Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT

NASA Astrophysics Data System (ADS)

Chen, Buxin; Zhang, Zheng; Sidky, Emil Y.; Xia, Dan; Pan, Xiaochuan

2017-11-01

Optimization-based algorithms for image reconstruction in multispectral (or photon-counting) computed tomography (MCT) remains a topic of active research. The challenge of optimization-based image reconstruction in MCT stems from the inherently non-linear data model that can lead to a non-convex optimization program for which no mathematically exact solver seems to exist for achieving globally optimal solutions. In this work, based upon a non-linear data model, we design a non-convex optimization program, derive its first-order-optimality conditions, and propose an algorithm to solve the program for image reconstruction in MCT. In addition to consideration of image reconstruction for the standard scan configuration, the emphasis is on investigating the algorithm’s potential for enabling non-standard scan configurations with no or minimum hardware modification to existing CT systems, which has potential practical implications for lowered hardware cost, enhanced scanning flexibility, and reduced imaging dose/time in MCT. Numerical studies are carried out for verification of the algorithm and its implementation, and for a preliminary demonstration and characterization of the algorithm in reconstructing images and in enabling non-standard configurations with varying scanning angular range and/or x-ray illumination coverage in MCT.

SU-E-I-33: Initial Evaluation of Model-Based Iterative CT Reconstruction Using Standard Image Quality Phantoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gingold, E; Dave, J

2014-06-01

Purpose: The purpose of this study was to compare a new model-based iterative reconstruction with existing reconstruction methods (filtered backprojection and basic iterative reconstruction) using quantitative analysis of standard image quality phantom images. Methods: An ACR accreditation phantom (Gammex 464) and a CATPHAN600 phantom were scanned using 3 routine clinical acquisition protocols (adult axial brain, adult abdomen, and pediatric abdomen) on a Philips iCT system. Each scan was acquired using default conditions and 75%, 50% and 25% dose levels. Images were reconstructed using standard filtered backprojection (FBP), conventional iterative reconstruction (iDose4) and a prototype model-based iterative reconstruction (IMR). Phantom measurementsmore » included CT number accuracy, contrast to noise ratio (CNR), modulation transfer function (MTF), low contrast detectability (LCD), and noise power spectrum (NPS). Results: The choice of reconstruction method had no effect on CT number accuracy, or MTF (p<0.01). The CNR of a 6 HU contrast target was improved by 1–67% with iDose4 relative to FBP, while IMR improved CNR by 145–367% across all protocols and dose levels. Within each scan protocol, the CNR improvement from IMR vs FBP showed a general trend of greater improvement at lower dose levels. NPS magnitude was greatest for FBP and lowest for IMR. The NPS of the IMR reconstruction showed a pronounced decrease with increasing spatial frequency, consistent with the unusual noise texture seen in IMR images. Conclusion: Iterative Model Reconstruction reduces noise and improves contrast-to-noise ratio without sacrificing spatial resolution in CT phantom images. This offers the possibility of radiation dose reduction and improved low contrast detectability compared with filtered backprojection or conventional iterative reconstruction.« less

Dynamic PET Image reconstruction for parametric imaging using the HYPR kernel method

NASA Astrophysics Data System (ADS)

Spencer, Benjamin; Qi, Jinyi; Badawi, Ramsey D.; Wang, Guobao

2017-03-01

Dynamic PET image reconstruction is a challenging problem because of the ill-conditioned nature of PET and the lowcounting statistics resulted from short time-frames in dynamic imaging. The kernel method for image reconstruction has been developed to improve image reconstruction of low-count PET data by incorporating prior information derived from high-count composite data. In contrast to most of the existing regularization-based methods, the kernel method embeds image prior information in the forward projection model and does not require an explicit regularization term in the reconstruction formula. Inspired by the existing highly constrained back-projection (HYPR) algorithm for dynamic PET image denoising, we propose in this work a new type of kernel that is simpler to implement and further improves the kernel-based dynamic PET image reconstruction. Our evaluation study using a physical phantom scan with synthetic FDG tracer kinetics has demonstrated that the new HYPR kernel-based reconstruction can achieve a better region-of-interest (ROI) bias versus standard deviation trade-off for dynamic PET parametric imaging than the post-reconstruction HYPR denoising method and the previously used nonlocal-means kernel.

Methodology for Image-Based Reconstruction of Ventricular Geometry for Patient-Specific Modeling of Cardiac Electrophysiology

PubMed Central

Prakosa, A.; Malamas, P.; Zhang, S.; Pashakhanloo, F.; Arevalo, H.; Herzka, D. A.; Lardo, A.; Halperin, H.; McVeigh, E.; Trayanova, N.; Vadakkumpadan, F.

2014-01-01

Patient-specific modeling of ventricular electrophysiology requires an interpolated reconstruction of the 3-dimensional (3D) geometry of the patient ventricles from the low-resolution (Lo-res) clinical images. The goal of this study was to implement a processing pipeline for obtaining the interpolated reconstruction, and thoroughly evaluate the efficacy of this pipeline in comparison with alternative methods. The pipeline implemented here involves contouring the epi- and endocardial boundaries in Lo-res images, interpolating the contours using the variational implicit functions method, and merging the interpolation results to obtain the ventricular reconstruction. Five alternative interpolation methods, namely linear, cubic spline, spherical harmonics, cylindrical harmonics, and shape-based interpolation were implemented for comparison. In the thorough evaluation of the processing pipeline, Hi-res magnetic resonance (MR), computed tomography (CT), and diffusion tensor (DT) MR images from numerous hearts were used. Reconstructions obtained from the Hi-res images were compared with the reconstructions computed by each of the interpolation methods from a sparse sample of the Hi-res contours, which mimicked Lo-res clinical images. Qualitative and quantitative comparison of these ventricular geometry reconstructions showed that the variational implicit functions approach performed better than others. Additionally, the outcomes of electrophysiological simulations (sinus rhythm activation maps and pseudo-ECGs) conducted using models based on the various reconstructions were compared. These electrophysiological simulations demonstrated that our implementation of the variational implicit functions-based method had the best accuracy. PMID:25148771

dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior images

NASA Astrophysics Data System (ADS)

Dang, H.; Wang, A. S.; Sussman, Marc S.; Siewerdsen, J. H.; Stayman, J. W.

2014-09-01

Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration, and prior image penalized-likelihood estimation with rigid registration of a prior image (PIRPLE) over a wide range of sampling sparsity and exposure levels.

Technical Note: FreeCT_ICD: An Open Source Implementation of a Model-Based Iterative Reconstruction Method using Coordinate Descent Optimization for CT Imaging Investigations.

PubMed

Hoffman, John M; Noo, Frédéric; Young, Stefano; Hsieh, Scott S; McNitt-Gray, Michael

2018-06-01

To facilitate investigations into the impacts of acquisition and reconstruction parameters on quantitative imaging, radiomics and CAD using CT imaging, we previously released an open source implementation of a conventional weighted filtered backprojection reconstruction called FreeCT_wFBP. Our purpose was to extend that work by providing an open-source implementation of a model-based iterative reconstruction method using coordinate descent optimization, called FreeCT_ICD. Model-based iterative reconstruction offers the potential for substantial radiation dose reduction, but can impose substantial computational processing and storage requirements. FreeCT_ICD is an open source implementation of a model-based iterative reconstruction method that provides a reasonable tradeoff between these requirements. This was accomplished by adapting a previously proposed method that allows the system matrix to be stored with a reasonable memory requirement. The method amounts to describing the attenuation coefficient using rotating slices that follow the helical geometry. In the initially-proposed version, the rotating slices are themselves described using blobs. We have replaced this description by a unique model that relies on tri-linear interpolation together with the principles of Joseph's method. This model offers an improvement in memory requirement while still allowing highly accurate reconstruction for conventional CT geometries. The system matrix is stored column-wise and combined with an iterative coordinate descent (ICD) optimization. The result is FreeCT_ICD, which is a reconstruction program developed on the Linux platform using C++ libraries and the open source GNU GPL v2.0 license. The software is capable of reconstructing raw projection data of helical CT scans. In this work, the software has been described and evaluated by reconstructing datasets exported from a clinical scanner which consisted of an ACR accreditation phantom dataset and a clinical pediatric thoracic scan. For the ACR phantom, image quality was comparable to clinical reconstructions as well as reconstructions using open-source FreeCT_wFBP software. The pediatric thoracic scan also yielded acceptable results. In addition, we did not observe any deleterious impact in image quality associated with the utilization of rotating slices. These evaluations also demonstrated reasonable tradeoffs in storage requirements and computational demands. FreeCT_ICD is an open-source implementation of a model-based iterative reconstruction method that extends the capabilities of previously released open source reconstruction software and provides the ability to perform vendor-independent reconstructions of clinically acquired raw projection data. This implementation represents a reasonable tradeoff between storage and computational requirements and has demonstrated acceptable image quality in both simulated and clinical image datasets. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Aberration correction for transcranial photoacoustic tomography of primates employing adjunct image data

NASA Astrophysics Data System (ADS)

Huang, Chao; Nie, Liming; Schoonover, Robert W.; Guo, Zijian; Schirra, Carsten O.; Anastasio, Mark A.; Wang, Lihong V.

2012-06-01

A challenge in photoacoustic tomography (PAT) brain imaging is to compensate for aberrations in the measured photoacoustic data due to their propagation through the skull. By use of information regarding the skull morphology and composition obtained from adjunct x-ray computed tomography image data, we developed a subject-specific imaging model that accounts for such aberrations. A time-reversal-based reconstruction algorithm was employed with this model for image reconstruction. The image reconstruction methodology was evaluated in experimental studies involving phantoms and monkey heads. The results establish that our reconstruction methodology can effectively compensate for skull-induced acoustic aberrations and improve image fidelity in transcranial PAT.

Tradeoff between noise reduction and inartificial visualization in a model-based iterative reconstruction algorithm on coronary computed tomography angiography.

PubMed

Hirata, Kenichiro; Utsunomiya, Daisuke; Kidoh, Masafumi; Funama, Yoshinori; Oda, Seitaro; Yuki, Hideaki; Nagayama, Yasunori; Iyama, Yuji; Nakaura, Takeshi; Sakabe, Daisuke; Tsujita, Kenichi; Yamashita, Yasuyuki

2018-05-01

We aimed to evaluate the image quality performance of coronary CT angiography (CTA) under the different settings of forward-projected model-based iterative reconstruction solutions (FIRST).Thirty patients undergoing coronary CTA were included. Each image was reconstructed using filtered back projection (FBP), adaptive iterative dose reduction 3D (AIDR-3D), and 2 model-based iterative reconstructions including FIRST-body and FIRST-cardiac sharp (CS). CT number and noise were measured in the coronary vessels and plaque. Subjective image-quality scores were obtained for noise and structure visibility.In the objective image analysis, FIRST-body produced the significantly highest contrast-to-noise ratio. Regarding subjective image quality, FIRST-CS had the highest score for structure visibility, although the image noise score was inferior to that of FIRST-body.In conclusion, FIRST provides significant improvements in objective and subjective image quality compared with FBP and AIDR-3D. FIRST-body effectively reduces image noise, but the structure visibility with FIRST-CS was superior to FIRST-body.

3D reconstruction of the magnetic vector potential using model based iterative reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prabhat, K. C.; Aditya Mohan, K.; Phatak, Charudatta

Lorentz transmission electron microscopy (TEM) observations of magnetic nanoparticles contain information on the magnetic and electrostatic potentials. Vector field electron tomography (VFET) can be used to reconstruct electromagnetic potentials of the nanoparticles from their corresponding LTEM images. The VFET approach is based on the conventional filtered back projection approach to tomographic reconstructions and the availability of an incomplete set of measurements due to experimental limitations means that the reconstructed vector fields exhibit significant artifacts. In this paper, we outline a model-based iterative reconstruction (MBIR) algorithm to reconstruct the magnetic vector potential of magnetic nanoparticles. We combine a forward model formore » image formation in TEM experiments with a prior model to formulate the tomographic problem as a maximum a-posteriori probability estimation problem (MAP). The MAP cost function is minimized iteratively to determine the vector potential. Here, a comparative reconstruction study of simulated as well as experimental data sets show that the MBIR approach yields quantifiably better reconstructions than the VFET approach.« less

3D reconstruction of the magnetic vector potential using model based iterative reconstruction.

PubMed

Prabhat, K C; Aditya Mohan, K; Phatak, Charudatta; Bouman, Charles; De Graef, Marc

2017-11-01

Lorentz transmission electron microscopy (TEM) observations of magnetic nanoparticles contain information on the magnetic and electrostatic potentials. Vector field electron tomography (VFET) can be used to reconstruct electromagnetic potentials of the nanoparticles from their corresponding LTEM images. The VFET approach is based on the conventional filtered back projection approach to tomographic reconstructions and the availability of an incomplete set of measurements due to experimental limitations means that the reconstructed vector fields exhibit significant artifacts. In this paper, we outline a model-based iterative reconstruction (MBIR) algorithm to reconstruct the magnetic vector potential of magnetic nanoparticles. We combine a forward model for image formation in TEM experiments with a prior model to formulate the tomographic problem as a maximum a-posteriori probability estimation problem (MAP). The MAP cost function is minimized iteratively to determine the vector potential. A comparative reconstruction study of simulated as well as experimental data sets show that the MBIR approach yields quantifiably better reconstructions than the VFET approach. Copyright © 2017 Elsevier B.V. All rights reserved.

3D reconstruction of the magnetic vector potential using model based iterative reconstruction

DOE PAGES

Prabhat, K. C.; Aditya Mohan, K.; Phatak, Charudatta; ...

2017-07-03

Lorentz transmission electron microscopy (TEM) observations of magnetic nanoparticles contain information on the magnetic and electrostatic potentials. Vector field electron tomography (VFET) can be used to reconstruct electromagnetic potentials of the nanoparticles from their corresponding LTEM images. The VFET approach is based on the conventional filtered back projection approach to tomographic reconstructions and the availability of an incomplete set of measurements due to experimental limitations means that the reconstructed vector fields exhibit significant artifacts. In this paper, we outline a model-based iterative reconstruction (MBIR) algorithm to reconstruct the magnetic vector potential of magnetic nanoparticles. We combine a forward model formore » image formation in TEM experiments with a prior model to formulate the tomographic problem as a maximum a-posteriori probability estimation problem (MAP). The MAP cost function is minimized iteratively to determine the vector potential. Here, a comparative reconstruction study of simulated as well as experimental data sets show that the MBIR approach yields quantifiably better reconstructions than the VFET approach.« less

Image-based Modeling of PSF Deformation with Application to Limited Angle PET Data

PubMed Central

Matej, Samuel; Li, Yusheng; Panetta, Joseph; Karp, Joel S.; Surti, Suleman

2016-01-01

The point-spread-functions (PSFs) of reconstructed images can be deformed due to detector effects such as resolution blurring and parallax error, data acquisition geometry such as insufficient sampling or limited angular coverage in dual-panel PET systems, or reconstruction imperfections/simplifications. PSF deformation decreases quantitative accuracy and its spatial variation lowers consistency of lesion uptake measurement across the imaging field-of-view (FOV). This can be a significant problem with dual panel PET systems even when using TOF data and image reconstruction models of the detector and data acquisition process. To correct for the spatially variant reconstructed PSF distortions we propose to use an image-based resolution model (IRM) that includes such image PSF deformation effects. Originally the IRM was mostly used for approximating data resolution effects of standard PET systems with full angular coverage in a computationally efficient way, but recently it was also used to mitigate effects of simplified geometric projectors. Our work goes beyond this by including into the IRM reconstruction imperfections caused by combination of the limited angle, parallax errors, and any other (residual) deformation effects and testing it for challenging dual panel data with strongly asymmetric and variable PSF deformations. We applied and tested these concepts using simulated data based on our design for a dedicated breast imaging geometry (B-PET) consisting of dual-panel, time-of-flight (TOF) detectors. We compared two image-based resolution models; i) a simple spatially invariant approximation to PSF deformation, which captures only the general PSF shape through an elongated 3D Gaussian function, and ii) a spatially variant model using a Gaussian mixture model (GMM) to more accurately capture the asymmetric PSF shape in images reconstructed from data acquired with the B-PET scanner geometry. Results demonstrate that while both IRMs decrease the overall uptake bias in the reconstructed image, the second one with the spatially variant and accurate PSF shape model is also able to ameliorate the spatially variant deformation effects to provide consistent uptake results independent of the lesion location within the FOV. PMID:27812222

Large-scale building scenes reconstruction from close-range images based on line and plane feature

NASA Astrophysics Data System (ADS)

Ding, Yi; Zhang, Jianqing

2007-11-01

Automatic generate 3D models of buildings and other man-made structures from images has become a topic of increasing importance, those models may be in applications such as virtual reality, entertainment industry and urban planning. In this paper we address the main problems and available solution for the generation of 3D models from terrestrial images. We first generate a coarse planar model of the principal scene planes and then reconstruct windows to refine the building models. There are several points of novelty: first we reconstruct the coarse wire frame model use the line segments matching with epipolar geometry constraint; Secondly, we detect the position of all windows in the image and reconstruct the windows by established corner points correspondences between images, then add the windows to the coarse model to refine the building models. The strategy is illustrated on image triple of college building.

4D-PET reconstruction using a spline-residue model with spatial and temporal roughness penalties

NASA Astrophysics Data System (ADS)

Ralli, George P.; Chappell, Michael A.; McGowan, Daniel R.; Sharma, Ricky A.; Higgins, Geoff S.; Fenwick, John D.

2018-05-01

4D reconstruction of dynamic positron emission tomography (dPET) data can improve the signal-to-noise ratio in reconstructed image sequences by fitting smooth temporal functions to the voxel time-activity-curves (TACs) during the reconstruction, though the optimal choice of function remains an open question. We propose a spline-residue model, which describes TACs as weighted sums of convolutions of the arterial input function with cubic B-spline basis functions. Convolution with the input function constrains the spline-residue model at early time-points, potentially enhancing noise suppression in early time-frames, while still allowing a wide range of TAC descriptions over the entire imaged time-course, thus limiting bias. Spline-residue based 4D-reconstruction is compared to that of a conventional (non-4D) maximum a posteriori (MAP) algorithm, and to 4D-reconstructions based on adaptive-knot cubic B-splines, the spectral model and an irreversible two-tissue compartment (‘2C3K’) model. 4D reconstructions were carried out using a nested-MAP algorithm including spatial and temporal roughness penalties. The algorithms were tested using Monte-Carlo simulated scanner data, generated for a digital thoracic phantom with uptake kinetics based on a dynamic [18F]-Fluromisonidazole scan of a non-small cell lung cancer patient. For every algorithm, parametric maps were calculated by fitting each voxel TAC within a sub-region of the reconstructed images with the 2C3K model. Compared to conventional MAP reconstruction, spline-residue-based 4D reconstruction achieved  >50% improvements for five of the eight combinations of the four kinetics parameters for which parametric maps were created with the bias and noise measures used to analyse them, and produced better results for 5/8 combinations than any of the other reconstruction algorithms studied, while spectral model-based 4D reconstruction produced the best results for 2/8. 2C3K model-based 4D reconstruction generated the most biased parametric maps. Inclusion of a temporal roughness penalty function improved the performance of 4D reconstruction based on the cubic B-spline, spectral and spline-residue models.

DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction.

PubMed

Yang, Guang; Yu, Simiao; Dong, Hao; Slabaugh, Greg; Dragotti, Pier Luigi; Ye, Xujiong; Liu, Fangde; Arridge, Simon; Keegan, Jennifer; Guo, Yike; Firmin, David; Keegan, Jennifer; Slabaugh, Greg; Arridge, Simon; Ye, Xujiong; Guo, Yike; Yu, Simiao; Liu, Fangde; Firmin, David; Dragotti, Pier Luigi; Yang, Guang; Dong, Hao

2018-06-01

Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which is highly desirable for numerous clinical applications. This can not only reduce the scanning cost and ease patient burden, but also potentially reduce motion artefacts and the effect of contrast washout, thus yielding better image quality. Different from parallel imaging-based fast MRI, which utilizes multiple coils to simultaneously receive MR signals, CS-MRI breaks the Nyquist-Shannon sampling barrier to reconstruct MRI images with much less required raw data. This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets. In particular, a novel conditional Generative Adversarial Networks-based model (DAGAN)-based model is proposed to reconstruct CS-MRI. In our DAGAN architecture, we have designed a refinement learning method to stabilize our U-Net based generator, which provides an end-to-end network to reduce aliasing artefacts. To better preserve texture and edges in the reconstruction, we have coupled the adversarial loss with an innovative content loss. In addition, we incorporate frequency-domain information to enforce similarity in both the image and frequency domains. We have performed comprehensive comparison studies with both conventional CS-MRI reconstruction methods and newly investigated deep learning approaches. Compared with these methods, our DAGAN method provides superior reconstruction with preserved perceptual image details. Furthermore, each image is reconstructed in about 5 ms, which is suitable for real-time processing.

Silhouette-based approach of 3D image reconstruction for automated image acquisition using robotic arm

NASA Astrophysics Data System (ADS)

Azhar, N.; Saad, W. H. M.; Manap, N. A.; Saad, N. M.; Syafeeza, A. R.

2017-06-01

This study presents the approach of 3D image reconstruction using an autonomous robotic arm for the image acquisition process. A low cost of the automated imaging platform is created using a pair of G15 servo motor connected in series to an Arduino UNO as a main microcontroller. Two sets of sequential images were obtained using different projection angle of the camera. The silhouette-based approach is used in this study for 3D reconstruction from the sequential images captured from several different angles of the object. Other than that, an analysis based on the effect of different number of sequential images on the accuracy of 3D model reconstruction was also carried out with a fixed projection angle of the camera. The effecting elements in the 3D reconstruction are discussed and the overall result of the analysis is concluded according to the prototype of imaging platform.

Acceleration of image-based resolution modelling reconstruction using an expectation maximization nested algorithm.

PubMed

Angelis, G I; Reader, A J; Markiewicz, P J; Kotasidis, F A; Lionheart, W R; Matthews, J C

2013-08-07

Recent studies have demonstrated the benefits of a resolution model within iterative reconstruction algorithms in an attempt to account for effects that degrade the spatial resolution of the reconstructed images. However, these algorithms suffer from slower convergence rates, compared to algorithms where no resolution model is used, due to the additional need to solve an image deconvolution problem. In this paper, a recently proposed algorithm, which decouples the tomographic and image deconvolution problems within an image-based expectation maximization (EM) framework, was evaluated. This separation is convenient, because more computational effort can be placed on the image deconvolution problem and therefore accelerate convergence. Since the computational cost of solving the image deconvolution problem is relatively small, multiple image-based EM iterations do not significantly increase the overall reconstruction time. The proposed algorithm was evaluated using 2D simulations, as well as measured 3D data acquired on the high-resolution research tomograph. Results showed that bias reduction can be accelerated by interleaving multiple iterations of the image-based EM algorithm solving the resolution model problem, with a single EM iteration solving the tomographic problem. Significant improvements were observed particularly for voxels that were located on the boundaries between regions of high contrast within the object being imaged and for small regions of interest, where resolution recovery is usually more challenging. Minor differences were observed using the proposed nested algorithm, compared to the single iteration normally performed, when an optimal number of iterations are performed for each algorithm. However, using the proposed nested approach convergence is significantly accelerated enabling reconstruction using far fewer tomographic iterations (up to 70% fewer iterations for small regions). Nevertheless, the optimal number of nested image-based EM iterations is hard to be defined and it should be selected according to the given application.

Texture-preserved penalized weighted least-squares reconstruction of low-dose CT image via image segmentation and high-order MRF modeling

NASA Astrophysics Data System (ADS)

Han, Hao; Zhang, Hao; Wei, Xinzhou; Moore, William; Liang, Zhengrong

2016-03-01

In this paper, we proposed a low-dose computed tomography (LdCT) image reconstruction method with the help of prior knowledge learning from previous high-quality or normal-dose CT (NdCT) scans. The well-established statistical penalized weighted least squares (PWLS) algorithm was adopted for image reconstruction, where the penalty term was formulated by a texture-based Gaussian Markov random field (gMRF) model. The NdCT scan was firstly segmented into different tissue types by a feature vector quantization (FVQ) approach. Then for each tissue type, a set of tissue-specific coefficients for the gMRF penalty was statistically learnt from the NdCT image via multiple-linear regression analysis. We also proposed a scheme to adaptively select the order of gMRF model for coefficients prediction. The tissue-specific gMRF patterns learnt from the NdCT image were finally used to form an adaptive MRF penalty for the PWLS reconstruction of LdCT image. The proposed texture-adaptive PWLS image reconstruction algorithm was shown to be more effective to preserve image textures than the conventional PWLS image reconstruction algorithm, and we further demonstrated the gain of high-order MRF modeling for texture-preserved LdCT PWLS image reconstruction.

Simbol-X Formation Flight and Image Reconstruction

NASA Astrophysics Data System (ADS)

Civitani, M.; Djalal, S.; Le Duigou, J. M.; La Marle, O.; Chipaux, R.

2009-05-01

Simbol-X is the first operational mission relying on two satellites flying in formation. The dynamics of the telescope, due to the formation flight concept, raises a variety of problematic, like image reconstruction, that can be better evaluated via a simulation tools. We present here the first results obtained with Simulos, simulation tool aimed to study the relative spacecrafts navigation and the weight of the different parameters in image reconstruction and telescope performance evaluation. The simulation relies on attitude and formation flight sensors models, formation flight dynamics and control, mirror model and focal plane model, while the image reconstruction is based on the Line of Sight (LOS) concept.

Effects of individualized electrical impedance tomography and image reconstruction settings upon the assessment of regional ventilation distribution: Comparison to 4-dimensional computed tomography in a porcine model

PubMed Central

Mudrak, Daniel; Kampusch, Stefan; Wielandner, Alice; Prosch, Helmut; Braun, Christina; Toemboel, Frédéric P. R.; Hofmanninger, Johannes; Kaniusas, Eugenijus

2017-01-01

Electrical impedance tomography (EIT) is a promising imaging technique for bedside monitoring of lung function. It is easily applicable, cheap and requires no ionizing radiation, but clinical interpretation of EIT-images is still not standardized. One of the reasons for this is the ill-posed nature of EIT, allowing a range of possible images to be produced–rather than a single explicit solution. Thus, to further advance the EIT technology for clinical application, thorough examinations of EIT-image reconstruction settings–i.e., mathematical parameters and addition of a priori (e.g., anatomical) information–is essential. In the present work, regional ventilation distribution profiles derived from different EIT finite-element reconstruction models and settings (for GREIT and Gauss Newton) were compared to regional aeration profiles assessed by the gold-standard of 4-dimensional computed tomography (4DCT) by calculating the root mean squared error (RMSE). Specifically, non-individualized reconstruction models (based on circular and averaged thoracic contours) and individualized reconstruction models (based on true thoracic contours) were compared. Our results suggest that GREIT with noise figure of 0.15 and non-uniform background works best for the assessment of regional ventilation distribution by EIT, as verified versus 4DCT. Furthermore, the RMSE of anteroposterior ventilation profiles decreased from 2.53±0.62% to 1.67±0.49% while correlation increased from 0.77 to 0.89 after embedding anatomical information into the reconstruction models. In conclusion, the present work reveals that anatomically enhanced EIT-image reconstruction is superior to non-individualized reconstruction models, but further investigations in humans, so as to standardize reconstruction settings, is warranted. PMID:28763474

Evaluation of an iterative model-based reconstruction of pediatric abdominal CT with regard to image quality and radiation dose.

PubMed

Aurumskjöld, Marie-Louise; Söderberg, Marcus; Stålhammar, Fredrik; von Steyern, Kristina Vult; Tingberg, Anders; Ydström, Kristina

2018-06-01

Background In pediatric patients, computed tomography (CT) is important in the medical chain of diagnosing and monitoring various diseases. Because children are more radiosensitive than adults, they require minimal radiation exposure. One way to achieve this goal is to implement new technical solutions, like iterative reconstruction. Purpose To evaluate the potential of a new, iterative, model-based method for reconstructing (IMR) pediatric abdominal CT at a low radiation dose and determine whether it maintains or improves image quality, compared to the current reconstruction method. Material and Methods Forty pediatric patients underwent abdominal CT. Twenty patients were examined with the standard dose settings and 20 patients were examined with a 32% lower radiation dose. Images from the standard examination were reconstructed with a hybrid iterative reconstruction method (iDose 4 ), and images from the low-dose examinations were reconstructed with both iDose 4 and IMR. Image quality was evaluated subjectively by three observers, according to modified EU image quality criteria, and evaluated objectively based on the noise observed in liver images. Results Visual grading characteristics analyses showed no difference in image quality between the standard dose examination reconstructed with iDose 4 and the low dose examination reconstructed with IMR. IMR showed lower image noise in the liver compared to iDose 4 images. Inter- and intra-observer variance was low: the intraclass coefficient was 0.66 (95% confidence interval = 0.60-0.71) for the three observers. Conclusion IMR provided image quality equivalent or superior to the standard iDose 4 method for evaluating pediatric abdominal CT, even with a 32% dose reduction.

Fast implementations of reconstruction-based scatter compensation in fully 3D SPECT image reconstruction

NASA Astrophysics Data System (ADS)

Kadrmas, Dan J.; Frey, Eric C.; Karimi, Seemeen S.; Tsui, Benjamin M. W.

1998-04-01

Accurate scatter compensation in SPECT can be performed by modelling the scatter response function during the reconstruction process. This method is called reconstruction-based scatter compensation (RBSC). It has been shown that RBSC has a number of advantages over other methods of compensating for scatter, but using RBSC for fully 3D compensation has resulted in prohibitively long reconstruction times. In this work we propose two new methods that can be used in conjunction with existing methods to achieve marked reductions in RBSC reconstruction times. The first method, coarse-grid scatter modelling, significantly accelerates the scatter model by exploiting the fact that scatter is dominated by low-frequency information. The second method, intermittent RBSC, further accelerates the reconstruction process by limiting the number of iterations during which scatter is modelled. The fast implementations were evaluated using a Monte Carlo simulated experiment of the 3D MCAT phantom with tracer, and also using experimentally acquired data with tracer. Results indicated that these fast methods can reconstruct, with fully 3D compensation, images very similar to those obtained using standard RBSC methods, and in reconstruction times that are an order of magnitude shorter. Using these methods, fully 3D iterative reconstruction with RBSC can be performed well within the realm of clinically realistic times (under 10 minutes for image reconstruction).

A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data.

PubMed

Luo, Jianhua; Mou, Zhiying; Qin, Binjie; Li, Wanqing; Ogunbona, Philip; Robini, Marc C; Zhu, Yuemin

2018-07-01

Reconstructing magnetic resonance images from undersampled k-space data is a challenging problem. This paper introduces a novel method of image reconstruction from undersampled k-space data based on the concept of singularizing operators and a novel singular k-space model. Exploring the sparsity of an image in the k-space, the singular k-space model (SKM) is proposed in terms of the k-space functions of a singularizing operator. The singularizing operator is constructed by combining basic difference operators. An algorithm is developed to reliably estimate the model parameters from undersampled k-space data. The estimated parameters are then used to recover the missing k-space data through the model, subsequently achieving high-quality reconstruction of the image using inverse Fourier transform. Experiments on physical phantom and real brain MR images have shown that the proposed SKM method constantly outperforms the popular total variation (TV) and the classical zero-filling (ZF) methods regardless of the undersampling rates, the noise levels, and the image structures. For the same objective quality of the reconstructed images, the proposed method requires much less k-space data than the TV method. The SKM method is an effective method for fast MRI reconstruction from the undersampled k-space data. Graphical abstract Two Real Images and their sparsified images by singularizing operator.

AFFINE-CORRECTED PARADISE: FREE-BREATHING PATIENT-ADAPTIVE CARDIAC MRI WITH SENSITIVITY ENCODING

PubMed Central

Sharif, Behzad; Bresler, Yoram

2013-01-01

We propose a real-time cardiac imaging method with parallel MRI that allows for free breathing during imaging and does not require cardiac or respiratory gating. The method is based on the recently proposed PARADISE (Patient-Adaptive Reconstruction and Acquisition Dynamic Imaging with Sensitivity Encoding) scheme. The new acquisition method adapts the PARADISE k-t space sampling pattern according to an affine model of the respiratory motion. The reconstruction scheme involves multi-channel time-sequential imaging with time-varying channels. All model parameters are adapted to the imaged patient as part of the experiment and drive both data acquisition and cine reconstruction. Simulated cardiac MRI experiments using the realistic NCAT phantom show high quality cine reconstructions and robustness to modeling inaccuracies. PMID:24390159

In vivo bioluminescence tomography based on multi-view projection and 3D surface reconstruction

NASA Astrophysics Data System (ADS)

Zhang, Shuang; Wang, Kun; Leng, Chengcai; Deng, Kexin; Hu, Yifang; Tian, Jie

2015-03-01

Bioluminescence tomography (BLT) is a powerful optical molecular imaging modality, which enables non-invasive realtime in vivo imaging as well as 3D quantitative analysis in preclinical studies. In order to solve the inverse problem and reconstruct inner light sources accurately, the prior structural information is commonly necessary and obtained from computed tomography or magnetic resonance imaging. This strategy requires expensive hybrid imaging system, complicated operation protocol and possible involvement of ionizing radiation. The overall robustness highly depends on the fusion accuracy between the optical and structural information. In this study we present a pure optical bioluminescence tomographic system (POBTS) and a novel BLT method based on multi-view projection acquisition and 3D surface reconstruction. The POBTS acquired a sparse set of white light surface images and bioluminescent images of a mouse. Then the white light images were applied to an approximate surface model to generate a high quality textured 3D surface reconstruction of the mouse. After that we integrated multi-view luminescent images based on the previous reconstruction, and applied an algorithm to calibrate and quantify the surface luminescent flux in 3D.Finally, the internal bioluminescence source reconstruction was achieved with this prior information. A BALB/C mouse with breast tumor of 4T1-fLuc cells mouse model were used to evaluate the performance of the new system and technique. Compared with the conventional hybrid optical-CT approach using the same inverse reconstruction method, the reconstruction accuracy of this technique was improved. The distance error between the actual and reconstructed internal source was decreased by 0.184 mm.

Denoised ordered subset statistically penalized algebraic reconstruction technique (DOS-SPART) in digital breast tomosynthesis

NASA Astrophysics Data System (ADS)

Garrett, John; Li, Yinsheng; Li, Ke; Chen, Guang-Hong

2017-03-01

Digital breast tomosynthesis (DBT) is a three dimensional (3D) breast imaging modality in which projections are acquired over a limited angular span around the compressed breast and reconstructed into image slices parallel to the detector. DBT has been shown to help alleviate the breast tissue overlapping issues of two dimensional (2D) mammography. Since the overlapping tissues may simulate cancer masses or obscure true cancers, this improvement is critically important for improved breast cancer screening and diagnosis. In this work, a model-based image reconstruction method is presented to show that spatial resolution in DBT volumes can be maintained while dose is reduced using the presented method when compared to that of a state-of-the-art commercial reconstruction technique. Spatial resolution was measured in phantom images and subjectively in a clinical dataset. Noise characteristics were explored in a cadaver study. In both the quantitative and subjective results the image sharpness was maintained and overall image quality was maintained at reduced doses when the model-based iterative reconstruction was used to reconstruct the volumes.

A variational reconstruction method for undersampled dynamic x-ray tomography based on physical motion models

NASA Astrophysics Data System (ADS)

Burger, Martin; Dirks, Hendrik; Frerking, Lena; Hauptmann, Andreas; Helin, Tapio; Siltanen, Samuli

2017-12-01

In this paper we study the reconstruction of moving object densities from undersampled dynamic x-ray tomography in two dimensions. A particular motivation of this study is to use realistic measurement protocols for practical applications, i.e. we do not assume to have a full Radon transform in each time step, but only projections in few angular directions. This restriction enforces a space-time reconstruction, which we perform by incorporating physical motion models and regularization of motion vectors in a variational framework. The methodology of optical flow, which is one of the most common methods to estimate motion between two images, is utilized to formulate a joint variational model for reconstruction and motion estimation. We provide a basic mathematical analysis of the forward model and the variational model for the image reconstruction. Moreover, we discuss the efficient numerical minimization based on alternating minimizations between images and motion vectors. A variety of results are presented for simulated and real measurement data with different sampling strategy. A key observation is that random sampling combined with our model allows reconstructions of similar amount of measurements and quality as a single static reconstruction.

Image reconstructions from super-sampled data sets with resolution modeling in PET imaging.

PubMed

Li, Yusheng; Matej, Samuel; Metzler, Scott D

2014-12-01

Spatial resolution in positron emission tomography (PET) is still a limiting factor in many imaging applications. To improve the spatial resolution for an existing scanner with fixed crystal sizes, mechanical movements such as scanner wobbling and object shifting have been considered for PET systems. Multiple acquisitions from different positions can provide complementary information and increased spatial sampling. The objective of this paper is to explore an efficient and useful reconstruction framework to reconstruct super-resolution images from super-sampled low-resolution data sets. The authors introduce a super-sampling data acquisition model based on the physical processes with tomographic, downsampling, and shifting matrices as its building blocks. Based on the model, we extend the MLEM and Landweber algorithms to reconstruct images from super-sampled data sets. The authors also derive a backprojection-filtration-like (BPF-like) method for the super-sampling reconstruction. Furthermore, they explore variant methods for super-sampling reconstructions: the separate super-sampling resolution-modeling reconstruction and the reconstruction without downsampling to further improve image quality at the cost of more computation. The authors use simulated reconstruction of a resolution phantom to evaluate the three types of algorithms with different super-samplings at different count levels. Contrast recovery coefficient (CRC) versus background variability, as an image-quality metric, is calculated at each iteration for all reconstructions. The authors observe that all three algorithms can significantly and consistently achieve increased CRCs at fixed background variability and reduce background artifacts with super-sampled data sets at the same count levels. For the same super-sampled data sets, the MLEM method achieves better image quality than the Landweber method, which in turn achieves better image quality than the BPF-like method. The authors also demonstrate that the reconstructions from super-sampled data sets using a fine system matrix yield improved image quality compared to the reconstructions using a coarse system matrix. Super-sampling reconstructions with different count levels showed that the more spatial-resolution improvement can be obtained with higher count at a larger iteration number. The authors developed a super-sampling reconstruction framework that can reconstruct super-resolution images using the super-sampling data sets simultaneously with known acquisition motion. The super-sampling PET acquisition using the proposed algorithms provides an effective and economic way to improve image quality for PET imaging, which has an important implication in preclinical and clinical region-of-interest PET imaging applications.

Computed Tomography Imaging of a Hip Prosthesis Using Iterative Model-Based Reconstruction and Orthopaedic Metal Artefact Reduction: A Quantitative Analysis.

PubMed

Wellenberg, Ruud H H; Boomsma, Martijn F; van Osch, Jochen A C; Vlassenbroek, Alain; Milles, Julien; Edens, Mireille A; Streekstra, Geert J; Slump, Cornelis H; Maas, Mario

To quantify the combined use of iterative model-based reconstruction (IMR) and orthopaedic metal artefact reduction (O-MAR) in reducing metal artefacts and improving image quality in a total hip arthroplasty phantom. Scans acquired at several dose levels and kVps were reconstructed with filtered back-projection (FBP), iterative reconstruction (iDose) and IMR, with and without O-MAR. Computed tomography (CT) numbers, noise levels, signal-to-noise-ratios and contrast-to-noise-ratios were analysed. Iterative model-based reconstruction results in overall improved image quality compared to iDose and FBP (P < 0.001). Orthopaedic metal artefact reduction is most effective in reducing severe metal artefacts improving CT number accuracy by 50%, 60%, and 63% (P < 0.05) and reducing noise by 1%, 62%, and 85% (P < 0.001) whereas improving signal-to-noise-ratios by 27%, 47%, and 46% (P < 0.001) and contrast-to-noise-ratios by 16%, 25%, and 19% (P < 0.001) with FBP, iDose, and IMR, respectively. The combined use of IMR and O-MAR strongly improves overall image quality and strongly reduces metal artefacts in the CT imaging of a total hip arthroplasty phantom.

PSF reconstruction for Compton-based prompt gamma imaging

NASA Astrophysics Data System (ADS)

Jan, Meei-Ling; Lee, Ming-Wei; Huang, Hsuan-Ming

2018-02-01

Compton-based prompt gamma (PG) imaging has been proposed for in vivo range verification in proton therapy. However, several factors degrade the image quality of PG images, some of which are due to inherent properties of a Compton camera such as spatial resolution and energy resolution. Moreover, Compton-based PG imaging has a spatially variant resolution loss. In this study, we investigate the performance of the list-mode ordered subset expectation maximization algorithm with a shift-variant point spread function (LM-OSEM-SV-PSF) model. We also evaluate how well the PG images reconstructed using an SV-PSF model reproduce the distal falloff of the proton beam. The SV-PSF parameters were estimated from simulation data of point sources at various positions. Simulated PGs were produced in a water phantom irradiated with a proton beam. Compared to the LM-OSEM algorithm, the LM-OSEM-SV-PSF algorithm improved the quality of the reconstructed PG images and the estimation of PG falloff positions. In addition, the 4.44 and 5.25 MeV PG emissions can be accurately reconstructed using the LM-OSEM-SV-PSF algorithm. However, for the 2.31 and 6.13 MeV PG emissions, the LM-OSEM-SV-PSF reconstruction provides limited improvement. We also found that the LM-OSEM algorithm followed by a shift-variant Richardson-Lucy deconvolution could reconstruct images with quality visually similar to the LM-OSEM-SV-PSF-reconstructed images, while requiring shorter computation time.

A simple method for low-contrast detectability, image quality and dose optimisation with CT iterative reconstruction algorithms and model observers.

PubMed

Bellesi, Luca; Wyttenbach, Rolf; Gaudino, Diego; Colleoni, Paolo; Pupillo, Francesco; Carrara, Mauro; Braghetti, Antonio; Puligheddu, Carla; Presilla, Stefano

2017-01-01

The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. Images of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four-alternative forced-choice test) and model observers were performed across the various images. Compared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result. IR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low-contrast detection tasks and in dose optimisation.

A novel super-resolution camera model

NASA Astrophysics Data System (ADS)

Shao, Xiaopeng; Wang, Yi; Xu, Jie; Wang, Lin; Liu, Fei; Luo, Qiuhua; Chen, Xiaodong; Bi, Xiangli

2015-05-01

Aiming to realize super resolution(SR) to single image and video reconstruction, a super resolution camera model is proposed for the problem that the resolution of the images obtained by traditional cameras behave comparatively low. To achieve this function we put a certain driving device such as piezoelectric ceramics in the camera. By controlling the driving device, a set of continuous low resolution(LR) images can be obtained and stored instantaneity, which reflect the randomness of the displacements and the real-time performance of the storage very well. The low resolution image sequences have different redundant information and some particular priori information, thus it is possible to restore super resolution image factually and effectively. The sample method is used to derive the reconstruction principle of super resolution, which analyzes the possible improvement degree of the resolution in theory. The super resolution algorithm based on learning is used to reconstruct single image and the variational Bayesian algorithm is simulated to reconstruct the low resolution images with random displacements, which models the unknown high resolution image, motion parameters and unknown model parameters in one hierarchical Bayesian framework. Utilizing sub-pixel registration method, a super resolution image of the scene can be reconstructed. The results of 16 images reconstruction show that this camera model can increase the image resolution to 2 times, obtaining images with higher resolution in currently available hardware levels.

Bayesian image reconstruction - The pixon and optimal image modeling

NASA Technical Reports Server (NTRS)

Pina, R. K.; Puetter, R. C.

1993-01-01

In this paper we describe the optimal image model, maximum residual likelihood method (OptMRL) for image reconstruction. OptMRL is a Bayesian image reconstruction technique for removing point-spread function blurring. OptMRL uses both a goodness-of-fit criterion (GOF) and an 'image prior', i.e., a function which quantifies the a priori probability of the image. Unlike standard maximum entropy methods, which typically reconstruct the image on the data pixel grid, OptMRL varies the image model in order to find the optimal functional basis with which to represent the image. We show how an optimal basis for image representation can be selected and in doing so, develop the concept of the 'pixon' which is a generalized image cell from which this basis is constructed. By allowing both the image and the image representation to be variable, the OptMRL method greatly increases the volume of solution space over which the image is optimized. Hence the likelihood of the final reconstructed image is greatly increased. For the goodness-of-fit criterion, OptMRL uses the maximum residual likelihood probability distribution introduced previously by Pina and Puetter (1992). This GOF probability distribution, which is based on the spatial autocorrelation of the residuals, has the advantage that it ensures spatially uncorrelated image reconstruction residuals.

3D image reconstruction algorithms for cryo-electron-microscopy images of virus particles

NASA Astrophysics Data System (ADS)

Doerschuk, Peter C.; Johnson, John E.

2000-11-01

A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are computed using the expectation maximization algorithm and an example based on Cowpea mosaic virus is provided.

Comparison of Knowledge-based Iterative Model Reconstruction and Hybrid Reconstruction Techniques for Liver CT Evaluation of Hypervascular Hepatocellular Carcinoma.

PubMed

Park, Hyun Jeong; Lee, Jeong Min; Park, Sung Bin; Lee, Jong Beum; Jeong, Yoong Ki; Yoon, Jeong Hee

The purpose of this work was to evaluate the image quality, lesion conspicuity, and dose reduction provided by knowledge-based iterative model reconstruction (IMR) in computed tomography (CT) of the liver compared with hybrid iterative reconstruction (IR) and filtered back projection (FBP) in patients with hepatocellular carcinoma (HCC). Fifty-six patients with 61 HCCs who underwent multiphasic reduced-dose CT (RDCT; n = 33) or standard-dose CT (SDCT; n = 28) were retrospectively evaluated. Reconstructed images with FBP, hybrid IR (iDose), IMR were evaluated for image quality using CT attenuation and image noise. Objective and subjective image quality of RDCT and SDCT sets were independently assessed by 2 observers in a blinded manner. Image quality and lesion conspicuity were better with IMR for both RDCT and SDCT than either FBP or IR (P < 0.001). Contrast-to-noise ratio of HCCs in IMR-RDCT was significantly higher on delayed phase (DP) (P < 0.001), and comparable on arterial phase, than with IR-SDCT (P = 0.501). Iterative model reconstruction RDCT was significantly superior to FBP-SDCT (P < 0.001). Compared with IR-SDCT, IMR-RDCT was comparable in image sharpness and tumor conspicuity on arterial phase, and superior in image quality, noise, and lesion conspicuity on DP. With the use of IMR, a 27% reduction of effective dose was achieved with RDCT (12.7 ± 0.6 mSv) compared with SDCT (17.4 ± 1.1 mSv) without loss of image quality (P < 0.001). Iterative model reconstruction provides better image quality and tumor conspicuity than FBP and IR with considerable noise reduction. In addition, more than comparable results were achieved with IMR-RDCT to IR-SDCT for the evaluation of HCCs.

Image quality of iterative reconstruction in cranial CT imaging: comparison of model-based iterative reconstruction (MBIR) and adaptive statistical iterative reconstruction (ASiR).

PubMed

Notohamiprodjo, S; Deak, Z; Meurer, F; Maertz, F; Mueck, F G; Geyer, L L; Wirth, S

2015-01-01

The purpose of this study was to compare cranial CT (CCT) image quality (IQ) of the MBIR algorithm with standard iterative reconstruction (ASiR). In this institutional review board (IRB)-approved study, raw data sets of 100 unenhanced CCT examinations (120 kV, 50-260 mAs, 20 mm collimation, 0.984 pitch) were reconstructed with both ASiR and MBIR. Signal-to-noise (SNR) and contrast-to-noise (CNR) were calculated from attenuation values measured in caudate nucleus, frontal white matter, anterior ventricle horn, fourth ventricle, and pons. Two radiologists, who were blinded to the reconstruction algorithms, evaluated anonymized multiplanar reformations of 2.5 mm with respect to depiction of different parenchymal structures and impact of artefacts on IQ with a five-point scale (0: unacceptable, 1: less than average, 2: average, 3: above average, 4: excellent). MBIR decreased artefacts more effectively than ASiR (p < 0.01). The median depiction score for MBIR was 3, whereas the median value for ASiR was 2 (p < 0.01). SNR and CNR were significantly higher in MBIR than ASiR (p < 0.01). MBIR showed significant improvement of IQ parameters compared to ASiR. As CCT is an examination that is frequently required, the use of MBIR may allow for substantial reduction of radiation exposure caused by medical diagnostics. • Model-Based iterative reconstruction (MBIR) effectively decreased artefacts in cranial CT. • MBIR reconstructed images were rated with significantly higher scores for image quality. • Model-Based iterative reconstruction may allow reduced-dose diagnostic examination protocols.

Forward model with space-variant of source size for reconstruction on X-ray radiographic image

NASA Astrophysics Data System (ADS)

Liu, Jin; Liu, Jun; Jing, Yue-feng; Xiao, Bo; Wei, Cai-hua; Guan, Yong-hong; Zhang, Xuan

2018-03-01

The Forward Imaging Technique is a method to solve the inverse problem of density reconstruction in radiographic imaging. In this paper, we introduce the forward projection equation (IFP model) for the radiographic system with areal source blur and detector blur. Our forward projection equation, based on X-ray tracing, is combined with the Constrained Conjugate Gradient method to form a new method for density reconstruction. We demonstrate the effectiveness of the new technique by reconstructing density distributions from simulated and experimental images. We show that for radiographic systems with source sizes larger than the pixel size, the effect of blur on the density reconstruction is reduced through our method and can be controlled within one or two pixels. The method is also suitable for reconstruction of non-homogeneousobjects.

3D prostate histology image reconstruction: Quantifying the impact of tissue deformation and histology section location

PubMed Central

Gibson, Eli; Gaed, Mena; Gómez, José A.; Moussa, Madeleine; Pautler, Stephen; Chin, Joseph L.; Crukley, Cathie; Bauman, Glenn S.; Fenster, Aaron; Ward, Aaron D.

2013-01-01

Background: Guidelines for localizing prostate cancer on imaging are ideally informed by registered post-prostatectomy histology. 3D histology reconstruction methods can support this by reintroducing 3D spatial information lost during histology processing. The need to register small, high-grade foci drives a need for high accuracy. Accurate 3D reconstruction method design is impacted by the answers to the following central questions of this work. (1) How does prostate tissue deform during histology processing? (2) What spatial misalignment of the tissue sections is induced by microtome cutting? (3) How does the choice of reconstruction model affect histology reconstruction accuracy? Materials and Methods: Histology, paraffin block face and magnetic resonance images were acquired for 18 whole mid-gland tissue slices from six prostates. 7-15 homologous landmarks were identified on each image. Tissue deformation due to histology processing was characterized using the target registration error (TRE) after landmark-based registration under four deformation models (rigid, similarity, affine and thin-plate-spline [TPS]). The misalignment of histology sections from the front faces of tissue slices was quantified using manually identified landmarks. The impact of reconstruction models on the TRE after landmark-based reconstruction was measured under eight reconstruction models comprising one of four deformation models with and without constraining histology images to the tissue slice front faces. Results: Isotropic scaling improved the mean TRE by 0.8-1.0 mm (all results reported as 95% confidence intervals), while skew or TPS deformation improved the mean TRE by <0.1 mm. The mean misalignment was 1.1-1.9° (angle) and 0.9-1.3 mm (depth). Using isotropic scaling, the front face constraint raised the mean TRE by 0.6-0.8 mm. Conclusions: For sub-millimeter accuracy, 3D reconstruction models should not constrain histology images to the tissue slice front faces and should be flexible enough to model isotropic scaling. PMID:24392245

Three Dimensional Reconstruction Workflows for Lost Cultural Heritage Monuments Exploiting Public Domain and Professional Photogrammetric Imagery

NASA Astrophysics Data System (ADS)

Wahbeh, W.; Nebiker, S.

2017-08-01

In our paper, we document experiments and results of image-based 3d reconstructions of famous heritage monuments which were recently damaged or completely destroyed by the so-called Islamic state in Syria and Iraq. The specific focus of our research is on the combined use of professional photogrammetric imagery and of publicly available imagery from the web for optimally 3d reconstructing those monuments. The investigated photogrammetric reconstruction techniques include automated bundle adjustment and dense multi-view 3d reconstruction using public domain and professional imagery on the one hand and an interactive polygonal modelling based on projected panoramas on the other. Our investigations show that the combination of these two image-based modelling techniques delivers better results in terms of model completeness, level of detail and appearance.

Knowledge-based iterative model reconstruction: comparative image quality and radiation dose with a pediatric computed tomography phantom.

PubMed

Ryu, Young Jin; Choi, Young Hun; Cheon, Jung-Eun; Ha, Seongmin; Kim, Woo Sun; Kim, In-One

2016-03-01

CT of pediatric phantoms can provide useful guidance to the optimization of knowledge-based iterative reconstruction CT. To compare radiation dose and image quality of CT images obtained at different radiation doses reconstructed with knowledge-based iterative reconstruction, hybrid iterative reconstruction and filtered back-projection. We scanned a 5-year anthropomorphic phantom at seven levels of radiation. We then reconstructed CT data with knowledge-based iterative reconstruction (iterative model reconstruction [IMR] levels 1, 2 and 3; Philips Healthcare, Andover, MA), hybrid iterative reconstruction (iDose(4), levels 3 and 7; Philips Healthcare, Andover, MA) and filtered back-projection. The noise, signal-to-noise ratio and contrast-to-noise ratio were calculated. We evaluated low-contrast resolutions and detectability by low-contrast targets and subjective and objective spatial resolutions by the line pairs and wire. With radiation at 100 peak kVp and 100 mAs (3.64 mSv), the relative doses ranged from 5% (0.19 mSv) to 150% (5.46 mSv). Lower noise and higher signal-to-noise, contrast-to-noise and objective spatial resolution were generally achieved in ascending order of filtered back-projection, iDose(4) levels 3 and 7, and IMR levels 1, 2 and 3, at all radiation dose levels. Compared with filtered back-projection at 100% dose, similar noise levels were obtained on IMR level 2 images at 24% dose and iDose(4) level 3 images at 50% dose, respectively. Regarding low-contrast resolution, low-contrast detectability and objective spatial resolution, IMR level 2 images at 24% dose showed comparable image quality with filtered back-projection at 100% dose. Subjective spatial resolution was not greatly affected by reconstruction algorithm. Reduced-dose IMR obtained at 0.92 mSv (24%) showed similar image quality to routine-dose filtered back-projection obtained at 3.64 mSv (100%), and half-dose iDose(4) obtained at 1.81 mSv.

Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images.

PubMed

Rydén, T; Heydorn Lagerlöf, J; Hemmingsson, J; Marin, I; Svensson, J; Båth, M; Gjertsson, P; Bernhardt, P

2018-01-04

Full Monte Carlo (MC)-based SPECT reconstructions have a strong potential for correcting for image degrading factors, but the reconstruction times are long. The objective of this study was to develop a highly parallel Monte Carlo code for fast, ordered subset expectation maximum (OSEM) reconstructions of SPECT/CT images. The MC code was written in the Compute Unified Device Architecture language for a computer with four graphics processing units (GPUs) (GeForce GTX Titan X, Nvidia, USA). This enabled simulations of parallel photon emissions from the voxels matrix (128 3 or 256 3 ). Each computed tomography (CT) number was converted to attenuation coefficients for photo absorption, coherent scattering, and incoherent scattering. For photon scattering, the deflection angle was determined by the differential scattering cross sections. An angular response function was developed and used to model the accepted angles for photon interaction with the crystal, and a detector scattering kernel was used for modeling the photon scattering in the detector. Predefined energy and spatial resolution kernels for the crystal were used. The MC code was implemented in the OSEM reconstruction of clinical and phantom 177 Lu SPECT/CT images. The Jaszczak image quality phantom was used to evaluate the performance of the MC reconstruction in comparison with attenuated corrected (AC) OSEM reconstructions and attenuated corrected OSEM reconstructions with resolution recovery corrections (RRC). The performance of the MC code was 3200 million photons/s. The required number of photons emitted per voxel to obtain a sufficiently low noise level in the simulated image was 200 for a 128 3 voxel matrix. With this number of emitted photons/voxel, the MC-based OSEM reconstruction with ten subsets was performed within 20 s/iteration. The images converged after around six iterations. Therefore, the reconstruction time was around 3 min. The activity recovery for the spheres in the Jaszczak phantom was clearly improved with MC-based OSEM reconstruction, e.g., the activity recovery was 88% for the largest sphere, while it was 66% for AC-OSEM and 79% for RRC-OSEM. The GPU-based MC code generated an MC-based SPECT/CT reconstruction within a few minutes, and reconstructed patient images of 177 Lu-DOTATATE treatments revealed clearly improved resolution and contrast.

Statistical iterative material image reconstruction for spectral CT using a semi-empirical forward model

NASA Astrophysics Data System (ADS)

Mechlem, Korbinian; Ehn, Sebastian; Sellerer, Thorsten; Pfeiffer, Franz; Noël, Peter B.

2017-03-01

In spectral computed tomography (spectral CT), the additional information about the energy dependence of attenuation coefficients can be exploited to generate material selective images. These images have found applications in various areas such as artifact reduction, quantitative imaging or clinical diagnosis. However, significant noise amplification on material decomposed images remains a fundamental problem of spectral CT. Most spectral CT algorithms separate the process of material decomposition and image reconstruction. Separating these steps is suboptimal because the full statistical information contained in the spectral tomographic measurements cannot be exploited. Statistical iterative reconstruction (SIR) techniques provide an alternative, mathematically elegant approach to obtaining material selective images with improved tradeoffs between noise and resolution. Furthermore, image reconstruction and material decomposition can be performed jointly. This is accomplished by a forward model which directly connects the (expected) spectral projection measurements and the material selective images. To obtain this forward model, detailed knowledge of the different photon energy spectra and the detector response was assumed in previous work. However, accurately determining the spectrum is often difficult in practice. In this work, a new algorithm for statistical iterative material decomposition is presented. It uses a semi-empirical forward model which relies on simple calibration measurements. Furthermore, an efficient optimization algorithm based on separable surrogate functions is employed. This partially negates one of the major shortcomings of SIR, namely high computational cost and long reconstruction times. Numerical simulations and real experiments show strongly improved image quality and reduced statistical bias compared to projection-based material decomposition.

Multi-modal molecular diffuse optical tomography system for small animal imaging

PubMed Central

Guggenheim, James A.; Basevi, Hector R. A.; Frampton, Jon; Styles, Iain B.; Dehghani, Hamid

2013-01-01

A multi-modal optical imaging system for quantitative 3D bioluminescence and functional diffuse imaging is presented, which has no moving parts and uses mirrors to provide multi-view tomographic data for image reconstruction. It is demonstrated that through the use of trans-illuminated spectral near infrared measurements and spectrally constrained tomographic reconstruction, recovered concentrations of absorbing agents can be used as prior knowledge for bioluminescence imaging within the visible spectrum. Additionally, the first use of a recently developed multi-view optical surface capture technique is shown and its application to model-based image reconstruction and free-space light modelling is demonstrated. The benefits of model-based tomographic image recovery as compared to 2D planar imaging are highlighted in a number of scenarios where the internal luminescence source is not visible or is confounding in 2D images. The results presented show that the luminescence tomographic imaging method produces 3D reconstructions of individual light sources within a mouse-sized solid phantom that are accurately localised to within 1.5mm for a range of target locations and depths indicating sensitivity and accurate imaging throughout the phantom volume. Additionally the total reconstructed luminescence source intensity is consistent to within 15% which is a dramatic improvement upon standard bioluminescence imaging. Finally, results from a heterogeneous phantom with an absorbing anomaly are presented demonstrating the use and benefits of a multi-view, spectrally constrained coupled imaging system that provides accurate 3D luminescence images. PMID:24954977

3DNOW: Image-Based 3d Reconstruction and Modeling via Web

NASA Astrophysics Data System (ADS)

Tefera, Y.; Poiesi, F.; Morabito, D.; Remondino, F.; Nocerino, E.; Chippendale, P.

2018-05-01

This paper presents a web-based 3D imaging pipeline, namely 3Dnow, that can be used by anyone without the need of installing additional software other than a browser. By uploading a set of images through the web interface, 3Dnow can generate sparse and dense point clouds as well as mesh models. 3D reconstructed models can be downloaded with standard formats or previewed directly on the web browser through an embedded visualisation interface. In addition to reconstructing objects, 3Dnow offers the possibility to evaluate and georeference point clouds. Reconstruction statistics, such as minimum, maximum and average intersection angles, point redundancy and density can also be accessed. The paper describes all features available in the web service and provides an analysis of the computational performance using servers with different GPU configurations.

Model-based iterative reconstruction for reduction of radiation dose in abdominopelvic CT: comparison to adaptive statistical iterative reconstruction.

PubMed

Yasaka, Koichiro; Katsura, Masaki; Akahane, Masaaki; Sato, Jiro; Matsuda, Izuru; Ohtomo, Kuni

2013-12-01

To evaluate dose reduction and image quality of abdominopelvic computed tomography (CT) reconstructed with model-based iterative reconstruction (MBIR) compared to adaptive statistical iterative reconstruction (ASIR). In this prospective study, 85 patients underwent referential-, low-, and ultralow-dose unenhanced abdominopelvic CT. Images were reconstructed with ASIR for low-dose (L-ASIR) and ultralow-dose CT (UL-ASIR), and with MBIR for ultralow-dose CT (UL-MBIR). Image noise was measured in the abdominal aorta and iliopsoas muscle. Subjective image analyses and a lesion detection study (adrenal nodules) were conducted by two blinded radiologists. A reference standard was established by a consensus panel of two different radiologists using referential-dose CT reconstructed with filtered back projection. Compared to low-dose CT, there was a 63% decrease in dose-length product with ultralow-dose CT. UL-MBIR had significantly lower image noise than L-ASIR and UL-ASIR (all p<0.01). UL-MBIR was significantly better for subjective image noise and streak artifacts than L-ASIR and UL-ASIR (all p<0.01). There were no significant differences between UL-MBIR and L-ASIR in diagnostic acceptability (p>0.65), or diagnostic performance for adrenal nodules (p>0.87). MBIR significantly improves image noise and streak artifacts compared to ASIR, and can achieve radiation dose reduction without severely compromising image quality.

The effects of iterative reconstruction in CT on low-contrast liver lesion volumetry: a phantom study

NASA Astrophysics Data System (ADS)

Li, Qin; Berman, Benjamin P.; Schumacher, Justin; Liang, Yongguang; Gavrielides, Marios A.; Yang, Hao; Zhao, Binsheng; Petrick, Nicholas

2017-03-01

Tumor volume measured from computed tomography images is considered a biomarker for disease progression or treatment response. The estimation of the tumor volume depends on the imaging system parameters selected, as well as lesion characteristics. In this study, we examined how different image reconstruction methods affect the measurement of lesions in an anthropomorphic liver phantom with a non-uniform background. Iterative statistics-based and model-based reconstructions, as well as filtered back-projection, were evaluated and compared in this study. Statistics-based and filtered back-projection yielded similar estimation performance, while model-based yielded higher precision but lower accuracy in the case of small lesions. Iterative reconstructions exhibited higher signal-to-noise ratio but slightly lower contrast of the lesion relative to the background. A better understanding of lesion volumetry performance as a function of acquisition parameters and lesion characteristics can lead to its incorporation as a routine sizing tool.

Restoration and reconstruction from overlapping images

NASA Technical Reports Server (NTRS)

Reichenbach, Stephen E.; Kaiser, Daniel J.; Hanson, Andrew L.; Li, Jing

1997-01-01

This paper describes a technique for restoring and reconstructing a scene from overlapping images. In situations where there are multiple, overlapping images of the same scene, it may be desirable to create a single image that most closely approximates the scene, based on all of the data in the available images. For example, successive swaths acquired by NASA's planned Moderate Imaging Spectrometer (MODIS) will overlap, particularly at wide scan angles, creating a severe visual artifact in the output image. Resampling the overlapping swaths to produce a more accurate image on a uniform grid requires restoration and reconstruction. The one-pass restoration and reconstruction technique developed in this paper yields mean-square-optimal resampling, based on a comprehensive end-to-end system model that accounts for image overlap, and subject to user-defined and data-availability constraints on the spatial support of the filter.

LORAKS Makes Better SENSE: Phase-Constrained Partial Fourier SENSE Reconstruction without Phase Calibration

PubMed Central

Kim, Tae Hyung; Setsompop, Kawin; Haldar, Justin P.

2016-01-01

Purpose Parallel imaging and partial Fourier acquisition are two classical approaches for accelerated MRI. Methods that combine these approaches often rely on prior knowledge of the image phase, but the need to obtain this prior information can place practical restrictions on the data acquisition strategy. In this work, we propose and evaluate SENSE-LORAKS, which enables combined parallel imaging and partial Fourier reconstruction without requiring prior phase information. Theory and Methods The proposed formulation is based on combining the classical SENSE model for parallel imaging data with the more recent LORAKS framework for MR image reconstruction using low-rank matrix modeling. Previous LORAKS-based methods have successfully enabled calibrationless partial Fourier parallel MRI reconstruction, but have been most successful with nonuniform sampling strategies that may be hard to implement for certain applications. By combining LORAKS with SENSE, we enable highly-accelerated partial Fourier MRI reconstruction for a broader range of sampling trajectories, including widely-used calibrationless uniformly-undersampled trajectories. Results Our empirical results with retrospectively undersampled datasets indicate that when SENSE-LORAKS reconstruction is combined with an appropriate k-space sampling trajectory, it can provide substantially better image quality at high-acceleration rates relative to existing state-of-the-art reconstruction approaches. Conclusion The SENSE-LORAKS framework provides promising new opportunities for highly-accelerated MRI. PMID:27037836

Parallel Reconstruction Using Null Operations (PRUNO)

PubMed Central

Zhang, Jian; Liu, Chunlei; Moseley, Michael E.

2011-01-01

A novel iterative k-space data-driven technique, namely Parallel Reconstruction Using Null Operations (PRUNO), is presented for parallel imaging reconstruction. In PRUNO, both data calibration and image reconstruction are formulated into linear algebra problems based on a generalized system model. An optimal data calibration strategy is demonstrated by using Singular Value Decomposition (SVD). And an iterative conjugate- gradient approach is proposed to efficiently solve missing k-space samples during reconstruction. With its generalized formulation and precise mathematical model, PRUNO reconstruction yields good accuracy, flexibility, stability. Both computer simulation and in vivo studies have shown that PRUNO produces much better reconstruction quality than autocalibrating partially parallel acquisition (GRAPPA), especially under high accelerating rates. With the aid of PRUO reconstruction, ultra high accelerating parallel imaging can be performed with decent image quality. For example, we have done successful PRUNO reconstruction at a reduction factor of 6 (effective factor of 4.44) with 8 coils and only a few autocalibration signal (ACS) lines. PMID:21604290

Computer-aided light sheet flow visualization using photogrammetry

NASA Technical Reports Server (NTRS)

Stacy, Kathryn; Severance, Kurt; Childers, Brooks A.

1994-01-01

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and a visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) results, was chosen to interactively display the reconstructed light sheet images with the numerical surface geometry for the model or aircraft under study. The photogrammetric reconstruction technique and the image processing and computer graphics techniques and equipment are described. Results of the computer-aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images with CFD solutions in the same graphics environment is also demonstrated.

Computer-Aided Light Sheet Flow Visualization

NASA Technical Reports Server (NTRS)

Stacy, Kathryn; Severance, Kurt; Childers, Brooks A.

1993-01-01

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) data sets, was chosen to interactively display the reconstructed light sheet images, along with the numerical surface geometry for the model or aircraft under study. A description is provided of the photogrammetric reconstruction technique, and the image processing and computer graphics techniques and equipment. Results of the computer aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images and CFD solutions in the same graphics environment is also demonstrated.

Computer-aided light sheet flow visualization

NASA Technical Reports Server (NTRS)

Stacy, Kathryn; Severance, Kurt; Childers, Brooks A.

1993-01-01

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) data sets, was chosen to interactively display the reconstructed light sheet images, along with the numerical surface geometry for the model or aircraft under study. A description is provided of the photogrammetric reconstruction technique, and the image processing and computer graphics techniques and equipment. Results of the computer aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images and CFD solutions in the same graphics environment is also demonstrated.

Quantitative photoacoustic imaging in the acoustic regime using SPIM

NASA Astrophysics Data System (ADS)

Beigl, Alexander; Elbau, Peter; Sadiq, Kamran; Scherzer, Otmar

2018-05-01

While in standard photoacoustic imaging the propagation of sound waves is modeled by the standard wave equation, our approach is based on a generalized wave equation with variable sound speed and material density, respectively. In this paper we present an approach for photoacoustic imaging, which in addition to the recovery of the absorption density parameter, the imaging parameter of standard photoacoustics, also allows us to reconstruct the spatially varying sound speed and density, respectively, of the medium. We provide analytical reconstruction formulas for all three parameters based in a linearized model based on single plane illumination microscopy (SPIM) techniques.

Fast magnetic resonance imaging based on high degree total variation

NASA Astrophysics Data System (ADS)

Wang, Sujie; Lu, Liangliang; Zheng, Junbao; Jiang, Mingfeng

2018-04-01

In order to eliminating the artifacts and "staircase effect" of total variation in Compressive Sensing MRI, high degree total variation model is proposed for dynamic MRI reconstruction. the high degree total variation regularization term is used as a constraint to reconstruct the magnetic resonance image, and the iterative weighted MM algorithm is proposed to solve the convex optimization problem of the reconstructed MR image model, In addtion, one set of cardiac magnetic resonance data is used to verify the proposed algorithm for MRI. The results show that the high degree total variation method has a better reconstruction effect than the total variation and the total generalized variation, which can obtain higher reconstruction SNR and better structural similarity.

A noise power spectrum study of a new model-based iterative reconstruction system: Veo 3.0.

PubMed

Li, Guang; Liu, Xinming; Dodge, Cristina T; Jensen, Corey T; Rong, X John

2016-09-08

The purpose of this study was to evaluate performance of the third generation of model-based iterative reconstruction (MBIR) system, Veo 3.0, based on noise power spectrum (NPS) analysis with various clinical presets over a wide range of clinically applicable dose levels. A CatPhan 600 surrounded by an oval, fat-equivalent ring to mimic patient size/shape was scanned 10 times at each of six dose levels on a GE HD 750 scanner. NPS analysis was performed on images reconstructed with various Veo 3.0 preset combinations for comparisons of those images reconstructed using Veo 2.0, filtered back projection (FBP) and adaptive statistical iterative reconstruc-tion (ASiR). The new Target Thickness setting resulted in higher noise in thicker axial images. The new Texture Enhancement function achieved a more isotropic noise behavior with less image artifacts. Veo 3.0 provides additional reconstruction options designed to allow the user choice of balance between spatial resolution and image noise, relative to Veo 2.0. Veo 3.0 provides more user selectable options and in general improved isotropic noise behavior in comparison to Veo 2.0. The overall noise reduction performance of both versions of MBIR was improved in comparison to FBP and ASiR, especially at low-dose levels. © 2016 The Authors.

Multiframe super resolution reconstruction method based on light field angular images

NASA Astrophysics Data System (ADS)

Zhou, Shubo; Yuan, Yan; Su, Lijuan; Ding, Xiaomin; Wang, Jichao

2017-12-01

The plenoptic camera can directly obtain 4-dimensional light field information from a 2-dimensional sensor. However, based on the sampling theorem, the spatial resolution is greatly limited by the microlenses. In this paper, we present a method of reconstructing high-resolution images from the angular images. First, the ray tracing method is used to model the telecentric-based light field imaging process. Then, we analyze the subpixel shifts between the angular images extracted from the defocused light field data and the blur in the angular images. According to the analysis above, we construct the observation model from the ideal high-resolution image to the angular images. Applying the regularized super resolution method, we can obtain the super resolution result with a magnification ratio of 8. The results demonstrate the effectiveness of the proposed observation model.

Comparison of 3d Reconstruction Services and Terrestrial Laser Scanning for Cultural Heritage Documentation

NASA Astrophysics Data System (ADS)

Rasztovits, S.; Dorninger, P.

2013-07-01

Terrestrial Laser Scanning (TLS) is an established method to reconstruct the geometrical surface of given objects. Current systems allow for fast and efficient determination of 3D models with high accuracy and richness in detail. Alternatively, 3D reconstruction services are using images to reconstruct the surface of an object. While the instrumental expenses for laser scanning systems are high, upcoming free software services as well as open source software packages enable the generation of 3D models using digital consumer cameras. In addition, processing TLS data still requires an experienced user while recent web-services operate completely automatically. An indisputable advantage of image based 3D modeling is its implicit capability for model texturing. However, the achievable accuracy and resolution of the 3D models is lower than those of laser scanning data. Within this contribution, we investigate the results of automated web-services for image based 3D model generation with respect to a TLS reference model. For this, a copper sculpture was acquired using a laser scanner and using image series of different digital cameras. Two different webservices, namely Arc3D and AutoDesk 123D Catch were used to process the image data. The geometric accuracy was compared for the entire model and for some highly structured details. The results are presented and interpreted based on difference models. Finally, an economical comparison of the generation of the models is given considering the interactive and processing time costs.

Three-dimensional electrical impedance tomography based on the complete electrode model.

PubMed

Vauhkonen, P J; Vauhkonen, M; Savolainen, T; Kaipio, J P

1999-09-01

In electrical impedance tomography an approximation for the internal resistivity distribution is computed based on the knowledge of the injected currents and measured voltages on the surface of the body. It is often assumed that the injected currents are confined to the two-dimensional (2-D) electrode plane and the reconstruction is based on 2-D assumptions. However, the currents spread out in three dimensions and, therefore, off-plane structures have significant effect on the reconstructed images. In this paper we propose a finite element-based method for the reconstruction of three-dimensional resistivity distributions. The proposed method is based on the so-called complete electrode model that takes into account the presence of the electrodes and the contact impedances. Both the forward and the inverse problems are discussed and results from static and dynamic (difference) reconstructions with real measurement data are given. It is shown that in phantom experiments with accurate finite element computations it is possible to obtain static images that are comparable with difference images that are reconstructed from the same object with the empty (saline filled) tank as a reference.

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

Reconstruction of fluorescence molecular tomography (FMT) is an ill-posed inverse problem. Anatomical guidance in the FMT reconstruction can improve FMT reconstruction efficiently. We have developed a kernel method to introduce the anatomical guidance into FMT robustly and easily. The kernel method is from machine learning for pattern analysis and is an efficient way to represent anatomical features. For the finite element method based FMT reconstruction, we calculate a kernel function for each finite element node from an anatomical image, such as a micro-CT image. Then the fluorophore concentration at each node is represented by a kernel coefficient vector and the corresponding kernel function. In the FMT forward model, we have a new system matrix by multiplying the sensitivity matrix with the kernel matrix. Thus, the kernel coefficient vector is the unknown to be reconstructed following a standard iterative reconstruction process. We convert the FMT reconstruction problem into the kernel coefficient reconstruction problem. The desired fluorophore concentration at each node can be calculated accordingly. Numerical simulation studies have demonstrated that the proposed kernel-based algorithm can improve the spatial resolution of the reconstructed FMT images. In the proposed kernel method, the anatomical guidance can be obtained directly from the anatomical image and is included in the forward modeling. One of the advantages is that we do not need to segment the anatomical image for the targets and background.

The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation.

PubMed

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2014-06-01

Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton-Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR(C)) and (4) GREIT with individual thorax geometry (GR(T)). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal-Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms.

Submillisievert Radiation Dose Coronary CT Angiography: Clinical Impact of the Knowledge-Based Iterative Model Reconstruction.

PubMed

Iyama, Yuji; Nakaura, Takeshi; Kidoh, Masafumi; Oda, Seitaro; Utsunomiya, Daisuke; Sakaino, Naritsugu; Tokuyasu, Shinichi; Osakabe, Hirokazu; Harada, Kazunori; Yamashita, Yasuyuki

2016-11-01

The purpose of this study was to evaluate the noise and image quality of images reconstructed with a knowledge-based iterative model reconstruction (knowledge-based IMR) in ultra-low dose cardiac computed tomography (CT). We performed submillisievert radiation dose coronary CT angiography on 43 patients. We also performed a phantom study to evaluate the influence of object size with the automatic exposure control phantom. We reconstructed clinical and phantom studies with filtered back projection (FBP), hybrid iterative reconstruction (hybrid IR), and knowledge-based IMR. We measured effective dose of patients and compared CT number, image noise, and contrast noise ratio in ascending aorta of each reconstruction technique. We compared the relationship between image noise and body mass index for the clinical study, and object size for phantom study. The mean effective dose was 0.98 ± 0.25 mSv. The image noise of knowledge-based IMR images was significantly lower than those of FBP and hybrid IR images (knowledge-based IMR: 19.4 ± 2.8; FBP: 126.7 ± 35.0; hybrid IR: 48.8 ± 12.8, respectively) (P < .01). The contrast noise ratio of knowledge-based IMR images was significantly higher than those of FBP and hybrid IR images (knowledge-based IMR: 29.1 ± 5.4; FBP: 4.6 ± 1.3; hybrid IR: 13.1 ± 3.5, respectively) (P < .01). There were moderate correlations between image noise and body mass index in FBP (r = 0.57, P < .01) and hybrid IR techniques (r = 0.42, P < .01); however, these correlations were weak in knowledge-based IMR (r = 0.27, P < .01). Compared to FBP and hybrid IR, the knowledge-based IMR offers significant noise reduction and improvement in image quality in submillisievert radiation dose cardiac CT. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging.

PubMed

Han, Yiyong; Tzoumas, Stratis; Nunes, Antonio; Ntziachristos, Vasilis; Rosenthal, Amir

2015-09-01

With recent advancement in hardware of optoacoustic imaging systems, highly detailed cross-sectional images may be acquired at a single laser shot, thus eliminating motion artifacts. Nonetheless, other sources of artifacts remain due to signal distortion or out-of-plane signals. The purpose of image reconstruction algorithms is to obtain the most accurate images from noisy, distorted projection data. In this paper, the authors use the model-based approach for acoustic inversion, combined with a sparsity-based inversion procedure. Specifically, a cost function is used that includes the L1 norm of the image in sparse representation and a total variation (TV) term. The optimization problem is solved by a numerically efficient implementation of a nonlinear gradient descent algorithm. TV-L1 model-based inversion is tested in the cross section geometry for numerically generated data as well as for in vivo experimental data from an adult mouse. In all cases, model-based TV-L1 inversion showed a better performance over the conventional Tikhonov regularization, TV inversion, and L1 inversion. In the numerical examples, the images reconstructed with TV-L1 inversion were quantitatively more similar to the originating images. In the experimental examples, TV-L1 inversion yielded sharper images and weaker streak artifact. The results herein show that TV-L1 inversion is capable of improving the quality of highly detailed, multiscale optoacoustic images obtained in vivo using cross-sectional imaging systems. As a result of its high fidelity, model-based TV-L1 inversion may be considered as the new standard for image reconstruction in cross-sectional imaging.

Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage

NASA Astrophysics Data System (ADS)

Liu, Tianshuai; Rong, Junyan; Gao, Peng; Zhang, Wenli; Liu, Wenlei; Zhang, Yuanke; Lu, Hongbing

2018-02-01

With the advances of x-ray excitable nanophosphors, x-ray luminescence computed tomography (XLCT) has become a promising hybrid imaging modality. In particular, a cone-beam XLCT (CB-XLCT) system has demonstrated its potential in in vivo imaging with the advantage of fast imaging speed over other XLCT systems. Currently, the imaging models of most XLCT systems assume that nanophosphors emit light based on the intensity distribution of x-ray within the object, not completely reflecting the nature of the x-ray excitation process. To improve the imaging quality of CB-XLCT, an imaging model that adopts an excitation model of nanophosphors based on x-ray absorption dosage is proposed in this study. To solve the ill-posed inverse problem, a reconstruction algorithm that combines the adaptive Tikhonov regularization method with the imaging model is implemented for CB-XLCT reconstruction. Numerical simulations and phantom experiments indicate that compared with the traditional forward model based on x-ray intensity, the proposed dose-based model could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy, and image contrast. In addition, the proposed model behaves better in distinguishing closer targets, demonstrating its advantage in improving spatial resolution.

SIMULTANEOUS MULTISLICE MAGNETIC RESONANCE FINGERPRINTING WITH LOW-RANK AND SUBSPACE MODELING

PubMed Central

Zhao, Bo; Bilgic, Berkin; Adalsteinsson, Elfar; Griswold, Mark A.; Wald, Lawrence L.; Setsompop, Kawin

2018-01-01

Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T1, T2, and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3x speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice. PMID:29060594

Simultaneous multislice magnetic resonance fingerprinting with low-rank and subspace modeling.

PubMed

Bo Zhao; Bilgic, Berkin; Adalsteinsson, Elfar; Griswold, Mark A; Wald, Lawrence L; Setsompop, Kawin

2017-07-01

Magnetic resonance fingerprinting (MRF) is a new quantitative imaging paradigm that enables simultaneous acquisition of multiple magnetic resonance tissue parameters (e.g., T 1 , T 2 , and spin density). Recently, MRF has been integrated with simultaneous multislice (SMS) acquisitions to enable volumetric imaging with faster scan time. In this paper, we present a new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF. Here the low-rank model exploits strong spatiotemporal correlation among contrast-weighted images, while the subspace model captures the temporal evolution of magnetization dynamics. With the proposed model, the image reconstruction problem is formulated as a convex optimization problem, for which we develop an algorithm based on variable splitting and the alternating direction method of multipliers. The performance of the proposed method has been evaluated by numerical experiments, and the results demonstrate that the proposed method leads to improved accuracy over the conventional approach. Practically, the proposed method has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.

Optimization of digital breast tomosynthesis (DBT) acquisition parameters for human observers: effect of reconstruction algorithms

NASA Astrophysics Data System (ADS)

Zeng, Rongping; Badano, Aldo; Myers, Kyle J.

2017-04-01

We showed in our earlier work that the choice of reconstruction methods does not affect the optimization of DBT acquisition parameters (angular span and number of views) using simulated breast phantom images in detecting lesions with a channelized Hotelling observer (CHO). In this work we investigate whether the model-observer based conclusion is valid when using humans to interpret images. We used previously generated DBT breast phantom images and recruited human readers to find the optimal geometry settings associated with two reconstruction algorithms, filtered back projection (FBP) and simultaneous algebraic reconstruction technique (SART). The human reader results show that image quality trends as a function of the acquisition parameters are consistent between FBP and SART reconstructions. The consistent trends confirm that the optimization of DBT system geometry is insensitive to the choice of reconstruction algorithm. The results also show that humans perform better in SART reconstructed images than in FBP reconstructed images. In addition, we applied CHOs with three commonly used channel models, Laguerre-Gauss (LG) channels, square (SQR) channels and sparse difference-of-Gaussian (sDOG) channels. We found that LG channels predict human performance trends better than SQR and sDOG channel models for the task of detecting lesions in tomosynthesis backgrounds. Overall, this work confirms that the choice of reconstruction algorithm is not critical for optimizing DBT system acquisition parameters.

Single image super-resolution reconstruction algorithm based on eage selection

NASA Astrophysics Data System (ADS)

Zhang, Yaolan; Liu, Yijun

2017-05-01

Super-resolution (SR) has become more important, because it can generate high-quality high-resolution (HR) images from low-resolution (LR) input images. At present, there are a lot of work is concentrated on developing sophisticated image priors to improve the image quality, while taking much less attention to estimating and incorporating the blur model that can also impact the reconstruction results. We present a new reconstruction method based on eager selection. This method takes full account of the factors that affect the blur kernel estimation and accurately estimating the blur process. When comparing with the state-of-the-art methods, our method has comparable performance.

Effects of the approximations of light propagation on quantitative photoacoustic tomography using two-dimensional photon diffusion equation and linearization

NASA Astrophysics Data System (ADS)

Okawa, Shinpei; Hirasawa, Takeshi; Kushibiki, Toshihiro; Ishihara, Miya

2017-12-01

Quantitative photoacoustic tomography (QPAT) employing a light propagation model will play an important role in medical diagnoses by quantifying the concentration of hemoglobin or a contrast agent. However, QPAT by the light propagation model with the three-dimensional (3D) radiative transfer equation (RTE) requires a huge computational load in the iterative forward calculations involved in the updating process to reconstruct the absorption coefficient. The approximations of the light propagation improve the efficiency of the image reconstruction for the QPAT. In this study, we compared the 3D/two-dimensional (2D) photon diffusion equation (PDE) approximating 3D RTE with the Monte Carlo simulation based on 3D RTE. Then, the errors in a 2D PDE-based linearized image reconstruction caused by the approximations were quantitatively demonstrated and discussed in the numerical simulations. It was clearly observed that the approximations affected the reconstructed absorption coefficient. The 2D PDE-based linearized algorithm succeeded in the image reconstruction of the region with a large absorption coefficient in the 3D phantom. The value reconstructed in the phantom experiment agreed with that in the numerical simulation, so that it was validated that the numerical simulation of the image reconstruction predicted the relationship between the true absorption coefficient of the target in the 3D medium and the reconstructed value with the 2D PDE-based linearized algorithm. Moreover, the the true absorption coefficient in 3D medium was estimated from the 2D reconstructed image on the basis of the prediction by the numerical simulation. The estimation was successful in the phantom experiment, although some limitations were revealed.

Efficient workflows for 3D building full-color model reconstruction using LIDAR long-range laser and image-based modeling techniques

NASA Astrophysics Data System (ADS)

Shih, Chihhsiong

2005-01-01

Two efficient workflow are developed for the reconstruction of a 3D full color building model. One uses a point wise sensing device to sample an unknown object densely and attach color textures from a digital camera separately. The other uses an image based approach to reconstruct the model with color texture automatically attached. The point wise sensing device reconstructs the CAD model using a modified best view algorithm that collects the maximum number of construction faces in one view. The partial views of the point clouds data are then glued together using a common face between two consecutive views. Typical overlapping mesh removal and coarsening procedures are adapted to generate a unified 3D mesh shell structure. A post processing step is then taken to combine the digital image content from a separate camera with the 3D mesh shell surfaces. An indirect uv mapping procedure first divide the model faces into groups within which every face share the same normal direction. The corresponding images of these faces in a group is then adjusted using the uv map as a guidance. The final assembled image is then glued back to the 3D mesh to present a full colored building model. The result is a virtual building that can reflect the true dimension and surface material conditions of a real world campus building. The image based modeling procedure uses a commercial photogrammetry package to reconstruct the 3D model. A novel view planning algorithm is developed to guide the photos taking procedure. This algorithm successfully generate a minimum set of view angles. The set of pictures taken at these view angles can guarantee that each model face shows up at least in two of the pictures set and no more than three. The 3D model can then be reconstructed with minimum amount of labor spent in correlating picture pairs. The finished model is compared with the original object in both the topological and dimensional aspects. All the test cases show exact same topology and reasonably low dimension error ratio. Again proving the applicability of the algorithm.

Development of acoustic model-based iterative reconstruction technique for thick-concrete imaging

NASA Astrophysics Data System (ADS)

Almansouri, Hani; Clayton, Dwight; Kisner, Roger; Polsky, Yarom; Bouman, Charles; Santos-Villalobos, Hector

2016-02-01

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structures are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.1

Evaluation of a 3D point cloud tetrahedral tomographic reconstruction method

PubMed Central

Pereira, N F; Sitek, A

2011-01-01

Tomographic reconstruction on an irregular grid may be superior to reconstruction on a regular grid. This is achieved through an appropriate choice of the image space model, the selection of an optimal set of points and the use of any available prior information during the reconstruction process. Accordingly, a number of reconstruction-related parameters must be optimized for best performance. In this work, a 3D point cloud tetrahedral mesh reconstruction method is evaluated for quantitative tasks. A linear image model is employed to obtain the reconstruction system matrix and five point generation strategies are studied. The evaluation is performed using the recovery coefficient, as well as voxel- and template-based estimates of bias and variance measures, computed over specific regions in the reconstructed image. A similar analysis is performed for regular grid reconstructions that use voxel basis functions. The maximum likelihood expectation maximization reconstruction algorithm is used. For the tetrahedral reconstructions, of the five point generation methods that are evaluated, three use image priors. For evaluation purposes, an object consisting of overlapping spheres with varying activity is simulated. The exact parallel projection data of this object are obtained analytically using a parallel projector, and multiple Poisson noise realizations of these exact data are generated and reconstructed using the different point generation strategies. The unconstrained nature of point placement in some of the irregular mesh-based reconstruction strategies has superior activity recovery for small, low-contrast image regions. The results show that, with an appropriately generated set of mesh points, the irregular grid reconstruction methods can out-perform reconstructions on a regular grid for mathematical phantoms, in terms of the performance measures evaluated. PMID:20736496

Evaluation of a 3D point cloud tetrahedral tomographic reconstruction method

NASA Astrophysics Data System (ADS)

Pereira, N. F.; Sitek, A.

2010-09-01

Tomographic reconstruction on an irregular grid may be superior to reconstruction on a regular grid. This is achieved through an appropriate choice of the image space model, the selection of an optimal set of points and the use of any available prior information during the reconstruction process. Accordingly, a number of reconstruction-related parameters must be optimized for best performance. In this work, a 3D point cloud tetrahedral mesh reconstruction method is evaluated for quantitative tasks. A linear image model is employed to obtain the reconstruction system matrix and five point generation strategies are studied. The evaluation is performed using the recovery coefficient, as well as voxel- and template-based estimates of bias and variance measures, computed over specific regions in the reconstructed image. A similar analysis is performed for regular grid reconstructions that use voxel basis functions. The maximum likelihood expectation maximization reconstruction algorithm is used. For the tetrahedral reconstructions, of the five point generation methods that are evaluated, three use image priors. For evaluation purposes, an object consisting of overlapping spheres with varying activity is simulated. The exact parallel projection data of this object are obtained analytically using a parallel projector, and multiple Poisson noise realizations of these exact data are generated and reconstructed using the different point generation strategies. The unconstrained nature of point placement in some of the irregular mesh-based reconstruction strategies has superior activity recovery for small, low-contrast image regions. The results show that, with an appropriately generated set of mesh points, the irregular grid reconstruction methods can out-perform reconstructions on a regular grid for mathematical phantoms, in terms of the performance measures evaluated.

An automated 3D reconstruction method of UAV images

NASA Astrophysics Data System (ADS)

Liu, Jun; Wang, He; Liu, Xiaoyang; Li, Feng; Sun, Guangtong; Song, Ping

2015-10-01

In this paper a novel fully automated 3D reconstruction approach based on low-altitude unmanned aerial vehicle system (UAVs) images will be presented, which does not require previous camera calibration or any other external prior knowledge. Dense 3D point clouds are generated by integrating orderly feature extraction, image matching, structure from motion (SfM) and multi-view stereo (MVS) algorithms, overcoming many of the cost, time limitations of rigorous photogrammetry techniques. An image topology analysis strategy is introduced to speed up large scene reconstruction by taking advantage of the flight-control data acquired by UAV. Image topology map can significantly reduce the running time of feature matching by limiting the combination of images. A high-resolution digital surface model of the study area is produced base on UAV point clouds by constructing the triangular irregular network. Experimental results show that the proposed approach is robust and feasible for automatic 3D reconstruction of low-altitude UAV images, and has great potential for the acquisition of spatial information at large scales mapping, especially suitable for rapid response and precise modelling in disaster emergency.

Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

MR Guided PET Image Reconstruction

PubMed Central

Bai, Bing; Li, Quanzheng; Leahy, Richard M.

2013-01-01

The resolution of PET images is limited by the physics of positron-electron annihilation and instrumentation for photon coincidence detection. Model based methods that incorporate accurate physical and statistical models have produced significant improvements in reconstructed image quality when compared to filtered backprojection reconstruction methods. However, it has often been suggested that by incorporating anatomical information, the resolution and noise properties of PET images could be improved, leading to better quantitation or lesion detection. With the recent development of combined MR-PET scanners, it is possible to collect intrinsically co-registered MR images. It is therefore now possible to routinely make use of anatomical information in PET reconstruction, provided appropriate methods are available. In this paper we review research efforts over the past 20 years to develop these methods. We discuss approaches based on the use of both Markov random field priors and joint information or entropy measures. The general framework for these methods is described and their performance and longer term potential and limitations discussed. PMID:23178087

Limiting CT radiation dose in children with craniosynostosis: phantom study using model-based iterative reconstruction.

PubMed

Kaasalainen, Touko; Palmu, Kirsi; Lampinen, Anniina; Reijonen, Vappu; Leikola, Junnu; Kivisaari, Riku; Kortesniemi, Mika

2015-09-01

Medical professionals need to exercise particular caution when developing CT scanning protocols for children who require multiple CT studies, such as those with craniosynostosis. To evaluate the utility of ultra-low-dose CT protocols with model-based iterative reconstruction techniques for craniosynostosis imaging. We scanned two pediatric anthropomorphic phantoms with a 64-slice CT scanner using different low-dose protocols for craniosynostosis. We measured organ doses in the head region with metal-oxide-semiconductor field-effect transistor (MOSFET) dosimeters. Numerical simulations served to estimate organ and effective doses. We objectively and subjectively evaluated the quality of images produced by adaptive statistical iterative reconstruction (ASiR) 30%, ASiR 50% and Veo (all by GE Healthcare, Waukesha, WI). Image noise and contrast were determined for different tissues. Mean organ dose with the newborn phantom was decreased up to 83% compared to the routine protocol when using ultra-low-dose scanning settings. Similarly, for the 5-year phantom the greatest radiation dose reduction was 88%. The numerical simulations supported the findings with MOSFET measurements. The image quality remained adequate with Veo reconstruction, even at the lowest dose level. Craniosynostosis CT with model-based iterative reconstruction could be performed with a 20-μSv effective dose, corresponding to the radiation exposure of plain skull radiography, without compromising required image quality.

LORAKS makes better SENSE: Phase-constrained partial fourier SENSE reconstruction without phase calibration.

PubMed

Kim, Tae Hyung; Setsompop, Kawin; Haldar, Justin P

2017-03-01

Parallel imaging and partial Fourier acquisition are two classical approaches for accelerated MRI. Methods that combine these approaches often rely on prior knowledge of the image phase, but the need to obtain this prior information can place practical restrictions on the data acquisition strategy. In this work, we propose and evaluate SENSE-LORAKS, which enables combined parallel imaging and partial Fourier reconstruction without requiring prior phase information. The proposed formulation is based on combining the classical SENSE model for parallel imaging data with the more recent LORAKS framework for MR image reconstruction using low-rank matrix modeling. Previous LORAKS-based methods have successfully enabled calibrationless partial Fourier parallel MRI reconstruction, but have been most successful with nonuniform sampling strategies that may be hard to implement for certain applications. By combining LORAKS with SENSE, we enable highly accelerated partial Fourier MRI reconstruction for a broader range of sampling trajectories, including widely used calibrationless uniformly undersampled trajectories. Our empirical results with retrospectively undersampled datasets indicate that when SENSE-LORAKS reconstruction is combined with an appropriate k-space sampling trajectory, it can provide substantially better image quality at high-acceleration rates relative to existing state-of-the-art reconstruction approaches. The SENSE-LORAKS framework provides promising new opportunities for highly accelerated MRI. Magn Reson Med 77:1021-1035, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Efficacy of model-based iterative reconstruction technique in non-enhanced CT of the renal tracts for ureteric calculi.

PubMed

Tan, T J; Lau, Kenneth K; Jackson, Dana; Ardley, Nicholas; Borasu, Adina

2017-04-01

The purpose of this study was to assess the efficacy of model-based iterative reconstruction (MBIR), statistical iterative reconstruction (SIR), and filtered back projection (FBP) image reconstruction algorithms in the delineation of ureters and overall image quality on non-enhanced computed tomography of the renal tracts (NECT-KUB). This was a prospective study of 40 adult patients who underwent NECT-KUB for investigation of ureteric colic. Images were reconstructed using FBP, SIR, and MBIR techniques and individually and randomly assessed by two blinded radiologists. Parameters measured were overall image quality, presence of ureteric calculus, presence of hydronephrosis or hydroureters, image quality of each ureteric segment, total length of ureters unable to be visualized, attenuation values of image noise, and retroperitoneal fat content for each patient. There were no diagnostic discrepancies between image reconstruction modalities for urolithiasis. Overall image qualities and for each ureteric segment were superior using MBIR (67.5 % rated as 'Good to Excellent' vs. 25 % in SIR and 2.5 % in FBP). The lengths of non-visualized ureteric segments were shortest using MBIR (55.0 % measured 'less than 5 cm' vs. ASIR 33.8 % and FBP 10 %). MBIR was able to reduce overall image noise by up to 49.36 % over SIR and 71.02 % over FBP. MBIR technique improves overall image quality and visualization of ureters over FBP and SIR.

LOR-interleaving image reconstruction for PET imaging with fractional-crystal collimation

NASA Astrophysics Data System (ADS)

Li, Yusheng; Matej, Samuel; Karp, Joel S.; Metzler, Scott D.

2015-01-01

Positron emission tomography (PET) has become an important modality in medical and molecular imaging. However, in most PET applications, the resolution is still mainly limited by the physical crystal sizes or the detector’s intrinsic spatial resolution. To achieve images with better spatial resolution in a central region of interest (ROI), we have previously proposed using collimation in PET scanners. The collimator is designed to partially mask detector crystals to detect lines of response (LORs) within fractional crystals. A sequence of collimator-encoded LORs is measured with different collimation configurations. This novel collimated scanner geometry makes the reconstruction problem challenging, as both detector and collimator effects need to be modeled to reconstruct high-resolution images from collimated LORs. In this paper, we present a LOR-interleaving (LORI) algorithm, which incorporates these effects and has the advantage of reusing existing reconstruction software, to reconstruct high-resolution images for PET with fractional-crystal collimation. We also develop a 3D ray-tracing model incorporating both the collimator and crystal penetration for simulations and reconstructions of the collimated PET. By registering the collimator-encoded LORs with the collimator configurations, high-resolution LORs are restored based on the modeled transfer matrices using the non-negative least-squares method and EM algorithm. The resolution-enhanced images are then reconstructed from the high-resolution LORs using the MLEM or OSEM algorithm. For validation, we applied the LORI method to a small-animal PET scanner, A-PET, with a specially designed collimator. We demonstrate through simulated reconstructions with a hot-rod phantom and MOBY phantom that the LORI reconstructions can substantially improve spatial resolution and quantification compared to the uncollimated reconstructions. The LORI algorithm is crucial to improve overall image quality of collimated PET, which can have significant implications in preclinical and clinical ROI imaging applications.

Toward morphological thoracic EIT: major signal sources correspond to respective organ locations in CT.

PubMed

Ferrario, Damien; Grychtol, Bartłomiej; Adler, Andy; Solà, Josep; Böhm, Stephan H; Bodenstein, Marc

2012-11-01

Lung and cardiovascular monitoring applications of electrical impedance tomography (EIT) require localization of relevant functional structures or organs of interest within the reconstructed images. We describe an algorithm for automatic detection of heart and lung regions in a time series of EIT images. Using EIT reconstruction based on anatomical models, candidate regions are identified in the frequency domain and image-based classification techniques applied. The algorithm was validated on a set of simultaneously recorded EIT and CT data in pigs. In all cases, identified regions in EIT images corresponded to those manually segmented in the matched CT image. Results demonstrate the ability of EIT technology to reconstruct relevant impedance changes at their anatomical locations, provided that information about the thoracic boundary shape (and electrode positions) are used for reconstruction.

Reconstruction of measurable three-dimensional point cloud model based on large-scene archaeological excavation sites

NASA Astrophysics Data System (ADS)

Zhang, Chun-Sen; Zhang, Meng-Meng; Zhang, Wei-Xing

2017-01-01

This paper outlines a low-cost, user-friendly photogrammetric technique with nonmetric cameras to obtain excavation site digital sequence images, based on photogrammetry and computer vision. Digital camera calibration, automatic aerial triangulation, image feature extraction, image sequence matching, and dense digital differential rectification are used, combined with a certain number of global control points of the excavation site, to reconstruct the high precision of measured three-dimensional (3-D) models. Using the acrobatic figurines in the Qin Shi Huang mausoleum excavation as an example, our method solves the problems of little base-to-height ratio, high inclination, unstable altitudes, and significant ground elevation changes affecting image matching. Compared to 3-D laser scanning, the 3-D color point cloud obtained by this method can maintain the same visual result and has advantages of low project cost, simple data processing, and high accuracy. Structure-from-motion (SfM) is often used to reconstruct 3-D models of large scenes and has lower accuracy if it is a reconstructed 3-D model of a small scene at close range. Results indicate that this method quickly achieves 3-D reconstruction of large archaeological sites and produces heritage site distribution of orthophotos providing a scientific basis for accurate location of cultural relics, archaeological excavations, investigation, and site protection planning. This proposed method has a comprehensive application value.

Iterative image reconstruction in elastic inhomogenous media with application to transcranial photoacoustic tomography

NASA Astrophysics Data System (ADS)

Poudel, Joemini; Matthews, Thomas P.; Mitsuhashi, Kenji; Garcia-Uribe, Alejandro; Wang, Lihong V.; Anastasio, Mark A.

2017-03-01

Photoacoustic computed tomography (PACT) is an emerging computed imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to a time-domain inverse source problem, where the initial pressure distribution is recovered from the measurements recorded on an aperture outside the support of the source. A major challenge in transcranial PACT brain imaging is to compensate for aberrations in the measured data due to the propagation of the photoacoustic wavefields through the skull. To properly account for these effects, a wave equation-based inversion method should be employed that can model the heterogeneous elastic properties of the medium. In this study, an iterative image reconstruction method for 3D transcranial PACT is developed based on the elastic wave equation. To accomplish this, a forward model based on a finite-difference time-domain discretization of the elastic wave equation is established. Subsequently, gradient-based methods are employed for computing penalized least squares estimates of the initial source distribution that produced the measured photoacoustic data. The developed reconstruction algorithm is validated and investigated through computer-simulation studies.

Limited-angle effect compensation for respiratory binned cardiac SPECT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qi, Wenyuan; Yang, Yongyi, E-mail: yy@ece.iit.edu; Wernick, Miles N.

Purpose: In cardiac single photon emission computed tomography (SPECT), respiratory-binned study is used to combat the motion blur associated with respiratory motion. However, owing to the variability in respiratory patterns during data acquisition, the acquired data counts can vary significantly both among respiratory bins and among projection angles within individual bins. If not properly accounted for, such variation could lead to artifacts similar to limited-angle effect in image reconstruction. In this work, the authors aim to investigate several reconstruction strategies for compensating the limited-angle effect in respiratory binned data for the purpose of reducing the image artifacts. Methods: The authorsmore » first consider a model based correction approach, in which the variation in acquisition time is directly incorporated into the imaging model, such that the data statistics are accurately described among both the projection angles and respiratory bins. Afterward, the authors consider an approximation approach, in which the acquired data are rescaled to accommodate the variation in acquisition time among different projection angles while the imaging model is kept unchanged. In addition, the authors also consider the use of a smoothing prior in reconstruction for suppressing the artifacts associated with limited-angle effect. In our evaluation study, the authors first used Monte Carlo simulated imaging with 4D NCAT phantom wherein the ground truth is known for quantitative comparison. The authors evaluated the accuracy of the reconstructed myocardium using a number of metrics, including regional and overall accuracy of the myocardium, uniformity and spatial resolution of the left ventricle (LV) wall, and detectability of perfusion defect using a channelized Hotelling observer. As a preliminary demonstration, the authors also tested the different approaches on five sets of clinical acquisitions. Results: The quantitative evaluation results show that the three compensation methods could all, but to different extents, reduce the reconstruction artifacts over no compensation. In particular, the model based approach reduced the mean-squared-error of the reconstructed myocardium by as much as 40%. Compared to the approach of data rescaling, the model based approach further improved both the overall and regional accuracy of the myocardium; it also further improved the lesion detectability and the uniformity of the LV wall. When ML reconstruction was used, the model based approach was notably more effective for improving the LV wall; when MAP reconstruction was used, the smoothing prior could reduce the noise level and artifacts with little or no increase in bias, but at the cost of a slight resolution loss of the LV wall. The improvements in image quality by the different compensation methods were also observed in the clinical acquisitions. Conclusions: Compensating for the uneven distribution of acquisition time among both projection angles and respiratory bins can effectively reduce the limited-angle artifacts in respiratory-binned cardiac SPECT reconstruction. Direct incorporation of the time variation into the imaging model together with a smoothing prior in reconstruction can lead to the most improvement in the accuracy of the reconstructed myocardium.« less

WE-G-18A-01: JUNIOR INVESTIGATOR WINNER - Low-Dose C-Arm Cone-Beam CT with Model-Based Image Reconstruction for High-Quality Guidance of Neurosurgical Intervention

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, A; Stayman, J; Otake, Y

Purpose: To address the challenges of image quality, radiation dose, and reconstruction speed in intraoperative cone-beam CT (CBCT) for neurosurgery by combining model-based image reconstruction (MBIR) with accelerated algorithmic and computational methods. Methods: Preclinical studies involved a mobile C-arm for CBCT imaging of two anthropomorphic head phantoms that included simulated imaging targets (ventricles, soft-tissue structures/bleeds) and neurosurgical procedures (deep brain stimulation (DBS) electrode insertion) for assessment of image quality. The penalized likelihood (PL) framework was used for MBIR, incorporating a statistical model with image regularization via an edgepreserving penalty. To accelerate PL reconstruction, the ordered-subset, separable quadratic surrogates (OS-SQS) algorithmmore » was modified to incorporate Nesterov's method and implemented on a multi-GPU system. A fair comparison of image quality between PL and conventional filtered backprojection (FBP) was performed by selecting reconstruction parameters that provided matched low-contrast spatial resolution. Results: CBCT images of the head phantoms demonstrated that PL reconstruction improved image quality (∼28% higher CNR) even at half the radiation dose (3.3 mGy) compared to FBP. A combination of Nesterov's method and fast projectors yielded a PL reconstruction run-time of 251 sec (cf., 5729 sec for OS-SQS, 13 sec for FBP). Insertion of a DBS electrode resulted in severe metal artifact streaks in FBP reconstructions, whereas PL was intrinsically robust against metal artifact. The combination of noise and artifact was reduced from 32.2 HU in FBP to 9.5 HU in PL, thereby providing better assessment of device placement and potential complications. Conclusion: The methods can be applied to intraoperative CBCT for guidance and verification of neurosurgical procedures (DBS electrode insertion, biopsy, tumor resection) and detection of complications (intracranial hemorrhage). Significant improvement in image quality, dose reduction, and reconstruction time of ∼4 min will enable practical deployment of low-dose C-arm CBCT within the operating room. AAPM Research Seed Funding (2013-2014); NIH Fellowship F32EB017571; Siemens Healthcare (XP Division)« less

Fast and accurate computation of system matrix for area integral model-based algebraic reconstruction technique

NASA Astrophysics Data System (ADS)

Zhang, Shunli; Zhang, Dinghua; Gong, Hao; Ghasemalizadeh, Omid; Wang, Ge; Cao, Guohua

2014-11-01

Iterative algorithms, such as the algebraic reconstruction technique (ART), are popular for image reconstruction. For iterative reconstruction, the area integral model (AIM) is more accurate for better reconstruction quality than the line integral model (LIM). However, the computation of the system matrix for AIM is more complex and time-consuming than that for LIM. Here, we propose a fast and accurate method to compute the system matrix for AIM. First, we calculate the intersection of each boundary line of a narrow fan-beam with pixels in a recursive and efficient manner. Then, by grouping the beam-pixel intersection area into six types according to the slopes of the two boundary lines, we analytically compute the intersection area of the narrow fan-beam with the pixels in a simple algebraic fashion. Overall, experimental results show that our method is about three times faster than the Siddon algorithm and about two times faster than the distance-driven model (DDM) in computation of the system matrix. The reconstruction speed of our AIM-based ART is also faster than the LIM-based ART that uses the Siddon algorithm and DDM-based ART, for one iteration. The fast reconstruction speed of our method was accomplished without compromising the image quality.

Sparsity-constrained PET image reconstruction with learned dictionaries

NASA Astrophysics Data System (ADS)

Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

2016-09-01

PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.

Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jing; Gu, Xuejun

2013-10-15

Purpose: Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR).Methods: The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstructionmore » to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT.Results: Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left–right (L-R), anterior–posterior (A-P), and superior–inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively.Conclusions: The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.« less

Superiorization-based multi-energy CT image reconstruction

PubMed Central

Yang, Q; Cong, W; Wang, G

2017-01-01

The recently-developed superiorization approach is efficient and robust for solving various constrained optimization problems. This methodology can be applied to multi-energy CT image reconstruction with the regularization in terms of the prior rank, intensity and sparsity model (PRISM). In this paper, we propose a superiorized version of the simultaneous algebraic reconstruction technique (SART) based on the PRISM model. Then, we compare the proposed superiorized algorithm with the Split-Bregman algorithm in numerical experiments. The results show that both the Superiorized-SART and the Split-Bregman algorithms generate good results with weak noise and reduced artefacts. PMID:28983142

Beyond maximum entropy: Fractal pixon-based image reconstruction

NASA Technical Reports Server (NTRS)

Puetter, R. C.; Pina, R. K.

1994-01-01

We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other methods, including Goodness-of-Fit (e.g. Least-Squares and Lucy-Richardson) and Maximum Entropy (ME). Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME.

Single particle analysis based on Zernike phase contrast transmission electron microscopy.

PubMed

Danev, Radostin; Nagayama, Kuniaki

2008-02-01

We present the first application of Zernike phase-contrast transmission electron microscopy to single-particle 3D reconstruction of a protein, using GroEL chaperonin as the test specimen. We evaluated the performance of the technique by comparing 3D models derived from Zernike phase contrast imaging, with models from conventional underfocus phase contrast imaging. The same resolution, about 12A, was achieved by both imaging methods. The reconstruction based on Zernike phase contrast data required about 30% fewer particles. The advantages and prospects of each technique are discussed.

Limited view angle iterative CT reconstruction

NASA Astrophysics Data System (ADS)

Kisner, Sherman J.; Haneda, Eri; Bouman, Charles A.; Skatter, Sondre; Kourinny, Mikhail; Bedford, Simon

2012-03-01

Computed Tomography (CT) is widely used for transportation security to screen baggage for potential threats. For example, many airports use X-ray CT to scan the checked baggage of airline passengers. The resulting reconstructions are then used for both automated and human detection of threats. Recently, there has been growing interest in the use of model-based reconstruction techniques for application in CT security systems. Model-based reconstruction offers a number of potential advantages over more traditional direct reconstruction such as filtered backprojection (FBP). Perhaps one of the greatest advantages is the potential to reduce reconstruction artifacts when non-traditional scan geometries are used. For example, FBP tends to produce very severe streaking artifacts when applied to limited view data, which can adversely affect subsequent processing such as segmentation and detection. In this paper, we investigate the use of model-based reconstruction in conjunction with limited-view scanning architectures, and we illustrate the value of these methods using transportation security examples. The advantage of limited view architectures is that it has the potential to reduce the cost and complexity of a scanning system, but its disadvantage is that limited-view data can result in structured artifacts in reconstructed images. Our method of reconstruction depends on the formulation of both a forward projection model for the system, and a prior model that accounts for the contents and densities of typical baggage. In order to evaluate our new method, we use realistic models of baggage with randomly inserted simple simulated objects. Using this approach, we show that model-based reconstruction can substantially reduce artifacts and improve important metrics of image quality such as the accuracy of the estimated CT numbers.

Renal Cyst Pseudoenhancement: Intraindividual Comparison Between Virtual Monochromatic Spectral Images and Conventional Polychromatic 120-kVp Images Obtained During the Same CT Examination and Comparisons Among Images Reconstructed Using Filtered Back Projection, Adaptive Statistical Iterative Reconstruction, and Model-Based Iterative Reconstruction

PubMed Central

Yamada, Yoshitake; Yamada, Minoru; Sugisawa, Koichi; Akita, Hirotaka; Shiomi, Eisuke; Abe, Takayuki; Okuda, Shigeo; Jinzaki, Masahiro

2015-01-01

Abstract The purpose of this study was to compare renal cyst pseudoenhancement between virtual monochromatic spectral (VMS) and conventional polychromatic 120-kVp images obtained during the same abdominal computed tomography (CT) examination and among images reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Our institutional review board approved this prospective study; each participant provided written informed consent. Thirty-one patients (19 men, 12 women; age range, 59–85 years; mean age, 73.2 ± 5.5 years) with renal cysts underwent unenhanced 120-kVp CT followed by sequential fast kVp-switching dual-energy (80/140 kVp) and 120-kVp abdominal enhanced CT in the nephrographic phase over a 10-cm scan length with a random acquisition order and 4.5-second intervals. Fifty-one renal cysts (maximal diameter, 18.0 ± 14.7 mm [range, 4–61 mm]) were identified. The CT attenuation values of the cysts as well as of the kidneys were measured on the unenhanced images, enhanced VMS images (at 70 keV) reconstructed using FBP and ASIR from dual-energy data, and enhanced 120-kVp images reconstructed using FBP, ASIR, and MBIR. The results were analyzed using the mixed-effects model and paired t test with Bonferroni correction. The attenuation increases (pseudoenhancement) of the renal cysts on the VMS images reconstructed using FBP/ASIR (least square mean, 5.0/6.0 Hounsfield units [HU]; 95% confidence interval, 2.6–7.4/3.6–8.4 HU) were significantly lower than those on the conventional 120-kVp images reconstructed using FBP/ASIR/MBIR (least square mean, 12.1/12.8/11.8 HU; 95% confidence interval, 9.8–14.5/10.4–15.1/9.4–14.2 HU) (all P < .001); on the other hand, the CT attenuation values of the kidneys on the VMS images were comparable to those on the 120-kVp images. Regardless of the reconstruction algorithm, 70-keV VMS images showed a lower degree of pseudoenhancement of renal cysts than 120-kVp images, while maintaining kidney contrast enhancement comparable to that on 120-kVp images. PMID:25881852

Sparsity-based acoustic inversion in cross-sectional multiscale optoacoustic imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, Yiyong; Tzoumas, Stratis; Nunes, Antonio

2015-09-15

Purpose: With recent advancement in hardware of optoacoustic imaging systems, highly detailed cross-sectional images may be acquired at a single laser shot, thus eliminating motion artifacts. Nonetheless, other sources of artifacts remain due to signal distortion or out-of-plane signals. The purpose of image reconstruction algorithms is to obtain the most accurate images from noisy, distorted projection data. Methods: In this paper, the authors use the model-based approach for acoustic inversion, combined with a sparsity-based inversion procedure. Specifically, a cost function is used that includes the L1 norm of the image in sparse representation and a total variation (TV) term. Themore » optimization problem is solved by a numerically efficient implementation of a nonlinear gradient descent algorithm. TV–L1 model-based inversion is tested in the cross section geometry for numerically generated data as well as for in vivo experimental data from an adult mouse. Results: In all cases, model-based TV–L1 inversion showed a better performance over the conventional Tikhonov regularization, TV inversion, and L1 inversion. In the numerical examples, the images reconstructed with TV–L1 inversion were quantitatively more similar to the originating images. In the experimental examples, TV–L1 inversion yielded sharper images and weaker streak artifact. Conclusions: The results herein show that TV–L1 inversion is capable of improving the quality of highly detailed, multiscale optoacoustic images obtained in vivo using cross-sectional imaging systems. As a result of its high fidelity, model-based TV–L1 inversion may be considered as the new standard for image reconstruction in cross-sectional imaging.« less

Dynamic PET image reconstruction integrating temporal regularization associated with respiratory motion correction for applications in oncology

NASA Astrophysics Data System (ADS)

Merlin, Thibaut; Visvikis, Dimitris; Fernandez, Philippe; Lamare, Frédéric

2018-02-01

Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging. Elastic transformation parameters for the motion correction are estimated from the non-attenuation-corrected PET images. The derived displacement matrices are subsequently used in a list-mode based OSEM reconstruction algorithm integrating a temporal regularization between the 3D dynamic PET frames, based on temporal basis functions. These functions are simultaneously estimated at each iteration, along with their relative coefficients for each image voxel. Quantitative evaluation has been performed using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE DRX system. The performance of the proposed method is compared with that of a standard multi-frame OSEM reconstruction algorithm. The proposed method achieved substantial improvements in terms of noise reduction while accounting for loss of contrast due to respiratory motion. Results on simulated data showed that the proposed 4D algorithms led to bias reduction values up to 40% in both tumor and blood regions for similar standard deviation levels, in comparison with a standard 3D reconstruction. Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area). Incorporation of the respiratory motion correction using an elastic model along with a temporal regularization in the reconstruction process of the PET dynamic series led to substantial quantitative improvements and motion artifact reduction. Future work will include the integration of a linear FDG kinetic model, in order to directly reconstruct parametric images.

Dynamic PET image reconstruction integrating temporal regularization associated with respiratory motion correction for applications in oncology.

PubMed

Merlin, Thibaut; Visvikis, Dimitris; Fernandez, Philippe; Lamare, Frédéric

2018-02-13

Respiratory motion reduces both the qualitative and quantitative accuracy of PET images in oncology. This impact is more significant for quantitative applications based on kinetic modeling, where dynamic acquisitions are associated with limited statistics due to the necessity of enhanced temporal resolution. The aim of this study is to address these drawbacks, by combining a respiratory motion correction approach with temporal regularization in a unique reconstruction algorithm for dynamic PET imaging. Elastic transformation parameters for the motion correction are estimated from the non-attenuation-corrected PET images. The derived displacement matrices are subsequently used in a list-mode based OSEM reconstruction algorithm integrating a temporal regularization between the 3D dynamic PET frames, based on temporal basis functions. These functions are simultaneously estimated at each iteration, along with their relative coefficients for each image voxel. Quantitative evaluation has been performed using dynamic FDG PET/CT acquisitions of lung cancer patients acquired on a GE DRX system. The performance of the proposed method is compared with that of a standard multi-frame OSEM reconstruction algorithm. The proposed method achieved substantial improvements in terms of noise reduction while accounting for loss of contrast due to respiratory motion. Results on simulated data showed that the proposed 4D algorithms led to bias reduction values up to 40% in both tumor and blood regions for similar standard deviation levels, in comparison with a standard 3D reconstruction. Patlak parameter estimations on reconstructed images with the proposed reconstruction methods resulted in 30% and 40% bias reduction in the tumor and lung region respectively for the Patlak slope, and a 30% bias reduction for the intercept in the tumor region (a similar Patlak intercept was achieved in the lung area). Incorporation of the respiratory motion correction using an elastic model along with a temporal regularization in the reconstruction process of the PET dynamic series led to substantial quantitative improvements and motion artifact reduction. Future work will include the integration of a linear FDG kinetic model, in order to directly reconstruct parametric images.

Target 3-D reconstruction of streak tube imaging lidar based on Gaussian fitting

NASA Astrophysics Data System (ADS)

Yuan, Qingyu; Niu, Lihong; Hu, Cuichun; Wu, Lei; Yang, Hongru; Yu, Bing

2018-02-01

Streak images obtained by the streak tube imaging lidar (STIL) contain the distance-azimuth-intensity information of a scanned target, and a 3-D reconstruction of the target can be carried out through extracting the characteristic data of multiple streak images. Significant errors will be caused in the reconstruction result by the peak detection method due to noise and other factors. So as to get a more precise 3-D reconstruction, a peak detection method based on Gaussian fitting of trust region is proposed in this work. Gaussian modeling is performed on the returned wave of single time channel of each frame, then the modeling result which can effectively reduce the noise interference and possesses a unique peak could be taken as the new returned waveform, lastly extracting its feature data through peak detection. The experimental data of aerial target is for verifying this method. This work shows that the peak detection method based on Gaussian fitting reduces the extraction error of the feature data to less than 10%; utilizing this method to extract the feature data and reconstruct the target make it possible to realize the spatial resolution with a minimum 30 cm in the depth direction, and improve the 3-D imaging accuracy of the STIL concurrently.

Reconstructing liver shape and position from MR image slices using an active shape model

NASA Astrophysics Data System (ADS)

Fenchel, Matthias; Thesen, Stefan; Schilling, Andreas

2008-03-01

We present an algorithm for fully automatic reconstruction of 3D position, orientation and shape of the human liver from a sparsely covering set of n 2D MR slice images. Reconstructing the shape of an organ from slice images can be used for scan planning, for surgical planning or other purposes where 3D anatomical knowledge has to be inferred from sparse slices. The algorithm is based on adapting an active shape model of the liver surface to a given set of slice images. The active shape model is created from a training set of liver segmentations from a group of volunteers. The training set is set up with semi-manual segmentations of T1-weighted volumetric MR images. Searching for the optimal shape model that best fits to the image data is done by maximizing a similarity measure based on local appearance at the surface. Two different algorithms for the active shape model search are proposed and compared: both algorithms seek to maximize the a-posteriori probability of the grey level appearance around the surface while constraining the surface to the space of valid shapes. The first algorithm works by using grey value profile statistics in normal direction. The second algorithm uses average and variance images to calculate the local surface appearance on the fly. Both algorithms are validated by fitting the active shape model to abdominal 2D slice images and comparing the shapes, which have been reconstructed, to the manual segmentations and to the results of active shape model searches from 3D image data. The results turn out to be promising and competitive to active shape model segmentations from 3D data.

Accelerating non-contrast-enhanced MR angiography with inflow inversion recovery imaging by skipped phase encoding and edge deghosting (SPEED).

PubMed

Chang, Zheng; Xiang, Qing-San; Shen, Hao; Yin, Fang-Fang

2010-03-01

To accelerate non-contrast-enhanced MR angiography (MRA) with inflow inversion recovery (IFIR) with a fast imaging method, Skipped Phase Encoding and Edge Deghosting (SPEED). IFIR imaging uses a preparatory inversion pulse to reduce signals from static tissue, while leaving inflow arterial blood unaffected, resulting in sparse arterial vasculature on modest tissue background. By taking advantage of vascular sparsity, SPEED can be simplified with a single-layer model to achieve higher efficiency in both scan time reduction and image reconstruction. SPEED can also make use of information available in multiple coils for further acceleration. The techniques are demonstrated with a three-dimensional renal non-contrast-enhanced IFIR MRA study. Images are reconstructed by SPEED based on a single-layer model to achieve an undersampling factor of up to 2.5 using one skipped phase encoding direction. By making use of information available in multiple coils, SPEED can achieve an undersampling factor of up to 8.3 with four receiver coils. The reconstructed images generally have comparable quality as that of the reference images reconstructed from full k-space data. As demonstrated with a three-dimensional renal IFIR scan, SPEED based on a single-layer model is able to reduce scan time further and achieve higher computational efficiency than the original SPEED.

Computational photoacoustic imaging with sparsity-based optimization of the initial pressure distribution

NASA Astrophysics Data System (ADS)

Shang, Ruibo; Archibald, Richard; Gelb, Anne; Luke, Geoffrey P.

2018-02-01

In photoacoustic (PA) imaging, the optical absorption can be acquired from the initial pressure distribution (IPD). An accurate reconstruction of the IPD will be very helpful for the reconstruction of the optical absorption. However, the image quality of PA imaging in scattering media is deteriorated by the acoustic diffraction, imaging artifacts, and weak PA signals. In this paper, we propose a sparsity-based optimization approach that improves the reconstruction of the IPD in PA imaging. A linear imaging forward model was set up based on time-and-delay method with the assumption that the point spread function (PSF) is spatial invariant. Then, an optimization equation was proposed with a regularization term to denote the sparsity of the IPD in a certain domain to solve this inverse problem. As a proof of principle, the approach was applied to reconstructing point objects and blood vessel phantoms. The resolution and signal-to-noise ratio (SNR) were compared between conventional back-projection and our proposed approach. Overall these results show that computational imaging can leverage the sparsity of PA images to improve the estimation of the IPD.

Ultrasound breast imaging using frequency domain reverse time migration

NASA Astrophysics Data System (ADS)

Roy, O.; Zuberi, M. A. H.; Pratt, R. G.; Duric, N.

2016-04-01

Conventional ultrasonography reconstruction techniques, such as B-mode, are based on a simple wave propagation model derived from a high frequency approximation. Therefore, to minimize model mismatch, the central frequency of the input pulse is typically chosen between 3 and 15 megahertz. Despite the increase in theoretical resolution, operating at higher frequencies comes at the cost of lower signal-to-noise ratio. This ultimately degrades the image contrast and overall quality at higher imaging depths. To address this issue, we investigate a reflection imaging technique, known as reverse time migration, which uses a more accurate propagation model for reconstruction. We present preliminary simulation results as well as physical phantom image reconstructions obtained using data acquired with a breast imaging ultrasound tomography prototype. The original reconstructions are filtered to remove low-wavenumber artifacts that arise due to the inclusion of the direct arrivals. We demonstrate the advantage of using an accurate sound speed model in the reverse time migration process. We also explain how the increase in computational complexity can be mitigated using a frequency domain approach and a parallel computing platform.

Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage.

PubMed

Liu, Tianshuai; Rong, Junyan; Gao, Peng; Zhang, Wenli; Liu, Wenlei; Zhang, Yuanke; Lu, Hongbing

2018-02-01

With the advances of x-ray excitable nanophosphors, x-ray luminescence computed tomography (XLCT) has become a promising hybrid imaging modality. In particular, a cone-beam XLCT (CB-XLCT) system has demonstrated its potential in in vivo imaging with the advantage of fast imaging speed over other XLCT systems. Currently, the imaging models of most XLCT systems assume that nanophosphors emit light based on the intensity distribution of x-ray within the object, not completely reflecting the nature of the x-ray excitation process. To improve the imaging quality of CB-XLCT, an imaging model that adopts an excitation model of nanophosphors based on x-ray absorption dosage is proposed in this study. To solve the ill-posed inverse problem, a reconstruction algorithm that combines the adaptive Tikhonov regularization method with the imaging model is implemented for CB-XLCT reconstruction. Numerical simulations and phantom experiments indicate that compared with the traditional forward model based on x-ray intensity, the proposed dose-based model could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy, and image contrast. In addition, the proposed model behaves better in distinguishing closer targets, demonstrating its advantage in improving spatial resolution. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Anthropometric body measurements based on multi-view stereo image reconstruction.

PubMed

Li, Zhaoxin; Jia, Wenyan; Mao, Zhi-Hong; Li, Jie; Chen, Hsin-Chen; Zuo, Wangmeng; Wang, Kuanquan; Sun, Mingui

2013-01-01

Anthropometric measurements, such as the circumferences of the hip, arm, leg and waist, waist-to-hip ratio, and body mass index, are of high significance in obesity and fitness evaluation. In this paper, we present a home based imaging system capable of conducting anthropometric measurements. Body images are acquired at different angles using a home camera and a simple rotating disk. Advanced image processing algorithms are utilized for 3D body surface reconstruction. A coarse body shape model is first established from segmented body silhouettes. Then, this model is refined through an inter-image consistency maximization process based on an energy function. Our experimental results using both a mannequin surrogate and a real human body validate the feasibility of the proposed system.

Anthropometric Body Measurements Based on Multi-View Stereo Image Reconstruction*

PubMed Central

Li, Zhaoxin; Jia, Wenyan; Mao, Zhi-Hong; Li, Jie; Chen, Hsin-Chen; Zuo, Wangmeng; Wang, Kuanquan; Sun, Mingui

2013-01-01

Anthropometric measurements, such as the circumferences of the hip, arm, leg and waist, waist-to-hip ratio, and body mass index, are of high significance in obesity and fitness evaluation. In this paper, we present a home based imaging system capable of conducting automatic anthropometric measurements. Body images are acquired at different angles using a home camera and a simple rotating disk. Advanced image processing algorithms are utilized for 3D body surface reconstruction. A coarse body shape model is first established from segmented body silhouettes. Then, this model is refined through an inter-image consistency maximization process based on an energy function. Our experimental results using both a mannequin surrogate and a real human body validate the feasibility of proposed system. PMID:24109700

Joint image and motion reconstruction for PET using a B-spline motion model.

PubMed

Blume, Moritz; Navab, Nassir; Rafecas, Magdalena

2012-12-21

We present a novel joint image and motion reconstruction method for PET. The method is based on gated data and reconstructs an image together with a motion function. The motion function can be used to transform the reconstructed image to any of the input gates. All available events (from all gates) are used in the reconstruction. The presented method uses a B-spline motion model, together with a novel motion regularization procedure that does not need a regularization parameter (which is usually extremely difficult to adjust). Several image and motion grid levels are used in order to reduce the reconstruction time. In a simulation study, the presented method is compared to a recently proposed joint reconstruction method. While the presented method provides comparable reconstruction quality, it is much easier to use since no regularization parameter has to be chosen. Furthermore, since the B-spline discretization of the motion function depends on fewer parameters than a displacement field, the presented method is considerably faster and consumes less memory than its counterpart. The method is also applied to clinical data, for which a novel purely data-driven gating approach is presented.

Development of Acoustic Model-Based Iterative Reconstruction Technique for Thick-Concrete Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Almansouri, Hani; Clayton, Dwight A; Kisner, Roger A

Ultrasound signals have been used extensively for non-destructive evaluation (NDE). However, typical reconstruction techniques, such as the synthetic aperture focusing technique (SAFT), are limited to quasi-homogenous thin media. New ultrasonic systems and reconstruction algorithms are in need for one-sided NDE of non-homogenous thick objects. An application example space is imaging of reinforced concrete structures for commercial nuclear power plants (NPPs). These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Another example is geothermal and oil/gas production wells. These multi-layered structuresmore » are composed of steel, cement, and several types of soil and rocks. Ultrasound systems with greater penetration range and image quality will allow for better monitoring of the well's health and prediction of high-pressure hydraulic fracturing of the rock. These application challenges need to be addressed with an integrated imaging approach, where the application, hardware, and reconstruction software are highly integrated and optimized. Therefore, we are developing an ultrasonic system with Model-Based Iterative Reconstruction (MBIR) as the image reconstruction backbone. As the first implementation of MBIR for ultrasonic signals, this paper document the first implementation of the algorithm and show reconstruction results for synthetically generated data.« less

Image-Based Reconstruction and Analysis of Dynamic Scenes in a Landslide Simulation Facility

NASA Astrophysics Data System (ADS)

Scaioni, M.; Crippa, J.; Longoni, L.; Papini, M.; Zanzi, L.

2017-12-01

The application of image processing and photogrammetric techniques to dynamic reconstruction of landslide simulations in a scaled-down facility is described. Simulations are also used here for active-learning purpose: students are helped understand how physical processes happen and which kinds of observations may be obtained from a sensor network. In particular, the use of digital images to obtain multi-temporal information is presented. On one side, using a multi-view sensor set up based on four synchronized GoPro 4 Black® cameras, a 4D (3D spatial position and time) reconstruction of the dynamic scene is obtained through the composition of several 3D models obtained from dense image matching. The final textured 4D model allows one to revisit in dynamic and interactive mode a completed experiment at any time. On the other side, a digital image correlation (DIC) technique has been used to track surface point displacements from the image sequence obtained from the camera in front of the simulation facility. While the 4D model may provide a qualitative description and documentation of the experiment running, DIC analysis output quantitative information such as local point displacements and velocities, to be related to physical processes and to other observations. All the hardware and software equipment adopted for the photogrammetric reconstruction has been based on low-cost and open-source solutions.

Determination of optimal imaging settings for urolithiasis CT using filtered back projection (FBP), statistical iterative reconstruction (IR) and knowledge-based iterative model reconstruction (IMR): a physical human phantom study

PubMed Central

Choi, Se Y; Ahn, Seung H; Choi, Jae D; Kim, Jung H; Lee, Byoung-Il; Kim, Jeong-In

2016-01-01

Objective: The purpose of this study was to compare CT image quality for evaluating urolithiasis using filtered back projection (FBP), statistical iterative reconstruction (IR) and knowledge-based iterative model reconstruction (IMR) according to various scan parameters and radiation doses. Methods: A 5 × 5 × 5 mm3 uric acid stone was placed in a physical human phantom at the level of the pelvis. 3 tube voltages (120, 100 and 80 kV) and 4 current–time products (100, 70, 30 and 15 mAs) were implemented in 12 scans. Each scan was reconstructed with FBP, statistical IR (Levels 5–7) and knowledge-based IMR (soft-tissue Levels 1–3). The radiation dose, objective image quality and signal-to-noise ratio (SNR) were evaluated, and subjective assessments were performed. Results: The effective doses ranged from 0.095 to 2.621 mSv. Knowledge-based IMR showed better objective image noise and SNR than did FBP and statistical IR. The subjective image noise of FBP was worse than that of statistical IR and knowledge-based IMR. The subjective assessment scores deteriorated after a break point of 100 kV and 30 mAs. Conclusion: At the setting of 100 kV and 30 mAs, the radiation dose can be decreased by approximately 84% while keeping the subjective image assessment. Advances in knowledge: Patients with urolithiasis can be evaluated with ultralow-dose non-enhanced CT using a knowledge-based IMR algorithm at a substantially reduced radiation dose with the imaging quality preserved, thereby minimizing the risks of radiation exposure while providing clinically relevant diagnostic benefits for patients. PMID:26577542

Influence of Iterative Reconstruction Algorithms on PET Image Resolution

NASA Astrophysics Data System (ADS)

Karpetas, G. E.; Michail, C. M.; Fountos, G. P.; Valais, I. G.; Nikolopoulos, D.; Kandarakis, I. S.; Panayiotakis, G. S.

2015-09-01

The aim of the present study was to assess image quality of PET scanners through a thin layer chromatography (TLC) plane source. The source was simulated using a previously validated Monte Carlo model. The model was developed by using the GATE MC package and reconstructed images obtained with the STIR software for tomographic image reconstruction. The simulated PET scanner was the GE DiscoveryST. A plane source consisted of a TLC plate, was simulated by a layer of silica gel on aluminum (Al) foil substrates, immersed in 18F-FDG bath solution (1MBq). Image quality was assessed in terms of the modulation transfer function (MTF). MTF curves were estimated from transverse reconstructed images of the plane source. Images were reconstructed by the maximum likelihood estimation (MLE)-OSMAPOSL, the ordered subsets separable paraboloidal surrogate (OSSPS), the median root prior (MRP) and OSMAPOSL with quadratic prior, algorithms. OSMAPOSL reconstruction was assessed by using fixed subsets and various iterations, as well as by using various beta (hyper) parameter values. MTF values were found to increase with increasing iterations. MTF also improves by using lower beta values. The simulated PET evaluation method, based on the TLC plane source, can be useful in the resolution assessment of PET scanners.

Noise and signal properties in PSF-based fully 3D PET image reconstruction: an experimental evaluation

NASA Astrophysics Data System (ADS)

Tong, S.; Alessio, A. M.; Kinahan, P. E.

2010-03-01

The addition of accurate system modeling in PET image reconstruction results in images with distinct noise texture and characteristics. In particular, the incorporation of point spread functions (PSF) into the system model has been shown to visually reduce image noise, but the noise properties have not been thoroughly studied. This work offers a systematic evaluation of noise and signal properties in different combinations of reconstruction methods and parameters. We evaluate two fully 3D PET reconstruction algorithms: (1) OSEM with exact scanner line of response modeled (OSEM+LOR), (2) OSEM with line of response and a measured point spread function incorporated (OSEM+LOR+PSF), in combination with the effects of four post-reconstruction filtering parameters and 1-10 iterations, representing a range of clinically acceptable settings. We used a modified NEMA image quality (IQ) phantom, which was filled with 68Ge and consisted of six hot spheres of different sizes with a target/background ratio of 4:1. The phantom was scanned 50 times in 3D mode on a clinical system to provide independent noise realizations. Data were reconstructed with OSEM+LOR and OSEM+LOR+PSF using different reconstruction parameters, and our implementations of the algorithms match the vendor's product algorithms. With access to multiple realizations, background noise characteristics were quantified with four metrics. Image roughness and the standard deviation image measured the pixel-to-pixel variation; background variability and ensemble noise quantified the region-to-region variation. Image roughness is the image noise perceived when viewing an individual image. At matched iterations, the addition of PSF leads to images with less noise defined as image roughness (reduced by 35% for unfiltered data) and as the standard deviation image, while it has no effect on background variability or ensemble noise. In terms of signal to noise performance, PSF-based reconstruction has a 7% improvement in contrast recovery at matched ensemble noise levels and 20% improvement of quantitation SNR in unfiltered data. In addition, the relations between different metrics are studied. A linear correlation is observed between background variability and ensemble noise for all different combinations of reconstruction methods and parameters, suggesting that background variability is a reasonable surrogate for ensemble noise when multiple realizations of scans are not available.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

NASA Astrophysics Data System (ADS)

Novosad, Philip; Reader, Andrew J.

2016-06-01

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [11C]SCH23390 data, showing promising results.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.

PubMed

Novosad, Philip; Reader, Andrew J

2016-06-21

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [(11)C]SCH23390 data, showing promising results.

Optical tomography in the presence of void regions

PubMed

Dehghani; Arridge; Schweiger; Delpy

2000-09-01

There is a growing interest in the use of near-infrared spectroscopy for the noninvasive determination of the oxygenation level within biological tissue. Stemming from this application, there has been further research in the use of this technique for obtaining tomographic images of the neonatal head, with the view of determining the levels of oxygenated and deoxygenated blood within the brain. Owing to computational complexity, methods used for numerical modeling of photon transfer within tissue have usually been limited to the diffusion approximation of the Boltzmann transport equation. The diffusion approximation, however, is not valid in regions of low scatter, such as the cerebrospinal fluid. Methods have been proposed for dealing with nonscattering regions within diffusing materials through the use of a radiosity-diffusion model. Currently, this new model assumes prior knowledge of the void region location; therefore it is instructive to examine the errors introduced in applying a simple diffusion-based reconstruction scheme in cases in which there exists a nonscattering region. We present reconstructed images of objects that contain a nonscattering region within a diffusive material. Here the forward data is calculated with the radiosity-diffusion model, and the inverse problem is solved with either the radiosity-diffusion model or the diffusion-only model. The reconstructed images show that even in the presence of only a thin nonscattering layer, a diffusion-only reconstruction will fail. When a radiosity-diffusion model is used for image reconstruction, together with a priori information about the position of the nonscattering region, the quality of the reconstructed image is considerably improved. The accuracy of the reconstructed images depends largely on the position of the anomaly with respect to the nonscattering region as well as the thickness of the nonscattering region.

A photoacoustic imaging reconstruction method based on directional total variation with adaptive directivity.

PubMed

Wang, Jin; Zhang, Chen; Wang, Yuanyuan

2017-05-30

In photoacoustic tomography (PAT), total variation (TV) based iteration algorithm is reported to have a good performance in PAT image reconstruction. However, classical TV based algorithm fails to preserve the edges and texture details of the image because it is not sensitive to the direction of the image. Therefore, it is of great significance to develop a new PAT reconstruction algorithm to effectively solve the drawback of TV. In this paper, a directional total variation with adaptive directivity (DDTV) model-based PAT image reconstruction algorithm, which weightedly sums the image gradients based on the spatially varying directivity pattern of the image is proposed to overcome the shortcomings of TV. The orientation field of the image is adaptively estimated through a gradient-based approach. The image gradients are weighted at every pixel based on both its anisotropic direction and another parameter, which evaluates the estimated orientation field reliability. An efficient algorithm is derived to solve the iteration problem associated with DDTV and possessing directivity of the image adaptively updated for each iteration step. Several texture images with various directivity patterns are chosen as the phantoms for the numerical simulations. The 180-, 90- and 30-view circular scans are conducted. Results obtained show that the DDTV-based PAT reconstructed algorithm outperforms the filtered back-projection method (FBP) and TV algorithms in the quality of reconstructed images with the peak signal-to-noise rations (PSNR) exceeding those of TV and FBP by about 10 and 18 dB, respectively, for all cases. The Shepp-Logan phantom is studied with further discussion of multimode scanning, convergence speed, robustness and universality aspects. In-vitro experiments are performed for both the sparse-view circular scanning and linear scanning. The results further prove the effectiveness of the DDTV, which shows better results than that of the TV with sharper image edges and clearer texture details. Both numerical simulation and in vitro experiments confirm that the DDTV provides a significant quality improvement of PAT reconstructed images for various directivity patterns.

Interior reconstruction method based on rotation-translation scanning model.

PubMed

Wang, Xianchao; Tang, Ziyue; Yan, Bin; Li, Lei; Bao, Shanglian

2014-01-01

In various applications of computed tomography (CT), it is common that the reconstructed object is over the field of view (FOV) or we may intend to sue a FOV which only covers the region of interest (ROI) for the sake of reducing radiation dose. These kinds of imaging situations often lead to interior reconstruction problems which are difficult cases in the reconstruction field of CT, due to the truncated projection data at every view angle. In this paper, an interior reconstruction method is developed based on a rotation-translation (RT) scanning model. The method is implemented by first scanning the reconstructed region, and then scanning a small region outside the support of the reconstructed object after translating the rotation centre. The differentiated backprojection (DBP) images of the reconstruction region and the small region outside the object can be respectively obtained from the two-time scanning data without data rebinning process. At last, the projection onto convex sets (POCS) algorithm is applied to reconstruct the interior region. Numerical simulations are conducted to validate the proposed reconstruction method.

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

NASA Astrophysics Data System (ADS)

Liu, Hao; Li, Kangda; Wang, Bing; Tang, Hainie; Gong, Xiaohui

2017-01-01

A quantized block compressive sensing (QBCS) framework, which incorporates the universal measurement, quantization/inverse quantization, entropy coder/decoder, and iterative projected Landweber reconstruction, is summarized. Under the QBCS framework, this paper presents an improved reconstruction algorithm for aerial imagery, QBCS, with entropy-aware projected Landweber (QBCS-EPL), which leverages the full-image sparse transform without Wiener filter and an entropy-aware thresholding model for wavelet-domain image denoising. Through analyzing the functional relation between the soft-thresholding factors and entropy-based bitrates for different quantization methods, the proposed model can effectively remove wavelet-domain noise of bivariate shrinkage and achieve better image reconstruction quality. For the overall performance of QBCS reconstruction, experimental results demonstrate that the proposed QBCS-EPL algorithm significantly outperforms several existing algorithms. With the experiment-driven methodology, the QBCS-EPL algorithm can obtain better reconstruction quality at a relatively moderate computational cost, which makes it more desirable for aerial imagery applications.

NOTE: Acceleration of Monte Carlo-based scatter compensation for cardiac SPECT

NASA Astrophysics Data System (ADS)

Sohlberg, A.; Watabe, H.; Iida, H.

2008-07-01

Single proton emission computed tomography (SPECT) images are degraded by photon scatter making scatter compensation essential for accurate reconstruction. Reconstruction-based scatter compensation with Monte Carlo (MC) modelling of scatter shows promise for accurate scatter correction, but it is normally hampered by long computation times. The aim of this work was to accelerate the MC-based scatter compensation using coarse grid and intermittent scatter modelling. The acceleration methods were compared to un-accelerated implementation using MC-simulated projection data of the mathematical cardiac torso (MCAT) phantom modelling 99mTc uptake and clinical myocardial perfusion studies. The results showed that when combined the acceleration methods reduced the reconstruction time for 10 ordered subset expectation maximization (OS-EM) iterations from 56 to 11 min without a significant reduction in image quality indicating that the coarse grid and intermittent scatter modelling are suitable for MC-based scatter compensation in cardiac SPECT.

Joint reconstruction of multiview compressed images.

PubMed

Thirumalai, Vijayaraghavan; Frossard, Pascal

2013-05-01

Distributed representation of correlated multiview images is an important problem that arises in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed images are decoded together in order to take benefit from the image correlation. We consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions (e.g., JPEG) with a balanced rate distribution among different cameras. A central decoder first estimates the inter-view image correlation from the independently compressed data. The joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total-variation (TV) smooth images, which comply with the estimated correlation model. At the same time, we add constraints that force the reconstructed images to be as close as possible to their compressed versions. We show through experiments that the proposed joint reconstruction scheme outperforms independent reconstruction in terms of image quality, for a given target bit rate. In addition, the decoding performance of our algorithm compares advantageously to state-of-the-art distributed coding schemes based on motion learning and on the DISCOVER algorithm.

A novel partial volume effects correction technique integrating deconvolution associated with denoising within an iterative PET image reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Merlin, Thibaut, E-mail: thibaut.merlin@telecom-bretagne.eu; Visvikis, Dimitris; Fernandez, Philippe

2015-02-15

Purpose: Partial volume effect (PVE) plays an important role in both qualitative and quantitative PET image accuracy, especially for small structures. A previously proposed voxelwise PVE correction method applied on PET reconstructed images involves the use of Lucy–Richardson deconvolution incorporating wavelet-based denoising to limit the associated propagation of noise. The aim of this study is to incorporate the deconvolution, coupled with the denoising step, directly inside the iterative reconstruction process to further improve PVE correction. Methods: The list-mode ordered subset expectation maximization (OSEM) algorithm has been modified accordingly with the application of the Lucy–Richardson deconvolution algorithm to the current estimationmore » of the image, at each reconstruction iteration. Acquisitions of the NEMA NU2-2001 IQ phantom were performed on a GE DRX PET/CT system to study the impact of incorporating the deconvolution inside the reconstruction [with and without the point spread function (PSF) model] in comparison to its application postreconstruction and to standard iterative reconstruction incorporating the PSF model. The impact of the denoising step was also evaluated. Images were semiquantitatively assessed by studying the trade-off between the intensity recovery and the noise level in the background estimated as relative standard deviation. Qualitative assessments of the developed methods were additionally performed on clinical cases. Results: Incorporating the deconvolution without denoising within the reconstruction achieved superior intensity recovery in comparison to both standard OSEM reconstruction integrating a PSF model and application of the deconvolution algorithm in a postreconstruction process. The addition of the denoising step permitted to limit the SNR degradation while preserving the intensity recovery. Conclusions: This study demonstrates the feasibility of incorporating the Lucy–Richardson deconvolution associated with a wavelet-based denoising in the reconstruction process to better correct for PVE. Future work includes further evaluations of the proposed method on clinical datasets and the use of improved PSF models.« less

Emerging Techniques for Dose Optimization in Abdominal CT

PubMed Central

Platt, Joel F.; Goodsitt, Mitchell M.; Al-Hawary, Mahmoud M.; Maturen, Katherine E.; Wasnik, Ashish P.; Pandya, Amit

2014-01-01

Recent advances in computed tomographic (CT) scanning technique such as automated tube current modulation (ATCM), optimized x-ray tube voltage, and better use of iterative image reconstruction have allowed maintenance of good CT image quality with reduced radiation dose. ATCM varies the tube current during scanning to account for differences in patient attenuation, ensuring a more homogeneous image quality, although selection of the appropriate image quality parameter is essential for achieving optimal dose reduction. Reducing the x-ray tube voltage is best suited for evaluating iodinated structures, since the effective energy of the x-ray beam will be closer to the k-edge of iodine, resulting in a higher attenuation for the iodine. The optimal kilovoltage for a CT study should be chosen on the basis of imaging task and patient habitus. The aim of iterative image reconstruction is to identify factors that contribute to noise on CT images with use of statistical models of noise (statistical iterative reconstruction) and selective removal of noise to improve image quality. The degree of noise suppression achieved with statistical iterative reconstruction can be customized to minimize the effect of altered image quality on CT images. Unlike with statistical iterative reconstruction, model-based iterative reconstruction algorithms model both the statistical noise and the physical acquisition process, allowing CT to be performed with further reduction in radiation dose without an increase in image noise or loss of spatial resolution. Understanding these recently developed scanning techniques is essential for optimization of imaging protocols designed to achieve the desired image quality with a reduced dose. © RSNA, 2014 PMID:24428277

Tracking boundary movement and exterior shape modelling in lung EIT imaging.

PubMed

Biguri, A; Grychtol, B; Adler, A; Soleimani, M

2015-06-01

Electrical impedance tomography (EIT) has shown significant promise for lung imaging. One key challenge for EIT in this application is the movement of electrodes during breathing, which introduces artefacts in reconstructed images. Various approaches have been proposed to compensate for electrode movement, but no comparison of these approaches is available. This paper analyses boundary model mismatch and electrode movement in lung EIT. The aim is to evaluate the extent to which various algorithms tolerate movement, and to determine if a patient specific model is required for EIT lung imaging. Movement data are simulated from a CT-based model, and image analysis is performed using quantitative figures of merit. The electrode movement is modelled based on expected values of chest movement and an extended Jacobian method is proposed to make use of exterior boundary tracking. Results show that a dynamical boundary tracking is the most robust method against any movement, but is computationally more expensive. Simultaneous electrode movement and conductivity reconstruction algorithms show increased robustness compared to only conductivity reconstruction. The results of this comparative study can help develop a better understanding of the impact of shape model mismatch and electrode movement in lung EIT.

Single-image-based Modelling Architecture from a Historical Photograph

NASA Astrophysics Data System (ADS)

Dzwierzynska, Jolanta

2017-10-01

Historical photographs are proved to be very useful to provide a dimensional and geometrical analysis of buildings as well as to generate 3D reconstruction of the whole structure. The paper addresses the problem of single historical photograph analysis and modelling of an architectural object from it. Especially, it focuses on reconstruction of the original look of New-Town synagogue from the single historic photograph, when camera calibration is completely unknown. Due to the fact that the photograph faithfully followed the geometric rules of perspective, it was possible to develop and apply the method to obtain a correct 3D reconstruction of the building. The modelling process consisted of a series of familiar steps: feature extraction, determination of base elements of perspective, dimensional analyses and 3D reconstruction. Simple formulas were proposed in order to estimate location of characteristic points of the building in 3D Cartesian system of axes on the base of their location in 2D Cartesian system of axes. The reconstruction process proceeded well, although slight corrections were necessary. It was possible to reconstruct the shape of the building in general, and two of its facades in detail. The reconstruction of the other two facades requires some additional information or the additional picture. The success of the presented reconstruction method depends on the geometrical content of the photograph as well as quality of the picture, which ensures the legibility of building edges. The presented method of reconstruction is a combination of the descriptive method of reconstruction and computer aid; therefore, it seems to be universal. It can prove useful for single-image-based modelling architecture.

Task-Driven Tube Current Modulation and Regularization Design in Computed Tomography with Penalized-Likelihood Reconstruction.

PubMed

Gang, G J; Siewerdsen, J H; Stayman, J W

2016-02-01

This work applies task-driven optimization to design CT tube current modulation and directional regularization in penalized-likelihood (PL) reconstruction. The relative performance of modulation schemes commonly adopted for filtered-backprojection (FBP) reconstruction were also evaluated for PL in comparison. We adopt a task-driven imaging framework that utilizes a patient-specific anatomical model and information of the imaging task to optimize imaging performance in terms of detectability index ( d' ). This framework leverages a theoretical model based on implicit function theorem and Fourier approximations to predict local spatial resolution and noise characteristics of PL reconstruction as a function of the imaging parameters to be optimized. Tube current modulation was parameterized as a linear combination of Gaussian basis functions, and regularization was based on the design of (directional) pairwise penalty weights for the 8 in-plane neighboring voxels. Detectability was optimized using a covariance matrix adaptation evolutionary strategy algorithm. Task-driven designs were compared to conventional tube current modulation strategies for a Gaussian detection task in an abdomen phantom. The task-driven design yielded the best performance, improving d' by ~20% over an unmodulated acquisition. Contrary to FBP, PL reconstruction using automatic exposure control and modulation based on minimum variance (in FBP) performed worse than the unmodulated case, decreasing d' by 16% and 9%, respectively. This work shows that conventional tube current modulation schemes suitable for FBP can be suboptimal for PL reconstruction. Thus, the proposed task-driven optimization provides additional opportunities for improved imaging performance and dose reduction beyond that achievable with conventional acquisition and reconstruction.

Image quality improvement using model-based iterative reconstruction in low dose chest CT for children with necrotizing pneumonia.

PubMed

Sun, Jihang; Yu, Tong; Liu, Jinrong; Duan, Xiaomin; Hu, Di; Liu, Yong; Peng, Yun

2017-03-16

Model-based iterative reconstruction (MBIR) is a promising reconstruction method which could improve CT image quality with low radiation dose. The purpose of this study was to demonstrate the advantage of using MBIR for noise reduction and image quality improvement in low dose chest CT for children with necrotizing pneumonia, over the adaptive statistical iterative reconstruction (ASIR) and conventional filtered back-projection (FBP) technique. Twenty-six children with necrotizing pneumonia (aged 2 months to 11 years) who underwent standard of care low dose CT scans were included. Thinner-slice (0.625 mm) images were retrospectively reconstructed using MBIR, ASIR and conventional FBP techniques. Image noise and signal-to-noise ratio (SNR) for these thin-slice images were measured and statistically analyzed using ANOVA. Two radiologists independently analyzed the image quality for detecting necrotic lesions, and results were compared using a Friedman's test. Radiation dose for the overall patient population was 0.59 mSv. There was a significant improvement in the high-density and low-contrast resolution of the MBIR reconstruction resulting in more detection and better identification of necrotic lesions (38 lesions in 0.625 mm MBIR images vs. 29 lesions in 0.625 mm FBP images). The subjective display scores (mean ± standard deviation) for the detection of necrotic lesions were 5.0 ± 0.0, 2.8 ± 0.4 and 2.5 ± 0.5 with MBIR, ASIR and FBP reconstruction, respectively, and the respective objective image noise was 13.9 ± 4.0HU, 24.9 ± 6.6HU and 33.8 ± 8.7HU. The image noise decreased by 58.9 and 26.3% in MBIR images as compared to FBP and ASIR images. Additionally, the SNR of MBIR images was significantly higher than FBP images and ASIR images. The quality of chest CT images obtained by MBIR in children with necrotizing pneumonia was significantly improved by the MBIR technique as compared to the ASIR and FBP reconstruction, to provide a more confident and accurate diagnosis for necrotizing pneumonia.

Modelling the physics in iterative reconstruction for transmission computed tomography

PubMed Central

Nuyts, Johan; De Man, Bruno; Fessler, Jeffrey A.; Zbijewski, Wojciech; Beekman, Freek J.

2013-01-01

There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of X-ray CT imaging. IR has the ability to significantly reduce patient dose, it provides the flexibility to reconstruct images from arbitrary X-ray system geometries and it allows to include detailed models of photon transport and detection physics, to accurately correct for a wide variety of image degrading effects. This paper reviews discretisation issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. Widespread implementation of IR with highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling. PMID:23739261

Analysis of acetabular orientation and femoral anteversion using images of three-dimensional reconstructed bone models.

PubMed

Park, Jaeyeong; Kim, Jun-Young; Kim, Hyun Deok; Kim, Young Cheol; Seo, Anna; Je, Minkyu; Mun, Jong Uk; Kim, Bia; Park, Il Hyung; Kim, Shin-Yoon

2017-05-01

Radiographic measurements using two-dimensional (2D) plain radiographs or planes from computed tomography (CT) scans have several drawbacks, while measurements using images of three-dimensional (3D) reconstructed bone models can provide more consistent anthropometric information. We compared the consistency of results using measurements based on images of 3D reconstructed bone models (3D measurements) with those using planes from CT scans (measurements using 2D slice images). Ninety-six of 561 patients who had undergone deep vein thrombosis-CT between January 2013 and November 2014 were randomly selected. We evaluated measurements using 2D slice images and 3D measurements. The images used for 3D reconstruction of bone models were obtained and measured using [Formula: see text] and [Formula: see text] (Materialize, Leuven, Belgium). The mean acetabular inclination, acetabular anteversion and femoral anteversion values on 2D slice images were 42.01[Formula: see text], 18.64[Formula: see text] and 14.44[Formula: see text], respectively, while those using images of 3D reconstructed bone models were 52.80[Formula: see text], 14.98[Formula: see text] and 17.26[Formula: see text]. Intra-rater reliabilities for acetabular inclination, acetabular anteversion, and femoral anteversion on 2D slice images were 0.55, 0.81, and 0.85, respectively, while those for 3D measurements were 0.98, 0.99, and 0.98. Inter-rater reliabilities for acetabular inclination, acetabular anteversion and femoral anteversion on 2D slice images were 0.48, 0.86, and 0.84, respectively, while those for 3D measurements were 0.97, 0.99, and 0.97. The differences between the two measurements are explained by the use of different tools. However, more consistent measurements were possible using the images of 3D reconstructed bone models. Therefore, 3D measurement can be a good alternative to measurement using 2D slice images.

Automated Recovery of Three-Dimensional Models of Plant Shoots from Multiple Color Images1[C][W][OPEN

PubMed Central

Pound, Michael P.; French, Andrew P.; Murchie, Erik H.; Pridmore, Tony P.

2014-01-01

Increased adoption of the systems approach to biological research has focused attention on the use of quantitative models of biological objects. This includes a need for realistic three-dimensional (3D) representations of plant shoots for quantification and modeling. Previous limitations in single-view or multiple-view stereo algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present a fully automatic approach to image-based 3D plant reconstruction that can be achieved using a single low-cost camera. The reconstructed plants are represented as a series of small planar sections that together model the more complex architecture of the leaf surfaces. The boundary of each leaf patch is refined using the level-set method, optimizing the model based on image information, curvature constraints, and the position of neighboring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed and, as such, is applicable to a wide variety of plant species and topologies and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on data sets of wheat (Triticum aestivum) and rice (Oryza sativa) plants as well as a unique virtual data set that allows us to compute quantitative measures of reconstruction accuracy. The output is a 3D mesh structure that is suitable for modeling applications in a format that can be imported in the majority of 3D graphics and software packages. PMID:25332504

3D Surface Reconstruction for Lower Limb Prosthetic Model using Radon Transform

NASA Astrophysics Data System (ADS)

Sobani, S. S. Mohd; Mahmood, N. H.; Zakaria, N. A.; Razak, M. A. Abdul

2018-03-01

This paper describes the idea to realize three-dimensional surfaces of objects with cylinder-based shapes where the techniques adopted and the strategy developed for a non-rigid three-dimensional surface reconstruction of an object from uncalibrated two-dimensional image sequences using multiple-view digital camera and turntable setup. The surface of an object is reconstructed based on the concept of tomography with the aid of performing several digital image processing algorithms on the two-dimensional images captured by a digital camera in thirty-six different projections and the three-dimensional structure of the surface is analysed. Four different objects are used as experimental models in the reconstructions and each object is placed on a manually rotated turntable. The results shown that the proposed method has successfully reconstruct the three-dimensional surface of the objects and practicable. The shape and size of the reconstructed three-dimensional objects are recognizable and distinguishable. The reconstructions of objects involved in the test are strengthened with the analysis where the maximum percent error obtained from the computation is approximately 1.4 % for the height whilst 4.0%, 4.79% and 4.7% for the diameters at three specific heights of the objects.

Kinematic reconstruction in cardiovascular imaging.

PubMed

Bastarrika, G; Huebra Rodríguez, I J González de la; Calvo-Imirizaldu, M; Suárez Vega, V M; Alonso-Burgos, A

2018-05-17

Advances in clinical applications of computed tomography have been accompanied by improvements in advanced post-processing tools. In addition to multiplanar reconstructions, curved planar reconstructions, maximum intensity projections, and volumetric reconstructions, very recently kinematic reconstruction has been developed. This new technique, based on mathematical models that simulate the propagation of light beams through a volume of data, makes it possible to obtain very realistic three dimensional images. This article illustrates examples of kinematic reconstructions and compares them with classical volumetric reconstructions in patients with cardiovascular disease in a way that makes it easy to establish the differences between the two types of reconstruction. Kinematic reconstruction is a new method for representing three dimensional images that facilitates the explanation and comprehension of the findings. Copyright © 2018 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Theoretical Analysis of Penalized Maximum-Likelihood Patlak Parametric Image Reconstruction in Dynamic PET for Lesion Detection.

PubMed

Yang, Li; Wang, Guobao; Qi, Jinyi

2016-04-01

Detecting cancerous lesions is a major clinical application of emission tomography. In a previous work, we studied penalized maximum-likelihood (PML) image reconstruction for lesion detection in static PET. Here we extend our theoretical analysis of static PET reconstruction to dynamic PET. We study both the conventional indirect reconstruction and direct reconstruction for Patlak parametric image estimation. In indirect reconstruction, Patlak parametric images are generated by first reconstructing a sequence of dynamic PET images, and then performing Patlak analysis on the time activity curves (TACs) pixel-by-pixel. In direct reconstruction, Patlak parametric images are estimated directly from raw sinogram data by incorporating the Patlak model into the image reconstruction procedure. PML reconstruction is used in both the indirect and direct reconstruction methods. We use a channelized Hotelling observer (CHO) to assess lesion detectability in Patlak parametric images. Simplified expressions for evaluating the lesion detectability have been derived and applied to the selection of the regularization parameter value to maximize detection performance. The proposed method is validated using computer-based Monte Carlo simulations. Good agreements between the theoretical predictions and the Monte Carlo results are observed. Both theoretical predictions and Monte Carlo simulation results show the benefit of the indirect and direct methods under optimized regularization parameters in dynamic PET reconstruction for lesion detection, when compared with the conventional static PET reconstruction.

Compressive Sensing via Nonlocal Smoothed Rank Function

PubMed Central

Fan, Ya-Ru; Liu, Jun; Zhao, Xi-Le

2016-01-01

Compressive sensing (CS) theory asserts that we can reconstruct signals and images with only a small number of samples or measurements. Recent works exploiting the nonlocal similarity have led to better results in various CS studies. To better exploit the nonlocal similarity, in this paper, we propose a non-convex smoothed rank function based model for CS image reconstruction. We also propose an efficient alternating minimization method to solve the proposed model, which reduces a difficult and coupled problem to two tractable subproblems. Experimental results have shown that the proposed method performs better than several existing state-of-the-art CS methods for image reconstruction. PMID:27583683

An accelerated photo-magnetic imaging reconstruction algorithm based on an analytical forward solution and a fast Jacobian assembly method

NASA Astrophysics Data System (ADS)

Nouizi, F.; Erkol, H.; Luk, A.; Marks, M.; Unlu, M. B.; Gulsen, G.

2016-10-01

We previously introduced photo-magnetic imaging (PMI), an imaging technique that illuminates the medium under investigation with near-infrared light and measures the induced temperature increase using magnetic resonance thermometry (MRT). Using a multiphysics solver combining photon migration and heat diffusion, PMI models the spatiotemporal distribution of temperature variation and recovers high resolution optical absorption images using these temperature maps. In this paper, we present a new fast non-iterative reconstruction algorithm for PMI. This new algorithm uses analytic methods during the resolution of the forward problem and the assembly of the sensitivity matrix. We validate our new analytic-based algorithm with the first generation finite element method (FEM) based reconstruction algorithm previously developed by our team. The validation is performed using, first synthetic data and afterwards, real MRT measured temperature maps. Our new method accelerates the reconstruction process 30-fold when compared to a single iteration of the FEM-based algorithm.

Influence of the partial volume correction method on 18F-fluorodeoxyglucose brain kinetic modelling from dynamic PET images reconstructed with resolution model based OSEM

PubMed Central

Bowen, Spencer L.; Byars, Larry G.; Michel, Christian J.; Chonde, Daniel B.; Catana, Ciprian

2014-01-01

Kinetic parameters estimated from dynamic 18F-fluorodeoxyglucose PET acquisitions have been used frequently to assess brain function in humans. Neglecting partial volume correction (PVC) for a dynamic series has been shown to produce significant bias in model estimates. Accurate PVC requires a space-variant model describing the reconstructed image spatial point spread function (PSF) that accounts for resolution limitations, including non-uniformities across the field of view due to the parallax effect. For OSEM, image resolution convergence is local and influenced significantly by the number of iterations, the count density, and background-to-target ratio. As both count density and background-to-target values for a brain structure can change during a dynamic scan, the local image resolution may also concurrently vary. When PVC is applied post-reconstruction the kinetic parameter estimates may be biased when neglecting the frame-dependent resolution. We explored the influence of the PVC method and implementation on kinetic parameters estimated by fitting 18F-fluorodeoxyglucose dynamic data acquired on a dedicated brain PET scanner and reconstructed with and without PSF modelling in the OSEM algorithm. The performance of several PVC algorithms was quantified with a phantom experiment, an anthropomorphic Monte Carlo simulation, and a patient scan. Using the last frame reconstructed image only for regional spread function (RSF) generation, as opposed to computing RSFs for each frame independently, and applying perturbation GTM PVC with PSF based OSEM produced the lowest magnitude bias kinetic parameter estimates in most instances, although at the cost of increased noise compared to the PVC methods utilizing conventional OSEM. Use of the last frame RSFs for PVC with no PSF modelling in the OSEM algorithm produced the lowest bias in CMRGlc estimates, although by less than 5% in most cases compared to the other PVC methods. The results indicate that the PVC implementation and choice of PSF modelling in the reconstruction can significantly impact model parameters. PMID:24052021

Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matenine, Dmitri, E-mail: dmitri.matenine.1@ulaval.ca; Mascolo-Fortin, Julia, E-mail: julia.mascolo-fortin.1@ulaval.ca; Goussard, Yves, E-mail: yves.goussard@polymtl.ca

Purpose: The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. Methods: This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numericalmore » simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. Results: The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. Conclusions: The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can potentially improve the rendering of spatial features and reduce cone-beam optical CT artifacts.« less

Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT.

PubMed

Matenine, Dmitri; Mascolo-Fortin, Julia; Goussard, Yves; Després, Philippe

2015-11-01

The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numerical simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can potentially improve the rendering of spatial features and reduce cone-beam optical CT artifacts.

LBP-based penalized weighted least-squares approach to low-dose cone-beam computed tomography reconstruction

NASA Astrophysics Data System (ADS)

Ma, Ming; Wang, Huafeng; Liu, Yan; Zhang, Hao; Gu, Xianfeng; Liang, Zhengrong

2014-03-01

Cone-beam computed tomography (CBCT) has attracted growing interest of researchers in image reconstruction. The mAs level of the X-ray tube current, in practical application of CBCT, is mitigated in order to reduce the CBCT dose. The lowering of the X-ray tube current, however, results in the degradation of image quality. Thus, low-dose CBCT image reconstruction is in effect a noise problem. To acquire clinically acceptable quality of image, and keep the X-ray tube current as low as achievable in the meanwhile, some penalized weighted least-squares (PWLS)-based image reconstruction algorithms have been developed. One representative strategy in previous work is to model the prior information for solution regularization using an anisotropic penalty term. To enhance the edge preserving and noise suppressing in a finer scale, a novel algorithm combining the local binary pattern (LBP) with penalized weighted leastsquares (PWLS), called LBP-PWLS-based image reconstruction algorithm, is proposed in this work. The proposed LBP-PWLS-based algorithm adaptively encourages strong diffusion on the local spot/flat region around a voxel and less diffusion on edge/corner ones by adjusting the penalty for cost function, after the LBP is utilized to detect the region around the voxel as spot, flat and edge ones. The LBP-PWLS-based reconstruction algorithm was evaluated using the sinogram data acquired by a clinical CT scanner from the CatPhan® 600 phantom. Experimental results on the noiseresolution tradeoff measurement and other quantitative measurements demonstrated its feasibility and effectiveness in edge preserving and noise suppressing in comparison with a previous PWLS reconstruction algorithm.

[The dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modeling language (VRML)].

PubMed

Yu, Zhengyang; Zheng, Shusen; Chen, Huaiqing; Wang, Jianjun; Xiong, Qingwen; Jing, Wanjun; Zeng, Yu

2006-10-01

This research studies the process of dynamic concision and 3D reconstruction from medical body data using VRML and JavaScript language, focuses on how to realize the dynamic concision of 3D medical model built with VRML. The 2D medical digital images firstly are modified and manipulated by 2D image software. Then, based on these images, 3D mould is built with VRML and JavaScript language. After programming in JavaScript to control 3D model, the function of dynamic concision realized by Script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be formed in high quality near to those got in traditional methods. By this way, with the function of dynamic concision, VRML browser can offer better windows of man-computer interaction in real time environment than before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and has a promising prospect in the fields of medical image.

A Reconstruction Algorithm for Breast Cancer Imaging With Electrical Impedance Tomography in Mammography Geometry

PubMed Central

Kao, Tzu-Jen; Isaacson, David; Saulnier, Gary J.; Newell, Jonathan C.

2009-01-01

The conductivity and permittivity of breast tumors are known to differ significantly from those of normal breast tissues, and electrical impedance tomography (EIT) is being studied as a modality for breast cancer imaging to exploit these differences. At present, X-ray mammography is the primary standard imaging modality used for breast cancer screening in clinical practice, so it is desirable to study EIT in the geometry of mammography. This paper presents a forward model of a simplified mammography geometry and a reconstruction algorithm for breast tumor imaging using EIT techniques. The mammography geometry is modeled as a rectangular box with electrode arrays on the top and bottom planes. A forward model for the electrical impedance imaging problem is derived for a homogeneous conductivity distribution and is validated by experiment using a phantom tank. A reconstruction algorithm for breast tumor imaging based on a linearization approach and the proposed forward model is presented. It is found that the proposed reconstruction algorithm performs well in the phantom experiment, and that the locations of a 5-mm-cube metal target and a 6-mm-cube agar target could be recovered at a target depth of 15 mm using a 32 electrode system. PMID:17405377

A 3D reconstruction algorithm for magneto-acoustic tomography with magnetic induction based on ultrasound transducer characteristics.

PubMed

Ma, Ren; Zhou, Xiaoqing; Zhang, Shunqi; Yin, Tao; Liu, Zhipeng

2016-12-21

In this study we present a three-dimensional (3D) reconstruction algorithm for magneto-acoustic tomography with magnetic induction (MAT-MI) based on the characteristics of the ultrasound transducer. The algorithm is investigated to solve the blur problem of the MAT-MI acoustic source image, which is caused by the ultrasound transducer and the scanning geometry. First, we established a transducer model matrix using measured data from the real transducer. With reference to the S-L model used in the computed tomography algorithm, a 3D phantom model of electrical conductivity is set up. Both sphere scanning and cylinder scanning geometries are adopted in the computer simulation. Then, using finite element analysis, the distribution of the eddy current and the acoustic source as well as the acoustic pressure can be obtained with the transducer model matrix. Next, using singular value decomposition, the inverse transducer model matrix together with the reconstruction algorithm are worked out. The acoustic source and the conductivity images are reconstructed using the proposed algorithm. Comparisons between an ideal point transducer and the realistic transducer are made to evaluate the algorithms. Finally, an experiment is performed using a graphite phantom. We found that images of the acoustic source reconstructed using the proposed algorithm are a better match than those using the previous one, the correlation coefficient of sphere scanning geometry is 98.49% and that of cylinder scanning geometry is 94.96%. Comparison between the ideal point transducer and the realistic transducer shows that the correlation coefficients are 90.2% in sphere scanning geometry and 86.35% in cylinder scanning geometry. The reconstruction of the graphite phantom experiment also shows a higher resolution using the proposed algorithm. We conclude that the proposed reconstruction algorithm, which considers the characteristics of the transducer, can obviously improve the resolution of the reconstructed image. This study can be applied to analyse the effect of the position of the transducer and the scanning geometry on imaging. It may provide a more precise method to reconstruct the conductivity distribution in MAT-MI.

Reconstruction of hyperspectral image using matting model for classification

NASA Astrophysics Data System (ADS)

Xie, Weiying; Li, Yunsong; Ge, Chiru

2016-05-01

Although hyperspectral images (HSIs) captured by satellites provide much information in spectral regions, some bands are redundant or have large amounts of noise, which are not suitable for image analysis. To address this problem, we introduce a method for reconstructing the HSI with noise reduction and contrast enhancement using a matting model for the first time. The matting model refers to each spectral band of an HSI that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, one spectral band of an HSI with more refined information than most other bands is selected, and is referred to as an alpha channel of the HSI to estimate the hyperspectral foreground and hyperspectral background. Finally, a combination operation is applied to reconstruct the HSI. In addition, the support vector machine (SVM) classifier and three sparsity-based classifiers, i.e., orthogonal matching pursuit (OMP), simultaneous OMP, and OMP based on first-order neighborhood system weighted classifiers, are utilized on the reconstructed HSI and the original HSI to verify the effectiveness of the proposed method. Specifically, using the reconstructed HSI, the average accuracy of the SVM classifier can be improved by as much as 19%.

Electron paramagnetic resonance image reconstruction with total variation and curvelets regularization

NASA Astrophysics Data System (ADS)

Durand, Sylvain; Frapart, Yves-Michel; Kerebel, Maud

2017-11-01

Spatial electron paramagnetic resonance imaging (EPRI) is a recent method to localize and characterize free radicals in vivo or in vitro, leading to applications in material and biomedical sciences. To improve the quality of the reconstruction obtained by EPRI, a variational method is proposed to inverse the image formation model. It is based on a least-square data-fidelity term and the total variation and Besov seminorm for the regularization term. To fully comprehend the Besov seminorm, an implementation using the curvelet transform and the L 1 norm enforcing the sparsity is proposed. It allows our model to reconstruct both image where acquisition information are missing and image with details in textured areas, thus opening possibilities to reduce acquisition times. To implement the minimization problem using the algorithm developed by Chambolle and Pock, a thorough analysis of the direct model is undertaken and the latter is inverted while avoiding the use of filtered backprojection (FBP) and of non-uniform Fourier transform. Numerical experiments are carried out on simulated data, where the proposed model outperforms both visually and quantitatively the classical model using deconvolution and FBP. Improved reconstructions on real data, acquired on an irradiated distal phalanx, were successfully obtained.

Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology

PubMed Central

Ukwatta, Eranga; Arevalo, Hermenegild; Rajchl, Martin; White, James; Pashakhanloo, Farhad; Prakosa, Adityo; Herzka, Daniel A.; McVeigh, Elliot; Lardo, Albert C.; Trayanova, Natalia A.; Vadakkumpadan, Fijoy

2015-01-01

Purpose: Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need. Methods: The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method. Results: The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs. Conclusions: The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations. PMID:26233186

Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ukwatta, Eranga, E-mail: eukwatt1@jhu.edu; Arevalo, Hermenegild; Pashakhanloo, Farhad

Purpose: Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need. Methods: The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitlymore » represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method. Results: The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs. Conclusions: The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.« less

Fast iterative image reconstruction using sparse matrix factorization with GPU acceleration

NASA Astrophysics Data System (ADS)

Zhou, Jian; Qi, Jinyi

2011-03-01

Statistically based iterative approaches for image reconstruction have gained much attention in medical imaging. An accurate system matrix that defines the mapping from the image space to the data space is the key to high-resolution image reconstruction. However, an accurate system matrix is often associated with high computational cost and huge storage requirement. Here we present a method to address this problem by using sparse matrix factorization and parallel computing on a graphic processing unit (GPU).We factor the accurate system matrix into three sparse matrices: a sinogram blurring matrix, a geometric projection matrix, and an image blurring matrix. The sinogram blurring matrix models the detector response. The geometric projection matrix is based on a simple line integral model. The image blurring matrix is to compensate for the line-of-response (LOR) degradation due to the simplified geometric projection matrix. The geometric projection matrix is precomputed, while the sinogram and image blurring matrices are estimated by minimizing the difference between the factored system matrix and the original system matrix. The resulting factored system matrix has much less number of nonzero elements than the original system matrix and thus substantially reduces the storage and computation cost. The smaller size also allows an efficient implement of the forward and back projectors on GPUs, which have limited amount of memory. Our simulation studies show that the proposed method can dramatically reduce the computation cost of high-resolution iterative image reconstruction. The proposed technique is applicable to image reconstruction for different imaging modalities, including x-ray CT, PET, and SPECT.

Reconstructing 3D Face Model with Associated Expression Deformation from a Single Face Image via Constructing a Low-Dimensional Expression Deformation Manifold.

PubMed

Wang, Shu-Fan; Lai, Shang-Hong

2011-10-01

Facial expression modeling is central to facial expression recognition and expression synthesis for facial animation. In this work, we propose a manifold-based 3D face reconstruction approach to estimating the 3D face model and the associated expression deformation from a single face image. With the proposed robust weighted feature map (RWF), we can obtain the dense correspondences between 3D face models and build a nonlinear 3D expression manifold from a large set of 3D facial expression models. Then a Gaussian mixture model in this manifold is learned to represent the distribution of expression deformation. By combining the merits of morphable neutral face model and the low-dimensional expression manifold, a novel algorithm is developed to reconstruct the 3D face geometry as well as the facial deformation from a single face image in an energy minimization framework. Experimental results on simulated and real images are shown to validate the effectiveness and accuracy of the proposed algorithm.

Techniques in helical scanning, dynamic imaging and image segmentation for improved quantitative analysis with X-ray micro-CT

NASA Astrophysics Data System (ADS)

Sheppard, Adrian; Latham, Shane; Middleton, Jill; Kingston, Andrew; Myers, Glenn; Varslot, Trond; Fogden, Andrew; Sawkins, Tim; Cruikshank, Ron; Saadatfar, Mohammad; Francois, Nicolas; Arns, Christoph; Senden, Tim

2014-04-01

This paper reports on recent advances at the micro-computed tomography facility at the Australian National University. Since 2000 this facility has been a significant centre for developments in imaging hardware and associated software for image reconstruction, image analysis and image-based modelling. In 2010 a new instrument was constructed that utilises theoretically-exact image reconstruction based on helical scanning trajectories, allowing higher cone angles and thus better utilisation of the available X-ray flux. We discuss the technical hurdles that needed to be overcome to allow imaging with cone angles in excess of 60°. We also present dynamic tomography algorithms that enable the changes between one moment and the next to be reconstructed from a sparse set of projections, allowing higher speed imaging of time-varying samples. Researchers at the facility have also created a sizeable distributed-memory image analysis toolkit with capabilities ranging from tomographic image reconstruction to 3D shape characterisation. We show results from image registration and present some of the new imaging and experimental techniques that it enables. Finally, we discuss the crucial question of image segmentation and evaluate some recently proposed techniques for automated segmentation.

Point-spread function reconstruction in ground-based astronomy by l(1)-l(p) model.

PubMed

Chan, Raymond H; Yuan, Xiaoming; Zhang, Wenxing

2012-11-01

In ground-based astronomy, images of objects in outer space are acquired via ground-based telescopes. However, the imaging system is generally interfered by atmospheric turbulence, and hence images so acquired are blurred with unknown point-spread function (PSF). To restore the observed images, the wavefront of light at the telescope's aperture is utilized to derive the PSF. A model with the Tikhonov regularization has been proposed to find the high-resolution phase gradients by solving a least-squares system. Here we propose the l(1)-l(p) (p=1, 2) model for reconstructing the phase gradients. This model can provide sharper edges in the gradients while removing noise. The minimization models can easily be solved by the Douglas-Rachford alternating direction method of a multiplier, and the convergence rate is readily established. Numerical results are given to illustrate that the model can give better phase gradients and hence a more accurate PSF. As a result, the restored images are much more accurate when compared to the traditional Tikhonov regularization model.

Does thorax EIT image analysis depend on the image reconstruction method?

NASA Astrophysics Data System (ADS)

Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut

2013-04-01

Different methods were proposed to analyze the resulting images of electrical impedance tomography (EIT) measurements during ventilation. The aim of our study was to examine if the analysis methods based on back-projection deliver the same results when applied on images based on other reconstruction algorithms. Seven mechanically ventilated patients with ARDS were examined by EIT. The thorax contours were determined from the routine CT images. EIT raw data was reconstructed offline with (1) filtered back-projection with circular forward model (BPC); (2) GREIT reconstruction method with circular forward model (GREITC) and (3) GREIT with individual thorax geometry (GREITT). Three parameters were calculated on the resulting images: linearity, global ventilation distribution and regional ventilation distribution. The results of linearity test are 5.03±2.45, 4.66±2.25 and 5.32±2.30 for BPC, GREITC and GREITT, respectively (median ±interquartile range). The differences among the three methods are not significant (p = 0.93, Kruskal-Wallis test). The proportions of ventilation in the right lung are 0.58±0.17, 0.59±0.20 and 0.59±0.25 for BPC, GREITC and GREITT, respectively (p = 0.98). The differences of the GI index based on different reconstruction methods (0.53±0.16, 0.51±0.25 and 0.54±0.16 for BPC, GREITC and GREITT, respectively) are also not significant (p = 0.93). We conclude that the parameters developed for images generated with GREITT are comparable with filtered back-projection and GREITC.

Projection model for flame chemiluminescence tomography based on lens imaging

NASA Astrophysics Data System (ADS)

Wan, Minggang; Zhuang, Jihui

2018-04-01

For flame chemiluminescence tomography (FCT) based on lens imaging, the projection model is essential because it formulates the mathematical relation between the flame projections captured by cameras and the chemiluminescence field, and, through this relation, the field is reconstructed. This work proposed the blurry-spot (BS) model, which takes more universal assumptions and has higher accuracy than the widely applied line-of-sight model. By combining the geometrical camera model and the thin-lens equation, the BS model takes into account perspective effect of the camera lens; by combining ray-tracing technique and Monte Carlo simulation, it also considers inhomogeneous distribution of captured radiance on the image plane. Performance of these two models in FCT was numerically compared, and results showed that using the BS model could lead to better reconstruction quality in wider application ranges.

Prospective regularization design in prior-image-based reconstruction

NASA Astrophysics Data System (ADS)

Dang, Hao; Siewerdsen, Jeffrey H.; Webster Stayman, J.

2015-12-01

Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in phantoms where the optimal parameters vary spatially by an order of magnitude or more. In a series of studies designed to explore potential unknowns associated with accurate PIBR, optimal prior image strength was found to vary with attenuation differences associated with anatomical change but exhibited only small variations as a function of the shape and size of the change. The results suggest that, given a target change attenuation, prospective patient-, change-, and data-specific customization of the prior image strength can be performed to ensure reliable reconstruction of specific anatomical changes.

SU-G-IeP4-03: Cone Beam X-Ray Luminescence Computed Tomography Based On Generalized Gaussian Markov Random Field

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, G; Xing, L

2016-06-15

Purpose: Cone beam X-ray luminescence computed tomography (CB-XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. However, the inverse problem of CB-XLCT is seriously ill-conditioned, hindering us to achieve good image quality. In this work, a novel reconstruction method based on Bayesian theory is proposed to tackle this problem Methods: Bayesian theory provides a natural framework for utilizing various kinds of available prior information to improve the reconstruction image quality. A generalized Gaussian Markov random field (GGMRF) model is proposed here to construct the prior model of the Bayesianmore » theory. The most important feature of GGMRF model is the adjustable shape parameter p, which can be continuously adjusted from 1 to 2. The reconstruction image tends to have more edge-preserving property when p is slide to 1, while having more noise tolerance property when p is slide to 2, just like the behavior of L1 and L2 regularization methods, respectively. The proposed method provides a flexible regularization framework to adapt to a wide range of applications. Results: Numerical simulations were implemented to test the performance of the proposed method. The Digimouse atlas were employed to construct a three-dimensional mouse model, and two small cylinders were placed inside to serve as the targets. Reconstruction results show that the proposed method tends to obtain better spatial resolution with a smaller shape parameter, while better signal-to-noise image with a larger shape parameter. Quantitative indexes, contrast-to-noise ratio (CNR) and full-width at half-maximum (FWHM), were used to assess the performance of the proposed method, and confirmed its effectiveness in CB-XLCT reconstruction. Conclusion: A novel reconstruction method for CB-XLCT is proposed based on GGMRF model, which enables an adjustable performance tradeoff between L1 and L2 regularization methods. Numerical simulations were conducted to demonstrate its performance.« less

SU-F-I-49: Vendor-Independent, Model-Based Iterative Reconstruction On a Rotating Grid with Coordinate-Descent Optimization for CT Imaging Investigations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, S; Hoffman, J; McNitt-Gray, M

Purpose: Iterative reconstruction methods show promise for improving image quality and lowering the dose in helical CT. We aim to develop a novel model-based reconstruction method that offers potential for dose reduction with reasonable computation speed and storage requirements for vendor-independent reconstruction from clinical data on a normal desktop computer. Methods: In 2012, Xu proposed reconstructing on rotating slices to exploit helical symmetry and reduce the storage requirements for the CT system matrix. Inspired by this concept, we have developed a novel reconstruction method incorporating the stored-system-matrix approach together with iterative coordinate-descent (ICD) optimization. A penalized-least-squares objective function with amore » quadratic penalty term is solved analytically voxel-by-voxel, sequentially iterating along the axial direction first, followed by the transaxial direction. 8 in-plane (transaxial) neighbors are used for the ICD algorithm. The forward problem is modeled via a unique approach that combines the principle of Joseph’s method with trilinear B-spline interpolation to enable accurate reconstruction with low storage requirements. Iterations are accelerated with multi-CPU OpenMP libraries. For preliminary evaluations, we reconstructed (1) a simulated 3D ellipse phantom and (2) an ACR accreditation phantom dataset exported from a clinical scanner (Definition AS, Siemens Healthcare). Image quality was evaluated in the resolution module. Results: Image quality was excellent for the ellipse phantom. For the ACR phantom, image quality was comparable to clinical reconstructions and reconstructions using open-source FreeCT-wFBP software. Also, we did not observe any deleterious impact associated with the utilization of rotating slices. The system matrix storage requirement was only 4.5GB, and reconstruction time was 50 seconds per iteration. Conclusion: Our reconstruction method shows potential for furthering research in low-dose helical CT, in particular as part of our ongoing development of an acquisition/reconstruction pipeline for generating images under a wide range of conditions. Our algorithm will be made available open-source as “FreeCT-ICD”. NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.« less

Magnetic Resonance-based Motion Correction for Quantitative PET in Simultaneous PET-MR Imaging.

PubMed

Rakvongthai, Yothin; El Fakhri, Georges

2017-07-01

Motion degrades image quality and quantitation of PET images, and is an obstacle to quantitative PET imaging. Simultaneous PET-MR offers a tool that can be used for correcting the motion in PET images by using anatomic information from MR imaging acquired concurrently. Motion correction can be performed by transforming a set of reconstructed PET images into the same frame or by incorporating the transformation into the system model and reconstructing the motion-corrected image. Several phantom and patient studies have validated that MR-based motion correction strategies have great promise for quantitative PET imaging in simultaneous PET-MR. Copyright © 2017 Elsevier Inc. All rights reserved.

Monte Carlo Simulation for Polychromatic X-Ray Fluorescence Computed Tomography with Sheet-Beam Geometry

PubMed Central

Jiang, Shanghai

2017-01-01

X-ray fluorescence computed tomography (XFCT) based on sheet beam can save a huge amount of time to obtain a whole set of projections using synchrotron. However, it is clearly unpractical for most biomedical research laboratories. In this paper, polychromatic X-ray fluorescence computed tomography with sheet-beam geometry is tested by Monte Carlo simulation. First, two phantoms (A and B) filled with PMMA are used to simulate imaging process through GEANT 4. Phantom A contains several GNP-loaded regions with the same size (10 mm) in height and diameter but different Au weight concentration ranging from 0.3% to 1.8%. Phantom B contains twelve GNP-loaded regions with the same Au weight concentration (1.6%) but different diameter ranging from 1 mm to 9 mm. Second, discretized presentation of imaging model is established to reconstruct more accurate XFCT images. Third, XFCT images of phantoms A and B are reconstructed by filter back-projection (FBP) and maximum likelihood expectation maximization (MLEM) with and without correction, respectively. Contrast-to-noise ratio (CNR) is calculated to evaluate all the reconstructed images. Our results show that it is feasible for sheet-beam XFCT system based on polychromatic X-ray source and the discretized imaging model can be used to reconstruct more accurate images. PMID:28567054

Connectome imaging for mapping human brain pathways

PubMed Central

Shi, Y; Toga, A W

2017-01-01

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research. PMID:28461700

Pan-sharpening via compressed superresolution reconstruction and multidictionary learning

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Fang; Li, Lingling; Jiao, Licheng; Hao, Hongxia; Shang, Ronghua; Li, Yangyang

2018-01-01

In recent compressed sensing (CS)-based pan-sharpening algorithms, pan-sharpening performance is affected by two key problems. One is that there are always errors between the high-resolution panchromatic (HRP) image and the linear weighted high-resolution multispectral (HRM) image, resulting in spatial and spectral information lost. The other is that the dictionary construction process depends on the nontruth training samples. These problems have limited applications to CS-based pan-sharpening algorithm. To solve these two problems, we propose a pan-sharpening algorithm via compressed superresolution reconstruction and multidictionary learning. Through a two-stage implementation, compressed superresolution reconstruction model reduces the error effectively between the HRP and the linear weighted HRM images. Meanwhile, the multidictionary with ridgelet and curvelet is learned for both the two stages in the superresolution reconstruction process. Since ridgelet and curvelet can better capture the structure and directional characteristics, a better reconstruction result can be obtained. Experiments are done on the QuickBird and IKONOS satellites images. The results indicate that the proposed algorithm is competitive compared with the recent CS-based pan-sharpening methods and other well-known methods.

A joint Richardson-Lucy deconvolution algorithm for the reconstruction of multifocal structured illumination microscopy data.

PubMed

Ströhl, Florian; Kaminski, Clemens F

2015-01-16

We demonstrate the reconstruction of images obtained by multifocal structured illumination microscopy, MSIM, using a joint Richardson-Lucy, jRL-MSIM, deconvolution algorithm, which is based on an underlying widefield image-formation model. The method is efficient in the suppression of out-of-focus light and greatly improves image contrast and resolution. Furthermore, it is particularly well suited for the processing of noise corrupted data. The principle is verified on simulated as well as experimental data and a comparison of the jRL-MSIM approach with the standard reconstruction procedure, which is based on image scanning microscopy, ISM, is made. Our algorithm is efficient and freely available in a user friendly software package.

A joint Richardson—Lucy deconvolution algorithm for the reconstruction of multifocal structured illumination microscopy data

NASA Astrophysics Data System (ADS)

Ströhl, Florian; Kaminski, Clemens F.

2015-03-01

We demonstrate the reconstruction of images obtained by multifocal structured illumination microscopy, MSIM, using a joint Richardson-Lucy, jRL-MSIM, deconvolution algorithm, which is based on an underlying widefield image-formation model. The method is efficient in the suppression of out-of-focus light and greatly improves image contrast and resolution. Furthermore, it is particularly well suited for the processing of noise corrupted data. The principle is verified on simulated as well as experimental data and a comparison of the jRL-MSIM approach with the standard reconstruction procedure, which is based on image scanning microscopy, ISM, is made. Our algorithm is efficient and freely available in a user friendly software package.

Fast implementation for compressive recovery of highly accelerated cardiac cine MRI using the balanced sparse model.

PubMed

Ting, Samuel T; Ahmad, Rizwan; Jin, Ning; Craft, Jason; Serafim da Silveira, Juliana; Xue, Hui; Simonetti, Orlando P

2017-04-01

Sparsity-promoting regularizers can enable stable recovery of highly undersampled magnetic resonance imaging (MRI), promising to improve the clinical utility of challenging applications. However, lengthy computation time limits the clinical use of these methods, especially for dynamic MRI with its large corpus of spatiotemporal data. Here, we present a holistic framework that utilizes the balanced sparse model for compressive sensing and parallel computing to reduce the computation time of cardiac MRI recovery methods. We propose a fast, iterative soft-thresholding method to solve the resulting ℓ1-regularized least squares problem. In addition, our approach utilizes a parallel computing environment that is fully integrated with the MRI acquisition software. The methodology is applied to two formulations of the multichannel MRI problem: image-based recovery and k-space-based recovery. Using measured MRI data, we show that, for a 224 × 144 image series with 48 frames, the proposed k-space-based approach achieves a mean reconstruction time of 2.35 min, a 24-fold improvement compared a reconstruction time of 55.5 min for the nonlinear conjugate gradient method, and the proposed image-based approach achieves a mean reconstruction time of 13.8 s. Our approach can be utilized to achieve fast reconstruction of large MRI datasets, thereby increasing the clinical utility of reconstruction techniques based on compressed sensing. Magn Reson Med 77:1505-1515, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

2009-09-01

This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

Reconstruction of dynamic image series from undersampled MRI data using data-driven model consistency condition (MOCCO).

PubMed

Velikina, Julia V; Samsonov, Alexey A

2015-11-01

To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models preestimated from training data. We introduce the model consistency condition (MOCCO) technique, which utilizes temporal models to regularize reconstruction without constraining the solution to be low-rank, as is performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Our method was compared with a standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE-MRA) and cardiac CINE imaging. We studied the sensitivity of all methods to rank reduction and temporal subspace modeling errors. MOCCO demonstrated reduced sensitivity to modeling errors compared with the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE-MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. © 2014 Wiley Periodicals, Inc.

RECONSTRUCTION OF DYNAMIC IMAGE SERIES FROM UNDERSAMPLED MRI DATA USING DATA-DRIVEN MODEL CONSISTENCY CONDITION (MOCCO)

PubMed Central

Velikina, Julia V.; Samsonov, Alexey A.

2014-01-01

Purpose To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models pre-estimated from training data. Theory We introduce the MOdel Consistency COndition (MOCCO) technique that utilizes temporal models to regularize the reconstruction without constraining the solution to be low-rank as performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Methods Our method was compared to standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE MRA) and cardiac CINE imaging. We studied sensitivity of all methods to rank-reduction and temporal subspace modeling errors. Results MOCCO demonstrated reduced sensitivity to modeling errors compared to the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. Conclusions MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. PMID:25399724

Effects of illumination differences on photometric stereo shape-and-albedo-from-shading for precision lunar surface reconstruction

NASA Astrophysics Data System (ADS)

Chung Liu, Wai; Wu, Bo; Wöhler, Christian

2018-02-01

Photoclinometric surface reconstruction techniques such as Shape-from-Shading (SfS) and Shape-and-Albedo-from-Shading (SAfS) retrieve topographic information of a surface on the basis of the reflectance information embedded in the image intensity of each pixel. SfS or SAfS techniques have been utilized to generate pixel-resolution digital elevation models (DEMs) of the Moon and other planetary bodies. Photometric stereo SAfS analyzes images under multiple illumination conditions to improve the robustness of reconstruction. In this case, the directional difference in illumination between the images is likely to affect the quality of the reconstruction result. In this study, we quantitatively investigate the effects of illumination differences on photometric stereo SAfS. Firstly, an algorithm for photometric stereo SAfS is developed, and then, an error model is derived to analyze the relationships between the azimuthal and zenith angles of illumination of the images and the reconstruction qualities. The developed algorithm and error model were verified with high-resolution images collected by the Narrow Angle Camera (NAC) of the Lunar Reconnaissance Orbiter Camera (LROC). Experimental analyses reveal that (1) the resulting error in photometric stereo SAfS depends on both the azimuthal and the zenith angles of illumination as well as the general intensity of the images and (2) the predictions from the proposed error model are consistent with the actual slope errors obtained by photometric stereo SAfS using the LROC NAC images. The proposed error model enriches the theory of photometric stereo SAfS and is of significance for optimized lunar surface reconstruction based on SAfS techniques.

Computational and human observer image quality evaluation of low dose, knowledge-based CT iterative reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eck, Brendan L.; Fahmi, Rachid; Miao, Jun

2015-10-15

Purpose: Aims in this study are to (1) develop a computational model observer which reliably tracks the detectability of human observers in low dose computed tomography (CT) images reconstructed with knowledge-based iterative reconstruction (IMR™, Philips Healthcare) and filtered back projection (FBP) across a range of independent variables, (2) use the model to evaluate detectability trends across reconstructions and make predictions of human observer detectability, and (3) perform human observer studies based on model predictions to demonstrate applications of the model in CT imaging. Methods: Detectability (d′) was evaluated in phantom studies across a range of conditions. Images were generated usingmore » a numerical CT simulator. Trained observers performed 4-alternative forced choice (4-AFC) experiments across dose (1.3, 2.7, 4.0 mGy), pin size (4, 6, 8 mm), contrast (0.3%, 0.5%, 1.0%), and reconstruction (FBP, IMR), at fixed display window. A five-channel Laguerre–Gauss channelized Hotelling observer (CHO) was developed with internal noise added to the decision variable and/or to channel outputs, creating six different internal noise models. Semianalytic internal noise computation was tested against Monte Carlo and used to accelerate internal noise parameter optimization. Model parameters were estimated from all experiments at once using maximum likelihood on the probability correct, P{sub C}. Akaike information criterion (AIC) was used to compare models of different orders. The best model was selected according to AIC and used to predict detectability in blended FBP-IMR images, analyze trends in IMR detectability improvements, and predict dose savings with IMR. Predicted dose savings were compared against 4-AFC study results using physical CT phantom images. Results: Detection in IMR was greater than FBP in all tested conditions. The CHO with internal noise proportional to channel output standard deviations, Model-k4, showed the best trade-off between fit and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.« less

Comparison of computational to human observer detection for evaluation of CT low dose iterative reconstruction

NASA Astrophysics Data System (ADS)

Eck, Brendan; Fahmi, Rachid; Brown, Kevin M.; Raihani, Nilgoun; Wilson, David L.

2014-03-01

Model observers were created and compared to human observers for the detection of low contrast targets in computed tomography (CT) images reconstructed with an advanced, knowledge-based, iterative image reconstruction method for low x-ray dose imaging. A 5-channel Laguerre-Gauss Hotelling Observer (CHO) was used with internal noise added to the decision variable (DV) and/or channel outputs (CO). Models were defined by parameters: (k1) DV-noise with standard deviation (std) proportional to DV std; (k2) DV-noise with constant std; (k3) CO-noise with constant std across channels; and (k4) CO-noise in each channel with std proportional to CO variance. Four-alternative forced choice (4AFC) human observer studies were performed on sub-images extracted from phantom images with and without a "pin" target. Model parameters were estimated using maximum likelihood comparison to human probability correct (PC) data. PC in human and all model observers increased with dose, contrast, and size, and was much higher for advanced iterative reconstruction (IMR) as compared to filtered back projection (FBP). Detection in IMR was better than FPB at 1/3 dose, suggesting significant dose savings. Model(k1,k2,k3,k4) gave the best overall fit to humans across independent variables (dose, size, contrast, and reconstruction) at fixed display window. However Model(k1) performed better when considering model complexity using the Akaike information criterion. Model(k1) fit the extraordinary detectability difference between IMR and FBP, despite the different noise quality. It is anticipated that the model observer will predict results from iterative reconstruction methods having similar noise characteristics, enabling rapid comparison of methods.

Image-based models of cardiac structure in health and disease

PubMed Central

Vadakkumpadan, Fijoy; Arevalo, Hermenegild; Prassl, Anton J.; Chen, Junjie; Kickinger, Ferdinand; Kohl, Peter; Plank, Gernot; Trayanova, Natalia

2010-01-01

Computational approaches to investigating the electromechanics of healthy and diseased hearts are becoming essential for the comprehensive understanding of cardiac function. In this article, we first present a brief review of existing image-based computational models of cardiac structure. We then provide a detailed explanation of a processing pipeline which we have recently developed for constructing realistic computational models of the heart from high resolution structural and diffusion tensor (DT) magnetic resonance (MR) images acquired ex vivo. The presentation of the pipeline incorporates a review of the methodologies that can be used to reconstruct models of cardiac structure. In this pipeline, the structural image is segmented to reconstruct the ventricles, normal myocardium, and infarct. A finite element mesh is generated from the segmented structural image, and fiber orientations are assigned to the elements based on DTMR data. The methods were applied to construct seven different models of healthy and diseased hearts. These models contain millions of elements, with spatial resolutions in the order of hundreds of microns, providing unprecedented detail in the representation of cardiac structure for simulation studies. PMID:20582162

A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning.

PubMed

Zhang, Cheng; Zhang, Tao; Zheng, Jian; Li, Ming; Lu, Yanfei; You, Jiali; Guan, Yihui

2015-01-01

In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time.

Level-set-based reconstruction algorithm for EIT lung images: first clinical results.

PubMed

Rahmati, Peyman; Soleimani, Manuchehr; Pulletz, Sven; Frerichs, Inéz; Adler, Andy

2012-05-01

We show the first clinical results using the level-set-based reconstruction algorithm for electrical impedance tomography (EIT) data. The level-set-based reconstruction method (LSRM) allows the reconstruction of non-smooth interfaces between image regions, which are typically smoothed by traditional voxel-based reconstruction methods (VBRMs). We develop a time difference formulation of the LSRM for 2D images. The proposed reconstruction method is applied to reconstruct clinical EIT data of a slow flow inflation pressure-volume manoeuvre in lung-healthy and adult lung-injury patients. Images from the LSRM and the VBRM are compared. The results show comparable reconstructed images, but with an improved ability to reconstruct sharp conductivity changes in the distribution of lung ventilation using the LSRM.

Optimization-based image reconstruction in x-ray computed tomography by sparsity exploitation of local continuity and nonlocal spatial self-similarity

NASA Astrophysics Data System (ADS)

Han-Ming, Zhang; Lin-Yuan, Wang; Lei, Li; Bin, Yan; Ai-Long, Cai; Guo-En, Hu

2016-07-01

The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography (CT) reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts. To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated. The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation. Project supported by the National Natural Science Foundation of China (Grant No. 61372172).

Hybrid-dual-fourier tomographic algorithm for a fast three-dimensionial optical image reconstruction in turbid media

NASA Technical Reports Server (NTRS)

Alfano, Robert R. (Inventor); Cai, Wei (Inventor)

2007-01-01

A reconstruction technique for reducing computation burden in the 3D image processes, wherein the reconstruction procedure comprises an inverse and a forward model. The inverse model uses a hybrid dual Fourier algorithm that combines a 2D Fourier inversion with a 1D matrix inversion to thereby provide high-speed inverse computations. The inverse algorithm uses a hybrid transfer to provide fast Fourier inversion for data of multiple sources and multiple detectors. The forward model is based on an analytical cumulant solution of a radiative transfer equation. The accurate analytical form of the solution to the radiative transfer equation provides an efficient formalism for fast computation of the forward model.

Beyond maximum entropy: Fractal Pixon-based image reconstruction

NASA Technical Reports Server (NTRS)

Puetter, Richard C.; Pina, R. K.

1994-01-01

We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other competing methods, including Goodness-of-Fit methods such as Least-Squares fitting and Lucy-Richardson reconstruction, as well as Maximum Entropy (ME) methods such as those embodied in the MEMSYS algorithms. Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME. Our past work has shown how uniform information content pixons can be used to develop a 'Super-ME' method in which entropy is maximized exactly. Recently, however, we have developed a superior pixon basis for the image, the Fractal Pixon Basis (FPB). Unlike the Uniform Pixon Basis (UPB) of our 'Super-ME' method, the FPB basis is selected by employing fractal dimensional concepts to assess the inherent structure in the image. The Fractal Pixon Basis results in the best image reconstructions to date, superior to both UPB and the best ME reconstructions. In this paper, we review the theory of the UPB and FPB pixon and apply our methodology to the reconstruction of far-infrared imaging of the galaxy M51. The results of our reconstruction are compared to published reconstructions of the same data using the Lucy-Richardson algorithm, the Maximum Correlation Method developed at IPAC, and the MEMSYS ME algorithms. The results show that our reconstructed image has a spatial resolution a factor of two better than best previous methods (and a factor of 20 finer than the width of the point response function), and detects sources two orders of magnitude fainter than other methods.

Accelerated Compressed Sensing Based CT Image Reconstruction.

PubMed

Hashemi, SayedMasoud; Beheshti, Soosan; Gill, Patrick R; Paul, Narinder S; Cobbold, Richard S C

2015-01-01

In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

Accelerated Compressed Sensing Based CT Image Reconstruction

PubMed Central

Hashemi, SayedMasoud; Beheshti, Soosan; Gill, Patrick R.; Paul, Narinder S.; Cobbold, Richard S. C.

2015-01-01

In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization. PMID:26167200

Computational microscopy: illumination coding and nonlinear optimization enables gigapixel 3D phase imaging

NASA Astrophysics Data System (ADS)

Tian, Lei; Waller, Laura

2017-05-01

Microscope lenses can have either large field of view (FOV) or high resolution, not both. Computational microscopy based on illumination coding circumvents this limit by fusing images from different illumination angles using nonlinear optimization algorithms. The result is a Gigapixel-scale image having both wide FOV and high resolution. We demonstrate an experimentally robust reconstruction algorithm based on a 2nd order quasi-Newton's method, combined with a novel phase initialization scheme. To further extend the Gigapixel imaging capability to 3D, we develop a reconstruction method to process the 4D light field measurements from sequential illumination scanning. The algorithm is based on a 'multislice' forward model that incorporates both 3D phase and diffraction effects, as well as multiple forward scatterings. To solve the inverse problem, an iterative update procedure that combines both phase retrieval and 'error back-propagation' is developed. To avoid local minimum solutions, we further develop a novel physical model-based initialization technique that accounts for both the geometric-optic and 1st order phase effects. The result is robust reconstructions of Gigapixel 3D phase images having both wide FOV and super resolution in all three dimensions. Experimental results from an LED array microscope were demonstrated.

Progress Implementing a Model-Based Iterative Reconstruction Algorithm for Ultrasound Imaging of Thick Concrete

DOE Office of Scientific and Technical Information (OSTI.GOV)

Almansouri, Hani; Johnson, Christi R; Clayton, Dwight A

All commercial nuclear power plants (NPPs) in the United States contain concrete structures. These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and the degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Concrete structures in NPPs are often inaccessible and contain large volumes of massively thick concrete. While acoustic imaging using the synthetic aperture focusing technique (SAFT) works adequately well for thin specimens of concrete such as concrete transportation structures, enhancements are needed for heavily reinforced, thick concrete. We argue that image reconstruction quality for acoustic imaging in thickmore » concrete could be improved with Model-Based Iterative Reconstruction (MBIR) techniques. MBIR works by designing a probabilistic model for the measurements (forward model) and a probabilistic model for the object (prior model). Both models are used to formulate an objective function (cost function). The final step in MBIR is to optimize the cost function. Previously, we have demonstrated a first implementation of MBIR for an ultrasonic transducer array system. The original forward model has been upgraded to account for direct arrival signal. Updates to the forward model will be documented and the new algorithm will be assessed with synthetic and empirical samples.« less

Progress implementing a model-based iterative reconstruction algorithm for ultrasound imaging of thick concrete

NASA Astrophysics Data System (ADS)

Almansouri, Hani; Johnson, Christi; Clayton, Dwight; Polsky, Yarom; Bouman, Charles; Santos-Villalobos, Hector

2017-02-01

All commercial nuclear power plants (NPPs) in the United States contain concrete structures. These structures provide important foundation, support, shielding, and containment functions. Identification and management of aging and the degradation of concrete structures is fundamental to the proposed long-term operation of NPPs. Concrete structures in NPPs are often inaccessible and contain large volumes of massively thick concrete. While acoustic imaging using the synthetic aperture focusing technique (SAFT) works adequately well for thin specimens of concrete such as concrete transportation structures, enhancements are needed for heavily reinforced, thick concrete. We argue that image reconstruction quality for acoustic imaging in thick concrete could be improved with Model-Based Iterative Reconstruction (MBIR) techniques. MBIR works by designing a probabilistic model for the measurements (forward model) and a probabilistic model for the object (prior model). Both models are used to formulate an objective function (cost function). The final step in MBIR is to optimize the cost function. Previously, we have demonstrated a first implementation of MBIR for an ultrasonic transducer array system. The original forward model has been upgraded to account for direct arrival signal. Updates to the forward model will be documented and the new algorithm will be assessed with synthetic and empirical samples.

Microstructure Images Restoration of Metallic Materials Based upon KSVD and Smoothing Penalty Sparse Representation Approach.

PubMed

Li, Qing; Liang, Steven Y

2018-04-20

Microstructure images of metallic materials play a significant role in industrial applications. To address image degradation problem of metallic materials, a novel image restoration technique based on K-means singular value decomposition (KSVD) and smoothing penalty sparse representation (SPSR) algorithm is proposed in this work, the microstructure images of aluminum alloy 7075 (AA7075) material are used as examples. To begin with, to reflect the detail structure characteristics of the damaged image, the KSVD dictionary is introduced to substitute the traditional sparse transform basis (TSTB) for sparse representation. Then, due to the image restoration, modeling belongs to a highly underdetermined equation, and traditional sparse reconstruction methods may cause instability and obvious artifacts in the reconstructed images, especially reconstructed image with many smooth regions and the noise level is strong, thus the SPSR (here, q = 0.5) algorithm is designed to reconstruct the damaged image. The results of simulation and two practical cases demonstrate that the proposed method has superior performance compared with some state-of-the-art methods in terms of restoration performance factors and visual quality. Meanwhile, the grain size parameters and grain boundaries of microstructure image are discussed before and after they are restored by proposed method.

Efficient robust reconstruction of dynamic PET activity maps with radioisotope decay constraints.

PubMed

Gao, Fei; Liu, Huafeng; Shi, Pengcheng

2010-01-01

Dynamic PET imaging performs sequence of data acquisition in order to provide visualization and quantification of physiological changes in specific tissues and organs. The reconstruction of activity maps is generally the first step in dynamic PET. State space Hinfinity approaches have been proved to be a robust method for PET image reconstruction where, however, temporal constraints are not considered during the reconstruction process. In addition, the state space strategies for PET image reconstruction have been computationally prohibitive for practical usage because of the need for matrix inversion. In this paper, we present a minimax formulation of the dynamic PET imaging problem where a radioisotope decay model is employed as physics-based temporal constraints on the photon counts. Furthermore, a robust steady state Hinfinity filter is developed to significantly improve the computational efficiency with minimal loss of accuracy. Experiments are conducted on Monte Carlo simulated image sequences for quantitative analysis and validation.

Accounting for hardware imperfections in EIT image reconstruction algorithms.

PubMed

Hartinger, Alzbeta E; Gagnon, Hervé; Guardo, Robert

2007-07-01

Electrical impedance tomography (EIT) is a non-invasive technique for imaging the conductivity distribution of a body section. Different types of EIT images can be reconstructed: absolute, time difference and frequency difference. Reconstruction algorithms are sensitive to many errors which translate into image artefacts. These errors generally result from incorrect modelling or inaccurate measurements. Every reconstruction algorithm incorporates a model of the physical set-up which must be as accurate as possible since any discrepancy with the actual set-up will cause image artefacts. Several methods have been proposed in the literature to improve the model realism, such as creating anatomical-shaped meshes, adding a complete electrode model and tracking changes in electrode contact impedances and positions. Absolute and frequency difference reconstruction algorithms are particularly sensitive to measurement errors and generally assume that measurements are made with an ideal EIT system. Real EIT systems have hardware imperfections that cause measurement errors. These errors translate into image artefacts since the reconstruction algorithm cannot properly discriminate genuine measurement variations produced by the medium under study from those caused by hardware imperfections. We therefore propose a method for eliminating these artefacts by integrating a model of the system hardware imperfections into the reconstruction algorithms. The effectiveness of the method has been evaluated by reconstructing absolute, time difference and frequency difference images with and without the hardware model from data acquired on a resistor mesh phantom. Results have shown that artefacts are smaller for images reconstructed with the model, especially for frequency difference imaging.

Exploiting Mirrors in 3d Reconstruction of Small Artefacts

NASA Astrophysics Data System (ADS)

Kontogianni, G.; Thomaidis, A. T.; Chliverou, R.; Georgopoulos, A.

2018-05-01

3D reconstruction of small artefacts is very significant in order to capture the details of the whole object irrespective of the documentation method which is used (Ranged Based or Image Based). Sometimes it is very difficult to achieve it because of hidden parts, occlusions, and obstructions which the object has. Hence, more data are necessary in order to 3D digitise the whole of the artefact leading to increased time for collecting and consequently processing the data. A methodology is necessary in order to reduce the collection of the data and therefore their processing time especially in cases of mass digitisation. So in this paper, the use of mirrors in particular high-quality mirrors in the data acquisition phase for the 3D reconstruction of small artefacts is investigated. Two case studies of 3D reconstruction are presented: the first one concerns Range-Based modelling especially a Time of Flight laser scanner is utilised and in the second one Image-Based modelling technique is implemented.

Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Samei, Ehsan, E-mail: samei@duke.edu; Richard, Samuel

2015-01-15

Purpose: Different computed tomography (CT) reconstruction techniques offer different image quality attributes of resolution and noise, challenging the ability to compare their dose reduction potential against each other. The purpose of this study was to evaluate and compare the task-based imaging performance of CT systems to enable the assessment of the dose performance of a model-based iterative reconstruction (MBIR) to that of an adaptive statistical iterative reconstruction (ASIR) and a filtered back projection (FBP) technique. Methods: The ACR CT phantom (model 464) was imaged across a wide range of mA setting on a 64-slice CT scanner (GE Discovery CT750 HD,more » Waukesha, WI). Based on previous work, the resolution was evaluated in terms of a task-based modulation transfer function (MTF) using a circular-edge technique and images from the contrast inserts located in the ACR phantom. Noise performance was assessed in terms of the noise-power spectrum (NPS) measured from the uniform section of the phantom. The task-based MTF and NPS were combined with a task function to yield a task-based estimate of imaging performance, the detectability index (d′). The detectability index was computed as a function of dose for two imaging tasks corresponding to the detection of a relatively small and a relatively large feature (1.5 and 25 mm, respectively). The performance of MBIR in terms of the d′ was compared with that of ASIR and FBP to assess its dose reduction potential. Results: Results indicated that MBIR exhibits a variability spatial resolution with respect to object contrast and noise while significantly reducing image noise. The NPS measurements for MBIR indicated a noise texture with a low-pass quality compared to the typical midpass noise found in FBP-based CT images. At comparable dose, the d′ for MBIR was higher than those of FBP and ASIR by at least 61% and 19% for the small feature and the large feature tasks, respectively. Compared to FBP and ASIR, MBIR indicated a 46%–84% dose reduction potential, depending on task, without compromising the modeled detection performance. Conclusions: The presented methodology based on ACR phantom measurements extends current possibilities for the assessment of CT image quality under the complex resolution and noise characteristics exhibited with statistical and iterative reconstruction algorithms. The findings further suggest that MBIR can potentially make better use of the projections data to reduce CT dose by approximately a factor of 2. Alternatively, if the dose held unchanged, it can improve image quality by different levels for different tasks.« less

Influence of the light propagation models on a linearized photoacoustic image reconstruction of the light absorption coefficient

NASA Astrophysics Data System (ADS)

Okawa, Shinpei; Hirasawa, Takeshi; Kushibiki, Toshihiro; Ishihara, Miya

2015-03-01

Quantification of the optical properties of the tissues and blood by noninvasive photoacoustic (PA) imaging may provide useful information for screening and early diagnosis of diseases. Linearized 2D image reconstruction algorithm based on PA wave equation and the photon diffusion equation (PDE) can reconstruct the image with computational cost smaller than a method based on 3D radiative transfer equation. However, the reconstructed image is affected by the differences between the actual and assumed light propagations. A quantitative capability of a linearized 2D image reconstruction was investigated and discussed by the numerical simulations and the phantom experiment in this study. The numerical simulations with the 3D Monte Carlo (MC) simulation and the 2D finite element calculation of the PDE were carried out. The phantom experiment was also conducted. In the phantom experiment, the PA pressures were acquired by a probe which had an optical fiber for illumination and the ring shaped P(VDF-TrFE) ultrasound transducer. The measured object was made of Intralipid and Indocyanine green. In the numerical simulations, it was shown that the linearized image reconstruction method recovered the absorption coefficients with alleviating the dependency of the PA amplitude on the depth of the photon absorber. The linearized image reconstruction method worked effectively under the light propagation calculated by 3D MC simulation, although some errors occurred. The phantom experiments validated the result of the numerical simulations.

A model-based reconstruction for undersampled radial spin echo DTI with variational penalties on the diffusion tensor

PubMed Central

Knoll, Florian; Raya, José G; Halloran, Rafael O; Baete, Steven; Sigmund, Eric; Bammer, Roland; Block, Tobias; Otazo, Ricardo; Sodickson, Daniel K

2015-01-01

Radial spin echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled DTI. A model-based reconstruction implicitly exploits redundancies in the diffusion weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a Total Variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (3 and 2 volunteers, respectively). Evaluation of the new approach was conducted by comparing the results to reconstructions performed with gridding, combined parallel imaging and compressed sensing, and a recently proposed model-based approach. The experiments demonstrated improvement in terms of reduction of noise and streaking artifacts in the quantitative parameter maps as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin echo diffusion tensor imaging without degrading parameter quantification and/or SNR. PMID:25594167

Photogrammetry for rapid prototyping: development of noncontact 3D reconstruction technologies

NASA Astrophysics Data System (ADS)

Knyaz, Vladimir A.

2002-04-01

An important stage of rapid prototyping technology is generating computer 3D model of an object to be reproduced. Wide variety of techniques for 3D model generation exists beginning with manual 3D models generation and finishing with full-automated reverse engineering system. The progress in CCD sensors and computers provides the background for integration of photogrammetry as an accurate 3D data source with CAD/CAM. The paper presents the results of developing photogrammetric methods for non-contact spatial coordinates measurements and generation of computer 3D model of real objects. The technology is based on object convergent images processing for calculating its 3D coordinates and surface reconstruction. The hardware used for spatial coordinates measurements is based on PC as central processing unit and video camera as image acquisition device. The original software for Windows 9X realizes the complete technology of 3D reconstruction for rapid input of geometry data in CAD/CAM systems. Technical characteristics of developed systems are given along with the results of applying for various tasks of 3D reconstruction. The paper describes the techniques used for non-contact measurements and the methods providing metric characteristics of reconstructed 3D model. Also the results of system application for 3D reconstruction of complex industrial objects are presented.

Four-dimensional reconstruction of cultural heritage sites based on photogrammetry and clustering

NASA Astrophysics Data System (ADS)

Voulodimos, Athanasios; Doulamis, Nikolaos; Fritsch, Dieter; Makantasis, Konstantinos; Doulamis, Anastasios; Klein, Michael

2017-01-01

A system designed and developed for the three-dimensional (3-D) reconstruction of cultural heritage (CH) assets is presented. Two basic approaches are presented. The first one, resulting in an "approximate" 3-D model, uses images retrieved in online multimedia collections; it employs a clustering-based technique to perform content-based filtering and eliminate outliers that significantly reduce the performance of 3-D reconstruction frameworks. The second one is based on input image data acquired through terrestrial laser scanning, as well as close range and airborne photogrammetry; it follows a sophisticated multistep strategy, which leads to a "precise" 3-D model. Furthermore, the concept of change history maps is proposed to address the computational limitations involved in four-dimensional (4-D) modeling, i.e., capturing 3-D models of a CH landmark or site at different time instances. The system also comprises a presentation viewer, which manages the display of the multifaceted CH content collected and created. The described methods have been successfully applied and evaluated in challenging real-world scenarios, including the 4-D reconstruction of the historic Market Square of the German city of Calw in the context of the 4-D-CH-World EU project.

Statistical analysis of nonlinearly reconstructed near-infrared tomographic images: Part I--Theory and simulations.

PubMed

Pogue, Brian W; Song, Xiaomei; Tosteson, Tor D; McBride, Troy O; Jiang, Shudong; Paulsen, Keith D

2002-07-01

Near-infrared (NIR) diffuse tomography is an emerging method for imaging the interior of tissues to quantify concentrations of hemoglobin and exogenous chromophores non-invasively in vivo. It often exploits an optical diffusion model-based image reconstruction algorithm to estimate spatial property values from measurements of the light flux at the surface of the tissue. In this study, mean-squared error (MSE) over the image is used to evaluate methods for regularizing the ill-posed inverse image reconstruction problem in NIR tomography. Estimates of image bias and image standard deviation were calculated based upon 100 repeated reconstructions of a test image with randomly distributed noise added to the light flux measurements. It was observed that the bias error dominates at high regularization parameter values while variance dominates as the algorithm is allowed to approach the optimal solution. This optimum does not necessarily correspond to the minimum projection error solution, but typically requires further iteration with a decreasing regularization parameter to reach the lowest image error. Increasing measurement noise causes a need to constrain the minimum regularization parameter to higher values in order to achieve a minimum in the overall image MSE.

Three-dimensional noninvasive monitoring iodine-131 uptake in the thyroid using a modified Cerenkov luminescence tomography approach.

PubMed

Hu, Zhenhua; Ma, Xiaowei; Qu, Xiaochao; Yang, Weidong; Liang, Jimin; Wang, Jing; Tian, Jie

2012-01-01

Cerenkov luminescence tomography (CLT) provides the three-dimensional (3D) radiopharmaceutical biodistribution in small living animals, which is vital to biomedical imaging. However, existing single-spectral and multispectral methods are not very efficient and effective at reconstructing the distribution of the radionuclide tracer. In this paper, we present a semi-quantitative Cerenkov radiation spectral characteristic-based source reconstruction method named the hybrid spectral CLT, to efficiently reconstruct the radionuclide tracer with both encouraging reconstruction results and less acquisition and image reconstruction time. We constructed the implantation mouse model implanted with a 400 µCi Na(131)I radioactive source and the physiological mouse model received an intravenous tail injection of 400 µCi radiopharmaceutical Iodine-131 (I-131) to validate the performance of the hybrid spectral CLT and compared the reconstruction results, acquisition, and image reconstruction time with that of single-spectral and multispectral CLT. Furthermore, we performed 3D noninvasive monitoring of I-131 uptake in the thyroid and quantified I-131 uptake in vivo using hybrid spectral CLT. Results showed that the reconstruction based on the hybrid spectral CLT was more accurate in localization and quantification than using single-spectral CLT, and was more efficient in the in vivo experiment compared with multispectral CLT. Additionally, 3D visualization of longitudinal observations suggested that the reconstructed energy of I-131 uptake in the thyroid increased with acquisition time and there was a robust correlation between the reconstructed energy versus the gamma ray counts of I-131 (r(2) = 0.8240). The ex vivo biodistribution experiment further confirmed the I-131 uptake in the thyroid for hybrid spectral CLT. Results indicated that hybrid spectral CLT could be potentially used for thyroid imaging to evaluate its function and monitor its treatment for thyroid cancer.

A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction.

PubMed

Yan, Yiming; Gao, Fengjiao; Deng, Shupei; Su, Nan

2017-01-24

In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed 'occlusions of random textures model' are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images.

ACCELERATING MR PARAMETER MAPPING USING SPARSITY-PROMOTING REGULARIZATION IN PARAMETRIC DIMENSION

PubMed Central

Velikina, Julia V.; Alexander, Andrew L.; Samsonov, Alexey

2013-01-01

MR parameter mapping requires sampling along additional (parametric) dimension, which often limits its clinical appeal due to a several-fold increase in scan times compared to conventional anatomic imaging. Data undersampling combined with parallel imaging is an attractive way to reduce scan time in such applications. However, inherent SNR penalties of parallel MRI due to noise amplification often limit its utility even at moderate acceleration factors, requiring regularization by prior knowledge. In this work, we propose a novel regularization strategy, which utilizes smoothness of signal evolution in the parametric dimension within compressed sensing framework (p-CS) to provide accurate and precise estimation of parametric maps from undersampled data. The performance of the method was demonstrated with variable flip angle T1 mapping and compared favorably to two representative reconstruction approaches, image space-based total variation regularization and an analytical model-based reconstruction. The proposed p-CS regularization was found to provide efficient suppression of noise amplification and preservation of parameter mapping accuracy without explicit utilization of analytical signal models. The developed method may facilitate acceleration of quantitative MRI techniques that are not suitable to model-based reconstruction because of complex signal models or when signal deviations from the expected analytical model exist. PMID:23213053

From Panoramic Photos to a Low-Cost Photogrammetric Workflow for Cultural Heritage 3d Documentation

NASA Astrophysics Data System (ADS)

D'Annibale, E.; Tassetti, A. N.; Malinverni, E. S.

2013-07-01

The research aims to optimize a workflow of architecture documentation: starting from panoramic photos, tackling available instruments and technologies to propose an integrated, quick and low-cost solution of Virtual Architecture. The broader research background shows how to use spherical panoramic images for the architectural metric survey. The input data (oriented panoramic photos), the level of reliability and Image-based Modeling methods constitute an integrated and flexible 3D reconstruction approach: from the professional survey of cultural heritage to its communication in virtual museum. The proposed work results from the integration and implementation of different techniques (Multi-Image Spherical Photogrammetry, Structure from Motion, Imagebased Modeling) with the aim to achieve high metric accuracy and photorealistic performance. Different documentation chances are possible within the proposed workflow: from the virtual navigation of spherical panoramas to complex solutions of simulation and virtual reconstruction. VR tools make for the integration of different technologies and the development of new solutions for virtual navigation. Image-based Modeling techniques allow 3D model reconstruction with photo realistic and high-resolution texture. High resolution of panoramic photo and algorithms of panorama orientation and photogrammetric restitution vouch high accuracy and high-resolution texture. Automated techniques and their following integration are subject of this research. Data, advisably processed and integrated, provide different levels of analysis and virtual reconstruction joining the photogrammetric accuracy to the photorealistic performance of the shaped surfaces. Lastly, a new solution of virtual navigation is tested. Inside the same environment, it proposes the chance to interact with high resolution oriented spherical panorama and 3D reconstructed model at once.

Improving the light quantification of near infrared (NIR) diffused light optical tomography with ultrasound localization

NASA Astrophysics Data System (ADS)

Ardeshirpour, Yasaman

According to the statistics published by the American Cancer Society, currently breast cancer is the second most common cancer after skin cancer and the second cause of cancer death after lung cancer in the female population. Diffuse optical tomography (DOT) using near-infrared (NIR) light, guided by ultrasound localization, has shown great promise in distinguishing benign from malignant breast tumors and in assessing the response of breast cancer to chemotherapy. Our ultrasound-guided DOT system is based on reflection geometry, with patients scanned in supine position using a hand-held probe. For patients with chest-wall located at a depth shallower than 1 to 2cm, as in about 10% of our clinical cases, the semi-infinite imaging medium is not a valid assumption and the chest-wall effect needs to be considered in the imaging reconstruction procedure. In this dissertation, co-registered ultrasound images were used to model the breast-tissue and chest-wall as a two-layer medium. The effect of the chest wall on breast lesion reconstruction was systematically investigated. The performance of the two-layer model-based reconstruction, using the Finite Element Method, was evaluated by simulation, phantom experiments and clinical studies. The results show that the two-layer model can improve the accuracy of estimated background optical properties, the reconstructed absorption map and the total hemoglobin concentration of the lesion. For patients' data affected by chest wall, the perturbation, which is the difference between measurements obtained at lesion and normal reference sites, may include the information of background mismatch between these two sites. Because the imaging reconstruction is based on the perturbation approach, the effect of this mismatch between the optical properties at the two sites on reconstructed optical absorption was studied and a guideline for imaging procedure was developed to reduce these effects during data capturing. To reduce the artifacts caused by the background mismatch between the lesion and reference sites, two solutions were introduced. The first solution uses a model-based approach and the second method uses an exogenous contrast agent. The results of phantom and animal studies show that both methods can significantly reduce artifacts generated by the background mismatch.

Accuracy and Specific Value of Cardiovascular 3D-Models in Pediatric CT-Angiography.

PubMed

Hammon, Matthias; Rompel, Oliver; Seuss, Hannes; Dittrich, Sven; Uder, Michael; Rüffer, Andrè; Cesnjevar, Robert; Ehret, Nicole; Glöckler, Martin

2017-12-01

Computed tomography (CT)-angiography is routinely performed prior to catheter-based and surgical treatment in congenital heart disease. To date, little is known about the accuracy and advantage of different 3D-reconstructions in CT-data. Exact anatomical information is crucial. We analyzed 35 consecutive CT-angiographies of infants with congenital heart disease. All datasets are reconstructed three-dimensionally using volume rendering technique (VRT) and threshold-based segmentation (stereolithographic model, STL). Additionally, the two-dimensional maximum intensity projection (MIP) reconstructs two-dimensional data. In each dataset and resulting image, measurements of vascular diameters for four different vessels were estimated and compared to the reference standard, measured via multiplanar reformation (MPR). The resulting measurements obtained via the STL-images, MIP-images, and the VRT-images were compared with the reference standard. There was a significant difference (p < 0.05) between measurements. The mean difference was 0.0 for STL-images, -0.1 for MIP-images, and -0.3 for VRT-images. The range of the differences was -0.7 to 1.0 mm for STL-images, -0.6 to 0.5 mm for MIP-images and -1.1 to 0.7 mm for VRT-images. There was an excellent correlation between the STL-, MIP-, VRT-measurements, and the reference standard. Inter-reader reliability was excellent (p < 0.01). STL-models of cardiovascular structures are more accurate than the traditional VRT-models. Additionally, they can be standardized and are reproducible.

X-ray dose reduction in abdominal computed tomography using advanced iterative reconstruction algorithms.

PubMed

Ning, Peigang; Zhu, Shaocheng; Shi, Dapeng; Guo, Ying; Sun, Minghua

2014-01-01

This work aims to explore the effects of adaptive statistical iterative reconstruction (ASiR) and model-based iterative reconstruction (MBIR) algorithms in reducing computed tomography (CT) radiation dosages in abdominal imaging. CT scans on a standard male phantom were performed at different tube currents. Images at the different tube currents were reconstructed with the filtered back-projection (FBP), 50% ASiR and MBIR algorithms and compared. The CT value, image noise and contrast-to-noise ratios (CNRs) of the reconstructed abdominal images were measured. Volumetric CT dose indexes (CTDIvol) were recorded. At different tube currents, 50% ASiR and MBIR significantly reduced image noise and increased the CNR when compared with FBP. The minimal tube current values required by FBP, 50% ASiR, and MBIR to achieve acceptable image quality using this phantom were 200, 140, and 80 mA, respectively. At the identical image quality, 50% ASiR and MBIR reduced the radiation dose by 35.9% and 59.9% respectively when compared with FBP. Advanced iterative reconstruction techniques are able to reduce image noise and increase image CNRs. Compared with FBP, 50% ASiR and MBIR reduced radiation doses by 35.9% and 59.9%, respectively.

Investigation of optimization-based reconstruction with an image-total-variation constraint in PET

NASA Astrophysics Data System (ADS)

Zhang, Zheng; Ye, Jinghan; Chen, Buxin; Perkins, Amy E.; Rose, Sean; Sidky, Emil Y.; Kao, Chien-Min; Xia, Dan; Tung, Chi-Hua; Pan, Xiaochuan

2016-08-01

Interest remains in reconstruction-algorithm research and development for possible improvement of image quality in current PET imaging and for enabling innovative PET systems to enhance existing, and facilitate new, preclinical and clinical applications. Optimization-based image reconstruction has been demonstrated in recent years of potential utility for CT imaging applications. In this work, we investigate tailoring the optimization-based techniques to image reconstruction for PET systems with standard and non-standard scan configurations. Specifically, given an image-total-variation (TV) constraint, we investigated how the selection of different data divergences and associated parameters impacts the optimization-based reconstruction of PET images. The reconstruction robustness was explored also with respect to different data conditions and activity up-takes of practical relevance. A study was conducted particularly for image reconstruction from data collected by use of a PET configuration with sparsely populated detectors. Overall, the study demonstrates the robustness of the TV-constrained, optimization-based reconstruction for considerably different data conditions in PET imaging, as well as its potential to enable PET configurations with reduced numbers of detectors. Insights gained in the study may be exploited for developing algorithms for PET-image reconstruction and for enabling PET-configuration design of practical usefulness in preclinical and clinical applications.

Research on the development of space target detecting system and three-dimensional reconstruction technology

NASA Astrophysics Data System (ADS)

Li, Dong; Wei, Zhen; Song, Dawei; Sun, Wenfeng; Fan, Xiaoyan

2016-11-01

With the development of space technology, the number of spacecrafts and debris are increasing year by year. The demand for detecting and identification of spacecraft is growing strongly, which provides support to the cataloguing, crash warning and protection of aerospace vehicles. The majority of existing approaches for three-dimensional reconstruction is scattering centres correlation, which is based on the radar high resolution range profile (HRRP). This paper proposes a novel method to reconstruct the threedimensional scattering centre structure of target from a sequence of radar ISAR images, which mainly consists of three steps. First is the azimuth scaling of consecutive ISAR images based on fractional Fourier transform (FrFT). The later is the extraction of scattering centres and matching between adjacent ISAR images using grid method. Finally, according to the coordinate matrix of scattering centres, the three-dimensional scattering centre structure is reconstructed using improved factorization method. The three-dimensional structure is featured with stable and intuitive characteristic, which provides a new way to improve the identification probability and reduce the complexity of the model matching library. A satellite model is reconstructed using the proposed method from four consecutive ISAR images. The simulation results prove that the method has gotten a satisfied consistency and accuracy.

Dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modelling language (VRML).

PubMed

Yu, Zheng-yang; Zheng, Shu-sen; Chen, Lei-ting; He, Xiao-qian; Wang, Jian-jun

2005-07-01

This research studies the process of 3D reconstruction and dynamic concision based on 2D medical digital images using virtual reality modelling language (VRML) and JavaScript language, with a focus on how to realize the dynamic concision of 3D medical model with script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be built with such high quality that they are better than those obtained from the traditional methods. With the function of dynamic concision, the VRML browser can offer better windows for man-computer interaction in real-time environment than ever before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and have a promising prospect in the fields of medical imaging.

Dynamic concision for three-dimensional reconstruction of human organ built with virtual reality modelling language (VRML)*

PubMed Central

Yu, Zheng-yang; Zheng, Shu-sen; Chen, Lei-ting; He, Xiao-qian; Wang, Jian-jun

2005-01-01

This research studies the process of 3D reconstruction and dynamic concision based on 2D medical digital images using virtual reality modelling language (VRML) and JavaScript language, with a focus on how to realize the dynamic concision of 3D medical model with script node and sensor node in VRML. The 3D reconstruction and concision of body internal organs can be built with such high quality that they are better than those obtained from the traditional methods. With the function of dynamic concision, the VRML browser can offer better windows for man-computer interaction in real-time environment than ever before. 3D reconstruction and dynamic concision with VRML can be used to meet the requirement for the medical observation of 3D reconstruction and have a promising prospect in the fields of medical imaging. PMID:15973760

A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction

NASA Astrophysics Data System (ADS)

Huang, Xiaokun; Zhang, You; Wang, Jing

2018-02-01

Reconstructing four-dimensional cone-beam computed tomography (4D-CBCT) images directly from respiratory phase-sorted traditional 3D-CBCT projections can capture target motion trajectory, reduce motion artifacts, and reduce imaging dose and time. However, the limited numbers of projections in each phase after phase-sorting decreases CBCT image quality under traditional reconstruction techniques. To address this problem, we developed a simultaneous motion estimation and image reconstruction (SMEIR) algorithm, an iterative method that can reconstruct higher quality 4D-CBCT images from limited projections using an inter-phase intensity-driven motion model. However, the accuracy of the intensity-driven motion model is limited in regions with fine details whose quality is degraded due to insufficient projection number, which consequently degrades the reconstructed image quality in corresponding regions. In this study, we developed a new 4D-CBCT reconstruction algorithm by introducing biomechanical modeling into SMEIR (SMEIR-Bio) to boost the accuracy of the motion model in regions with small fine structures. The biomechanical modeling uses tetrahedral meshes to model organs of interest and solves internal organ motion using tissue elasticity parameters and mesh boundary conditions. This physics-driven approach enhances the accuracy of solved motion in the organ’s fine structures regions. This study used 11 lung patient cases to evaluate the performance of SMEIR-Bio, making both qualitative and quantitative comparisons between SMEIR-Bio, SMEIR, and the algebraic reconstruction technique with total variation regularization (ART-TV). The reconstruction results suggest that SMEIR-Bio improves the motion model’s accuracy in regions containing small fine details, which consequently enhances the accuracy and quality of the reconstructed 4D-CBCT images.

Ill-posed problem and regularization in reconstruction of radiobiological parameters from serial tumor imaging data

NASA Astrophysics Data System (ADS)

Chvetsov, Alevei V.; Sandison, George A.; Schwartz, Jeffrey L.; Rengan, Ramesh

2015-11-01

The main objective of this article is to improve the stability of reconstruction algorithms for estimation of radiobiological parameters using serial tumor imaging data acquired during radiation therapy. Serial images of tumor response to radiation therapy represent a complex summation of several exponential processes as treatment induced cell inactivation, tumor growth rates, and the rate of cell loss. Accurate assessment of treatment response would require separation of these processes because they define radiobiological determinants of treatment response and, correspondingly, tumor control probability. However, the estimation of radiobiological parameters using imaging data can be considered an inverse ill-posed problem because a sum of several exponentials would produce the Fredholm integral equation of the first kind which is ill posed. Therefore, the stability of reconstruction of radiobiological parameters presents a problem even for the simplest models of tumor response. To study stability of the parameter reconstruction problem, we used a set of serial CT imaging data for head and neck cancer and a simplest case of a two-level cell population model of tumor response. Inverse reconstruction was performed using a simulated annealing algorithm to minimize a least squared objective function. Results show that the reconstructed values of cell surviving fractions and cell doubling time exhibit significant nonphysical fluctuations if no stabilization algorithms are applied. However, after applying a stabilization algorithm based on variational regularization, the reconstruction produces statistical distributions for survival fractions and doubling time that are comparable to published in vitro data. This algorithm is an advance over our previous work where only cell surviving fractions were reconstructed. We conclude that variational regularization allows for an increase in the number of free parameters in our model which enables development of more-advanced parameter reconstruction algorithms.

Real-time three-dimensional soft tissue reconstruction for laparoscopic surgery.

PubMed

Kowalczuk, Jędrzej; Meyer, Avishai; Carlson, Jay; Psota, Eric T; Buettner, Shelby; Pérez, Lance C; Farritor, Shane M; Oleynikov, Dmitry

2012-12-01

Accurate real-time 3D models of the operating field have the potential to enable augmented reality for endoscopic surgery. A new system is proposed to create real-time 3D models of the operating field that uses a custom miniaturized stereoscopic video camera attached to a laparoscope and an image-based reconstruction algorithm implemented on a graphics processing unit (GPU). The proposed system was evaluated in a porcine model that approximates the viewing conditions of in vivo surgery. To assess the quality of the models, a synthetic view of the operating field was produced by overlaying a color image on the reconstructed 3D model, and an image rendered from the 3D model was compared with a 2D image captured from the same view. Experiments conducted with an object of known geometry demonstrate that the system produces 3D models accurate to within 1.5 mm. The ability to produce accurate real-time 3D models of the operating field is a significant advancement toward augmented reality in minimally invasive surgery. An imaging system with this capability will potentially transform surgery by helping novice and expert surgeons alike to delineate variance in internal anatomy accurately.

Evaluation of the CT Parameters to Suppress Renal Cysts Pseudoenhancement Effect: Influence of the Virtual Monochromatic Spectral Images, the Model-based Iterative Reconstruction Algorithm and the Aperture Size in Phantom Model.

PubMed

Sugisawa, Koichi; Ichikawa, Katsuhiro; Minamishima, Kazuya; Hasegawa, Masakazu; Yamada, Yoshitake; Jinzaki, Masahiro

2017-01-01

The purpose of this study was to evaluate the effect of the virtual monochromatic spectral images (VMSI) and the model-based iterative reconstruction (MBIR) images, to evaluate the influence of the aperture size (40- and 20-mm beam) on renal pseudoenhancement (PE) compared with the filtered back projection (FBP) images. The renal compartment-CT phantom was filled with iodinated contrast material diluted to the attenuation of 180 Hounsfield units (HU) at 120 kV. The water-filled spherical structures, which simulate cyst, were inserted into the renal compartment. Those diameters were 7, 15 and 25 mm. These were scanned by conventional mode (helical scan, 120 kV-FBP) and dual energy mode. 70 keV-VMSI were reconstructed from the dual energy mode, and MBIR images were reconstructed from conventional mode at 40- and 20-mm aperture. Additionally, the phantom was scanned using non-helical mode with 20-mm aperture, and FBP images were reconstructed. The CT value of the PE for cyst areas was measured for these images. The CT values of the cysts were 20.0-14.3 HU on the FBP images, 12.8-12.7 HU on the 70 keV-VMSI (PE-inhibition ratio was 36.0-11.2%) and 16.2-14.0 HU on the MBIR images (19.0-2.1%), respectively, at 40-mm aperture. The PE-inhibition ratio scanned by 20-mm aperture was improved by 28.0% with FBP, 32.8% with 70 keV-VMSI and 29.6% with MBIR compared with 40-mm aperture. One of the FBP images with non-helical mode was 11.6 HU. The best CT technique to minimize PE was the combination of 70 keV-VMSI and 20-mm aperture.

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography.

PubMed

Karimi, Davood; Ward, Rabab K

2016-10-01

Image models are central to all image processing tasks. The great advancements in digital image processing would not have been made possible without powerful models which, themselves, have evolved over time. In the past decade, "patch-based" models have emerged as one of the most effective models for natural images. Patch-based methods have outperformed other competing methods in many image processing tasks. These developments have come at a time when greater availability of powerful computational resources and growing concerns over the health risks of the ionizing radiation encourage research on image processing algorithms for computed tomography (CT). The goal of this paper is to explain the principles of patch-based methods and to review some of their recent applications in CT. We first review the central concepts in patch-based image processing and explain some of the state-of-the-art algorithms, with a focus on aspects that are more relevant to CT. Then, we review some of the recent application of patch-based methods in CT. Patch-based methods have already transformed the field of image processing, leading to state-of-the-art results in many applications. More recently, several studies have proposed patch-based algorithms for various image processing tasks in CT, from denoising and restoration to iterative reconstruction. Although these studies have reported good results, the true potential of patch-based methods for CT has not been yet appreciated. Patch-based methods can play a central role in image reconstruction and processing for CT. They have the potential to lead to substantial improvements in the current state of the art.

Impact of joint statistical dual-energy CT reconstruction of proton stopping power images: Comparison to image- and sinogram-domain material decomposition approaches.

PubMed

Zhang, Shuangyue; Han, Dong; Politte, David G; Williamson, Jeffrey F; O'Sullivan, Joseph A

2018-05-01

The purpose of this study was to assess the performance of a novel dual-energy CT (DECT) approach for proton stopping power ratio (SPR) mapping that integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). A systematic comparison between the JSIR-BVM method and previously described DECT image- and sinogram-domain decomposition approaches is also carried out on synthetic data. The JSIR-BVM method was implemented to estimate the electron densities and mean excitation energies (I-values) required by the Bethe equation for SPR mapping. In addition, image- and sinogram-domain DECT methods based on three available SPR models including BVM were implemented for comparison. The intrinsic SPR modeling accuracy of the three models was first validated. Synthetic DECT transmission sinograms of two 330 mm diameter phantoms each containing 17 soft and bony tissues (for a total of 34) of known composition were then generated with spectra of 90 and 140 kVp. The estimation accuracy of the reconstructed SPR images were evaluated for the seven investigated methods. The impact of phantom size and insert location on SPR estimation accuracy was also investigated. All three selected DECT-SPR models predict the SPR of all tissue types with less than 0.2% RMS errors under idealized conditions with no reconstruction uncertainties. When applied to synthetic sinograms, the JSIR-BVM method achieves the best performance with mean and RMS-average errors of less than 0.05% and 0.3%, respectively, for all noise levels, while the image- and sinogram-domain decomposition methods show increasing mean and RMS-average errors with increasing noise level. The JSIR-BVM method also reduces statistical SPR variation by sixfold compared to other methods. A 25% phantom diameter change causes up to 4% SPR differences for the image-domain decomposition approach, while the JSIR-BVM method and sinogram-domain decomposition methods are insensitive to size change. Among all the investigated methods, the JSIR-BVM method achieves the best performance for SPR estimation in our simulation phantom study. This novel method is robust with respect to sinogram noise and residual beam-hardening effects, yielding SPR estimation errors comparable to intrinsic BVM modeling error. In contrast, the achievable SPR estimation accuracy of the image- and sinogram-domain decomposition methods is dominated by the CT image intensity uncertainties introduced by the reconstruction and decomposition processes. © 2018 American Association of Physicists in Medicine.

Task-based data-acquisition optimization for sparse image reconstruction systems

NASA Astrophysics Data System (ADS)

Chen, Yujia; Lou, Yang; Kupinski, Matthew A.; Anastasio, Mark A.

2017-03-01

Conventional wisdom dictates that imaging hardware should be optimized by use of an ideal observer (IO) that exploits full statistical knowledge of the class of objects to be imaged, without consideration of the reconstruction method to be employed. However, accurate and tractable models of the complete object statistics are often difficult to determine in practice. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and (sparse) image reconstruction are innately coupled technologies. We have previously proposed a sparsity-driven ideal observer (SDIO) that can be employed to optimize hardware by use of a stochastic object model that describes object sparsity. The SDIO and sparse reconstruction method can therefore be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute SDIO performance, the posterior distribution is estimated by use of computational tools developed recently for variational Bayesian inference. Subsequently, the SDIO test statistic can be computed semi-analytically. The advantages of employing the SDIO instead of a Hotelling observer are systematically demonstrated in case studies in which magnetic resonance imaging (MRI) data acquisition schemes are optimized for signal detection tasks.

Influence of the partial volume correction method on 18F-fluorodeoxyglucose brain kinetic modelling from dynamic PET images reconstructed with resolution model based OSEM

NASA Astrophysics Data System (ADS)

Bowen, Spencer L.; Byars, Larry G.; Michel, Christian J.; Chonde, Daniel B.; Catana, Ciprian

2013-10-01

Kinetic parameters estimated from dynamic 18F-fluorodeoxyglucose (18F-FDG) PET acquisitions have been used frequently to assess brain function in humans. Neglecting partial volume correction (PVC) for a dynamic series has been shown to produce significant bias in model estimates. Accurate PVC requires a space-variant model describing the reconstructed image spatial point spread function (PSF) that accounts for resolution limitations, including non-uniformities across the field of view due to the parallax effect. For ordered subsets expectation maximization (OSEM), image resolution convergence is local and influenced significantly by the number of iterations, the count density, and background-to-target ratio. As both count density and background-to-target values for a brain structure can change during a dynamic scan, the local image resolution may also concurrently vary. When PVC is applied post-reconstruction the kinetic parameter estimates may be biased when neglecting the frame-dependent resolution. We explored the influence of the PVC method and implementation on kinetic parameters estimated by fitting 18F-FDG dynamic data acquired on a dedicated brain PET scanner and reconstructed with and without PSF modelling in the OSEM algorithm. The performance of several PVC algorithms was quantified with a phantom experiment, an anthropomorphic Monte Carlo simulation, and a patient scan. Using the last frame reconstructed image only for regional spread function (RSF) generation, as opposed to computing RSFs for each frame independently, and applying perturbation geometric transfer matrix PVC with PSF based OSEM produced the lowest magnitude bias kinetic parameter estimates in most instances, although at the cost of increased noise compared to the PVC methods utilizing conventional OSEM. Use of the last frame RSFs for PVC with no PSF modelling in the OSEM algorithm produced the lowest bias in cerebral metabolic rate of glucose estimates, although by less than 5% in most cases compared to the other PVC methods. The results indicate that the PVC implementation and choice of PSF modelling in the reconstruction can significantly impact model parameters.

Influence of the partial volume correction method on (18)F-fluorodeoxyglucose brain kinetic modelling from dynamic PET images reconstructed with resolution model based OSEM.

PubMed

Bowen, Spencer L; Byars, Larry G; Michel, Christian J; Chonde, Daniel B; Catana, Ciprian

2013-10-21

Kinetic parameters estimated from dynamic (18)F-fluorodeoxyglucose ((18)F-FDG) PET acquisitions have been used frequently to assess brain function in humans. Neglecting partial volume correction (PVC) for a dynamic series has been shown to produce significant bias in model estimates. Accurate PVC requires a space-variant model describing the reconstructed image spatial point spread function (PSF) that accounts for resolution limitations, including non-uniformities across the field of view due to the parallax effect. For ordered subsets expectation maximization (OSEM), image resolution convergence is local and influenced significantly by the number of iterations, the count density, and background-to-target ratio. As both count density and background-to-target values for a brain structure can change during a dynamic scan, the local image resolution may also concurrently vary. When PVC is applied post-reconstruction the kinetic parameter estimates may be biased when neglecting the frame-dependent resolution. We explored the influence of the PVC method and implementation on kinetic parameters estimated by fitting (18)F-FDG dynamic data acquired on a dedicated brain PET scanner and reconstructed with and without PSF modelling in the OSEM algorithm. The performance of several PVC algorithms was quantified with a phantom experiment, an anthropomorphic Monte Carlo simulation, and a patient scan. Using the last frame reconstructed image only for regional spread function (RSF) generation, as opposed to computing RSFs for each frame independently, and applying perturbation geometric transfer matrix PVC with PSF based OSEM produced the lowest magnitude bias kinetic parameter estimates in most instances, although at the cost of increased noise compared to the PVC methods utilizing conventional OSEM. Use of the last frame RSFs for PVC with no PSF modelling in the OSEM algorithm produced the lowest bias in cerebral metabolic rate of glucose estimates, although by less than 5% in most cases compared to the other PVC methods. The results indicate that the PVC implementation and choice of PSF modelling in the reconstruction can significantly impact model parameters.

Model-Based Building Detection from Low-Cost Optical Sensors Onboard Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Karantzalos, K.; Koutsourakis, P.; Kalisperakis, I.; Grammatikopoulos, L.

2015-08-01

The automated and cost-effective building detection in ultra high spatial resolution is of major importance for various engineering and smart city applications. To this end, in this paper, a model-based building detection technique has been developed able to extract and reconstruct buildings from UAV aerial imagery and low-cost imaging sensors. In particular, the developed approach through advanced structure from motion, bundle adjustment and dense image matching computes a DSM and a true orthomosaic from the numerous GoPro images which are characterised by important geometric distortions and fish-eye effect. An unsupervised multi-region, graphcut segmentation and a rule-based classification is responsible for delivering the initial multi-class classification map. The DTM is then calculated based on inpaininting and mathematical morphology process. A data fusion process between the detected building from the DSM/DTM and the classification map feeds a grammar-based building reconstruction and scene building are extracted and reconstructed. Preliminary experimental results appear quite promising with the quantitative evaluation indicating detection rates at object level of 88% regarding the correctness and above 75% regarding the detection completeness.

Reconstructing 3-D skin surface motion for the DIET breast cancer screening system.

PubMed

Botterill, Tom; Lotz, Thomas; Kashif, Amer; Chase, J Geoffrey

2014-05-01

Digital image-based elasto-tomography (DIET) is a prototype system for breast cancer screening. A breast is imaged while being vibrated, and the observed surface motion is used to infer the internal stiffness of the breast, hence identifying tumors. This paper describes a computer vision system for accurately measuring 3-D surface motion. A model-based segmentation is used to identify the profile of the breast in each image, and the 3-D surface is reconstructed by fitting a model to the profiles. The surface motion is measured using a modern optical flow implementation customized to the application, then trajectories of points on the 3-D surface are given by fusing the optical flow with the reconstructed surfaces. On data from human trials, the system is shown to exceed the performance of an earlier marker-based system at tracking skin surface motion. We demonstrate that the system can detect a 10 mm tumor in a silicone phantom breast.

Anisotropic elastic moduli reconstruction in transversely isotropic model using MRE

NASA Astrophysics Data System (ADS)

Song, Jiah; In Kwon, Oh; Seo, Jin Keun

2012-11-01

Magnetic resonance elastography (MRE) is an elastic tissue property imaging modality in which the phase-contrast based MRI imaging technique is used to measure internal displacement induced by a harmonically oscillating mechanical vibration. MRE has made rapid technological progress in the past decade and has now reached the stage of clinical use. Most of the research outcomes are based on the assumption of isotropy. Since soft tissues like skeletal muscles show anisotropic behavior, the MRE technique should be extended to anisotropic elastic property imaging. This paper considers reconstruction in a transversely isotropic model, which is the simplest case of anisotropy, and develops a new non-iterative reconstruction method for visualizing the elastic moduli distribution. This new method is based on an explicit representation formula using the Newtonian potential of measured displacement. Hence, the proposed method does not require iterations since it directly recovers the anisotropic elastic moduli. We perform numerical simulations in order to demonstrate the feasibility of the proposed method in recovering a two-dimensional anisotropic tensor.

Model-based iterative reconstruction in low-dose CT colonography-feasibility study in 65 patients for symptomatic investigation.

PubMed

Vardhanabhuti, Varut; James, Julia; Nensey, Rehaan; Hyde, Christopher; Roobottom, Carl

2015-05-01

To compare image quality on computed tomographic colonography (CTC) acquired at standard dose (STD) and low dose (LD) using filtered-back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) techniques. A total of 65 symptomatic patients were prospectively enrolled for the study and underwent STD and LD CTC with filtered-back projection, adaptive statistical iterative reconstruction, and MBIR to allow direct per-patient comparison. Objective image noise, subjective image analyses, and polyp detection were assessed. Objective image noise analysis demonstrates significant noise reduction using MBIR technique (P < .05) despite being acquired at lower doses. Subjective image analyses were superior for LD MBIR in all parameters except visibility of extracolonic lesions (two-dimensional) and visibility of colonic wall (three-dimensional) where there were no significant differences. There was no significant difference in polyp detection rates (P > .05). Doses: LD (dose-length product, 257.7), STD (dose-length product, 483.6). LD MBIR CTC objectively shows improved image noise using parameters in our study. Subjectively, image quality is maintained. Polyp detection shows no significant difference but because of small numbers needs further validation. Average dose reduction of 47% can be achieved. This study confirms feasibility of using MBIR in this context of CTC in symptomatic population. Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prakash, P.; Zbijewski, W.; Gang, G. J.

2011-10-15

Purpose: This work applies a cascaded systems model for cone-beam CT imaging performance to the design and optimization of a system for musculoskeletal extremity imaging. The model provides a quantitative guide to the selection of system geometry, source and detector components, acquisition techniques, and reconstruction parameters. Methods: The model is based on cascaded systems analysis of the 3D noise-power spectrum (NPS) and noise-equivalent quanta (NEQ) combined with factors of system geometry (magnification, focal spot size, and scatter-to-primary ratio) and anatomical background clutter. The model was extended to task-based analysis of detectability index (d') for tasks ranging in contrast and frequencymore » content, and d' was computed as a function of system magnification, detector pixel size, focal spot size, kVp, dose, electronic noise, voxel size, and reconstruction filter to examine trade-offs and optima among such factors in multivariate analysis. The model was tested quantitatively versus the measured NPS and qualitatively in cadaver images as a function of kVp, dose, pixel size, and reconstruction filter under conditions corresponding to the proposed scanner. Results: The analysis quantified trade-offs among factors of spatial resolution, noise, and dose. System magnification (M) was a critical design parameter with strong effect on spatial resolution, dose, and x-ray scatter, and a fairly robust optimum was identified at M {approx} 1.3 for the imaging tasks considered. The results suggested kVp selection in the range of {approx}65-90 kVp, the lower end (65 kVp) maximizing subject contrast and the upper end maximizing NEQ (90 kVp). The analysis quantified fairly intuitive results--e.g., {approx}0.1-0.2 mm pixel size (and a sharp reconstruction filter) optimal for high-frequency tasks (bone detail) compared to {approx}0.4 mm pixel size (and a smooth reconstruction filter) for low-frequency (soft-tissue) tasks. This result suggests a specific protocol for 1 x 1 (full-resolution) projection data acquisition followed by full-resolution reconstruction with a sharp filter for high-frequency tasks along with 2 x 2 binning reconstruction with a smooth filter for low-frequency tasks. The analysis guided selection of specific source and detector components implemented on the proposed scanner. The analysis also quantified the potential benefits and points of diminishing return in focal spot size, reduced electronic noise, finer detector pixels, and low-dose limits of detectability. Theoretical results agreed quantitatively with the measured NPS and qualitatively with evaluation of cadaver images by a musculoskeletal radiologist. Conclusions: A fairly comprehensive model for 3D imaging performance in cone-beam CT combines factors of quantum noise, system geometry, anatomical background, and imaging task. The analysis provided a valuable, quantitative guide to design, optimization, and technique selection for a musculoskeletal extremities imaging system under development.« less

Quantitative PET/CT scanner performance characterization based upon the society of nuclear medicine and molecular imaging clinical trials network oncology clinical simulator phantom.

PubMed

Sunderland, John J; Christian, Paul E

2015-01-01

The Clinical Trials Network (CTN) of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) operates a PET/CT phantom imaging program using the CTN's oncology clinical simulator phantom, designed to validate scanners at sites that wish to participate in oncology clinical trials. Since its inception in 2008, the CTN has collected 406 well-characterized phantom datasets from 237 scanners at 170 imaging sites covering the spectrum of commercially available PET/CT systems. The combined and collated phantom data describe a global profile of quantitative performance and variability of PET/CT data used in both clinical practice and clinical trials. Individual sites filled and imaged the CTN oncology PET phantom according to detailed instructions. Standard clinical reconstructions were requested and submitted. The phantom itself contains uniform regions suitable for scanner calibration assessment, lung fields, and 6 hot spheric lesions with diameters ranging from 7 to 20 mm at a 4:1 contrast ratio with primary background. The CTN Phantom Imaging Core evaluated the quality of the phantom fill and imaging and measured background standardized uptake values to assess scanner calibration and maximum standardized uptake values of all 6 lesions to review quantitative performance. Scanner make-and-model-specific measurements were pooled and then subdivided by reconstruction to create scanner-specific quantitative profiles. Different makes and models of scanners predictably demonstrated different quantitative performance profiles including, in some cases, small calibration bias. Differences in site-specific reconstruction parameters increased the quantitative variability among similar scanners, with postreconstruction smoothing filters being the most influential parameter. Quantitative assessment of this intrascanner variability over this large collection of phantom data gives, for the first time, estimates of reconstruction variance introduced into trials from allowing trial sites to use their preferred reconstruction methodologies. Predictably, time-of-flight-enabled scanners exhibited less size-based partial-volume bias than non-time-of-flight scanners. The CTN scanner validation experience over the past 5 y has generated a rich, well-curated phantom dataset from which PET/CT make-and-model and reconstruction-dependent quantitative behaviors were characterized for the purposes of understanding and estimating scanner-based variances in clinical trials. These results should make it possible to identify and recommend make-and-model-specific reconstruction strategies to minimize measurement variability in cancer clinical trials. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Design and assessment of a novel SPECT system for desktop open-gantry imaging of small animals: A simulation study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeraatkar, Navid; Farahani, Mohammad Hossein; Rahmim, Arman

Purpose: Given increasing efforts in biomedical research utilizing molecular imaging methods, development of dedicated high-performance small-animal SPECT systems has been growing rapidly in the last decade. In the present work, we propose and assess an alternative concept for SPECT imaging enabling desktop open-gantry imaging of small animals. Methods: The system, PERSPECT, consists of an imaging desk, with a set of tilted detector and pinhole collimator placed beneath it. The object to be imaged is simply placed on the desk. Monte Carlo (MC) and analytical simulations were utilized to accurately model and evaluate the proposed concept and design. Furthermore, a dedicatedmore » image reconstruction algorithm, finite-aperture-based circular projections (FABCP), was developed and validated for the system, enabling more accurate modeling of the system and higher quality reconstructed images. Image quality was quantified as a function of different tilt angles in the acquisition and number of iterations in the reconstruction algorithm. Furthermore, more complex phantoms including Derenzo, Defrise, and mouse whole body were simulated and studied. Results: The sensitivity of the PERSPECT was 207 cps/MBq. It was quantitatively demonstrated that for a tilt angle of 30°, comparable image qualities were obtained in terms of normalized squared error, contrast, uniformity, noise, and spatial resolution measurements, the latter at ∼0.6 mm. Furthermore, quantitative analyses demonstrated that 3 iterations of FABCP image reconstruction (16 subsets/iteration) led to optimally reconstructed images. Conclusions: The PERSPECT, using a novel imaging protocol, can achieve comparable image quality performance in comparison with a conventional pinhole SPECT with the same configuration. The dedicated FABCP algorithm, which was developed for reconstruction of data from the PERSPECT system, can produce high quality images for small-animal imaging via accurate modeling of the system as incorporated in the forward- and back-projection steps. Meanwhile, the developed MC model and the analytical simulator of the system can be applied for further studies on development and evaluation of the system.« less

Accelerated gradient based diffuse optical tomographic image reconstruction.

PubMed

Biswas, Samir Kumar; Rajan, K; Vasu, R M

2011-01-01

Fast reconstruction of interior optical parameter distribution using a new approach called Broyden-based model iterative image reconstruction (BMOBIIR) and adjoint Broyden-based MOBIIR (ABMOBIIR) of a tissue and a tissue mimicking phantom from boundary measurement data in diffuse optical tomography (DOT). DOT is a nonlinear and ill-posed inverse problem. Newton-based MOBIIR algorithm, which is generally used, requires repeated evaluation of the Jacobian which consumes bulk of the computation time for reconstruction. In this study, we propose a Broyden approach-based accelerated scheme for Jacobian computation and it is combined with conjugate gradient scheme (CGS) for fast reconstruction. The method makes explicit use of secant and adjoint information that can be obtained from forward solution of the diffusion equation. This approach reduces the computational time many fold by approximating the system Jacobian successively through low-rank updates. Simulation studies have been carried out with single as well as multiple inhomogeneities. Algorithms are validated using an experimental study carried out on a pork tissue with fat acting as an inhomogeneity. The results obtained through the proposed BMOBIIR and ABMOBIIR approaches are compared with those of Newton-based MOBIIR algorithm. The mean squared error and execution time are used as metrics for comparing the results of reconstruction. We have shown through experimental and simulation studies that Broyden-based MOBIIR and adjoint Broyden-based methods are capable of reconstructing single as well as multiple inhomogeneities in tissue and a tissue-mimicking phantom. Broyden MOBIIR and adjoint Broyden MOBIIR methods are computationally simple and they result in much faster implementations because they avoid direct evaluation of Jacobian. The image reconstructions have been carried out with different initial values using Newton, Broyden, and adjoint Broyden approaches. These algorithms work well when the initial guess is close to the true solution. However, when initial guess is far away from true solution, Newton-based MOBIIR gives better reconstructed images. The proposed methods are found to be stable with noisy measurement data.

Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish

PubMed Central

Correia, Teresa; Yin, Jun; Ramel, Marie-Christine; Andrews, Natalie; Katan, Matilda; Bugeon, Laurence; Dallman, Margaret J.; McGinty, James; Frankel, Paul; French, Paul M. W.; Arridge, Simon

2015-01-01

Optical projection tomography (OPT) provides a non-invasive 3-D imaging modality that can be applied to longitudinal studies of live disease models, including in zebrafish. Current limitations include the requirement of a minimum number of angular projections for reconstruction of reasonable OPT images using filtered back projection (FBP), which is typically several hundred, leading to acquisition times of several minutes. It is highly desirable to decrease the number of required angular projections to decrease both the total acquisition time and the light dose to the sample. This is particularly important to enable longitudinal studies, which involve measurements of the same fish at different time points. In this work, we demonstrate that the use of an iterative algorithm to reconstruct sparsely sampled OPT data sets can provide useful 3-D images with 50 or fewer projections, thereby significantly decreasing the minimum acquisition time and light dose while maintaining image quality. A transgenic zebrafish embryo with fluorescent labelling of the vasculature was imaged to acquire densely sampled (800 projections) and under-sampled data sets of transmitted and fluorescence projection images. The under-sampled OPT data sets were reconstructed using an iterative total variation-based image reconstruction algorithm and compared against FBP reconstructions of the densely sampled data sets. To illustrate the potential for quantitative analysis following rapid OPT data acquisition, a Hessian-based method was applied to automatically segment the reconstructed images to select the vasculature network. Results showed that 3-D images of the zebrafish embryo and its vasculature of sufficient visual quality for quantitative analysis can be reconstructed using the iterative algorithm from only 32 projections—achieving up to 28 times improvement in imaging speed and leading to total acquisition times of a few seconds. PMID:26308086

Nonuniform multiview color texture mapping of image sequence and three-dimensional model for faded cultural relics with sift feature points

NASA Astrophysics Data System (ADS)

Li, Na; Gong, Xingyu; Li, Hongan; Jia, Pengtao

2018-01-01

For faded relics, such as Terracotta Army, the 2D-3D registration between an optical camera and point cloud model is an important part for color texture reconstruction and further applications. This paper proposes a nonuniform multiview color texture mapping for the image sequence and the three-dimensional (3D) model of point cloud collected by Handyscan3D. We first introduce nonuniform multiview calibration, including the explanation of its algorithm principle and the analysis of its advantages. We then establish transformation equations based on sift feature points for the multiview image sequence. At the same time, the selection of nonuniform multiview sift feature points is introduced in detail. Finally, the solving process of the collinear equations based on multiview perspective projection is given with three steps and the flowchart. In the experiment, this method is applied to the color reconstruction of the kneeling figurine, Tangsancai lady, and general figurine. These results demonstrate that the proposed method provides an effective support for the color reconstruction of the faded cultural relics and be able to improve the accuracy of 2D-3D registration between the image sequence and the point cloud model.

An iterative reduced field-of-view reconstruction for periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI.

PubMed

Lin, Jyh-Miin; Patterson, Andrew J; Chang, Hing-Chiu; Gillard, Jonathan H; Graves, Martin J

2015-10-01

To propose a new reduced field-of-view (rFOV) strategy for iterative reconstructions in a clinical environment. Iterative reconstructions can incorporate regularization terms to improve the image quality of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI. However, the large amount of calculations required for full FOV iterative reconstructions has posed a huge computational challenge for clinical usage. By subdividing the entire problem into smaller rFOVs, the iterative reconstruction can be accelerated on a desktop with a single graphic processing unit (GPU). This rFOV strategy divides the iterative reconstruction into blocks, based on the block-diagonal dominant structure. A near real-time reconstruction system was developed for the clinical MR unit, and parallel computing was implemented using the object-oriented model. In addition, the Toeplitz method was implemented on the GPU to reduce the time required for full interpolation. Using the data acquired from the PROPELLER MRI, the reconstructed images were then saved in the digital imaging and communications in medicine format. The proposed rFOV reconstruction reduced the gridding time by 97%, as the total iteration time was 3 s even with multiple processes running. A phantom study showed that the structure similarity index for rFOV reconstruction was statistically superior to conventional density compensation (p < 0.001). In vivo study validated the increased signal-to-noise ratio, which is over four times higher than with density compensation. Image sharpness index was improved using the regularized reconstruction implemented. The rFOV strategy permits near real-time iterative reconstruction to improve the image quality of PROPELLER images. Substantial improvements in image quality metrics were validated in the experiments. The concept of rFOV reconstruction may potentially be applied to other kinds of iterative reconstructions for shortened reconstruction duration.

A Hierarchical Building Segmentation in Digital Surface Models for 3D Reconstruction

PubMed Central

Yan, Yiming; Gao, Fengjiao; Deng, Shupei; Su, Nan

2017-01-01

In this study, a hierarchical method for segmenting buildings in a digital surface model (DSM), which is used in a novel framework for 3D reconstruction, is proposed. Most 3D reconstructions of buildings are model-based. However, the limitations of these methods are overreliance on completeness of the offline-constructed models of buildings, and the completeness is not easily guaranteed since in modern cities buildings can be of a variety of types. Therefore, a model-free framework using high precision DSM and texture-images buildings was introduced. There are two key problems with this framework. The first one is how to accurately extract the buildings from the DSM. Most segmentation methods are limited by either the terrain factors or the difficult choice of parameter-settings. A level-set method are employed to roughly find the building regions in the DSM, and then a recently proposed ‘occlusions of random textures model’ are used to enhance the local segmentation of the buildings. The second problem is how to generate the facades of buildings. Synergizing with the corresponding texture-images, we propose a roof-contour guided interpolation of building facades. The 3D reconstruction results achieved by airborne-like images and satellites are compared. Experiments show that the segmentation method has good performance, and 3D reconstruction is easily performed by our framework, and better visualization results can be obtained by airborne-like images, which can be further replaced by UAV images. PMID:28125018

Efficient 3D porous microstructure reconstruction via Gaussian random field and hybrid optimization.

PubMed

Jiang, Z; Chen, W; Burkhart, C

2013-11-01

Obtaining an accurate three-dimensional (3D) structure of a porous microstructure is important for assessing the material properties based on finite element analysis. Whereas directly obtaining 3D images of the microstructure is impractical under many circumstances, two sets of methods have been developed in literature to generate (reconstruct) 3D microstructure from its 2D images: one characterizes the microstructure based on certain statistical descriptors, typically two-point correlation function and cluster correlation function, and then performs an optimization process to build a 3D structure that matches those statistical descriptors; the other method models the microstructure using stochastic models like a Gaussian random field and generates a 3D structure directly from the function. The former obtains a relatively accurate 3D microstructure, but computationally the optimization process can be very intensive, especially for problems with large image size; the latter generates a 3D microstructure quickly but sacrifices the accuracy due to issues in numerical implementations. A hybrid optimization approach of modelling the 3D porous microstructure of random isotropic two-phase materials is proposed in this paper, which combines the two sets of methods and hence maintains the accuracy of the correlation-based method with improved efficiency. The proposed technique is verified for 3D reconstructions based on silica polymer composite images with different volume fractions. A comparison of the reconstructed microstructures and the optimization histories for both the original correlation-based method and our hybrid approach demonstrates the improved efficiency of the approach. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Combined magnetic resonance, fluorescence, and histology imaging strategy in a human breast tumor xenograft model

PubMed Central

Jiang, Lu; Greenwood, Tiffany R.; Amstalden van Hove, Erika R.; Chughtai, Kamila; Raman, Venu; Winnard, Paul T.; Heeren, Ron; Artemov, Dmitri; Glunde, Kristine

2014-01-01

Applications of molecular imaging in cancer and other diseases frequently require combining in vivo imaging modalities, such as magnetic resonance and optical imaging, with ex vivo optical, fluorescence, histology, and immunohistochemical (IHC) imaging, to investigate and relate molecular and biological processes to imaging parameters within the same region of interest. We have developed a multimodal image reconstruction and fusion framework that accurately combines in vivo magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI), ex vivo brightfield and fluorescence microscopic imaging, and ex vivo histology imaging. Ex vivo brightfield microscopic imaging was used as an intermediate modality to facilitate the ultimate link between ex vivo histology and in vivo MRI/MRSI. Tissue sectioning necessary for optical and histology imaging required generation of a three-dimensional (3D) reconstruction module for 2D ex vivo optical and histology imaging data. We developed an external fiducial marker based 3D reconstruction method, which was able to fuse optical brightfield and fluorescence with histology imaging data. Registration of 3D tumor shape was pursued to combine in vivo MRI/MRSI and ex vivo optical brightfield and fluorescence imaging data. This registration strategy was applied to in vivo MRI/MRSI, ex vivo optical brightfield/fluorescence, as well as histology imaging data sets obtained from human breast tumor models. 3D human breast tumor data sets were successfully reconstructed and fused with this platform. PMID:22945331

Polyenergetic known-component reconstruction without prior shape models

NASA Astrophysics Data System (ADS)

Zhang, C.; Zbijewski, W.; Zhang, X.; Xu, S.; Stayman, J. W.

2017-03-01

Purpose: Previous work has demonstrated that structural models of surgical tools and implants can be integrated into model-based CT reconstruction to greatly reduce metal artifacts and improve image quality. This work extends a polyenergetic formulation of known-component reconstruction (Poly-KCR) by removing the requirement that a physical model (e.g. CAD drawing) be known a priori, permitting much more widespread application. Methods: We adopt a single-threshold segmentation technique with the help of morphological structuring elements to build a shape model of metal components in a patient scan based on initial filtered-backprojection (FBP) reconstruction. This shape model is used as an input to Poly-KCR, a formulation of known-component reconstruction that does not require a prior knowledge of beam quality or component material composition. An investigation of performance as a function of segmentation thresholds is performed in simulation studies, and qualitative comparisons to Poly-KCR with an a priori shape model are made using physical CBCT data of an implanted cadaver and in patient data from a prototype extremities scanner. Results: We find that model-free Poly-KCR (MF-Poly-KCR) provides much better image quality compared to conventional reconstruction techniques (e.g. FBP). Moreover, the performance closely approximates that of Poly- KCR with an a prior shape model. In simulation studies, we find that imaging performance generally follows segmentation accuracy with slight under- or over-estimation based on the shape of the implant. In both simulation and physical data studies we find that the proposed approach can remove most of the blooming and streak artifacts around the component permitting visualization of the surrounding soft-tissues. Conclusion: This work shows that it is possible to perform known-component reconstruction without prior knowledge of the known component. In conjunction with the Poly-KCR technique that does not require knowledge of beam quality or material composition, very little needs to be known about the metal implant and system beforehand. These generalizations will allow more widespread application of KCR techniques in real patient studies where the information of surgical tools and implants is limited or not available.

Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints.

PubMed

Duan, Yuping; Bouslimi, Dalel; Yang, Guanyu; Shu, Huazhong; Coatrieux, Gouenou

2017-07-01

In this paper, we focus on the "blind" identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94% and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.

Dictionary-based image reconstruction for superresolution in integrated circuit imaging.

PubMed

Cilingiroglu, T Berkin; Uyar, Aydan; Tuysuzoglu, Ahmet; Karl, W Clem; Konrad, Janusz; Goldberg, Bennett B; Ünlü, M Selim

2015-06-01

Resolution improvement through signal processing techniques for integrated circuit imaging is becoming more crucial as the rapid decrease in integrated circuit dimensions continues. Although there is a significant effort to push the limits of optical resolution for backside fault analysis through the use of solid immersion lenses, higher order laser beams, and beam apodization, signal processing techniques are required for additional improvement. In this work, we propose a sparse image reconstruction framework which couples overcomplete dictionary-based representation with a physics-based forward model to improve resolution and localization accuracy in high numerical aperture confocal microscopy systems for backside optical integrated circuit analysis. The effectiveness of the framework is demonstrated on experimental data.

Parallelized Bayesian inversion for three-dimensional dental X-ray imaging.

PubMed

Kolehmainen, Ville; Vanne, Antti; Siltanen, Samuli; Järvenpää, Seppo; Kaipio, Jari P; Lassas, Matti; Kalke, Martti

2006-02-01

Diagnostic and operational tasks based on dental radiology often require three-dimensional (3-D) information that is not available in a single X-ray projection image. Comprehensive 3-D information about tissues can be obtained by computerized tomography (CT) imaging. However, in dental imaging a conventional CT scan may not be available or practical because of high radiation dose, low-resolution or the cost of the CT scanner equipment. In this paper, we consider a novel type of 3-D imaging modality for dental radiology. We consider situations in which projection images of the teeth are taken from a few sparsely distributed projection directions using the dentist's regular (digital) X-ray equipment and the 3-D X-ray attenuation function is reconstructed. A complication in these experiments is that the reconstruction of the 3-D structure based on a few projection images becomes an ill-posed inverse problem. Bayesian inversion is a well suited framework for reconstruction from such incomplete data. In Bayesian inversion, the ill-posed reconstruction problem is formulated in a well-posed probabilistic form in which a priori information is used to compensate for the incomplete information of the projection data. In this paper we propose a Bayesian method for 3-D reconstruction in dental radiology. The method is partially based on Kolehmainen et al. 2003. The prior model for dental structures consist of a weighted l1 and total variation (TV)-prior together with the positivity prior. The inverse problem is stated as finding the maximum a posteriori (MAP) estimate. To make the 3-D reconstruction computationally feasible, a parallelized version of an optimization algorithm is implemented for a Beowulf cluster computer. The method is tested with projection data from dental specimens and patient data. Tomosynthetic reconstructions are given as reference for the proposed method.

A new Mumford-Shah total variation minimization based model for sparse-view x-ray computed tomography image reconstruction.

PubMed

Chen, Bo; Bian, Zhaoying; Zhou, Xiaohui; Chen, Wensheng; Ma, Jianhua; Liang, Zhengrong

2018-04-12

Total variation (TV) minimization for the sparse-view x-ray computer tomography (CT) reconstruction has been widely explored to reduce radiation dose. However, due to the piecewise constant assumption for the TV model, the reconstructed images often suffer from over-smoothness on the image edges. To mitigate this drawback of TV minimization, we present a Mumford-Shah total variation (MSTV) minimization algorithm in this paper. The presented MSTV model is derived by integrating TV minimization and Mumford-Shah segmentation. Subsequently, a penalized weighted least-squares (PWLS) scheme with MSTV is developed for the sparse-view CT reconstruction. For simplicity, the proposed algorithm is named as 'PWLS-MSTV.' To evaluate the performance of the present PWLS-MSTV algorithm, both qualitative and quantitative studies were conducted by using a digital XCAT phantom and a physical phantom. Experimental results show that the present PWLS-MSTV algorithm has noticeable gains over the existing algorithms in terms of noise reduction, contrast-to-ratio measure and edge-preservation.

Accurate and efficient modeling of the detector response in small animal multi-head PET systems.

PubMed

Cecchetti, Matteo; Moehrs, Sascha; Belcari, Nicola; Del Guerra, Alberto

2013-10-07

In fully three-dimensional PET imaging, iterative image reconstruction techniques usually outperform analytical algorithms in terms of image quality provided that an appropriate system model is used. In this study we concentrate on the calculation of an accurate system model for the YAP-(S)PET II small animal scanner, with the aim to obtain fully resolution- and contrast-recovered images at low levels of image roughness. For this purpose we calculate the system model by decomposing it into a product of five matrices: (1) a detector response component obtained via Monte Carlo simulations, (2) a geometric component which describes the scanner geometry and which is calculated via a multi-ray method, (3) a detector normalization component derived from the acquisition of a planar source, (4) a photon attenuation component calculated from x-ray computed tomography data, and finally, (5) a positron range component is formally included. This system model factorization allows the optimization of each component in terms of computation time, storage requirements and accuracy. The main contribution of this work is a new, efficient way to calculate the detector response component for rotating, planar detectors, that consists of a GEANT4 based simulation of a subset of lines of flight (LOFs) for a single detector head whereas the missing LOFs are obtained by using intrinsic detector symmetries. Additionally, we introduce and analyze a probability threshold for matrix elements of the detector component to optimize the trade-off between the matrix size in terms of non-zero elements and the resulting quality of the reconstructed images. In order to evaluate our proposed system model we reconstructed various images of objects, acquired according to the NEMA NU 4-2008 standard, and we compared them to the images reconstructed with two other system models: a model that does not include any detector response component and a model that approximates analytically the depth of interaction as detector response component. The comparisons confirm previous research results, showing that the usage of an accurate system model with a realistic detector response leads to reconstructed images with better resolution and contrast recovery at low levels of image roughness.

Accurate and efficient modeling of the detector response in small animal multi-head PET systems

NASA Astrophysics Data System (ADS)

Cecchetti, Matteo; Moehrs, Sascha; Belcari, Nicola; Del Guerra, Alberto

2013-10-01

In fully three-dimensional PET imaging, iterative image reconstruction techniques usually outperform analytical algorithms in terms of image quality provided that an appropriate system model is used. In this study we concentrate on the calculation of an accurate system model for the YAP-(S)PET II small animal scanner, with the aim to obtain fully resolution- and contrast-recovered images at low levels of image roughness. For this purpose we calculate the system model by decomposing it into a product of five matrices: (1) a detector response component obtained via Monte Carlo simulations, (2) a geometric component which describes the scanner geometry and which is calculated via a multi-ray method, (3) a detector normalization component derived from the acquisition of a planar source, (4) a photon attenuation component calculated from x-ray computed tomography data, and finally, (5) a positron range component is formally included. This system model factorization allows the optimization of each component in terms of computation time, storage requirements and accuracy. The main contribution of this work is a new, efficient way to calculate the detector response component for rotating, planar detectors, that consists of a GEANT4 based simulation of a subset of lines of flight (LOFs) for a single detector head whereas the missing LOFs are obtained by using intrinsic detector symmetries. Additionally, we introduce and analyze a probability threshold for matrix elements of the detector component to optimize the trade-off between the matrix size in terms of non-zero elements and the resulting quality of the reconstructed images. In order to evaluate our proposed system model we reconstructed various images of objects, acquired according to the NEMA NU 4-2008 standard, and we compared them to the images reconstructed with two other system models: a model that does not include any detector response component and a model that approximates analytically the depth of interaction as detector response component. The comparisons confirm previous research results, showing that the usage of an accurate system model with a realistic detector response leads to reconstructed images with better resolution and contrast recovery at low levels of image roughness.

Microscopic vision modeling method by direct mapping analysis for micro-gripping system with stereo light microscope.

PubMed

Wang, Yuezong; Zhao, Zhizhong; Wang, Junshuai

2016-04-01

We present a novel and high-precision microscopic vision modeling method, which can be used for 3D data reconstruction in micro-gripping system with stereo light microscope. This method consists of four parts: image distortion correction, disparity distortion correction, initial vision model and residual compensation model. First, the method of image distortion correction is proposed. Image data required by image distortion correction comes from stereo images of calibration sample. The geometric features of image distortions can be predicted though the shape deformation of lines constructed by grid points in stereo images. Linear and polynomial fitting methods are applied to correct image distortions. Second, shape deformation features of disparity distribution are discussed. The method of disparity distortion correction is proposed. Polynomial fitting method is applied to correct disparity distortion. Third, a microscopic vision model is derived, which consists of two models, i.e., initial vision model and residual compensation model. We derive initial vision model by the analysis of direct mapping relationship between object and image points. Residual compensation model is derived based on the residual analysis of initial vision model. The results show that with maximum reconstruction distance of 4.1mm in X direction, 2.9mm in Y direction and 2.25mm in Z direction, our model achieves a precision of 0.01mm in X and Y directions and 0.015mm in Z direction. Comparison of our model with traditional pinhole camera model shows that two kinds of models have a similar reconstruction precision of X coordinates. However, traditional pinhole camera model has a lower precision of Y and Z coordinates than our model. The method proposed in this paper is very helpful for the micro-gripping system based on SLM microscopic vision. Copyright © 2016 Elsevier Ltd. All rights reserved.

Algorithm-enabled exploration of image-quality potential of cone-beam CT in image-guided radiation therapy

NASA Astrophysics Data System (ADS)

Han, Xiao; Pearson, Erik; Pelizzari, Charles; Al-Hallaq, Hania; Sidky, Emil Y.; Bian, Junguo; Pan, Xiaochuan

2015-06-01

Kilo-voltage (KV) cone-beam computed tomography (CBCT) unit mounted onto a linear accelerator treatment system, often referred to as on-board imager (OBI), plays an increasingly important role in image-guided radiation therapy. While the FDK algorithm is currently used for reconstructing images from clinical OBI data, optimization-based reconstruction has also been investigated for OBI CBCT. An optimization-based reconstruction involves numerous parameters, which can significantly impact reconstruction properties (or utility). The success of an optimization-based reconstruction for a particular class of practical applications thus relies strongly on appropriate selection of parameter values. In the work, we focus on tailoring the constrained-TV-minimization-based reconstruction, an optimization-based reconstruction previously shown of some potential for CBCT imaging conditions of practical interest, to OBI imaging through appropriate selection of parameter values. In particular, for given real data of phantoms and patient collected with OBI CBCT, we first devise utility metrics specific to OBI-quality-assurance tasks and then apply them to guiding the selection of parameter values in constrained-TV-minimization-based reconstruction. The study results show that the reconstructions are with improvement, relative to clinical FDK reconstruction, in both visualization and quantitative assessments in terms of the devised utility metrics.

Statistical reconstruction for cone-beam CT with a post-artifact-correction noise model: application to high-quality head imaging

NASA Astrophysics Data System (ADS)

Dang, H.; Stayman, J. W.; Sisniega, A.; Xu, J.; Zbijewski, W.; Wang, X.; Foos, D. H.; Aygun, N.; Koliatsos, V. E.; Siewerdsen, J. H.

2015-08-01

Non-contrast CT reliably detects fresh blood in the brain and is the current front-line imaging modality for intracranial hemorrhage such as that occurring in acute traumatic brain injury (contrast ~40-80 HU, size  >  1 mm). We are developing flat-panel detector (FPD) cone-beam CT (CBCT) to facilitate such diagnosis in a low-cost, mobile platform suitable for point-of-care deployment. Such a system may offer benefits in the ICU, urgent care/concussion clinic, ambulance, and sports and military theatres. However, current FPD-CBCT systems face significant challenges that confound low-contrast, soft-tissue imaging. Artifact correction can overcome major sources of bias in FPD-CBCT but imparts noise amplification in filtered backprojection (FBP). Model-based reconstruction improves soft-tissue image quality compared to FBP by leveraging a high-fidelity forward model and image regularization. In this work, we develop a novel penalized weighted least-squares (PWLS) image reconstruction method with a noise model that includes accurate modeling of the noise characteristics associated with the two dominant artifact corrections (scatter and beam-hardening) in CBCT and utilizes modified weights to compensate for noise amplification imparted by each correction. Experiments included real data acquired on a FPD-CBCT test-bench and an anthropomorphic head phantom emulating intra-parenchymal hemorrhage. The proposed PWLS method demonstrated superior noise-resolution tradeoffs in comparison to FBP and PWLS with conventional weights (viz. at matched 0.50 mm spatial resolution, CNR = 11.9 compared to CNR = 5.6 and CNR = 9.9, respectively) and substantially reduced image noise especially in challenging regions such as skull base. The results support the hypothesis that with high-fidelity artifact correction and statistical reconstruction using an accurate post-artifact-correction noise model, FPD-CBCT can achieve image quality allowing reliable detection of intracranial hemorrhage.

Image-Based Computational Fluid Dynamics in Blood Vessel Models: Toward Developing a Prognostic Tool to Assess Cardiovascular Function Changes in Prolonged Space Flights

NASA Technical Reports Server (NTRS)

Chatzimavroudis, George P.; Spirka, Thomas A.; Setser, Randolph M.; Myers, Jerry G.

2004-01-01

One of NASA's objectives is to be able to perform a complete, pre-flight, evaluation of cardiovascular changes in astronauts scheduled for prolonged space missions. Computational fluid dynamics (CFD) has shown promise as a method for estimating cardiovascular function during reduced gravity conditions. For this purpose, MRI can provide geometrical information, to reconstruct vessel geometries, and measure all spatial velocity components, providing location specific boundary conditions. The objective of this study was to investigate the reliability of MRI-based model reconstruction and measured boundary conditions for CFD simulations. An aortic arch model and a carotid bifurcation model were scanned in a 1.5T Siemens MRI scanner. Axial MRI acquisitions provided images for geometry reconstruction (slice thickness 3 and 5 mm; pixel size 1x1 and 0.5x0.5 square millimeters). Velocity acquisitions provided measured inlet boundary conditions and localized three-directional steady-flow velocity data (0.7-3.0 L/min). The vessel walls were isolated using NIH provided software (ImageJ) and lofted to form the geometric surface. Constructed and idealized geometries were imported into a commercial CFD code for meshing and simulation. Contour and vector plots of the velocity showed identical features between the MRI velocity data, the MRI-based CFD data, and the idealized-geometry CFD data, with less than 10% differences in the local velocity values. CFD results on models reconstructed from different MRI resolution settings showed insignificant differences (less than 5%). This study illustrated, quantitatively, that reliable CFD simulations can be performed with MRI reconstructed models and gives evidence that a future, subject-specific, computational evaluation of the cardiovascular system alteration during space travel is feasible.

SPIDER image processing for single-particle reconstruction of biological macromolecules from electron micrographs

PubMed Central

Shaikh, Tanvir R; Gao, Haixiao; Baxter, William T; Asturias, Francisco J; Boisset, Nicolas; Leith, Ardean; Frank, Joachim

2009-01-01

This protocol describes the reconstruction of biological molecules from the electron micrographs of single particles. Computation here is performed using the image-processing software SPIDER and can be managed using a graphical user interface, termed the SPIDER Reconstruction Engine. Two approaches are described to obtain an initial reconstruction: random-conical tilt and common lines. Once an existing model is available, reference-based alignment can be used, a procedure that can be iterated. Also described is supervised classification, a method to look for homogeneous subsets when multiple known conformations of the molecule may coexist. PMID:19180078

Microstructure Images Restoration of Metallic Materials Based upon KSVD and Smoothing Penalty Sparse Representation Approach

PubMed Central

Liang, Steven Y.

2018-01-01

Microstructure images of metallic materials play a significant role in industrial applications. To address image degradation problem of metallic materials, a novel image restoration technique based on K-means singular value decomposition (KSVD) and smoothing penalty sparse representation (SPSR) algorithm is proposed in this work, the microstructure images of aluminum alloy 7075 (AA7075) material are used as examples. To begin with, to reflect the detail structure characteristics of the damaged image, the KSVD dictionary is introduced to substitute the traditional sparse transform basis (TSTB) for sparse representation. Then, due to the image restoration, modeling belongs to a highly underdetermined equation, and traditional sparse reconstruction methods may cause instability and obvious artifacts in the reconstructed images, especially reconstructed image with many smooth regions and the noise level is strong, thus the SPSR (here, q = 0.5) algorithm is designed to reconstruct the damaged image. The results of simulation and two practical cases demonstrate that the proposed method has superior performance compared with some state-of-the-art methods in terms of restoration performance factors and visual quality. Meanwhile, the grain size parameters and grain boundaries of microstructure image are discussed before and after they are restored by proposed method. PMID:29677163

Multi-ray-based system matrix generation for 3D PET reconstruction

NASA Astrophysics Data System (ADS)

Moehrs, Sascha; Defrise, Michel; Belcari, Nicola; DelGuerra, Alberto; Bartoli, Antonietta; Fabbri, Serena; Zanetti, Gianluigi

2008-12-01

Iterative image reconstruction algorithms for positron emission tomography (PET) require a sophisticated system matrix (model) of the scanner. Our aim is to set up such a model offline for the YAP-(S)PET II small animal imaging tomograph in order to use it subsequently with standard ML-EM (maximum-likelihood expectation maximization) and OSEM (ordered subset expectation maximization) for fully three-dimensional image reconstruction. In general, the system model can be obtained analytically, via measurements or via Monte Carlo simulations. In this paper, we present the multi-ray method, which can be considered as a hybrid method to set up the system model offline. It incorporates accurate analytical (geometric) considerations as well as crystal depth and crystal scatter effects. At the same time, it has the potential to model seamlessly other physical aspects such as the positron range. The proposed method is based on multiple rays which are traced from/to the detector crystals through the image volume. Such a ray-tracing approach itself is not new; however, we derive a novel mathematical formulation of the approach and investigate the positioning of the integration (ray-end) points. First, we study single system matrix entries and show that the positioning and weighting of the ray-end points according to Gaussian integration give better results compared to equally spaced integration points (trapezoidal integration), especially if only a small number of integration points (rays) are used. Additionally, we show that, for a given variance of the single matrix entries, the number of rays (events) required to calculate the whole matrix is a factor of 20 larger when using a pure Monte-Carlo-based method. Finally, we analyse the quality of the model by reconstructing phantom data from the YAP-(S)PET II scanner.

Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms.

PubMed

Tang, Jie; Nett, Brian E; Chen, Guang-Hong

2009-10-07

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

A validated methodology for the 3D reconstruction of cochlea geometries using human microCT images

NASA Astrophysics Data System (ADS)

Sakellarios, A. I.; Tachos, N. S.; Rigas, G.; Bibas, T.; Ni, G.; Böhnke, F.; Fotiadis, D. I.

2017-05-01

Accurate reconstruction of the inner ear is a prerequisite for the modelling and understanding of the inner ear mechanics. In this study, we present a semi-automated methodology for accurate reconstruction of the major inner ear structures (scalae, basilar membrane, stapes and semicircular canals). For this purpose, high resolution microCT images of a human specimen were used. The segmentation methodology is based on an iterative level set algorithm which provides the borders of the structures of interest. An enhanced coupled level set method which allows the simultaneous multiple image labeling without any overlapping regions has been developed for this purpose. The marching cube algorithm was applied in order to extract the surface from the segmented volume. The reconstructed geometries are then post-processed to improve the basilar membrane geometry to realistically represent physiologic dimensions. The final reconstructed model is compared to the available data from the literature. The results show that our generated inner ear structures are in good agreement with the published ones, while our approach is the most realistic in terms of the basilar membrane thickness and width reconstruction.

Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.

PubMed

Wu, Junfeng; Dai, Fang; Hu, Gang; Mou, Xuanqin

2018-04-18

Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.

Acceleration of the direct reconstruction of linear parametric images using nested algorithms.

PubMed

Wang, Guobao; Qi, Jinyi

2010-03-07

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.

Linear single-step image reconstruction in the presence of nonscattering regions.

PubMed

Dehghani, H; Delpy, D T

2002-06-01

There is growing interest in the use of near-infrared spectroscopy for the noninvasive determination of the oxygenation level within biological tissue. Stemming from this application, there has been further research in using this technique for obtaining tomographic images of the neonatal head, with the view of determining the level of oxygenated and deoxygenated blood within the brain. Because of computational complexity, methods used for numerical modeling of photon transfer within tissue have usually been limited to the diffusion approximation of the Boltzmann transport equation. The diffusion approximation, however, is not valid in regions of low scatter, such as the cerebrospinal fluid. Methods have been proposed for dealing with nonscattering regions within diffusing materials through the use of a radiosity-diffusion model. Currently, this new model assumes prior knowledge of the void region; therefore it is instructive to examine the errors introduced in applying a simple diffusion-based reconstruction scheme in cases where a nonscattering region exists. We present reconstructed images, using linear algorithms, of models that contain a nonscattering region within a diffusing material. The forward data are calculated by using the radiosity-diffusion model, and the inverse problem is solved by using either the radiosity-diffusion model or the diffusion-only model. When using data from a model containing a clear layer and reconstructing with the correct model, one can reconstruct the anomaly, but the qualitative accuracy and the position of the reconstructed anomaly depend on the size and the position of the clear regions. If the inverse model has no information about the clear regions (i.e., it is a purely diffusing model), an anomaly can be reconstructed, but the resulting image has very poor qualitative accuracy and poor localization of the anomaly. The errors in quantitative and localization accuracies depend on the size and location of the clear regions.

Linear single-step image reconstruction in the presence of nonscattering regions

NASA Astrophysics Data System (ADS)

Dehghani, H.; Delpy, D. T.

2002-06-01

There is growing interest in the use of near-infrared spectroscopy for the noninvasive determination of the oxygenation level within biological tissue. Stemming from this application, there has been further research in using this technique for obtaining tomographic images of the neonatal head, with the view of determining the level of oxygenated and deoxygenated blood within the brain. Because of computational complexity, methods used for numerical modeling of photon transfer within tissue have usually been limited to the diffusion approximation of the Boltzmann transport equation. The diffusion approximation, however, is not valid in regions of low scatter, such as the cerebrospinal fluid. Methods have been proposed for dealing with nonscattering regions within diffusing materials through the use of a radiosity-diffusion model. Currently, this new model assumes prior knowledge of the void region; therefore it is instructive to examine the errors introduced in applying a simple diffusion-based reconstruction scheme in cases where a nonscattering region exists. We present reconstructed images, using linear algorithms, of models that contain a nonscattering region within a diffusing material. The forward data are calculated by using the radiosity-diffusion model, and the inverse problem is solved by using either the radiosity-diffusion model or the diffusion-only model. When using data from a model containing a clear layer and reconstructing with the correct model, one can reconstruct the anomaly, but the qualitative accuracy and the position of the reconstructed anomaly depend on the size and the position of the clear regions. If the inverse model has no information about the clear regions (i.e., it is a purely diffusing model), an anomaly can be reconstructed, but the resulting image has very poor qualitative accuracy and poor localization of the anomaly. The errors in quantitative and localization accuracies depend on the size and location of the clear regions.

A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning

PubMed Central

Zhang, Cheng; Zhang, Tao; Li, Ming; Lu, Yanfei; You, Jiali; Guan, Yihui

2015-01-01

In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time. PMID:26550024

SU-E-J-17: Evaluation of Metal Artifact Reduction in MVCTs Using a Model Based Image Reconstruction Method.

PubMed

Paudel, M; MacKenzie, M; Fallone, B; Rathee, S

2012-06-01

To evaluate the performance of a model based image reconstruction in reducing metal artifacts in MVCT systems, and to compare with filtered-back projection (FBP) technique. Iterative maximum likelihood polychromatic algorithm for CT (IMPACT) is used with pair/triplet production process and the energy dependent response of detectors. The beam spectra for in-house bench-top and TomotherapyTM MVCT are modelled for use in IMPACT. The energy dependent gain of detectors is calculated using a constrained optimization technique and measured attenuation produced by 0 - 24 cm thick solid water slabs. A cylindrical (19 cm diameter) plexiglass phantom containing various central cylindrical inserts (relative electron density of 0.28-1.69) between two steel rods (2 cm diameter) is scanned in the bench-top [the bremsstrahlung radiation from 6 MeV electron beam passed through 4 cm solid water on the Varian Clinac 2300C] and TomotherapyTM MVCTs. The FBP reconstructs images from raw signal normalised to air scan and corrected for beam hardening using a uniform plexi-glass cylinder (20 cm diameter). IMPACT starts with FBP reconstructed seed image and reconstructs final image at 1.25 MeV in 150 iterations. FBP produces a visible dark shading in the image between two steel rods that becomes darker with higher density central insert causing 5-8 % underestimation of electron density compared to the case without the steel rods. In the IMPACT image the dark shading connecting the steel rods is nearly removed and the uniform background restored. The average attenuation coefficients of the inserts and the background are very close to the corresponding theoretical values at 1.25 MeV. The dark shading metal artifact due to beam hardening can be removed in MVCT using the iterative reconstruction algorithm such as IMPACT. However, the accurate modelling of detectors' energy dependent response and physical processes are crucial for successful implementation. Funding support for the research is obtained from "Vanier Canada Graduate Scholarship" and "Canadian Institute of Health Research". © 2012 American Association of Physicists in Medicine.

Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform.

PubMed

Lai, Zongying; Zhang, Xinlin; Guo, Di; Du, Xiaofeng; Yang, Yonggui; Guo, Gang; Chen, Zhong; Qu, Xiaobo

2018-05-03

Multi-contrast images in magnetic resonance imaging (MRI) provide abundant contrast information reflecting the characteristics of the internal tissues of human bodies, and thus have been widely utilized in clinical diagnosis. However, long acquisition time limits the application of multi-contrast MRI. One efficient way to accelerate data acquisition is to under-sample the k-space data and then reconstruct images with sparsity constraint. However, images are compromised at high acceleration factor if images are reconstructed individually. We aim to improve the images with a jointly sparse reconstruction and Graph-based redundant wavelet transform (GBRWT). First, a sparsifying transform, GBRWT, is trained to reflect the similarity of tissue structures in multi-contrast images. Second, joint multi-contrast image reconstruction is formulated as a ℓ 2, 1 norm optimization problem under GBRWT representations. Third, the optimization problem is numerically solved using a derived alternating direction method. Experimental results in synthetic and in vivo MRI data demonstrate that the proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods. Besides, the proposed method outperforms single image reconstruction with joint sparsity constraint of multi-contrast images. The proposed method explores the joint sparsity of multi-contrast MRI images under graph-based redundant wavelet transform and realizes joint sparse reconstruction of multi-contrast images. Experiment demonstrate that the proposed method outperforms the compared joint reconstruction methods as well as individual reconstructions. With this high quality image reconstruction method, it is possible to achieve the high acceleration factors by exploring the complementary information provided by multi-contrast MRI.

MO-DE-207A-08: Four-Dimensional Cone-Beam CT Iterative Reconstruction with Time-Ordered Chain Graph Model for Non-Periodic Organ Motion and Deformation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakano, M; Haga, A; Hanaoka, S

2016-06-15

Purpose: The purpose of this study is to propose a new concept of four-dimensional (4D) cone-beam CT (CBCT) reconstruction for non-periodic organ motion using the Time-ordered Chain Graph Model (TCGM), and to compare the reconstructed results with the previously proposed methods, the total variation-based compressed sensing (TVCS) and prior-image constrained compressed sensing (PICCS). Methods: CBCT reconstruction method introduced in this study consisted of maximum a posteriori (MAP) iterative reconstruction combined with a regularization term derived from a concept of TCGM, which includes a constraint coming from the images of neighbouring time-phases. The time-ordered image series were concurrently reconstructed in themore » MAP iterative reconstruction framework. Angular range of projections for each time-phase was 90 degrees for TCGM and PICCS, and 200 degrees for TVCS. Two kinds of projection data, an elliptic-cylindrical digital phantom data and two clinical patients’ data, were used for reconstruction. The digital phantom contained an air sphere moving 3 cm along longitudinal axis, and temporal resolution of each method was evaluated by measuring the penumbral width of reconstructed moving air sphere. The clinical feasibility of non-periodic time-ordered 4D CBCT reconstruction was also examined using projection data of prostate cancer patients. Results: The results of reconstructed digital phantom shows that the penumbral widths of TCGM yielded the narrowest result; PICCS and TCGM were 10.6% and 17.4% narrower than that of TVCS, respectively. This suggests that the TCGM has the better temporal resolution than the others. Patients’ CBCT projection data were also reconstructed and all three reconstructed results showed motion of rectal gas and stool. The result of TCGM provided visually clearer and less blurring images. Conclusion: The present study demonstrates that the new concept for 4D CBCT reconstruction, TCGM, combined with MAP iterative reconstruction framework enables time-ordered image reconstruction with narrower time-window.« less

Image restoration by minimizing zero norm of wavelet frame coefficients

NASA Astrophysics Data System (ADS)

Bao, Chenglong; Dong, Bin; Hou, Likun; Shen, Zuowei; Zhang, Xiaoqun; Zhang, Xue

2016-11-01

In this paper, we propose two algorithms, namely the extrapolated proximal iterative hard thresholding (EPIHT) algorithm and the EPIHT algorithm with line-search, for solving the {{\\ell }}0-norm regularized wavelet frame balanced approach for image restoration. Under the theoretical framework of Kurdyka-Łojasiewicz property, we show that the sequences generated by the two algorithms converge to a local minimizer with linear convergence rate. Moreover, extensive numerical experiments on sparse signal reconstruction and wavelet frame based image restoration problems including CT reconstruction, image deblur, demonstrate the improvement of {{\\ell }}0-norm based regularization models over some prevailing ones, as well as the computational efficiency of the proposed algorithms.

A new method for electric impedance imaging using an eddy current with a tetrapolar circuit.

PubMed

Ahsan-Ul-Ambia; Toda, Shogo; Takemae, Tadashi; Kosugi, Yukio; Hongo, Minoru

2009-02-01

A new contactless technique for electrical impedance imaging, using an eddy current managed along with the tetrapolar circuit method, is proposed. The eddy current produced by a magnetic field is superimposed on a constant current that is normally used in the tetrapolar circuit method, and thus is used to control the current distribution in the body. By changing the current distribution, a set of voltage differences is measured with a pair of electrodes. This set of voltage differences is used in the image reconstruction of the resistivity distribution. The least square error minimization method is used in the reconstruction algorithm. The principle of this method is explained theoretically. A backprojection algorithm was used to get 2-D images. Based on this principle, a measurement system was developed and model experiments were conducted with a saline-filled phantom. The estimated shape of each model in the reconstructed image was similar to that of the corresponding model. From the results of these experiments, it is confirmed that the proposed method is applicable to the realization of electrical conductivity imaging.

Contrast improvement of continuous wave diffuse optical tomography reconstruction by hybrid approach using least square and genetic algorithm

NASA Astrophysics Data System (ADS)

Patra, Rusha; Dutta, Pranab K.

2015-07-01

Reconstruction of the absorption coefficient of tissue with good contrast is of key importance in functional diffuse optical imaging. A hybrid approach using model-based iterative image reconstruction and a genetic algorithm is proposed to enhance the contrast of the reconstructed image. The proposed method yields an observed contrast of 98.4%, mean square error of 0.638×10-3, and object centroid error of (0.001 to 0.22) mm. Experimental validation of the proposed method has also been provided with tissue-like phantoms which shows a significant improvement in image quality and thus establishes the potential of the method for functional diffuse optical tomography reconstruction with continuous wave setup. A case study of finger joint imaging is illustrated as well to show the prospect of the proposed method in clinical diagnosis. The method can also be applied to the concentration measurement of a region of interest in a turbid medium.

MO-DE-207A-02: A Feature-Preserving Image Reconstruction Method for Improved Pancreaticlesion Classification in Diagnostic CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, J; Tsui, B; Noo, F

Purpose: To develop a feature-preserving model based image reconstruction (MBIR) method that improves performance in pancreatic lesion classification at equal or reduced radiation dose. Methods: A set of pancreatic lesion models was created with both benign and premalignant lesion types. These two classes of lesions are distinguished by their fine internal structures; their delineation is therefore crucial to the task of pancreatic lesion classification. To reduce image noise while preserving the features of the lesions, we developed a MBIR method with curvature-based regularization. The novel regularization encourages formation of smooth surfaces that model both the exterior shape and the internalmore » features of pancreatic lesions. Given that the curvature depends on the unknown image, image reconstruction or denoising becomes a non-convex optimization problem; to address this issue an iterative-reweighting scheme was used to calculate and update the curvature using the image from the previous iteration. Evaluation was carried out with insertion of the lesion models into the pancreas of a patient CT image. Results: Visual inspection was used to compare conventional TV regularization with our curvature-based regularization. Several penalty-strengths were considered for TV regularization, all of which resulted in erasing portions of the septation (thin partition) in a premalignant lesion. At matched noise variance (50% noise reduction in the patient stomach region), the connectivity of the septation was well preserved using the proposed curvature-based method. Conclusion: The curvature-based regularization is able to reduce image noise while simultaneously preserving the lesion features. This method could potentially improve task performance for pancreatic lesion classification at equal or reduced radiation dose. The result is of high significance for longitudinal surveillance studies of patients with pancreatic cysts, which may develop into pancreatic cancer. The Senior Author receives financial support from Siemens GmbH Healthcare.« less

Data consistency criterion for selecting parameters for k-space-based reconstruction in parallel imaging.

PubMed

Nana, Roger; Hu, Xiaoping

2010-01-01

k-space-based reconstruction in parallel imaging depends on the reconstruction kernel setting, including its support. An optimal choice of the kernel depends on the calibration data, coil geometry and signal-to-noise ratio, as well as the criterion used. In this work, data consistency, imposed by the shift invariance requirement of the kernel, is introduced as a goodness measure of k-space-based reconstruction in parallel imaging and demonstrated. Data consistency error (DCE) is calculated as the sum of squared difference between the acquired signals and their estimates obtained based on the interpolation of the estimated missing data. A resemblance between DCE and the mean square error in the reconstructed image was found, demonstrating DCE's potential as a metric for comparing or choosing reconstructions. When used for selecting the kernel support for generalized autocalibrating partially parallel acquisition (GRAPPA) reconstruction and the set of frames for calibration as well as the kernel support in temporal GRAPPA reconstruction, DCE led to improved images over existing methods. Data consistency error is efficient to evaluate, robust for selecting reconstruction parameters and suitable for characterizing and optimizing k-space-based reconstruction in parallel imaging.

Study on the algorithm of computational ghost imaging based on discrete fourier transform measurement matrix

NASA Astrophysics Data System (ADS)

Zhang, Leihong; Liang, Dong; Li, Bei; Kang, Yi; Pan, Zilan; Zhang, Dawei; Gao, Xiumin; Ma, Xiuhua

2016-07-01

On the basis of analyzing the cosine light field with determined analytic expression and the pseudo-inverse method, the object is illuminated by a presetting light field with a determined discrete Fourier transform measurement matrix, and the object image is reconstructed by the pseudo-inverse method. The analytic expression of the algorithm of computational ghost imaging based on discrete Fourier transform measurement matrix is deduced theoretically, and compared with the algorithm of compressive computational ghost imaging based on random measurement matrix. The reconstruction process and the reconstruction error are analyzed. On this basis, the simulation is done to verify the theoretical analysis. When the sampling measurement number is similar to the number of object pixel, the rank of discrete Fourier transform matrix is the same as the one of the random measurement matrix, the PSNR of the reconstruction image of FGI algorithm and PGI algorithm are similar, the reconstruction error of the traditional CGI algorithm is lower than that of reconstruction image based on FGI algorithm and PGI algorithm. As the decreasing of the number of sampling measurement, the PSNR of reconstruction image based on FGI algorithm decreases slowly, and the PSNR of reconstruction image based on PGI algorithm and CGI algorithm decreases sharply. The reconstruction time of FGI algorithm is lower than that of other algorithms and is not affected by the number of sampling measurement. The FGI algorithm can effectively filter out the random white noise through a low-pass filter and realize the reconstruction denoising which has a higher denoising capability than that of the CGI algorithm. The FGI algorithm can improve the reconstruction accuracy and the reconstruction speed of computational ghost imaging.

Imaging Internal Structure of Long Bones Using Wave Scattering Theory.

PubMed

Zheng, Rui; Le, Lawrence H; Sacchi, Mauricio D; Lou, Edmond

2015-11-01

An ultrasonic wavefield imaging method is developed to reconstruct the internal geometric properties of long bones using zero-offset data acquired axially on the bone surface. The imaging algorithm based on Born scattering theory is implemented with the conjugate gradient iterative method to reconstruct an optimal image. In the case of a multilayered velocity model, ray tracing through a smooth medium is used to calculate the traveled distance and traveling time. The method has been applied to simulated and real data. The results indicate that the interfaces of the top cortex are accurately imaged and correspond favorably to the original model. The reconstructed bottom cortex below the marrow is less accurate mainly because of the low signal-to-noise ratio. The current imaging method has successfully recovered the top cortical layer, providing a potential tool to investigate the internal structures of long bone cortex for osteoporosis assessment. Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

A new method of morphological comparison for bony reconstructive surgery: maxillary reconstruction using scapular tip bone

NASA Astrophysics Data System (ADS)

Chan, Harley; Gilbert, Ralph W.; Pagedar, Nitin A.; Daly, Michael J.; Irish, Jonathan C.; Siewerdsen, Jeffrey H.

2010-02-01

esthetic appearance is one of the most important factors for reconstructive surgery. The current practice of maxillary reconstruction chooses radial forearm, fibula or iliac rest osteocutaneous to recreate three-dimensional complex structures of the palate and maxilla. However, these bone flaps lack shape similarity to the palate and result in a less satisfactory esthetic. Considering similarity factors and vasculature advantages, reconstructive surgeons recently explored the use of scapular tip myo-osseous free flaps to restore the excised site. We have developed a new method that quantitatively evaluates the morphological similarity of the scapula tip bone and palate based on a diagnostic volumetric computed tomography (CT) image. This quantitative result was further interpreted as a color map that rendered on the surface of a three-dimensional computer model. For surgical planning, this color interpretation could potentially assist the surgeon to maximize the orientation of the bone flaps for best fit of the reconstruction site. With approval from the Research Ethics Board (REB) of the University Health Network, we conducted a retrospective analysis with CT image obtained from 10 patients. Each patient had a CT scans including the maxilla and chest on the same day. Based on this image set, we simulated total, subtotal and hemi palate reconstruction. The procedure of simulation included volume segmentation, conversing the segmented volume to a stereo lithography (STL) model, manual registration, computation of minimum geometric distances and curvature between STL model. Across the 10 patients data, we found the overall root-mean-square (RMS) conformance was 3.71+/- 0.16 mm

Two-Dimensional Lorentz Force Image Reconstruction for Magnetoacoustic Tomography with Magnetic Induction

NASA Astrophysics Data System (ADS)

Li, Yi-Ling; Liu, Zhen-Bo; Ma, Qing-Yu; Guo, Xia-Sheng; Zhang, Dong

2010-08-01

Magnetoacoustic tomography with magnetic induction has shown potential applications in imaging the electrical impedance for biological tissues. We present a novel methodology for the inverse problem solution of the 2-D Lorentz force distribution reconstruction based on the acoustic straight line propagation theory. The magnetic induction and acoustic generation as well as acoustic detection are theoretically provided as explicit formulae and also validated by the numerical simulations for a multilayered cylindrical phantom model. The reconstructed 2-D Lorentz force distribution reveals not only the conductivity configuration in terms of shape and size but also the amplitude value of the Lorentz force in the examined layer. This study provides a basis for further study of conductivity distribution reconstruction of MAT-MI in medical imaging.

Sparse-view proton computed tomography using modulated proton beams.

PubMed

Lee, Jiseoc; Kim, Changhwan; Min, Byungjun; Kwak, Jungwon; Park, Seyjoon; Lee, Se Byeong; Park, Sungyong; Cho, Seungryong

2015-02-01

Proton imaging that uses a modulated proton beam and an intensity detector allows a relatively fast image acquisition compared to the imaging approach based on a trajectory tracking detector. In addition, it requires a relatively simple implementation in a conventional proton therapy equipment. The model of geometric straight ray assumed in conventional computed tomography (CT) image reconstruction is however challenged by multiple-Coulomb scattering and energy straggling in the proton imaging. Radiation dose to the patient is another important issue that has to be taken care of for practical applications. In this work, the authors have investigated iterative image reconstructions after a deconvolution of the sparsely view-sampled data to address these issues in proton CT. Proton projection images were acquired using the modulated proton beams and the EBT2 film as an intensity detector. Four electron-density cylinders representing normal soft tissues and bone were used as imaged object and scanned at 40 views that are equally separated over 360°. Digitized film images were converted to water-equivalent thickness by use of an empirically derived conversion curve. For improving the image quality, a deconvolution-based image deblurring with an empirically acquired point spread function was employed. They have implemented iterative image reconstruction algorithms such as adaptive steepest descent-projection onto convex sets (ASD-POCS), superiorization method-projection onto convex sets (SM-POCS), superiorization method-expectation maximization (SM-EM), and expectation maximization-total variation minimization (EM-TV). Performance of the four image reconstruction algorithms was analyzed and compared quantitatively via contrast-to-noise ratio (CNR) and root-mean-square-error (RMSE). Objects of higher electron density have been reconstructed more accurately than those of lower density objects. The bone, for example, has been reconstructed within 1% error. EM-based algorithms produced an increased image noise and RMSE as the iteration reaches about 20, while the POCS-based algorithms showed a monotonic convergence with iterations. The ASD-POCS algorithm outperformed the others in terms of CNR, RMSE, and the accuracy of the reconstructed relative stopping power in the region of lung and soft tissues. The four iterative algorithms, i.e., ASD-POCS, SM-POCS, SM-EM, and EM-TV, have been developed and applied for proton CT image reconstruction. Although it still seems that the images need to be improved for practical applications to the treatment planning, proton CT imaging by use of the modulated beams in sparse-view sampling has demonstrated its feasibility.

Right adrenal vein: comparison between adaptive statistical iterative reconstruction and model-based iterative reconstruction.

PubMed

Noda, Y; Goshima, S; Nagata, S; Miyoshi, T; Kawada, H; Kawai, N; Tanahashi, Y; Matsuo, M

2018-06-01

To compare right adrenal vein (RAV) visualisation and contrast enhancement degree on adrenal venous phase images reconstructed using adaptive statistical iterative reconstruction (ASiR) and model-based iterative reconstruction (MBIR) techniques. This prospective study was approved by the institutional review board, and written informed consent was waived. Fifty-seven consecutive patients who underwent adrenal venous phase imaging were enrolled. The same raw data were reconstructed using ASiR 40% and MBIR. The expert and beginner independently reviewed computed tomography (CT) images. RAV visualisation rates, background noise, and CT attenuation of the RAV, right adrenal gland, inferior vena cava (IVC), hepatic vein, and bilateral renal veins were compared between the two reconstruction techniques. RAV visualisation rates were higher with MBIR than with ASiR (95% versus 88%, p=0.13 in expert and 93% versus 75%, p=0.002 in beginner, respectively). RAV visualisation confidence ratings with MBIR were significantly greater than with ASiR (p<0.0001, both in the beginner and the expert). The mean background noise was significantly lower with MBIR than with ASiR (p<0.0001). Mean CT attenuation values of the RAV, right adrenal gland, IVC, and hepatic vein were comparable between the two techniques (p=0.12-0.91). Mean CT attenuation values of the bilateral renal veins were significantly higher with MBIR than with ASiR (p=0.0013 and 0.02). Reconstruction of adrenal venous phase images using MBIR significantly reduces background noise, leading to an improvement in the RAV visualisation compared with ASiR. Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Optimization-Based Image Reconstruction with Artifact Reduction in C-Arm CBCT

PubMed Central

Xia, Dan; Langan, David A.; Solomon, Stephen B.; Zhang, Zheng; Chen, Buxin; Lai, Hao; Sidky, Emil Y.; Pan, Xiaochuan

2016-01-01

We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g., data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility. PMID:27694700

Optimization-based image reconstruction with artifact reduction in C-arm CBCT

NASA Astrophysics Data System (ADS)

Xia, Dan; Langan, David A.; Solomon, Stephen B.; Zhang, Zheng; Chen, Buxin; Lai, Hao; Sidky, Emil Y.; Pan, Xiaochuan

2016-10-01

We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g. data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.

Modeling and image reconstruction in spectrally resolved bioluminescence tomography

NASA Astrophysics Data System (ADS)

Dehghani, Hamid; Pogue, Brian W.; Davis, Scott C.; Patterson, Michael S.

2007-02-01

Recent interest in modeling and reconstruction algorithms for Bioluminescence Tomography (BLT) has increased and led to the general consensus that non-spectrally resolved intensity-based BLT results in a non-unique problem. However, the light emitted from, for example firefly Luciferase, is widely distributed over the band of wavelengths from 500 nm to 650 nm and above, with the dominant fraction emitted from tissue being above 550 nm. This paper demonstrates the development of an algorithm used for multi-wavelength 3D spectrally resolved BLT image reconstruction in a mouse model. It is shown that using a single view data, bioluminescence sources of up to 15 mm deep can be successfully recovered given correct information about the underlying tissue absorption and scatter.

Micro-tomography based Geometry Modeling of Three-Dimensional Braided Composites

NASA Astrophysics Data System (ADS)

Fang, Guodong; Chen, Chenghua; Yuan, Shenggang; Meng, Songhe; Liang, Jun

2018-06-01

A tracking and recognizing algorithm is proposed to automatically generate irregular cross-sections and central path of braid yarn within the 3D braided composites by using sets of high resolution tomography images. Only the initial cross-sections of braid yarns in a tomography image after treatment are required to be calibrated manually as searching cross-section template. The virtual geometry of 3D braided composites including some detailed geometry information, such as the braid yarn squeezing deformation, braid yarn distortion and braid yarn path deviation etc., can be reconstructed. The reconstructed geometry model can reflect the change of braid configurations during solidification process. The geometry configurations and mechanical properties of the braided composites are analyzed by using the reconstructed geometry model.

Priori mask guided image reconstruction (p-MGIR) for ultra-low dose cone-beam computed tomography

NASA Astrophysics Data System (ADS)

Park, Justin C.; Zhang, Hao; Chen, Yunmei; Fan, Qiyong; Kahler, Darren L.; Liu, Chihray; Lu, Bo

2015-11-01

Recently, the compressed sensing (CS) based iterative reconstruction method has received attention because of its ability to reconstruct cone beam computed tomography (CBCT) images with good quality using sparsely sampled or noisy projections, thus enabling dose reduction. However, some challenges remain. In particular, there is always a tradeoff between image resolution and noise/streak artifact reduction based on the amount of regularization weighting that is applied uniformly across the CBCT volume. The purpose of this study is to develop a novel low-dose CBCT reconstruction algorithm framework called priori mask guided image reconstruction (p-MGIR) that allows reconstruction of high-quality low-dose CBCT images while preserving the image resolution. In p-MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions: (1) where anatomical structures are complex, and (2) where intensities are relatively uniform. The priori mask, which is the key concept of the p-MGIR algorithm, was defined as the matrix that distinguishes between the two separate CBCT regions where the resolution needs to be preserved and where streak or noise needs to be suppressed. We then alternately updated each part of image by solving two sub-minimization problems iteratively, where one minimization was focused on preserving the edge information of the first part while the other concentrated on the removal of noise/artifacts from the latter part. To evaluate the performance of the p-MGIR algorithm, a numerical head-and-neck phantom, a Catphan 600 physical phantom, and a clinical head-and-neck cancer case were used for analysis. The results were compared with the standard Feldkamp-Davis-Kress as well as conventional CS-based algorithms. Examination of the p-MGIR algorithm showed that high-quality low-dose CBCT images can be reconstructed without compromising the image resolution. For both phantom and the patient cases, the p-MGIR is able to achieve a clinically-reasonable image with 60 projections. Therefore, a clinically-viable, high-resolution head-and-neck CBCT image can be obtained while cutting the dose by 83%. Moreover, the image quality obtained using p-MGIR is better than the quality obtained using other algorithms. In this work, we propose a novel low-dose CBCT reconstruction algorithm called p-MGIR. It can be potentially used as a CBCT reconstruction algorithm with low dose scan requests

Ultrasound guided fluorescence molecular tomography with improved quantification by an attenuation compensated born-normalization and in vivo preclinical study of cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Baoqiang; Berti, Romain; Abran, Maxime

2014-05-15

Ultrasound imaging, having the advantages of low-cost and non-invasiveness over MRI and X-ray CT, was reported by several studies as an adequate complement to fluorescence molecular tomography with the perspective of improving localization and quantification of fluorescent molecular targets in vivo. Based on the previous work, an improved dual-modality Fluorescence-Ultrasound imaging system was developed and then validated in imaging study with preclinical tumor model. Ultrasound imaging and a profilometer were used to obtain the anatomical prior information and 3D surface, separately, to precisely extract the tissue boundary on both sides of sample in order to achieve improved fluorescence reconstruction. Furthermore,more » a pattern-based fluorescence reconstruction on the detection side was incorporated to enable dimensional reduction of the dataset while keeping the useful information for reconstruction. Due to its putative role in the current imaging geometry and the chosen reconstruction technique, we developed an attenuation compensated Born-normalization method to reduce the attenuation effects and cancel off experimental factors when collecting quantitative fluorescence datasets over large area. Results of both simulation and phantom study demonstrated that fluorescent targets could be recovered accurately and quantitatively using this reconstruction mechanism. Finally, in vivo experiment confirms that the imaging system associated with the proposed image reconstruction approach was able to extract both functional and anatomical information, thereby improving quantification and localization of molecular targets.« less

Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction.

PubMed

Zhang, Hanming; Wang, Linyuan; Yan, Bin; Li, Lei; Cai, Ailong; Hu, Guoen

2016-01-01

Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of unfavorable staircase effects. However, conventional TGV regularization employs l1-based form, which is not the most direct method for maximizing sparsity prior. In this study, we propose a total generalized p-variation (TGpV) regularization model to improve the sparsity exploitation of TGV and offer efficient solutions to few-view CT image reconstruction problems. To solve the nonconvex optimization problem of the TGpV minimization model, we then present an efficient iterative algorithm based on the alternating minimization of augmented Lagrangian function. All of the resulting subproblems decoupled by variable splitting admit explicit solutions by applying alternating minimization method and generalized p-shrinkage mapping. In addition, approximate solutions that can be easily performed and quickly calculated through fast Fourier transform are derived using the proximal point method to reduce the cost of inner subproblems. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to validate the efficiency and feasibility of the proposed method. Overall, the proposed method exhibits reasonable performance and outperforms the original TGV-based method when applied to few-view problems.

An Imaging Model Incorporating Ultrasonic Transducer Properties for Three-Dimensional Optoacoustic Tomography

PubMed Central

Wang, Kun; Ermilov, Sergey A.; Su, Richard; Brecht, Hans-Peter; Oraevsky, Alexander A.; Anastasio, Mark A.

2010-01-01

Optoacoustic Tomography (OAT) is a hybrid imaging modality that combines the advantages of optical and ultrasound imaging. Most existing reconstruction algorithms for OAT assume that the ultrasound transducers employed to record the measurement data are point-like. When transducers with large detecting areas and/or compact measurement geometries are utilized, this assumption can result in conspicuous image blurring and distortions in the reconstructed images. In this work, a new OAT imaging model that incorporates the spatial and temporal responses of an ultrasound transducer is introduced. A discrete form of the imaging model is implemented and its numerical properties are investigated. We demonstrate that use of the imaging model in an iterative reconstruction method can improve the spatial resolution of the optoacoustic images as compared to those reconstructed assuming point-like ultrasound transducers. PMID:20813634

2-D Fused Image Reconstruction approach for Microwave Tomography: a theoretical assessment using FDTD Model.

PubMed

Bindu, G; Semenov, S

2013-01-01

This paper describes an efficient two-dimensional fused image reconstruction approach for Microwave Tomography (MWT). Finite Difference Time Domain (FDTD) models were created for a viable MWT experimental system having the transceivers modelled using thin wire approximation with resistive voltage sources. Born Iterative and Distorted Born Iterative methods have been employed for image reconstruction with the extremity imaging being done using a differential imaging technique. The forward solver in the imaging algorithm employs the FDTD method of solving the time domain Maxwell's equations with the regularisation parameter computed using a stochastic approach. The algorithm is tested with 10% noise inclusion and successful image reconstruction has been shown implying its robustness.

Model-based Iterative Reconstruction: Effect on Patient Radiation Dose and Image Quality in Pediatric Body CT

PubMed Central

Dillman, Jonathan R.; Goodsitt, Mitchell M.; Christodoulou, Emmanuel G.; Keshavarzi, Nahid; Strouse, Peter J.

2014-01-01

Purpose To retrospectively compare image quality and radiation dose between a reduced-dose computed tomographic (CT) protocol that uses model-based iterative reconstruction (MBIR) and a standard-dose CT protocol that uses 30% adaptive statistical iterative reconstruction (ASIR) with filtered back projection. Materials and Methods Institutional review board approval was obtained. Clinical CT images of the chest, abdomen, and pelvis obtained with a reduced-dose protocol were identified. Images were reconstructed with two algorithms: MBIR and 100% ASIR. All subjects had undergone standard-dose CT within the prior year, and the images were reconstructed with 30% ASIR. Reduced- and standard-dose images were evaluated objectively and subjectively. Reduced-dose images were evaluated for lesion detectability. Spatial resolution was assessed in a phantom. Radiation dose was estimated by using volumetric CT dose index (CTDIvol) and calculated size-specific dose estimates (SSDE). A combination of descriptive statistics, analysis of variance, and t tests was used for statistical analysis. Results In the 25 patients who underwent the reduced-dose protocol, mean decrease in CTDIvol was 46% (range, 19%–65%) and mean decrease in SSDE was 44% (range, 19%–64%). Reduced-dose MBIR images had less noise (P > .004). Spatial resolution was superior for reduced-dose MBIR images. Reduced-dose MBIR images were equivalent to standard-dose images for lungs and soft tissues (P > .05) but were inferior for bones (P = .004). Reduced-dose 100% ASIR images were inferior for soft tissues (P < .002), lungs (P < .001), and bones (P < .001). By using the same reduced-dose acquisition, lesion detectability was better (38% [32 of 84 rated lesions]) or the same (62% [52 of 84 rated lesions]) with MBIR as compared with 100% ASIR. Conclusion CT performed with a reduced-dose protocol and MBIR is feasible in the pediatric population, and it maintains diagnostic quality. © RSNA, 2013 Online supplemental material is available for this article. PMID:24091359

GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation.

PubMed

Jia, Xun; Lou, Yifei; Li, Ruijiang; Song, William Y; Jiang, Steve B

2010-04-01

Cone-beam CT (CBCT) plays an important role in image guided radiation therapy (IGRT). However, the large radiation dose from serial CBCT scans in most IGRT procedures raises a clinical concern, especially for pediatric patients who are essentially excluded from receiving IGRT for this reason. The goal of this work is to develop a fast GPU-based algorithm to reconstruct CBCT from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. The authors developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multigrid technique is also employed. It is found that 20-40 x-ray projections are sufficient to reconstruct images with satisfactory quality for IGRT. The reconstruction time ranges from 77 to 130 s on an NVIDIA Tesla C1060 (NVIDIA, Santa Clara, CA) GPU card, depending on the number of projections used, which is estimated about 100 times faster than similar iterative reconstruction approaches. Moreover, phantom studies indicate that the algorithm enables the CBCT to be reconstructed under a scanning protocol with as low as 0.1 mA s/projection. Comparing with currently widely used full-fan head and neck scanning protocol of approximately 360 projections with 0.4 mA s/projection, it is estimated that an overall 36-72 times dose reduction has been achieved in our fast CBCT reconstruction algorithm. This work indicates that the developed GPU-based CBCT reconstruction algorithm is capable of lowering imaging dose considerably. The high computation efficiency in this algorithm makes the iterative CBCT reconstruction approach applicable in real clinical environments.

Model-based tomographic reconstruction of objects containing known components.

PubMed

Stayman, J Webster; Otake, Yoshito; Prince, Jerry L; Khanna, A Jay; Siewerdsen, Jeffrey H

2012-10-01

The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the target anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.

Image- and model-based surgical planning in otolaryngology.

PubMed

Korves, B; Klimek, L; Klein, H M; Mösges, R

1995-10-01

Preoperative evaluation of any operating field is essential for the preparation of surgical procedures. The relationship between pathology and adjacent structures, and anatomically dangerous sites need to be analyzed for the determination of intraoperative action. For the simulation of surgery using three-dimensional imaging or individually manufactured plastic patient models, the authors have worked out different procedures. A total of 481 surgical interventions in the maxillofacial region, paranasal sinuses, orbit, and the anterior and middle skull base, in addition to neurotologic procedures were presurgically simulated using three-dimensional imaging and image manipulation. An intraoperative simulation device, part of the Aachen Computer-Assisted Surgery System, had been applied in 407 of these cases. In seven patients, stereolithography was used to create plastic patient models for the preparation of reconstructive surgery and prostheses fabrication. The disadvantages of this process include time and cost; however, the advantages included (1) a better understanding of the anatomic relationships, (2) the feasibility of presurgical simulation of the prevailing procedure, (3) an improved intraoperative localization accuracy, (4) prostheses fabrication in reconstructive procedures with an approach to more accuracy, (5) permanent recordings for future requirements or reconstructions, and (6) improved residency education.

Correcting electrode modelling errors in EIT on realistic 3D head models.

PubMed

Jehl, Markus; Avery, James; Malone, Emma; Holder, David; Betcke, Timo

2015-12-01

Electrical impedance tomography (EIT) is a promising medical imaging technique which could aid differentiation of haemorrhagic from ischaemic stroke in an ambulance. One challenge in EIT is the ill-posed nature of the image reconstruction, i.e., that small measurement or modelling errors can result in large image artefacts. It is therefore important that reconstruction algorithms are improved with regard to stability to modelling errors. We identify that wrongly modelled electrode positions constitute one of the biggest sources of image artefacts in head EIT. Therefore, the use of the Fréchet derivative on the electrode boundaries in a realistic three-dimensional head model is investigated, in order to reconstruct electrode movements simultaneously to conductivity changes. We show a fast implementation and analyse the performance of electrode position reconstructions in time-difference and absolute imaging for simulated and experimental voltages. Reconstructing the electrode positions and conductivities simultaneously increased the image quality significantly in the presence of electrode movement.

Using statistical deformable models to reconstruct vocal tract shape from magnetic resonance images.

PubMed

Vasconcelos, M J M; Rua Ventura, S M; Freitas, D R S; Tavares, J M R S

2010-10-01

The mechanisms involved in speech production are complex and have thus been subject to growing attention by the scientific community. It has been demonstrated that magnetic resonance imaging (MRI) is a powerful means in the understanding of the morphology of the vocal tract. Over the last few years, statistical deformable models have been successfully used to identify and characterize bones and organs in medical images and point distribution models (PDMs) have gained particular relevance. In this work, the suitability of these models has been studied to characterize and further reconstruct the shape of the vocal tract in the articulation of Portuguese European (EP) speech sounds, one of the most spoken languages worldwide, with the aid of MR images. Therefore, a PDM has been built from a set of MR images acquired during the artificially sustained articulation of 25 EP speech sounds. Following this, the capacity of this statistical model to characterize the shape deformation of the vocal tract during the production of sounds was analysed. Next, the model was used to reconstruct five EP oral vowels and the EP fricative consonants. As far as a study on speech production is concerned, this study is considered to be the first approach to characterize and reconstruct the vocal tract shape from MR images by using PDMs. In addition, the findings achieved permit one to conclude that this modelling technique compels an enhanced understanding of the dynamic speech events involved in sustained articulations based on MRI, which are of particular interest for speech rehabilitation and simulation.

White light-informed optical properties improve ultrasound-guided fluorescence tomography of photoactive protoporphyrin IX

NASA Astrophysics Data System (ADS)

Flynn, Brendan P.; DSouza, Alisha V.; Kanick, Stephen C.; Davis, Scott C.; Pogue, Brian W.

2013-04-01

Subsurface fluorescence imaging is desirable for medical applications, including protoporphyrin-IX (PpIX)-based skin tumor diagnosis, surgical guidance, and dosimetry in photodynamic therapy. While tissue optical properties and heterogeneities make true subsurface fluorescence mapping an ill-posed problem, ultrasound-guided fluorescence-tomography (USFT) provides regional fluorescence mapping. Here USFT is implemented with spectroscopic decoupling of fluorescence signals (auto-fluorescence, PpIX, photoproducts), and white light spectroscopy-determined bulk optical properties. Segmented US images provide a priori spatial information for fluorescence reconstruction using region-based, diffuse FT. The method was tested in simulations, tissue homogeneous and inclusion phantoms, and an injected-inclusion animal model. Reconstructed fluorescence yield was linear with PpIX concentration, including the lowest concentration used, 0.025 μg/ml. White light spectroscopy informed optical properties, which improved fluorescence reconstruction accuracy compared to the use of fixed, literature-based optical properties, reduced reconstruction error and reconstructed fluorescence standard deviation by factors of 8.9 and 2.0, respectively. Recovered contrast-to-background error was 25% and 74% for inclusion phantoms without and with a 2-mm skin-like layer, respectively. Preliminary mouse-model imaging demonstrated system feasibility for subsurface fluorescence measurement in vivo. These data suggest that this implementation of USFT is capable of regional PpIX mapping in human skin tumors during photodynamic therapy, to be used in dosimetric evaluations.

Task-based statistical image reconstruction for high-quality cone-beam CT

NASA Astrophysics Data System (ADS)

Dang, Hao; Webster Stayman, J.; Xu, Jennifer; Zbijewski, Wojciech; Sisniega, Alejandro; Mow, Michael; Wang, Xiaohui; Foos, David H.; Aygun, Nafi; Koliatsos, Vassilis E.; Siewerdsen, Jeffrey H.

2017-11-01

Task-based analysis of medical imaging performance underlies many ongoing efforts in the development of new imaging systems. In statistical image reconstruction, regularization is often formulated in terms to encourage smoothness and/or sharpness (e.g. a linear, quadratic, or Huber penalty) but without explicit formulation of the task. We propose an alternative regularization approach in which a spatially varying penalty is determined that maximizes task-based imaging performance at every location in a 3D image. We apply the method to model-based image reconstruction (MBIR—viz., penalized weighted least-squares, PWLS) in cone-beam CT (CBCT) of the head, focusing on the task of detecting a small, low-contrast intracranial hemorrhage (ICH), and we test the performance of the algorithm in the context of a recently developed CBCT prototype for point-of-care imaging of brain injury. Theoretical predictions of local spatial resolution and noise are computed via an optimization by which regularization (specifically, the quadratic penalty strength) is allowed to vary throughout the image to maximize local task-based detectability index ({{d}\\prime} ). Simulation studies and test-bench experiments were performed using an anthropomorphic head phantom. Three PWLS implementations were tested: conventional (constant) penalty; a certainty-based penalty derived to enforce constant point-spread function, PSF; and the task-based penalty derived to maximize local detectability at each location. Conventional (constant) regularization exhibited a fairly strong degree of spatial variation in {{d}\\prime} , and the certainty-based method achieved uniform PSF, but each exhibited a reduction in detectability compared to the task-based method, which improved detectability up to ~15%. The improvement was strongest in areas of high attenuation (skull base), where the conventional and certainty-based methods tended to over-smooth the data. The task-driven reconstruction method presents a promising regularization method in MBIR by explicitly incorporating task-based imaging performance as the objective. The results demonstrate improved ICH conspicuity and support the development of high-quality CBCT systems.

An Automatic Image-Based Modelling Method Applied to Forensic Infography

PubMed Central

Zancajo-Blazquez, Sandra; Gonzalez-Aguilera, Diego; Gonzalez-Jorge, Higinio; Hernandez-Lopez, David

2015-01-01

This paper presents a new method based on 3D reconstruction from images that demonstrates the utility and integration of close-range photogrammetry and computer vision as an efficient alternative to modelling complex objects and scenarios of forensic infography. The results obtained confirm the validity of the method compared to other existing alternatives as it guarantees the following: (i) flexibility, permitting work with any type of camera (calibrated and non-calibrated, smartphone or tablet) and image (visible, infrared, thermal, etc.); (ii) automation, allowing the reconstruction of three-dimensional scenarios in the absence of manual intervention, and (iii) high quality results, sometimes providing higher resolution than modern laser scanning systems. As a result, each ocular inspection of a crime scene with any camera performed by the scientific police can be transformed into a scaled 3d model. PMID:25793628

SU-E-J-246: A Deformation-Field Map Based Liver 4D CBCT Reconstruction Method Using Gold Nanoparticles as Constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Harris, W; Zhang, Y; Ren, L

2014-06-01

Purpose: To investigate the feasibility of using nanoparticle markers to validate liver tumor motion together with a deformation field map-based four dimensional (4D) cone-beam computed tomography (CBCT) reconstruction method. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In this method, each phase of the 4D-CBCT is considered as a deformation of a prior CT volume. The DFM is solved by a motion modeling and free-form deformation (MM-FD) technique, using a data fidelity constraint and the deformation energy minimization. For liver imaging, there is low contrast of a liver tumor inmore » on-board projections. A validation of liver tumor motion using implanted gold nanoparticles, along with the MM-FD deformation technique is implemented to reconstruct onboard 4D CBCT liver radiotherapy images. These nanoparticles were placed around the liver tumor to reflect the tumor positions in both CT simulation and on-board image acquisition. When reconstructing each phase of the 4D-CBCT, the migrations of the gold nanoparticles act as a constraint to regularize the deformation field, along with the data fidelity and the energy minimization constraints. In this study, multiple tumor diameters and positions were simulated within the liver for on-board 4D-CBCT imaging. The on-board 4D-CBCT reconstructed by the proposed method was compared with the “ground truth” image. Results: The preliminary data, which uses reconstruction for lung radiotherapy suggests that the advanced reconstruction algorithm including the gold nanoparticle constraint will Resultin volume percentage differences (VPD) between lesions in reconstructed images by MM-FD and “ground truth” on-board images of 11.5% (± 9.4%) and a center of mass shift of 1.3 mm (± 1.3 mm) for liver radiotherapy. Conclusion: The advanced MM-FD technique enforcing the additional constraints from gold nanoparticles, results in improved accuracy for reconstructing on-board 4D-CBCT of liver tumor. Varian medical systems research grant.« less

Empirical Modeling of the Plasmasphere Dynamics Using Neural Networks

NASA Astrophysics Data System (ADS)

Zhelavskaya, I. S.; Shprits, Y.; Spasojevic, M.

2017-12-01

We present a new empirical model for reconstructing the global dynamics of the cold plasma density distribution based only on solar wind data and geomagnetic indices. Utilizing the density database obtained using the NURD (Neural-network-based Upper hybrid Resonance Determination) algorithm for the period of October 1, 2012 - July 1, 2016, in conjunction with solar wind data and geomagnetic indices, we develop a neural network model that is capable of globally reconstructing the dynamics of the cold plasma density distribution for 2 ≤ L ≤ 6 and all local times. We validate and test the model by measuring its performance on independent datasets withheld from the training set and by comparing the model predicted global evolution with global images of He+ distribution in the Earth's plasmasphere from the IMAGE Extreme UltraViolet (EUV) instrument. We identify the parameters that best quantify the plasmasphere dynamics by training and comparing multiple neural networks with different combinations of input parameters (geomagnetic indices, solar wind data, and different durations of their time history). We demonstrate results of both local and global plasma density reconstruction. This study illustrates how global dynamics can be reconstructed from local in-situ observations by using machine learning techniques.

Acoustic dipole radiation based electrical impedance contrast imaging approach of magnetoacoustic tomography with magnetic induction.

PubMed

Sun, Xiaodong; Fang, Dawei; Zhang, Dong; Ma, Qingyu

2013-05-01

Different from the theory of acoustic monopole spherical radiation, the acoustic dipole radiation based theory introduces the radiation pattern of Lorentz force induced dipole sources to describe the principle of magnetoacoustic tomography with magnetic induction (MAT-MI). Although two-dimensional (2D) simulations have been studied for cylindrical phantom models, layer effects of the dipole sources within the entire object along the z direction still need to be investigated to evaluate the performance of MAT-MI for different geometric specifications. The purpose of this work is further verifying the validity and generality of acoustic dipole radiation based theory for MAT-MI with two new models in different shapes, dimensions, and conductivities. Based on the theory of acoustic dipole radiation, the principles of MAT-MI were analyzed with derived analytic formulae. 2D and 3D numerical studies for two new models of aluminum foil and cooked egg were conducted to simulate acoustic pressures and corresponding waveforms, and 2D images of the scanned layers were reconstructed with the simplified back projection algorithm for the waveforms collected around the models. The spatial resolution for conductivity boundary differentiation was also analyzed with different foil thickness. For comparison, two experimental measurements were conducted for a cylindrical aluminum foil phantom and a shell-peeled cooked egg. The collected waveforms and the reconstructed images of the scanned layers were achieved to verify the validation of the acoustic dipole radiation based theory for MAT-MI. Despite the difference between the 2D and 3D simulated pressures, good consistence of the collected waveforms proves that wave clusters are generated by the abrupt pressure changes with bipolar vibration phases, representing the opposite polarities of the conductivity changes along the measurement direction. The configuration of the scanned layer can be reconstructed in terms of shape and size, and the conductivity boundaries are displayed in stripes with different contrast and bipolar intensities. Layer effects are demonstrated to have little influence on the collected waveforms and the reconstructed images of the scanned layers for the two new models. The experimental results have good agreements with numerical simulations, and the reconstructed 2D images provide conductivity configurations in the scanned layers of the aluminum foil and the egg models. It can be concluded that the acoustic pressure of MAT-MI is produced by the divergence of the induced Lorentz force, and the collected waveforms comprise wave clusters with bipolar vibration phases and different amplitudes, providing the information of conductivity boundaries in the scanned layer. With the simplified back projection algorithm for diffraction sources, collected waveforms can be used to reconstruct 2D conductivity contrast image and the conductivity configuration in the scanned layer can be obtained in terms of shape and size in stripes with the spatial resolution of the acoustic wavelength. The favorable results further verify the validity and generality of the acoustic dipole radiation based theory and suggest the feasibility of MAT-MI as an effective electrical impedance contrast imaging approach for medical imaging.

Left ventricular endocardial surface detection based on real-time 3D echocardiographic data

NASA Technical Reports Server (NTRS)

Corsi, C.; Borsari, M.; Consegnati, F.; Sarti, A.; Lamberti, C.; Travaglini, A.; Shiota, T.; Thomas, J. D.

2001-01-01

OBJECTIVE: A new computerized semi-automatic method for left ventricular (LV) chamber segmentation is presented. METHODS: The LV is imaged by real-time three-dimensional echocardiography (RT3DE). The surface detection model, based on level set techniques, is applied to RT3DE data for image analysis. The modified level set partial differential equation we use is solved by applying numerical methods for conservation laws. The initial conditions are manually established on some slices of the entire volume. The solution obtained for each slice is a contour line corresponding with the boundary between LV cavity and LV endocardium. RESULTS: The mathematical model has been applied to sequences of frames of human hearts (volume range: 34-109 ml) imaged by 2D and reconstructed off-line and RT3DE data. Volume estimation obtained by this new semi-automatic method shows an excellent correlation with those obtained by manual tracing (r = 0.992). Dynamic change of LV volume during the cardiac cycle is also obtained. CONCLUSION: The volume estimation method is accurate; edge based segmentation, image completion and volume reconstruction can be accomplished. The visualization technique also allows to navigate into the reconstructed volume and to display any section of the volume.

Three-dimensional assessment of scoliosis based on ultrasound data

NASA Astrophysics Data System (ADS)

Zhang, Junhua; Li, Hongjian; Yu, Bo

2015-12-01

In this study, an approach was proposed to assess the 3D scoliotic deformity based on ultrasound data. The 3D spine model was reconstructed by using a freehand 3D ultrasound imaging system. The geometric torsion was then calculated from the reconstructed spine model. A thoracic spine phantom set at a given pose was used in the experiment. The geometric torsion of the spine phantom calculated from the freehand ultrasound imaging system was 0.041 mm-1 which was close to that calculated from the biplanar radiographs (0.025 mm-1). Therefore, ultrasound is a promising technique for the 3D assessment of scoliosis.

Dictionary learning based noisy image super-resolution via distance penalty weight model

PubMed Central

Han, Yulan; Zhao, Yongping; Wang, Qisong

2017-01-01

In this study, we address the problem of noisy image super-resolution. Noisy low resolution (LR) image is always obtained in applications, while most of the existing algorithms assume that the LR image is noise-free. As to this situation, we present an algorithm for noisy image super-resolution which can achieve simultaneously image super-resolution and denoising. And in the training stage of our method, LR example images are noise-free. For different input LR images, even if the noise variance varies, the dictionary pair does not need to be retrained. For the input LR image patch, the corresponding high resolution (HR) image patch is reconstructed through weighted average of similar HR example patches. To reduce computational cost, we use the atoms of learned sparse dictionary as the examples instead of original example patches. We proposed a distance penalty model for calculating the weight, which can complete a second selection on similar atoms at the same time. Moreover, LR example patches removed mean pixel value are also used to learn dictionary rather than just their gradient features. Based on this, we can reconstruct initial estimated HR image and denoised LR image. Combined with iterative back projection, the two reconstructed images are applied to obtain final estimated HR image. We validate our algorithm on natural images and compared with the previously reported algorithms. Experimental results show that our proposed method performs better noise robustness. PMID:28759633

Three Dimentional Reconstruction of Large Cultural Heritage Objects Based on Uav Video and Tls Data

NASA Astrophysics Data System (ADS)

Xu, Z.; Wu, T. H.; Shen, Y.; Wu, L.

2016-06-01

This paper investigates the synergetic use of unmanned aerial vehicle (UAV) and terrestrial laser scanner (TLS) in 3D reconstruction of cultural heritage objects. Rather than capturing still images, the UAV that equips a consumer digital camera is used to collect dynamic videos to overcome its limited endurance capacity. Then, a set of 3D point-cloud is generated from video image sequences using the automated structure-from-motion (SfM) and patch-based multi-view stereo (PMVS) methods. The TLS is used to collect the information that beyond the reachability of UAV imaging e.g., partial building facades. A coarse to fine method is introduced to integrate the two sets of point clouds UAV image-reconstruction and TLS scanning for completed 3D reconstruction. For increased reliability, a variant of ICP algorithm is introduced using local terrain invariant regions in the combined designation. The experimental study is conducted in the Tulou culture heritage building in Fujian province, China, which is focused on one of the TuLou clusters built several hundred years ago. Results show a digital 3D model of the Tulou cluster with complete coverage and textural information. This paper demonstrates the usability of the proposed method for efficient 3D reconstruction of heritage object based on UAV video and TLS data.

Comparison among Reconstruction Algorithms for Quantitative Analysis of 11C-Acetate Cardiac PET Imaging.

PubMed

Shi, Ximin; Li, Nan; Ding, Haiyan; Dang, Yonghong; Hu, Guilan; Liu, Shuai; Cui, Jie; Zhang, Yue; Li, Fang; Zhang, Hui; Huo, Li

2018-01-01

Kinetic modeling of dynamic 11 C-acetate PET imaging provides quantitative information for myocardium assessment. The quality and quantitation of PET images are known to be dependent on PET reconstruction methods. This study aims to investigate the impacts of reconstruction algorithms on the quantitative analysis of dynamic 11 C-acetate cardiac PET imaging. Suspected alcoholic cardiomyopathy patients ( N = 24) underwent 11 C-acetate dynamic PET imaging after low dose CT scan. PET images were reconstructed using four algorithms: filtered backprojection (FBP), ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), and OSEM with both time-of-flight and point-spread-function (TPSF). Standardized uptake values (SUVs) at different time points were compared among images reconstructed using the four algorithms. Time-activity curves (TACs) in myocardium and blood pools of ventricles were generated from the dynamic image series. Kinetic parameters K 1 and k 2 were derived using a 1-tissue-compartment model for kinetic modeling of cardiac flow from 11 C-acetate PET images. Significant image quality improvement was found in the images reconstructed using iterative OSEM-type algorithms (OSME, TOF, and TPSF) compared with FBP. However, no statistical differences in SUVs were observed among the four reconstruction methods at the selected time points. Kinetic parameters K 1 and k 2 also exhibited no statistical difference among the four reconstruction algorithms in terms of mean value and standard deviation. However, for the correlation analysis, OSEM reconstruction presented relatively higher residual in correlation with FBP reconstruction compared with TOF and TPSF reconstruction, and TOF and TPSF reconstruction were highly correlated with each other. All the tested reconstruction algorithms performed similarly for quantitative analysis of 11 C-acetate cardiac PET imaging. TOF and TPSF yielded highly consistent kinetic parameter results with superior image quality compared with FBP. OSEM was relatively less reliable. Both TOF and TPSF were recommended for cardiac 11 C-acetate kinetic analysis.

Three-dimensional Noninvasive Monitoring Iodine-131 Uptake in the Thyroid Using a Modified Cerenkov Luminescence Tomography Approach

PubMed Central

Qu, Xiaochao; Yang, Weidong; Liang, Jimin; Wang, Jing; Tian, Jie

2012-01-01

Background Cerenkov luminescence tomography (CLT) provides the three-dimensional (3D) radiopharmaceutical biodistribution in small living animals, which is vital to biomedical imaging. However, existing single-spectral and multispectral methods are not very efficient and effective at reconstructing the distribution of the radionuclide tracer. In this paper, we present a semi-quantitative Cerenkov radiation spectral characteristic-based source reconstruction method named the hybrid spectral CLT, to efficiently reconstruct the radionuclide tracer with both encouraging reconstruction results and less acquisition and image reconstruction time. Methodology/Principal Findings We constructed the implantation mouse model implanted with a 400 µCi Na131I radioactive source and the physiological mouse model received an intravenous tail injection of 400 µCi radiopharmaceutical Iodine-131 (I-131) to validate the performance of the hybrid spectral CLT and compared the reconstruction results, acquisition, and image reconstruction time with that of single-spectral and multispectral CLT. Furthermore, we performed 3D noninvasive monitoring of I-131 uptake in the thyroid and quantified I-131 uptake in vivo using hybrid spectral CLT. Results showed that the reconstruction based on the hybrid spectral CLT was more accurate in localization and quantification than using single-spectral CLT, and was more efficient in the in vivo experiment compared with multispectral CLT. Additionally, 3D visualization of longitudinal observations suggested that the reconstructed energy of I-131 uptake in the thyroid increased with acquisition time and there was a robust correlation between the reconstructed energy versus the gamma ray counts of I-131 (). The ex vivo biodistribution experiment further confirmed the I-131 uptake in the thyroid for hybrid spectral CLT. Conclusions/Significance Results indicated that hybrid spectral CLT could be potentially used for thyroid imaging to evaluate its function and monitor its treatment for thyroid cancer. PMID:22629431

A novel algorithm of super-resolution image reconstruction based on multi-class dictionaries for natural scene

NASA Astrophysics Data System (ADS)

Wu, Wei; Zhao, Dewei; Zhang, Huan

2015-12-01

Super-resolution image reconstruction is an effective method to improve the image quality. It has important research significance in the field of image processing. However, the choice of the dictionary directly affects the efficiency of image reconstruction. A sparse representation theory is introduced into the problem of the nearest neighbor selection. Based on the sparse representation of super-resolution image reconstruction method, a super-resolution image reconstruction algorithm based on multi-class dictionary is analyzed. This method avoids the redundancy problem of only training a hyper complete dictionary, and makes the sub-dictionary more representatives, and then replaces the traditional Euclidean distance computing method to improve the quality of the whole image reconstruction. In addition, the ill-posed problem is introduced into non-local self-similarity regularization. Experimental results show that the algorithm is much better results than state-of-the-art algorithm in terms of both PSNR and visual perception.

A fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration.

PubMed

Song, Xiaolei; Xiong, Xiaoyun; Bai, Jing

2007-01-01

Fluorescence optical diffusion tomography in the near-infrared (NIR) bandwidth is considered to be one of the most promising ways for noninvasive molecular-based imaging. Many reconstructive approaches to it utilize iterative methods for data inversion. However, they are time-consuming and they are far from meeting the real-time imaging demands. In this work, a fast preiteration algorithm based on the generalized inverse matrix is proposed. This method needs only one step of matrix-vector multiplication online, by pushing the iteration process to be executed offline. In the preiteration process, the second-order iterative format is employed to exponentially accelerate the convergence. Simulations based on an analytical diffusion model show that the distribution of fluorescent yield can be well estimated by this algorithm and the reconstructed speed is remarkably increased.

3D/3D registration of coronary CTA and biplane XA reconstructions for improved image guidance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dibildox, Gerardo, E-mail: g.dibildox@erasmusmc.nl; Baka, Nora; Walsum, Theo van

2014-09-15

Purpose: The authors aim to improve image guidance during percutaneous coronary interventions of chronic total occlusions (CTO) by providing information obtained from computed tomography angiography (CTA) to the cardiac interventionist. To this end, the authors investigate a method to register a 3D CTA model to biplane reconstructions. Methods: The authors developed a method for registering preoperative coronary CTA with intraoperative biplane x-ray angiography (XA) images via 3D models of the coronary arteries. The models are extracted from the CTA and biplane XA images, and are temporally aligned based on CTA reconstruction phase and XA ECG signals. Rigid spatial alignment ismore » achieved with a robust probabilistic point set registration approach using Gaussian mixture models (GMMs). This approach is extended by including orientation in the Gaussian mixtures and by weighting bifurcation points. The method is evaluated on retrospectively acquired coronary CTA datasets of 23 CTO patients for which biplane XA images are available. Results: The Gaussian mixture model approach achieved a median registration accuracy of 1.7 mm. The extended GMM approach including orientation was not significantly different (P > 0.1) but did improve robustness with regards to the initialization of the 3D models. Conclusions: The authors demonstrated that the GMM approach can effectively be applied to register CTA to biplane XA images for the purpose of improving image guidance in percutaneous coronary interventions.« less

Heliospheric Imaging of 3D Density Structures During the Multiple Coronal Mass Ejections of Late July to Early August 2010

NASA Astrophysics Data System (ADS)

Webb, D. F.; Möstl, C.; Jackson, B. V.; Bisi, M. M.; Howard, T. A.; Mulligan, T.; Jensen, E. A.; Jian, L. K.; Davies, J. A.; de Koning, C. A.; Liu, Y.; Temmer, M.; Clover, J. M.; Farrugia, C. J.; Harrison, R. A.; Nitta, N.; Odstrcil, D.; Tappin, S. J.; Yu, H.-S.

2013-07-01

It is usually difficult to gain a consistent global understanding of a coronal mass ejection (CME) eruption and its propagation when only near-Sun imagery and the local measurements derived from single-spacecraft observations are available. Three-dimensional (3D) density reconstructions based on heliospheric imaging allow us to "fill in" the temporal and spatial gaps between the near-Sun and in situ data to provide a truly global picture of the propagation and interactions of the CME as it moves through the inner heliosphere. In recent years the heliospheric propagation of dense structures has been observed and measured by the heliospheric imagers of the Solar Mass Ejection Imager (SMEI) and on the twin Solar TErrestrial RElations Observatory (STEREO) spacecraft. We describe the use of several 3D reconstruction techniques based on these heliospheric imaging data sets to distinguish and track the propagation of multiple CMEs in the inner heliosphere during the very active period of solar activity in late July - early August 2010. We employ 3D reconstruction techniques used at the University of California, San Diego (UCSD) based on a kinematic solar wind model, and also the empirical Tappin-Howard model. We compare our results with those from other studies of this active period, in particular the heliospheric simulations made with the ENLIL model by Odstrcil et al. ( J. Geophys. Res., 2013) and the in situ results from multiple spacecraft provided by Möstl et al. ( Astrophys. J. 758, 10 - 28, 2012). We find that the SMEI results in particular provide an overall context for the multiple-density flows associated with these CMEs. For the first time we are able to intercompare the 3D reconstructed densities with the timing and magnitude of in situ density structures at five spacecraft spread over 150° in ecliptic longitude and from 0.4 to 1 AU in radial distance. We also model the magnetic flux-rope structures at three spacecraft using both force-free and non-force-free modelling, and compare their timing and spatial structure with the reconstructed density flows.

Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging.

PubMed

Pickhardt, Perry J; Lubner, Meghan G; Kim, David H; Tang, Jie; Ruma, Julie A; del Rio, Alejandro Muñoz; Chen, Guang-Hong

2012-12-01

The purpose of this study was to report preliminary results of an ongoing prospective trial of ultralow-dose abdominal MDCT. Imaging with standard-dose contrast-enhanced (n = 21) and unenhanced (n = 24) clinical abdominal MDCT protocols was immediately followed by ultralow-dose imaging of a matched series of 45 consecutively registered adults (mean age, 57.9 years; mean body mass index, 28.5). The ultralow-dose images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and model-based iterative reconstruction (MBIR). Standard-dose series were reconstructed with FBP (reference standard). Image noise was measured at multiple predefined sites. Two blinded abdominal radiologists interpreted randomly presented ultralow-dose images for multilevel subjective image quality (5-point scale) and depiction of organ-based focal lesions. Mean dose reduction relative to the standard series was 74% (median, 78%; range, 57-88%; mean effective dose, 1.90 mSv). Mean multiorgan image noise for low-dose MBIR was 14.7 ± 2.6 HU, significantly lower than standard-dose FBP (28.9 ± 9.9 HU), low-dose FBP (59.2 ± 23.3 HU), and ASIR (45.6 ± 14.1 HU) (p < 0.001). The mean subjective image quality score for low-dose MBIR (3.0 ± 0.5) was significantly higher than for low-dose FBP (1.6 ± 0.7) and ASIR (1.8 ± 0.7) (p < 0.001). Readers identified 213 focal noncalcific lesions with standard-dose FBP. Pooled lesion detection was higher for low-dose MBIR (79.3% [169/213]) compared with low-dose FBP (66.2% [141/213]) and ASIR (62.0% [132/213]) (p < 0.05). MBIR shows great potential for substantially reducing radiation doses at routine abdominal CT. Both FBP and ASIR are limited in this regard owing to reduced image quality and diagnostic capability. Further investigation is needed to determine the optimal dose level for MBIR that maintains adequate diagnostic performance. In general, objective and subjective image quality measurements do not necessarily correlate with diagnostic performance at ultralow-dose CT.

Intra-patient comparison of reduced-dose model-based iterative reconstruction with standard-dose adaptive statistical iterative reconstruction in the CT diagnosis and follow-up of urolithiasis.

PubMed

Tenant, Sean; Pang, Chun Lap; Dissanayake, Prageeth; Vardhanabhuti, Varut; Stuckey, Colin; Gutteridge, Catherine; Hyde, Christopher; Roobottom, Carl

2017-10-01

To evaluate the accuracy of reduced-dose CT scans reconstructed using a new generation of model-based iterative reconstruction (MBIR) in the imaging of urinary tract stone disease, compared with a standard-dose CT using 30% adaptive statistical iterative reconstruction. This single-institution prospective study recruited 125 patients presenting either with acute renal colic or for follow-up of known urinary tract stones. They underwent two immediately consecutive scans, one at standard dose settings and one at the lowest dose (highest noise index) the scanner would allow. The reduced-dose scans were reconstructed using both ASIR 30% and MBIR algorithms and reviewed independently by two radiologists. Objective and subjective image quality measures as well as diagnostic data were obtained. The reduced-dose MBIR scan was 100% concordant with the reference standard for the assessment of ureteric stones. It was extremely accurate at identifying calculi of 3 mm and above. The algorithm allowed a dose reduction of 58% without any loss of scan quality. A reduced-dose CT scan using MBIR is accurate in acute imaging for renal colic symptoms and for urolithiasis follow-up and allows a significant reduction in dose. • MBIR allows reduced CT dose with similar diagnostic accuracy • MBIR outperforms ASIR when used for the reconstruction of reduced-dose scans • MBIR can be used to accurately assess stones 3 mm and above.

Accurate reconstruction of 3D cardiac geometry from coarsely-sliced MRI.

PubMed

Ringenberg, Jordan; Deo, Makarand; Devabhaktuni, Vijay; Berenfeld, Omer; Snyder, Brett; Boyers, Pamela; Gold, Jeffrey

2014-02-01

We present a comprehensive validation analysis to assess the geometric impact of using coarsely-sliced short-axis images to reconstruct patient-specific cardiac geometry. The methods utilize high-resolution diffusion tensor MRI (DTMRI) datasets as reference geometries from which synthesized coarsely-sliced datasets simulating in vivo MRI were produced. 3D models are reconstructed from the coarse data using variational implicit surfaces through a commonly used modeling tool, CardioViz3D. The resulting geometries were then compared to the reference DTMRI models from which they were derived to analyze how well the synthesized geometries approximate the reference anatomy. Averaged over seven hearts, 95% spatial overlap, less than 3% volume variability, and normal-to-surface distance of 0.32 mm was observed between the synthesized myocardial geometries reconstructed from 8 mm sliced images and the reference data. The results provide strong supportive evidence to validate the hypothesis that coarsely-sliced MRI may be used to accurately reconstruct geometric ventricular models. Furthermore, the use of DTMRI for validation of in vivo MRI presents a novel benchmark procedure for studies which aim to substantiate their modeling and simulation methods using coarsely-sliced cardiac data. In addition, the paper outlines a suggested original procedure for deriving image-based ventricular models using the CardioViz3D software. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Partition-based acquisition model for speed up navigated beta-probe surface imaging

NASA Astrophysics Data System (ADS)

Monge, Frédéric; Shakir, Dzhoshkun I.; Navab, Nassir; Jannin, Pierre

2016-03-01

Although gross total resection in low-grade glioma surgery leads to a better patient outcome, the in-vivo control of resection borders remains challenging. For this purpose, navigated beta-probe systems combined with 18F-based radiotracer, relying on activity distribution surface estimation, have been proposed to generate reconstructed images. The clinical relevancy has been outlined by early studies where intraoperative functional information is leveraged although inducing low spatial resolution in reconstruction. To improve reconstruction quality, multiple acquisition models have been proposed. They involve the definition of attenuation matrix for designing radiation detection physics. Yet, they require high computational power for efficient intraoperative use. To address the problem, we propose a new acquisition model called Partition Model (PM) considering an existing model where coefficients of the matrix are taken from a look-up table (LUT). Our model is based upon the division of the LUT into averaged homogeneous values for assigning attenuation coefficients. We validated our model using in vitro datasets, where tumors and peri-tumoral tissues have been simulated. We compared our acquisition model with the o_-the-shelf LUT and the raw method. Acquisition models outperformed the raw method in term of tumor contrast (7.97:1 mean T:B) but with a difficulty of real-time use. Both acquisition models reached the same detection performance with references (0.8 mean AUC and 0.77 mean NCC), where PM slightly improves the mean tumor contrast up to 10.1:1 vs 9.9:1 with the LUT model and more importantly, it reduces the mean computation time by 7.5%. Our model gives a faster solution for an intraoperative use of navigated beta-probe surface imaging system, with improved image quality.

Low Dose PET Image Reconstruction with Total Variation Using Alternating Direction Method.

PubMed

Yu, Xingjian; Wang, Chenye; Hu, Hongjie; Liu, Huafeng

2016-01-01

In this paper, a total variation (TV) minimization strategy is proposed to overcome the problem of sparse spatial resolution and large amounts of noise in low dose positron emission tomography (PET) imaging reconstruction. Two types of objective function were established based on two statistical models of measured PET data, least-square (LS) TV for the Gaussian distribution and Poisson-TV for the Poisson distribution. To efficiently obtain high quality reconstructed images, the alternating direction method (ADM) is used to solve these objective functions. As compared with the iterative shrinkage/thresholding (IST) based algorithms, the proposed ADM can make full use of the TV constraint and its convergence rate is faster. The performance of the proposed approach is validated through comparisons with the expectation-maximization (EM) method using synthetic and experimental biological data. In the comparisons, the results of both LS-TV and Poisson-TV are taken into consideration to find which models are more suitable for PET imaging, in particular low-dose PET. To evaluate the results quantitatively, we computed bias, variance, and the contrast recovery coefficient (CRC) and drew profiles of the reconstructed images produced by the different methods. The results show that both Poisson-TV and LS-TV can provide a high visual quality at a low dose level. The bias and variance of the proposed LS-TV and Poisson-TV methods are 20% to 74% less at all counting levels than those of the EM method. Poisson-TV gives the best performance in terms of high-accuracy reconstruction with the lowest bias and variance as compared to the ground truth (14.3% less bias and 21.9% less variance). In contrast, LS-TV gives the best performance in terms of the high contrast of the reconstruction with the highest CRC.

Low Dose PET Image Reconstruction with Total Variation Using Alternating Direction Method

PubMed Central

Yu, Xingjian; Wang, Chenye; Hu, Hongjie; Liu, Huafeng

2016-01-01

In this paper, a total variation (TV) minimization strategy is proposed to overcome the problem of sparse spatial resolution and large amounts of noise in low dose positron emission tomography (PET) imaging reconstruction. Two types of objective function were established based on two statistical models of measured PET data, least-square (LS) TV for the Gaussian distribution and Poisson-TV for the Poisson distribution. To efficiently obtain high quality reconstructed images, the alternating direction method (ADM) is used to solve these objective functions. As compared with the iterative shrinkage/thresholding (IST) based algorithms, the proposed ADM can make full use of the TV constraint and its convergence rate is faster. The performance of the proposed approach is validated through comparisons with the expectation-maximization (EM) method using synthetic and experimental biological data. In the comparisons, the results of both LS-TV and Poisson-TV are taken into consideration to find which models are more suitable for PET imaging, in particular low-dose PET. To evaluate the results quantitatively, we computed bias, variance, and the contrast recovery coefficient (CRC) and drew profiles of the reconstructed images produced by the different methods. The results show that both Poisson-TV and LS-TV can provide a high visual quality at a low dose level. The bias and variance of the proposed LS-TV and Poisson-TV methods are 20% to 74% less at all counting levels than those of the EM method. Poisson-TV gives the best performance in terms of high-accuracy reconstruction with the lowest bias and variance as compared to the ground truth (14.3% less bias and 21.9% less variance). In contrast, LS-TV gives the best performance in terms of the high contrast of the reconstruction with the highest CRC. PMID:28005929

Computed Tomography Image Quality Evaluation of a New Iterative Reconstruction Algorithm in the Abdomen (Adaptive Statistical Iterative Reconstruction-V) a Comparison With Model-Based Iterative Reconstruction, Adaptive Statistical Iterative Reconstruction, and Filtered Back Projection Reconstructions.

PubMed

Goodenberger, Martin H; Wagner-Bartak, Nicolaus A; Gupta, Shiva; Liu, Xinming; Yap, Ramon Q; Sun, Jia; Tamm, Eric P; Jensen, Corey T

The purpose of this study was to compare abdominopelvic computed tomography images reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) with model-based iterative reconstruction (Veo 3.0), ASIR, and filtered back projection (FBP). Abdominopelvic computed tomography scans for 36 patients (26 males and 10 females) were reconstructed using FBP, ASIR (80%), Veo 3.0, and ASIR-V (30%, 60%, 90%). Mean ± SD patient age was 32 ± 10 years with mean ± SD body mass index of 26.9 ± 4.4 kg/m. Images were reviewed by 2 independent readers in a blinded, randomized fashion. Hounsfield unit, noise, and contrast-to-noise ratio (CNR) values were calculated for each reconstruction algorithm for further comparison. Phantom evaluation of low-contrast detectability (LCD) and high-contrast resolution was performed. Adaptive statistical iterative reconstruction-V 30%, ASIR-V 60%, and ASIR 80% were generally superior qualitatively compared with ASIR-V 90%, Veo 3.0, and FBP (P < 0.05). Adaptive statistical iterative reconstruction-V 90% showed superior LCD and had the highest CNR in the liver, aorta, and, pancreas, measuring 7.32 ± 3.22, 11.60 ± 4.25, and 4.60 ± 2.31, respectively, compared with the next best series of ASIR-V 60% with respective CNR values of 5.54 ± 2.39, 8.78 ± 3.15, and 3.49 ± 1.77 (P <0.0001). Veo 3.0 and ASIR 80% had the best and worst spatial resolution, respectively. Adaptive statistical iterative reconstruction-V 30% and ASIR-V 60% provided the best combination of qualitative and quantitative performance. Adaptive statistical iterative reconstruction 80% was equivalent qualitatively, but demonstrated inferior spatial resolution and LCD.

2-D Fused Image Reconstruction approach for Microwave Tomography: a theoretical assessment using FDTD Model

PubMed Central

Bindu, G.; Semenov, S.

2013-01-01

This paper describes an efficient two-dimensional fused image reconstruction approach for Microwave Tomography (MWT). Finite Difference Time Domain (FDTD) models were created for a viable MWT experimental system having the transceivers modelled using thin wire approximation with resistive voltage sources. Born Iterative and Distorted Born Iterative methods have been employed for image reconstruction with the extremity imaging being done using a differential imaging technique. The forward solver in the imaging algorithm employs the FDTD method of solving the time domain Maxwell’s equations with the regularisation parameter computed using a stochastic approach. The algorithm is tested with 10% noise inclusion and successful image reconstruction has been shown implying its robustness. PMID:24058889

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms

NASA Astrophysics Data System (ADS)

Ott, Julien G.; Becce, Fabio; Monnin, Pascal; Schmidt, Sabine; Bochud, François O.; Verdun, Francis R.

2014-08-01

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Quantitative Susceptibility Mapping using Structural Feature based Collaborative Reconstruction (SFCR) in the Human Brain

PubMed Central

Cai, Congbo; Chen, Zhong; van Zijl, Peter C.M.

2017-01-01

The reconstruction of MR quantitative susceptibility mapping (QSM) from local phase measurements is an ill posed inverse problem and different regularization strategies incorporating a priori information extracted from magnitude and phase images have been proposed. However, the anatomy observed in magnitude and phase images does not always coincide spatially with that in susceptibility maps, which could give erroneous estimation in the reconstructed susceptibility map. In this paper, we develop a structural feature based collaborative reconstruction (SFCR) method for QSM including both magnitude and susceptibility based information. The SFCR algorithm is composed of two consecutive steps corresponding to complementary reconstruction models, each with a structural feature based l1 norm constraint and a voxel fidelity based l2 norm constraint, which allows both the structure edges and tiny features to be recovered, whereas the noise and artifacts could be reduced. In the M-step, the initial susceptibility map is reconstructed by employing a k-space based compressed sensing model incorporating magnitude prior. In the S-step, the susceptibility map is fitted in spatial domain using weighted constraints derived from the initial susceptibility map from the M-step. Simulations and in vivo human experiments at 7T MRI show that the SFCR method provides high quality susceptibility maps with improved RMSE and MSSIM. Finally, the susceptibility values of deep gray matter are analyzed in multiple head positions, with the supine position most approximate to the gold standard COSMOS result. PMID:27019480

Adaptive 3D Face Reconstruction from Unconstrained Photo Collections.

PubMed

Roth, Joseph; Tong, Yiying; Liu, Xiaoming

2016-12-07

Given a photo collection of "unconstrained" face images of one individual captured under a variety of unknown pose, expression, and illumination conditions, this paper presents a method for reconstructing a 3D face surface model of the individual along with albedo information. Unlike prior work on face reconstruction that requires large photo collections, we formulate an approach to adapt to photo collections with a high diversity in both the number of images and the image quality. To achieve this, we incorporate prior knowledge about face shape by fitting a 3D morphable model to form a personalized template, following by using a novel photometric stereo formulation to complete the fine details, under a coarse-to-fine scheme. Our scheme incorporates a structural similarity-based local selection step to help identify a common expression for reconstruction while discarding occluded portions of faces. The evaluation of reconstruction performance is through a novel quality measure, in the absence of ground truth 3D scans. Superior large-scale experimental results are reported on synthetic, Internet, and personal photo collections.

In vivo quantitative bioluminescence tomography using heterogeneous and homogeneous mouse models.

PubMed

Liu, Junting; Wang, Yabin; Qu, Xiaochao; Li, Xiangsi; Ma, Xiaopeng; Han, Runqiang; Hu, Zhenhua; Chen, Xueli; Sun, Dongdong; Zhang, Rongqing; Chen, Duofang; Chen, Dan; Chen, Xiaoyuan; Liang, Jimin; Cao, Feng; Tian, Jie

2010-06-07

Bioluminescence tomography (BLT) is a new optical molecular imaging modality, which can monitor both physiological and pathological processes by using bioluminescent light-emitting probes in small living animal. Especially, this technology possesses great potential in drug development, early detection, and therapy monitoring in preclinical settings. In the present study, we developed a dual modality BLT prototype system with Micro-computed tomography (MicroCT) registration approach, and improved the quantitative reconstruction algorithm based on adaptive hp finite element method (hp-FEM). Detailed comparisons of source reconstruction between the heterogeneous and homogeneous mouse models were performed. The models include mice with implanted luminescence source and tumor-bearing mice with firefly luciferase report gene. Our data suggest that the reconstruction based on heterogeneous mouse model is more accurate in localization and quantification than the homogeneous mouse model with appropriate optical parameters and that BLT allows super-early tumor detection in vivo based on tomographic reconstruction of heterogeneous mouse model signal.

A combined reconstruction-classification method for diffuse optical tomography.

PubMed

Hiltunen, P; Prince, S J D; Arridge, S

2009-11-07

We present a combined classification and reconstruction algorithm for diffuse optical tomography (DOT). DOT is a nonlinear ill-posed inverse problem. Therefore, some regularization is needed. We present a mixture of Gaussians prior, which regularizes the DOT reconstruction step. During each iteration, the parameters of a mixture model are estimated. These associate each reconstructed pixel with one of several classes based on the current estimate of the optical parameters. This classification is exploited to form a new prior distribution to regularize the reconstruction step and update the optical parameters. The algorithm can be described as an iteration between an optimization scheme with zeroth-order variable mean and variance Tikhonov regularization and an expectation-maximization scheme for estimation of the model parameters. We describe the algorithm in a general Bayesian framework. Results from simulated test cases and phantom measurements show that the algorithm enhances the contrast of the reconstructed images with good spatial accuracy. The probabilistic classifications of each image contain only a few misclassified pixels.

Evaluation of phase-diversity techniques for solar-image restoration

NASA Technical Reports Server (NTRS)

Paxman, Richard G.; Seldin, John H.; Lofdahl, Mats G.; Scharmer, Goran B.; Keller, Christoph U.

1995-01-01

Phase-diversity techniques provide a novel observational method for overcomming the effects of turbulence and instrument-induced aberrations in ground-based astronomy. Two implementations of phase-diversity techniques that differ with regard to noise model, estimator, optimization algorithm, method of regularization, and treatment of edge effects are described. Reconstructions of solar granulation derived by applying these two implementations to common data sets are shown to yield nearly identical images. For both implementations, reconstructions from phase-diverse speckle data (involving multiple realizations of turbulence) are shown to be superior to those derived from conventional phase-diversity data (involving a single realization). Phase-diverse speckle reconstructions are shown to achieve near diffraction-limited resolution and are validated by internal and external consistency tests, including a comparison with a reconstruction using a well-accepted speckle-imaging method.

Functional imaging of small tissue volumes with diffuse optical tomography

NASA Astrophysics Data System (ADS)

Klose, Alexander D.; Hielscher, Andreas H.

2006-03-01

Imaging of dynamic changes in blood parameters, functional brain imaging, and tumor imaging are the most advanced application areas of diffuse optical tomography (DOT). When dealing with the image reconstruction problem one is faced with the fact that near-infrared photons, unlike X-rays, are highly scattered when they traverse biological tissue. Image reconstruction schemes are required that model the light propagation inside biological tissue and predict measurements on the tissue surface. By iteratively changing the tissue-parameters until the predictions agree with the real measurements, a spatial distribution of optical properties inside the tissue is found. The optical properties can be related to the tissue oxygenation, inflammation, or to the fluorophore concentration of a biochemical marker. If the model of light propagation is inaccurate, the reconstruction process will lead to an inaccurate result as well. Here, we focus on difficulties that are encountered when DOT is employed for functional imaging of small tissue volumes, for example, in cancer studies involving small animals, or human finger joints for early diagnosis of rheumatoid arthritis. Most of the currently employed image reconstruction methods rely on the diffusion theory that is an approximation to the equation of radiative transfer. But, in the cases of small tissue volumes and tissues that contain low scattering regions diffusion theory has been shown to be of limited applicability Therefore, we employ a light propagation model that is based on the equation of radiative transfer, which promises to overcome the limitations.

Using compressive measurement to obtain images at ultra low-light-level

NASA Astrophysics Data System (ADS)

Ke, Jun; Wei, Ping

2013-08-01

In this paper, a compressive imaging architecture is used for ultra low-light-level imaging. In such a system, features, instead of object pixels, are imaged onto a photocathode, and then magnified by an image intensifier. By doing so, system measurement SNR is increased significantly. Therefore, the new system can image objects at ultra low-ligh-level, while a conventional system has difficulty. PCA projection is used to collect feature measurements in this work. Linear Wiener operator and nonlinear method based on FoE model are used to reconstruct objects. Root mean square error (RMSE) is used to quantify system reconstruction quality.

Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

PubMed Central

Wang, Kun; Su, Richard; Oraevsky, Alexander A; Anastasio, Mark A

2012-01-01

Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response, and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely, a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By use of quantitative measures of image quality, we demonstrate that the iterative reconstruction algorithms can mitigate image artifacts and preserve spatial resolution more effectively than FBP algorithms. These features suggest that the use of advanced image reconstruction algorithms can improve the effectiveness of 3D OAT while reducing the amount of data required for biomedical applications. PMID:22864062

Influence of speckle image reconstruction on photometric precision for large solar telescopes

NASA Astrophysics Data System (ADS)

Peck, C. L.; Wöger, F.; Marino, J.

2017-11-01

Context. High-resolution observations from large solar telescopes require adaptive optics (AO) systems to overcome image degradation caused by Earth's turbulent atmosphere. AO corrections are, however, only partial. Achieving near-diffraction limited resolution over a large field of view typically requires post-facto image reconstruction techniques to reconstruct the source image. Aims: This study aims to examine the expected photometric precision of amplitude reconstructed solar images calibrated using models for the on-axis speckle transfer functions and input parameters derived from AO control data. We perform a sensitivity analysis of the photometric precision under variations in the model input parameters for high-resolution solar images consistent with four-meter class solar telescopes. Methods: Using simulations of both atmospheric turbulence and partial compensation by an AO system, we computed the speckle transfer function under variations in the input parameters. We then convolved high-resolution numerical simulations of the solar photosphere with the simulated atmospheric transfer function, and subsequently deconvolved them with the model speckle transfer function to obtain a reconstructed image. To compute the resulting photometric precision, we compared the intensity of the original image with the reconstructed image. Results: The analysis demonstrates that high photometric precision can be obtained for speckle amplitude reconstruction using speckle transfer function models combined with AO-derived input parameters. Additionally, it shows that the reconstruction is most sensitive to the input parameter that characterizes the atmospheric distortion, and sub-2% photometric precision is readily obtained when it is well estimated.

Color filter array design based on a human visual model

NASA Astrophysics Data System (ADS)

Parmar, Manu; Reeves, Stanley J.

2004-05-01

To reduce cost and complexity associated with registering multiple color sensors, most consumer digital color cameras employ a single sensor. A mosaic of color filters is overlaid on a sensor array such that only one color channel is sampled per pixel location. The missing color values must be reconstructed from available data before the image is displayed. The quality of the reconstructed image depends fundamentally on the array pattern and the reconstruction technique. We present a design method for color filter array patterns that use red, green, and blue color channels in an RGB array. A model of the human visual response for luminance and opponent chrominance channels is used to characterize the perceptual error between a fully sampled and a reconstructed sparsely-sampled image. Demosaicking is accomplished using Wiener reconstruction. To ensure that the error criterion reflects perceptual effects, reconstruction is done in a perceptually uniform color space. A sequential backward selection algorithm is used to optimize the error criterion to obtain the sampling arrangement. Two different types of array patterns are designed: non-periodic and periodic arrays. The resulting array patterns outperform commonly used color filter arrays in terms of the error criterion.

Direct Estimation of Kinetic Parametric Images for Dynamic PET

PubMed Central

Wang, Guobao; Qi, Jinyi

2013-01-01

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed. PMID:24396500

Integrating prior information into microwave tomography Part 1: Impact of detail on image quality.

PubMed

Kurrant, Douglas; Baran, Anastasia; LoVetri, Joe; Fear, Elise

2017-12-01

The authors investigate the impact that incremental increases in the level of detail of patient-specific prior information have on image quality and the convergence behavior of an inversion algorithm in the context of near-field microwave breast imaging. A methodology is presented that uses image quality measures to characterize the ability of the algorithm to reconstruct both internal structures and lesions embedded in fibroglandular tissue. The approach permits key aspects that impact the quality of reconstruction of these structures to be identified and quantified. This provides insight into opportunities to improve image reconstruction performance. Patient-specific information is acquired using radar-based methods that form a regional map of the breast. This map is then incorporated into a microwave tomography algorithm. Previous investigations have demonstrated the effectiveness of this approach to improve image quality when applied to data generated with two-dimensional (2D) numerical models. The present study extends this work by generating prior information that is customized to vary the degree of structural detail to facilitate the investigation of the role of prior information in image formation. Numerical 2D breast models constructed from magnetic resonance (MR) scans, and reconstructions formed with a three-dimensional (3D) numerical breast model are used to assess if trends observed for the 2D results can be extended to 3D scenarios. For the blind reconstruction scenario (i.e., no prior information), the breast surface is not accurately identified and internal structures are not clearly resolved. A substantial improvement in image quality is achieved by incorporating the skin surface map and constraining the imaging domain to the breast. Internal features within the breast appear in the reconstructed image. However, it is challenging to discriminate between adipose and glandular regions and there are inaccuracies in both the structural properties of the glandular region and the dielectric properties reconstructed within this structure. Using a regional map with a skin layer only marginally improves this situation. Increasing the structural detail in the prior information to include internal features leads to reconstructions for which the interface that delineates the fat and gland regions can be inferred. Different features within the glandular region corresponding to tissues with varying relative permittivity values, such as a lesion embedded within glandular structure, emerge in the reconstructed images. Including knowledge of the breast surface and skin layer leads to a substantial improvement in image quality compared to the blind case, but the images have limited diagnostic utility for applications such as tumor response tracking. The diagnostic utility of the reconstruction technique is improved considerably when patient-specific structural information is used. This qualitative observation is supported quantitatively with image metrics. © 2017 American Association of Physicists in Medicine.

Cascaded systems analysis of noise and detectability in dual-energy cone-beam CT

PubMed Central

Gang, Grace J.; Zbijewski, Wojciech; Webster Stayman, J.; Siewerdsen, Jeffrey H.

2012-01-01

Purpose: Dual-energy computed tomography and dual-energy cone-beam computed tomography (DE-CBCT) are promising modalities for applications ranging from vascular to breast, renal, hepatic, and musculoskeletal imaging. Accordingly, the optimization of imaging techniques for such applications would benefit significantly from a general theoretical description of image quality that properly incorporates factors of acquisition, reconstruction, and tissue decomposition in DE tomography. This work reports a cascaded systems analysis model that includes the Poisson statistics of x rays (quantum noise), detector model (flat-panel detectors), anatomical background, image reconstruction (filtered backprojection), DE decomposition (weighted subtraction), and simple observer models to yield a task-based framework for DE technique optimization. Methods: The theoretical framework extends previous modeling of DE projection radiography and CBCT. Signal and noise transfer characteristics are propagated through physical and mathematical stages of image formation and reconstruction. Dual-energy decomposition was modeled according to weighted subtraction of low- and high-energy images to yield the 3D DE noise-power spectrum (NPS) and noise-equivalent quanta (NEQ), which, in combination with observer models and the imaging task, yields the dual-energy detectability index (d′). Model calculations were validated with NPS and NEQ measurements from an experimental imaging bench simulating the geometry of a dedicated musculoskeletal extremities scanner. Imaging techniques, including kVp pair and dose allocation, were optimized using d′ as an objective function for three example imaging tasks: (1) kidney stone discrimination; (2) iodine vs bone in a uniform, soft-tissue background; and (3) soft tissue tumor detection on power-law anatomical background. Results: Theoretical calculations of DE NPS and NEQ demonstrated good agreement with experimental measurements over a broad range of imaging conditions. Optimization results suggest a lower fraction of total dose imparted by the low-energy acquisition, a finding consistent with previous literature. The selection of optimal kVp pair reveals the combined effect of both quantum noise and contrast in the kidney stone discrimination and soft-tissue tumor detection tasks, whereas the K-edge effect of iodine was the dominant factor in determining kVp pairs in the iodine vs bone task. The soft-tissue tumor task illustrated the benefit of dual-energy imaging in eliminating anatomical background noise and improving detectability beyond that achievable by single-energy scans. Conclusions: This work established a task-based theoretical framework that is predictive of DE image quality. The model can be utilized in optimizing a broad range of parameters in image acquisition, reconstruction, and decomposition, providing a useful tool for maximizing DE-CBCT image quality and reducing dose. PMID:22894440

3D Reconstruction of Static Human Body with a Digital Camera

NASA Astrophysics Data System (ADS)

Remondino, Fabio

2003-01-01

Nowadays the interest in 3D reconstruction and modeling of real humans is one of the most challenging problems and a topic of great interest. The human models are used for movies, video games or ergonomics applications and they are usually created with 3D scanner devices. In this paper a new method to reconstruct the shape of a static human is presented. Our approach is based on photogrammetric techniques and uses a sequence of images acquired around a standing person with a digital still video camera or with a camcorder. First the images are calibrated and orientated using a bundle adjustment. After the establishment of a stable adjusted image block, an image matching process is performed between consecutive triplets of images. Finally the 3D coordinates of the matched points are computed with a mean accuracy of ca 2 mm by forward ray intersection. The obtained point cloud can then be triangulated to generate a surface model of the body or a virtual human model can be fitted to the recovered 3D data. Results of the 3D human point cloud with pixel color information are presented.

Fine-resolution imaging of solar features using Phase-Diverse Speckle

NASA Technical Reports Server (NTRS)

Paxman, Richard G.

1995-01-01

Phase-diverse speckle (PDS) is a novel imaging technique intended to overcome the degrading effects of atmospheric turbulence on fine-resolution imaging. As its name suggests, PDS is a blend of phase-diversity and speckle-imaging concepts. PDS reconstructions on solar data were validated by simulation, by demonstrating internal consistency of PDS estimates, and by comparing PDS reconstructions with those produced from well accepted speckle-imaging processing. Several sources of error in data collected with the Swedish Vacuum Solar Telescope (SVST) were simulated: CCD noise, quantization error, image misalignment, and defocus error, as well as atmospheric turbulence model error. The simulations demonstrate that fine-resolution information can be reliably recovered out to at least 70% of the diffraction limit without significant introduction of image artifacts. Additional confidence in the SVST restoration is obtained by comparing its spatial power spectrum with previously-published power spectra derived from both space-based images and earth-based images corrected with traditional speckle-imaging techniques; the shape of the spectrum is found to match well the previous measurements. In addition, the imagery is found to be consistent with, but slightly sharper than, imagery reconstructed with accepted speckle-imaging techniques.

Respiratory motion correction in emission tomography image reconstruction.

PubMed

Reyes, Mauricio; Malandain, Grégoire; Koulibaly, Pierre Malick; González Ballester, Miguel A; Darcourt, Jacques

2005-01-01

In Emission Tomography imaging, respiratory motion causes artifacts in lungs and cardiac reconstructed images, which lead to misinterpretations and imprecise diagnosis. Solutions like respiratory gating, correlated dynamic PET techniques, list-mode data based techniques and others have been tested with improvements over the spatial activity distribution in lungs lesions, but with the disadvantages of requiring additional instrumentation or discarding part of the projection data used for reconstruction. The objective of this study is to incorporate respiratory motion correction directly into the image reconstruction process, without any additional acquisition protocol consideration. To this end, we propose an extension to the Maximum Likelihood Expectation Maximization (MLEM) algorithm that includes a respiratory motion model, which takes into account the displacements and volume deformations produced by the respiratory motion during the data acquisition process. We present results from synthetic simulations incorporating real respiratory motion as well as from phantom and patient data.

Cone-beam CT of traumatic brain injury using statistical reconstruction with a post-artifact-correction noise model

NASA Astrophysics Data System (ADS)

Dang, H.; Stayman, J. W.; Sisniega, A.; Xu, J.; Zbijewski, W.; Yorkston, J.; Aygun, N.; Koliatsos, V.; Siewerdsen, J. H.

2015-03-01

Traumatic brain injury (TBI) is a major cause of death and disability. The current front-line imaging modality for TBI detection is CT, which reliably detects intracranial hemorrhage (fresh blood contrast 30-50 HU, size down to 1 mm) in non-contrast-enhanced exams. Compared to CT, flat-panel detector (FPD) cone-beam CT (CBCT) systems offer lower cost, greater portability, and smaller footprint suitable for point-of-care deployment. We are developing FPD-CBCT to facilitate TBI detection at the point-of-care such as in emergent, ambulance, sports, and military applications. However, current FPD-CBCT systems generally face challenges in low-contrast, soft-tissue imaging. Model-based reconstruction can improve image quality in soft-tissue imaging compared to conventional filtered back-projection (FBP) by leveraging high-fidelity forward model and sophisticated regularization. In FPD-CBCT TBI imaging, measurement noise characteristics undergo substantial change following artifact correction, resulting in non-negligible noise amplification. In this work, we extend the penalized weighted least-squares (PWLS) image reconstruction to include the two dominant artifact corrections (scatter and beam hardening) in FPD-CBCT TBI imaging by correctly modeling the variance change following each correction. Experiments were performed on a CBCT test-bench using an anthropomorphic phantom emulating intra-parenchymal hemorrhage in acute TBI, and the proposed method demonstrated an improvement in blood-brain contrast-to-noise ratio (CNR = 14.2) compared to FBP (CNR = 9.6) and PWLS using conventional weights (CNR = 11.6) at fixed spatial resolution (1 mm edge-spread width at the target contrast). The results support the hypothesis that FPD-CBCT can fulfill the image quality requirements for reliable TBI detection, using high-fidelity artifact correction and statistical reconstruction with accurate post-artifact-correction noise models.

Optical tomographic imaging for breast cancer detection

NASA Astrophysics Data System (ADS)

Cong, Wenxiang; Intes, Xavier; Wang, Ge

2017-09-01

Diffuse optical breast imaging utilizes near-infrared (NIR) light propagation through tissues to assess the optical properties of tissues for the identification of abnormal tissue. This optical imaging approach is sensitive, cost-effective, and does not involve any ionizing radiation. However, the image reconstruction of diffuse optical tomography (DOT) is a nonlinear inverse problem and suffers from severe illposedness due to data noise, NIR light scattering, and measurement incompleteness. An image reconstruction method is proposed for the detection of breast cancer. This method splits the image reconstruction problem into the localization of abnormal tissues and quantification of absorption variations. The localization of abnormal tissues is performed based on a well-posed optimization model, which can be solved via a differential evolution optimization method to achieve a stable reconstruction. The quantification of abnormal absorption is then determined in localized regions of relatively small extents, in which a potential tumor might be. Consequently, the number of unknown absorption variables can be greatly reduced to overcome the underdetermined nature of DOT. Numerical simulation experiments are performed to verify merits of the proposed method, and the results show that the image reconstruction method is stable and accurate for the identification of abnormal tissues, and robust against the measurement noise of data.

D Reconstruction with a Collaborative Approach Based on Smartphones and a Cloud-Based Server

NASA Astrophysics Data System (ADS)

Nocerino, E.; Poiesi, F.; Locher, A.; Tefera, Y. T.; Remondino, F.; Chippendale, P.; Van Gool, L.

2017-11-01

The paper presents a collaborative image-based 3D reconstruction pipeline to perform image acquisition with a smartphone and geometric 3D reconstruction on a server during concurrent or disjoint acquisition sessions. Images are selected from the video feed of the smartphone's camera based on their quality and novelty. The smartphone's app provides on-the-fly reconstruction feedback to users co-involved in the acquisitions. The server is composed of an incremental SfM algorithm that processes the received images by seamlessly merging them into a single sparse point cloud using bundle adjustment. Dense image matching algorithm can be lunched to derive denser point clouds. The reconstruction details, experiments and performance evaluation are presented and discussed.

Three-dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation.

PubMed

Naumovich, S S; Naumovich, S A; Goncharenko, V G

2015-01-01

The objective of the present study was the development and clinical testing of a three-dimensional (3D) reconstruction method of teeth and a bone tissue of the jaw on the basis of CT images of the maxillofacial region. 3D reconstruction was performed using the specially designed original software based on watershed transformation. Computed tomograms in digital imaging and communications in medicine format obtained on multispiral CT and CBCT scanners were used for creation of 3D models of teeth and the jaws. The processing algorithm is realized in the stepwise threshold image segmentation with the placement of markers in the mode of a multiplanar projection in areas relating to the teeth and a bone tissue. The developed software initially creates coarse 3D models of the entire dentition and the jaw. Then, certain procedures specify the model of the jaw and cut the dentition into separate teeth. The proper selection of the segmentation threshold is very important for CBCT images having a low contrast and high noise level. The developed semi-automatic algorithm of multispiral and cone beam computed tomogram processing allows 3D models of teeth to be created separating them from a bone tissue of the jaws. The software is easy to install in a dentist's workplace, has an intuitive interface and takes little time in processing. The obtained 3D models can be used for solving a wide range of scientific and clinical tasks.

A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection datasets obtained with a synchrotron X-ray source

NASA Astrophysics Data System (ADS)

Melli, S. Ali; Wahid, Khan A.; Babyn, Paul; Cooper, David M. L.; Gopi, Varun P.

2016-12-01

Synchrotron X-ray Micro Computed Tomography (Micro-CT) is an imaging technique which is increasingly used for non-invasive in vivo preclinical imaging. However, it often requires a large number of projections from many different angles to reconstruct high-quality images leading to significantly high radiation doses and long scan times. To utilize this imaging technique further for in vivo imaging, we need to design reconstruction algorithms that reduce the radiation dose and scan time without reduction of reconstructed image quality. This research is focused on using a combination of gradient-based Douglas-Rachford splitting and discrete wavelet packet shrinkage image denoising methods to design an algorithm for reconstruction of large-scale reduced-view synchrotron Micro-CT images with acceptable quality metrics. These quality metrics are computed by comparing the reconstructed images with a high-dose reference image reconstructed from 1800 equally spaced projections spanning 180°. Visual and quantitative-based performance assessment of a synthetic head phantom and a femoral cortical bone sample imaged in the biomedical imaging and therapy bending magnet beamline at the Canadian Light Source demonstrates that the proposed algorithm is superior to the existing reconstruction algorithms. Using the proposed reconstruction algorithm to reduce the number of projections in synchrotron Micro-CT is an effective way to reduce the overall radiation dose and scan time which improves in vivo imaging protocols.

WE-FG-207B-02: Material Reconstruction for Spectral Computed Tomography with Detector Response Function

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, J; Gao, H

2016-06-15

Purpose: Different from the conventional computed tomography (CT), spectral CT based on energy-resolved photon-counting detectors is able to provide the unprecedented material composition. However, an important missing piece for accurate spectral CT is to incorporate the detector response function (DRF), which is distorted by factors such as pulse pileup and charge-sharing. In this work, we propose material reconstruction methods for spectral CT with DRF. Methods: The polyenergetic X-ray forward model takes the DRF into account for accurate material reconstruction. Two image reconstruction methods are proposed: a direct method based on the nonlinear data fidelity from DRF-based forward model; a linear-data-fidelitymore » based method that relies on the spectral rebinning so that the corresponding DRF matrix is invertible. Then the image reconstruction problem is regularized with the isotropic TV term and solved by alternating direction method of multipliers. Results: The simulation results suggest that the proposed methods provided more accurate material compositions than the standard method without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity. Conclusion: We have proposed material reconstruction methods for spectral CT with DRF, whichprovided more accurate material compositions than the standard methods without DRF. Moreover, the proposed method with linear data fidelity had improved reconstruction quality from the proposed method with nonlinear data fidelity. Jiulong Liu and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).« less

Volume-of-interest reconstruction from severely truncated data in dental cone-beam CT

NASA Astrophysics Data System (ADS)

Zhang, Zheng; Kusnoto, Budi; Han, Xiao; Sidky, E. Y.; Pan, Xiaochuan

2015-03-01

As cone-beam computed tomography (CBCT) has gained popularity rapidly in dental imaging applications in the past two decades, radiation dose in CBCT imaging remains a potential, health concern to the patients. It is a common practice in dental CBCT imaging that only a small volume of interest (VOI) containing the teeth of interest is illuminated, thus substantially lowering imaging radiation dose. However, this would yield data with severe truncations along both transverse and longitudinal directions. Although images within the VOI reconstructed from truncated data can be of some practical utility, they often are compromised significantly by truncation artifacts. In this work, we investigate optimization-based reconstruction algorithms for VOI image reconstruction from CBCT data of dental patients containing severe truncations. In an attempt to further reduce imaging dose, we also investigate optimization-based image reconstruction from severely truncated data collected at projection views substantially fewer than those used in clinical dental applications. Results of our study show that appropriately designed optimization-based reconstruction can yield VOI images with reduced truncation artifacts, and that, when reconstructing from only one half, or even one quarter, of clinical data, it can also produce VOI images comparable to that of clinical images.

Mirror-Imaged Rapid Prototype Skull Model and Pre-Molded Synthetic Scaffold to Achieve Optimal Orbital Cavity Reconstruction.

PubMed

Park, Sung Woo; Choi, Jong Woo; Koh, Kyung S; Oh, Tae Suk

2015-08-01

Reconstruction of traumatic orbital wall defects has evolved to restore the original complex anatomy with the rapidly growing use of computer-aided design and prototyping. This study evaluated a mirror-imaged rapid prototype skull model and a pre-molded synthetic scaffold for traumatic orbital wall reconstruction. A single-center retrospective review was performed of patients who underwent orbital wall reconstruction after trauma from 2012 to 2014. Patients were included by admission through the emergency department after facial trauma or by a tertiary referral for post-traumatic orbital deformity. Three-dimensional (3D) computed tomogram-based mirror-imaged reconstruction images of the orbit and an individually manufactured rapid prototype skull model by a 3D printing technique were obtained for each case. Synthetic scaffolds were anatomically pre-molded using the skull model as guide and inserted at the individual orbital defect. Postoperative complications were assessed and 3D volumetric measurements of the orbital cavity were performed. Paired samples t test was used for statistical analysis. One hundred four patients with immediate orbital defect reconstructions and 23 post-traumatic orbital deformity reconstructions were included in this study. All reconstructions were successful without immediate postoperative complications, although there were 10 cases with mild enophthalmos and 2 cases with persistent diplopia. Reoperations were performed for 2 cases of persistent diplopia and secondary touchup procedures were performed to contour soft tissue in 4 cases. Postoperative volumetric measurement of the orbital cavity showed nonsignificant volume differences between the damaged orbit and the reconstructed orbit (21.35 ± 1.93 vs 20.93 ± 2.07 cm(2); P = .98). This protocol was extended to severe cases in which more than 40% of the orbital frame was lost and combined with extensive soft tissue defects. Traumatic orbital reconstruction can be optimized and successful using an individually manufactured rapid prototype skull model and a pre-molded synthetic scaffold by computer-aid design and manufacturing. Copyright © 2015 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

A promising limited angular computed tomography reconstruction via segmentation based regional enhancement and total variation minimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Wenkun; Zhang, Hanming; Li, Lei

2016-08-15

X-ray computed tomography (CT) is a powerful and common inspection technique used for the industrial non-destructive testing. However, large-sized and heavily absorbing objects cause the formation of artifacts because of either the lack of specimen penetration in specific directions or the acquisition of data from only a limited angular range of views. Although the sparse optimization-based methods, such as the total variation (TV) minimization method, can suppress artifacts to some extent, reconstructing the images such that they converge to accurate values remains difficult because of the deficiency in continuous angular data and inconsistency in the projections. To address this problem,more » we use the idea of regional enhancement of the true values and suppression of the illusory artifacts outside the region to develop an efficient iterative algorithm. This algorithm is based on the combination of regional enhancement of the true values and TV minimization for the limited angular reconstruction. In this algorithm, the segmentation approach is introduced to distinguish the regions of different image knowledge and generate the support mask of the image. A new regularization term, which contains the support knowledge to enhance the true values of the image, is incorporated into the objective function. Then, the proposed optimization model is solved by variable splitting and the alternating direction method efficiently. A compensation approach is also designed to extract useful information from the initial projections and thus reduce false segmentation result and correct the segmentation support and the segmented image. The results obtained from comparing both simulation studies and real CT data set reconstructions indicate that the proposed algorithm generates a more accurate image than do the other reconstruction methods. The experimental results show that this algorithm can produce high-quality reconstructed images for the limited angular reconstruction and suppress the illusory artifacts caused by the deficiency in valid data.« less

A promising limited angular computed tomography reconstruction via segmentation based regional enhancement and total variation minimization

NASA Astrophysics Data System (ADS)

Zhang, Wenkun; Zhang, Hanming; Li, Lei; Wang, Linyuan; Cai, Ailong; Li, Zhongguo; Yan, Bin

2016-08-01

X-ray computed tomography (CT) is a powerful and common inspection technique used for the industrial non-destructive testing. However, large-sized and heavily absorbing objects cause the formation of artifacts because of either the lack of specimen penetration in specific directions or the acquisition of data from only a limited angular range of views. Although the sparse optimization-based methods, such as the total variation (TV) minimization method, can suppress artifacts to some extent, reconstructing the images such that they converge to accurate values remains difficult because of the deficiency in continuous angular data and inconsistency in the projections. To address this problem, we use the idea of regional enhancement of the true values and suppression of the illusory artifacts outside the region to develop an efficient iterative algorithm. This algorithm is based on the combination of regional enhancement of the true values and TV minimization for the limited angular reconstruction. In this algorithm, the segmentation approach is introduced to distinguish the regions of different image knowledge and generate the support mask of the image. A new regularization term, which contains the support knowledge to enhance the true values of the image, is incorporated into the objective function. Then, the proposed optimization model is solved by variable splitting and the alternating direction method efficiently. A compensation approach is also designed to extract useful information from the initial projections and thus reduce false segmentation result and correct the segmentation support and the segmented image. The results obtained from comparing both simulation studies and real CT data set reconstructions indicate that the proposed algorithm generates a more accurate image than do the other reconstruction methods. The experimental results show that this algorithm can produce high-quality reconstructed images for the limited angular reconstruction and suppress the illusory artifacts caused by the deficiency in valid data.

Towards Complete, Geo-Referenced 3d Models from Crowd-Sourced Amateur Images

NASA Astrophysics Data System (ADS)

Hartmann, W.; Havlena, M.; Schindler, K.

2016-06-01

Despite a lot of recent research, photogrammetric reconstruction from crowd-sourced imagery is plagued by a number of recurrent problems. (i) The resulting models are chronically incomplete, because even touristic landmarks are photographed mostly from a few "canonical" viewpoints. (ii) Man-made constructions tend to exhibit repetitive structure and rotational symmetries, which lead to gross errors in the 3D reconstruction and aggravate the problem of incomplete reconstruction. (iii) The models are normally not geo-referenced. In this paper, we investigate the possibility of using sparse GNSS geo-tags from digital cameras to address these issues and push the boundaries of crowd-sourced photogrammetry. A small proportion of the images in Internet collections (≍ 10 %) do possess geo-tags. While the individual geo-tags are very inaccurate, they nevertheless can help to address the problems above. By providing approximate geo-reference for partial reconstructions they make it possible to fuse those pieces into more complete models; the capability to fuse partial reconstruction opens up the possibility to be more restrictive in the matching phase and avoid errors due to repetitive structure; and collectively, the redundant set of low-quality geo-tags can provide reasonably accurate absolute geo-reference. We show that even few, noisy geo-tags can help to improve architectural models, compared to puristic structure-from-motion only based on image correspondence.

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

PubMed

Chen, Shuo; Luo, Chenggao; Wang, Hongqiang; Deng, Bin; Cheng, Yongqiang; Zhuang, Zhaowen

2018-04-26

As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing the 3D imaging area creates a heavy computational burden, thus leading to unsatisfactory efficiency. Secondly, it is difficult to resolve the target under low signal-to-noise ratio (SNR). In this paper, we propose a 3D imaging method based on matched filtering (MF) and convolutional neural network (CNN), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. In terms of the frequency-hopping (FH) signal, the original echo is processed with MF. By extracting the processed echo in different spike pulses separately, targets in different imaging planes are reconstructed simultaneously to decompose the global computational complexity, and then are synthesized together to reconstruct the 3D target. Based on the conventional TCAI model, we deduce and build a new TCAI model based on MF. Furthermore, the convolutional neural network (CNN) is designed to teach the MF-TCAI how to reconstruct the low SNR target better. The experimental results demonstrate that the MF-TCAI achieves impressive performance on imaging ability and efficiency under low SNR. Moreover, the MF-TCAI has learned to better resolve the low-SNR 3D target with the help of CNN. In summary, the proposed 3D TCAI can achieve: (1) low-SNR high-resolution imaging by using MF; (2) efficient 3D imaging by downsizing the large-scale reference-signal matrix; and (3) intelligent imaging with CNN. Therefore, the TCAI based on MF and CNN has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.

Penalized maximum likelihood reconstruction for x-ray differential phase-contrast tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brendel, Bernhard, E-mail: bernhard.brendel@philips.com; Teuffenbach, Maximilian von; Noël, Peter B.

2016-01-15

Purpose: The purpose of this work is to propose a cost function with regularization to iteratively reconstruct attenuation, phase, and scatter images simultaneously from differential phase contrast (DPC) acquisitions, without the need of phase retrieval, and examine its properties. Furthermore this reconstruction method is applied to an acquisition pattern that is suitable for a DPC tomographic system with continuously rotating gantry (sliding window acquisition), overcoming the severe smearing in noniterative reconstruction. Methods: We derive a penalized maximum likelihood reconstruction algorithm to directly reconstruct attenuation, phase, and scatter image from the measured detector values of a DPC acquisition. The proposed penaltymore » comprises, for each of the three images, an independent smoothing prior. Image quality of the proposed reconstruction is compared to images generated with FBP and iterative reconstruction after phase retrieval. Furthermore, the influence between the priors is analyzed. Finally, the proposed reconstruction algorithm is applied to experimental sliding window data acquired at a synchrotron and results are compared to reconstructions based on phase retrieval. Results: The results show that the proposed algorithm significantly increases image quality in comparison to reconstructions based on phase retrieval. No significant mutual influence between the proposed independent priors could be observed. Further it could be illustrated that the iterative reconstruction of a sliding window acquisition results in images with substantially reduced smearing artifacts. Conclusions: Although the proposed cost function is inherently nonconvex, it can be used to reconstruct images with less aliasing artifacts and less streak artifacts than reconstruction methods based on phase retrieval. Furthermore, the proposed method can be used to reconstruct images of sliding window acquisitions with negligible smearing artifacts.« less

A 3D Freehand Ultrasound System for Multi-view Reconstructions from Sparse 2D Scanning Planes

PubMed Central

2011-01-01

Background A significant limitation of existing 3D ultrasound systems comes from the fact that the majority of them work with fixed acquisition geometries. As a result, the users have very limited control over the geometry of the 2D scanning planes. Methods We present a low-cost and flexible ultrasound imaging system that integrates several image processing components to allow for 3D reconstructions from limited numbers of 2D image planes and multiple acoustic views. Our approach is based on a 3D freehand ultrasound system that allows users to control the 2D acquisition imaging using conventional 2D probes. For reliable performance, we develop new methods for image segmentation and robust multi-view registration. We first present a new hybrid geometric level-set approach that provides reliable segmentation performance with relatively simple initializations and minimum edge leakage. Optimization of the segmentation model parameters and its effect on performance is carefully discussed. Second, using the segmented images, a new coarse to fine automatic multi-view registration method is introduced. The approach uses a 3D Hotelling transform to initialize an optimization search. Then, the fine scale feature-based registration is performed using a robust, non-linear least squares algorithm. The robustness of the multi-view registration system allows for accurate 3D reconstructions from sparse 2D image planes. Results Volume measurements from multi-view 3D reconstructions are found to be consistently and significantly more accurate than measurements from single view reconstructions. The volume error of multi-view reconstruction is measured to be less than 5% of the true volume. We show that volume reconstruction accuracy is a function of the total number of 2D image planes and the number of views for calibrated phantom. In clinical in-vivo cardiac experiments, we show that volume estimates of the left ventricle from multi-view reconstructions are found to be in better agreement with clinical measures than measures from single view reconstructions. Conclusions Multi-view 3D reconstruction from sparse 2D freehand B-mode images leads to more accurate volume quantification compared to single view systems. The flexibility and low-cost of the proposed system allow for fine control of the image acquisition planes for optimal 3D reconstructions from multiple views. PMID:21251284

A 3D freehand ultrasound system for multi-view reconstructions from sparse 2D scanning planes.

PubMed

Yu, Honggang; Pattichis, Marios S; Agurto, Carla; Beth Goens, M

2011-01-20

A significant limitation of existing 3D ultrasound systems comes from the fact that the majority of them work with fixed acquisition geometries. As a result, the users have very limited control over the geometry of the 2D scanning planes. We present a low-cost and flexible ultrasound imaging system that integrates several image processing components to allow for 3D reconstructions from limited numbers of 2D image planes and multiple acoustic views. Our approach is based on a 3D freehand ultrasound system that allows users to control the 2D acquisition imaging using conventional 2D probes.For reliable performance, we develop new methods for image segmentation and robust multi-view registration. We first present a new hybrid geometric level-set approach that provides reliable segmentation performance with relatively simple initializations and minimum edge leakage. Optimization of the segmentation model parameters and its effect on performance is carefully discussed. Second, using the segmented images, a new coarse to fine automatic multi-view registration method is introduced. The approach uses a 3D Hotelling transform to initialize an optimization search. Then, the fine scale feature-based registration is performed using a robust, non-linear least squares algorithm. The robustness of the multi-view registration system allows for accurate 3D reconstructions from sparse 2D image planes. Volume measurements from multi-view 3D reconstructions are found to be consistently and significantly more accurate than measurements from single view reconstructions. The volume error of multi-view reconstruction is measured to be less than 5% of the true volume. We show that volume reconstruction accuracy is a function of the total number of 2D image planes and the number of views for calibrated phantom. In clinical in-vivo cardiac experiments, we show that volume estimates of the left ventricle from multi-view reconstructions are found to be in better agreement with clinical measures than measures from single view reconstructions. Multi-view 3D reconstruction from sparse 2D freehand B-mode images leads to more accurate volume quantification compared to single view systems. The flexibility and low-cost of the proposed system allow for fine control of the image acquisition planes for optimal 3D reconstructions from multiple views.

Super-Resolution Image Reconstruction Applied to Medical Ultrasound

NASA Astrophysics Data System (ADS)

Ellis, Michael

Ultrasound is the preferred imaging modality for many diagnostic applications due to its real-time image reconstruction and low cost. Nonetheless, conventional ultrasound is not used in many applications because of limited spatial resolution and soft tissue contrast. Most commercial ultrasound systems reconstruct images using a simple delay-and-sum architecture on receive, which is fast and robust but does not utilize all information available in the raw data. Recently, more sophisticated image reconstruction methods have been developed that make use of far more information in the raw data to improve resolution and contrast. One such method is the Time-Domain Optimized Near-Field Estimator (TONE), which employs a maximum a priori estimation to solve a highly underdetermined problem, given a well-defined system model. TONE has been shown to significantly improve both the contrast and resolution of ultrasound images when compared to conventional methods. However, TONE's lack of robustness to variations from the system model and extremely high computational cost hinder it from being readily adopted in clinical scanners. This dissertation aims to reduce the impact of TONE's shortcomings, transforming it from an academic construct to a clinically viable image reconstruction algorithm. By altering the system model from a collection of individual hypothetical scatterers to a collection of weighted, diffuse regions, dTONE is able to achieve much greater robustness to modeling errors. A method for efficient parallelization of dTONE is presented that reduces reconstruction time by more than an order of magnitude with little loss in image fidelity. An alternative reconstruction algorithm, called qTONE, is also developed and is able to reduce reconstruction times by another two orders of magnitude while simultaneously improving image contrast. Each of these methods for improving TONE are presented, their limitations are explored, and all are used in concert to reconstruct in vivo images of a human testicle. In all instances, the methods presented here outperform conventional image reconstruction methods by a significant margin. As TONE and its variants are general image reconstruction techniques, the theories and research presented here have the potential to significantly improve not only ultrasound's clinical utility, but that of other imaging modalities as well.

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Shiyu, E-mail: shiyu.xu@gmail.com; Chen, Ying, E-mail: adachen@siu.edu; Lu, Jianping

2015-09-15

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair basedmore » prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.« less

SU-E-J-76: CBCT Reconstruction of a Full Couch Using Rigid Registration and Pre-Scanned Couch Image and Its Clinical Application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, E; Lasio, G; Lee, M

Purpose: Only a part of a treatment couch is reconstructed in CBCT due to the limited field of view (FOV). This often generates inaccurate results in the delivered dose evaluation with CBCT and more noise in the CBCT reconstruction. Full reconstruction of the couch at treatment setup can be used for more accurate exit beam dosimetry. The goal of this study is to develop a method to reconstruct a full treatment couch using a pre-scanned couch image and rigid registration. Methods: A full couch (Exact Couch, Varian) model image was reconstructed by rigidly registering and combining two sets of partialmore » CBCT images. The full couch model includes three parts: two side rails and a couch top. A patient CBCT was reconstructed with reconstruction grid size larger than the physical field of view to include the full couch. The image quality of the couch is not good due to data truncation, but good enough to allow rigid registration of the couch. A composite CBCT image of the patient plus couch has been generated from the original reconstruction by replacing couch portion with the pre-acquired model couch, rigidly registered to the original scan. We evaluated the clinical usefulness of this method by comparing treatment plans generated on the original and on the modified scans. Results: The full couch model could be attached to a patient CBCT image set via rigid image registration. Plan DVHs showed 1∼2% difference between plans with and without full couch modeling. Conclusion: The proposed method generated a full treatment couch CBCT model, which can be successfully registered to the original patient image. This method was also shown to be useful in generating more accurate dose distributions, by lowering 1∼2% dose in PTV and a few other critical organs. Part of this study is supported by NIH R01CA133539.« less

Dynamic SPECT reconstruction from few projections: a sparsity enforced matrix factorization approach

NASA Astrophysics Data System (ADS)

Ding, Qiaoqiao; Zan, Yunlong; Huang, Qiu; Zhang, Xiaoqun

2015-02-01

The reconstruction of dynamic images from few projection data is a challenging problem, especially when noise is present and when the dynamic images are vary fast. In this paper, we propose a variational model, sparsity enforced matrix factorization (SEMF), based on low rank matrix factorization of unknown images and enforced sparsity constraints for representing both coefficients and bases. The proposed model is solved via an alternating iterative scheme for which each subproblem is convex and involves the efficient alternating direction method of multipliers (ADMM). The convergence of the overall alternating scheme for the nonconvex problem relies upon the Kurdyka-Łojasiewicz property, recently studied by Attouch et al (2010 Math. Oper. Res. 35 438) and Attouch et al (2013 Math. Program. 137 91). Finally our proof-of-concept simulation on 2D dynamic images shows the advantage of the proposed method compared to conventional methods.

Surgical planning and manual image fusion based on 3D model facilitate laparoscopic partial nephrectomy for intrarenal tumors.

PubMed

Chen, Yuanbo; Li, Hulin; Wu, Dingtao; Bi, Keming; Liu, Chunxiao

2014-12-01

Construction of three-dimensional (3D) model of renal tumor facilitated surgical planning and imaging guidance of manual image fusion in laparoscopic partial nephrectomy (LPN) for intrarenal tumors. Fifteen patients with intrarenal tumors underwent LPN between January and December 2012. Computed tomography-based reconstruction of the 3D models of renal tumors was performed using Mimics 12.1 software. Surgical planning was performed through morphometry and multi-angle visual views of the tumor model. Two-step manual image fusion superimposed 3D model images onto 2D laparoscopic images. The image fusion was verified by intraoperative ultrasound. Imaging-guided laparoscopic hilar clamping and tumor excision was performed. Manual fusion time, patient demographics, surgical details, and postoperative treatment parameters were analyzed. The reconstructed 3D tumor models accurately represented the patient's physiological anatomical landmarks. The surgical planning markers were marked successfully. Manual image fusion was flexible and feasible with fusion time of 6 min (5-7 min). All surgeries were completed laparoscopically. The median tumor excision time was 5.4 min (3.5-10 min), whereas the median warm ischemia time was 25.5 min (16-32 min). Twelve patients (80 %) demonstrated renal cell carcinoma on final pathology, and all surgical margins were negative. No tumor recurrence was detected after a media follow-up of 1 year (3-15 months). The surgical planning and two-step manual image fusion based on 3D model of renal tumor facilitated visible-imaging-guided tumor resection with negative margin in LPN for intrarenal tumor. It is promising and moves us one step closer to imaging-guided surgery.

A Fourier-based compressed sensing technique for accelerated CT image reconstruction using first-order methods.

PubMed

Choi, Kihwan; Li, Ruijiang; Nam, Haewon; Xing, Lei

2014-06-21

As a solution to iterative CT image reconstruction, first-order methods are prominent for the large-scale capability and the fast convergence rate [Formula: see text]. In practice, the CT system matrix with a large condition number may lead to slow convergence speed despite the theoretically promising upper bound. The aim of this study is to develop a Fourier-based scaling technique to enhance the convergence speed of first-order methods applied to CT image reconstruction. Instead of working in the projection domain, we transform the projection data and construct a data fidelity model in Fourier space. Inspired by the filtered backprojection formalism, the data are appropriately weighted in Fourier space. We formulate an optimization problem based on weighted least-squares in the Fourier space and total-variation (TV) regularization in image space for parallel-beam, fan-beam and cone-beam CT geometry. To achieve the maximum computational speed, the optimization problem is solved using a fast iterative shrinkage-thresholding algorithm with backtracking line search and GPU implementation of projection/backprojection. The performance of the proposed algorithm is demonstrated through a series of digital simulation and experimental phantom studies. The results are compared with the existing TV regularized techniques based on statistics-based weighted least-squares as well as basic algebraic reconstruction technique. The proposed Fourier-based compressed sensing (CS) method significantly improves both the image quality and the convergence rate compared to the existing CS techniques.

Design, fabrication, and implementation of voxel-based 3D printed textured phantoms for task-based image quality assessment in CT

NASA Astrophysics Data System (ADS)

Solomon, Justin; Ba, Alexandre; Diao, Andrew; Lo, Joseph; Bier, Elianna; Bochud, François; Gehm, Michael; Samei, Ehsan

2016-03-01

In x-ray computed tomography (CT), task-based image quality studies are typically performed using uniform background phantoms with low-contrast signals. Such studies may have limited clinical relevancy for modern non-linear CT systems due to possible influence of background texture on image quality. The purpose of this study was to design and implement anatomically informed textured phantoms for task-based assessment of low-contrast detection. Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find the CLB parameters that were most reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, a cylinder phantom (165 mm in diameter and 30 mm height) was designed, containing 20 low-contrast spherical signals (6 mm in diameter at targeted contrast levels of ~3.2, 5.2, 7.2, 10, and 14 HU, 4 repeats per signal). The phantom was voxelized and input into a commercial multi-material 3D printer (Object Connex 350), with custom software for voxel-based printing. Using principles of digital half-toning and dithering, the 3D printer was programmed to distribute two base materials (VeroWhite and TangoPlus, nominal voxel size of 42x84x30 microns) to achieve the targeted spatial distribution of x-ray attenuation properties. The phantom was used for task-based image quality assessment of a clinically available iterative reconstruction algorithm (Sinogram Affirmed Iterative Reconstruction, SAFIRE) using a channelized Hotelling observer paradigm. Images of the textured phantom and a corresponding uniform phantom were acquired at six dose levels and observer model performance was estimated for each condition (5 contrasts x 6 doses x 2 reconstructions x 2 backgrounds = 120 total conditions). Based on the observer model results, the dose reduction potential of SAFIRE was computed and compared between the uniform and textured phantom. The dose reduction potential of SAFIRE was found to be 23% based on the uniform phantom and 17% based on the textured phantom. This discrepancy demonstrates the need to consider background texture when assessing non-linear reconstruction algorithms.

Reconstruction of pulse noisy images via stochastic resonance

PubMed Central

Han, Jing; Liu, Hongjun; Sun, Qibing; Huang, Nan

2015-01-01

We investigate a practical technology for reconstructing nanosecond pulse noisy images via stochastic resonance, which is based on the modulation instability. A theoretical model of this method for optical pulse signal is built to effectively recover the pulse image. The nanosecond noise-hidden images grow at the expense of noise during the stochastic resonance process in a photorefractive medium. The properties of output images are mainly determined by the input signal-to-noise intensity ratio, the applied voltage across the medium, and the correlation length of noise background. A high cross-correlation gain is obtained by optimizing these parameters. This provides a potential method for detecting low-level or hidden pulse images in various imaging applications. PMID:26067911

Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm.

PubMed

Solomon, Justin; Mileto, Achille; Nelson, Rendon C; Roy Choudhury, Kingshuk; Samei, Ehsan

2016-04-01

To determine if radiation dose and reconstruction algorithm affect the computer-based extraction and analysis of quantitative imaging features in lung nodules, liver lesions, and renal stones at multi-detector row computed tomography (CT). Retrospective analysis of data from a prospective, multicenter, HIPAA-compliant, institutional review board-approved clinical trial was performed by extracting 23 quantitative imaging features (size, shape, attenuation, edge sharpness, pixel value distribution, and texture) of lesions on multi-detector row CT images of 20 adult patients (14 men, six women; mean age, 63 years; range, 38-72 years) referred for known or suspected focal liver lesions, lung nodules, or kidney stones. Data were acquired between September 2011 and April 2012. All multi-detector row CT scans were performed at two different radiation dose levels; images were reconstructed with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) algorithms. A linear mixed-effects model was used to assess the effect of radiation dose and reconstruction algorithm on extracted features. Among the 23 imaging features assessed, radiation dose had a significant effect on five, three, and four of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Adaptive statistical iterative reconstruction had a significant effect on three, one, and one of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). MBIR reconstruction had a significant effect on nine, 11, and 15 of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Of note, the measured size of lung nodules and renal stones with MBIR was significantly different than those for the other two algorithms (P < .002 for all comparisons). Although lesion texture was significantly affected by the reconstruction algorithm used (average of 3.33 features affected by MBIR throughout lesion types; P < .002, for all comparisons), no significant effect of the radiation dose setting was observed for all but one of the texture features (P = .002-.998). Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

Crack Modelling for Radiography

NASA Astrophysics Data System (ADS)

Chady, T.; Napierała, L.

2010-02-01

In this paper, possibility of creation of three-dimensional crack models, both random type and based on real-life radiographic images is discussed. Method for storing cracks in a number of two-dimensional matrices, as well algorithm for their reconstruction into three-dimensional objects is presented. Also the possibility of using iterative algorithm for matching simulated images of cracks to real-life radiographic images is discussed.

Tensor-based dynamic reconstruction method for electrical capacitance tomography

NASA Astrophysics Data System (ADS)

Lei, J.; Mu, H. P.; Liu, Q. B.; Li, Z. H.; Liu, S.; Wang, X. Y.

2017-03-01

Electrical capacitance tomography (ECT) is an attractive visualization measurement method, in which the acquisition of high-quality images is beneficial for the understanding of the underlying physical or chemical mechanisms of the dynamic behaviors of the measurement objects. In real-world measurement environments, imaging objects are often in a dynamic process, and the exploitation of the spatial-temporal correlations related to the dynamic nature will contribute to improving the imaging quality. Different from existing imaging methods that are often used in ECT measurements, in this paper a dynamic image sequence is stacked into a third-order tensor that consists of a low rank tensor and a sparse tensor within the framework of the multiple measurement vectors model and the multi-way data analysis method. The low rank tensor models the similar spatial distribution information among frames, which is slowly changing over time, and the sparse tensor captures the perturbations or differences introduced in each frame, which is rapidly changing over time. With the assistance of the Tikhonov regularization theory and the tensor-based multi-way data analysis method, a new cost function, with the considerations of the multi-frames measurement data, the dynamic evolution information of a time-varying imaging object and the characteristics of the low rank tensor and the sparse tensor, is proposed to convert the imaging task in the ECT measurement into a reconstruction problem of a third-order image tensor. An effective algorithm is developed to search for the optimal solution of the proposed cost function, and the images are reconstructed via a batching pattern. The feasibility and effectiveness of the developed reconstruction method are numerically validated.

Edge guided image reconstruction in linear scan CT by weighted alternating direction TV minimization.

PubMed

Cai, Ailong; Wang, Linyuan; Zhang, Hanming; Yan, Bin; Li, Lei; Xi, Xiaoqi; Li, Jianxin

2014-01-01

Linear scan computed tomography (CT) is a promising imaging configuration with high scanning efficiency while the data set is under-sampled and angularly limited for which high quality image reconstruction is challenging. In this work, an edge guided total variation minimization reconstruction (EGTVM) algorithm is developed in dealing with this problem. The proposed method is modeled on the combination of total variation (TV) regularization and iterative edge detection strategy. In the proposed method, the edge weights of intermediate reconstructions are incorporated into the TV objective function. The optimization is efficiently solved by applying alternating direction method of multipliers. A prudential and conservative edge detection strategy proposed in this paper can obtain the true edges while restricting the errors within an acceptable degree. Based on the comparison on both simulation studies and real CT data set reconstructions, EGTVM provides comparable or even better quality compared to the non-edge guided reconstruction and adaptive steepest descent-projection onto convex sets method. With the utilization of weighted alternating direction TV minimization and edge detection, EGTVM achieves fast and robust convergence and reconstructs high quality image when applied in linear scan CT with under-sampled data set.

Three-Dimensional (3D) Nanometrology Based on Scanning Electron Microscope (SEM) Stereophotogrammetry.

PubMed

Tondare, Vipin N; Villarrubia, John S; Vlada R, András E

2017-10-01

Three-dimensional (3D) reconstruction of a sample surface from scanning electron microscope (SEM) images taken at two perspectives has been known for decades. Nowadays, there exist several commercially available stereophotogrammetry software packages. For testing these software packages, in this study we used Monte Carlo simulated SEM images of virtual samples. A virtual sample is a model in a computer, and its true dimensions are known exactly, which is impossible for real SEM samples due to measurement uncertainty. The simulated SEM images can be used for algorithm testing, development, and validation. We tested two stereophotogrammetry software packages and compared their reconstructed 3D models with the known geometry of the virtual samples used to create the simulated SEM images. Both packages performed relatively well with simulated SEM images of a sample with a rough surface. However, in a sample containing nearly uniform and therefore low-contrast zones, the height reconstruction error was ≈46%. The present stereophotogrammetry software packages need further improvement before they can be used reliably with SEM images with uniform zones.

Constrained Total Generalized p-Variation Minimization for Few-View X-Ray Computed Tomography Image Reconstruction

PubMed Central

Zhang, Hanming; Wang, Linyuan; Yan, Bin; Li, Lei; Cai, Ailong; Hu, Guoen

2016-01-01

Total generalized variation (TGV)-based computed tomography (CT) image reconstruction, which utilizes high-order image derivatives, is superior to total variation-based methods in terms of the preservation of edge information and the suppression of unfavorable staircase effects. However, conventional TGV regularization employs l1-based form, which is not the most direct method for maximizing sparsity prior. In this study, we propose a total generalized p-variation (TGpV) regularization model to improve the sparsity exploitation of TGV and offer efficient solutions to few-view CT image reconstruction problems. To solve the nonconvex optimization problem of the TGpV minimization model, we then present an efficient iterative algorithm based on the alternating minimization of augmented Lagrangian function. All of the resulting subproblems decoupled by variable splitting admit explicit solutions by applying alternating minimization method and generalized p-shrinkage mapping. In addition, approximate solutions that can be easily performed and quickly calculated through fast Fourier transform are derived using the proximal point method to reduce the cost of inner subproblems. The accuracy and efficiency of the simulated and real data are qualitatively and quantitatively evaluated to validate the efficiency and feasibility of the proposed method. Overall, the proposed method exhibits reasonable performance and outperforms the original TGV-based method when applied to few-view problems. PMID:26901410

3D reconstructions with pixel-based images are made possible by digitally clearing plant and animal tissue

USDA-ARS?s Scientific Manuscript database

Reconstruction of 3D images from a series of 2D images has been restricted by the limited capacity to decrease the opacity of surrounding tissue. Commercial software that allows color-keying and manipulation of 2D images in true 3D space allowed us to produce 3D reconstructions from pixel based imag...

Bessel Fourier Orientation Reconstruction (BFOR): An Analytical Diffusion Propagator Reconstruction for Hybrid Diffusion Imaging and Computation of q-Space Indices

PubMed Central

Hosseinbor, A. Pasha; Chung, Moo K.; Wu, Yu-Chien; Alexander, Andrew L.

2012-01-01

The ensemble average propagator (EAP) describes the 3D average diffusion process of water molecules, capturing both its radial and angular contents. The EAP can thus provide richer information about complex tissue microstructure properties than the orientation distribution function (ODF), an angular feature of the EAP. Recently, several analytical EAP reconstruction schemes for multiple q-shell acquisitions have been proposed, such as diffusion propagator imaging (DPI) and spherical polar Fourier imaging (SPFI). In this study, a new analytical EAP reconstruction method is proposed, called Bessel Fourier orientation reconstruction (BFOR), whose solution is based on heat equation estimation of the diffusion signal for each shell acquisition, and is validated on both synthetic and real datasets. A significant portion of the paper is dedicated to comparing BFOR, SPFI, and DPI using hybrid, non-Cartesian sampling for multiple b-value acquisitions. Ways to mitigate the effects of Gibbs ringing on EAP reconstruction are also explored. In addition to analytical EAP reconstruction, the aforementioned modeling bases can be used to obtain rotationally invariant q-space indices of potential clinical value, an avenue which has not yet been thoroughly explored. Three such measures are computed: zero-displacement probability (Po), mean squared displacement (MSD), and generalized fractional anisotropy (GFA). PMID:22963853

Ultralow-dose CT of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction and model-based iterative reconstruction: 2D and 3D image quality.

PubMed

Widmann, Gerlig; Schullian, Peter; Gassner, Eva-Maria; Hoermann, Romed; Bale, Reto; Puelacher, Wolfgang

2015-03-01

OBJECTIVE. The purpose of this article is to evaluate 2D and 3D image quality of high-resolution ultralow-dose CT images of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) in comparison with standard filtered backprojection (FBP). MATERIALS AND METHODS. A formalin-fixed human cadaver head was scanned using a clinical reference protocol at a CT dose index volume of 30.48 mGy and a series of five ultralow-dose protocols at 3.48, 2.19, 0.82, 0.44, and 0.22 mGy using FBP and ASIR at 50% (ASIR-50), ASIR at 100% (ASIR-100), and MBIR. Blinded 2D axial and 3D volume-rendered images were compared with each other by three readers using top-down scoring. Scores were analyzed per protocol or dose and reconstruction. All images were compared with the FBP reference at 30.48 mGy. A nonparametric Mann-Whitney U test was used. Statistical significance was set at p < 0.05. RESULTS. For 2D images, the FBP reference at 30.48 mGy did not statistically significantly differ from ASIR-100 at 3.48 mGy, ASIR-100 at 2.19 mGy, and MBIR at 0.82 mGy. MBIR at 2.19 and 3.48 mGy scored statistically significantly better than the FBP reference (p = 0.032 and 0.001, respectively). For 3D images, the FBP reference at 30.48 mGy did not statistically significantly differ from all reconstructions at 3.48 mGy; FBP and ASIR-100 at 2.19 mGy; FBP, ASIR-100, and MBIR at 0.82 mGy; MBIR at 0.44 mGy; and MBIR at 0.22 mGy. CONCLUSION. MBIR (2D and 3D) and ASIR-100 (2D) may significantly improve subjective image quality of ultralow-dose images and may allow more than 90% dose reductions.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies.

PubMed

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-07

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18 F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans-each containing 1/8th of the total number of events-were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18 F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of [Formula: see text], the tracer transport rate (ml · min -1 · ml -1 ), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced [Formula: see text] maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced [Formula: see text] estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in-vivo studies

PubMed Central

Petibon, Yoann; Rakvongthai, Yothin; Fakhri, Georges El; Ouyang, Jinsong

2017-01-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves -TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in-vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans - each containing 1/8th of the total number of events - were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard Ordered Subset Expectation Maximization (OSEM) reconstruction algorithm on one side, and the One-Step Late Maximum a Posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of K1, the tracer transport rate (mL.min−1.mL−1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced K1 maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced K1 estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in-vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance. PMID:28379843

Direct parametric reconstruction in dynamic PET myocardial perfusion imaging: in vivo studies

NASA Astrophysics Data System (ADS)

Petibon, Yoann; Rakvongthai, Yothin; El Fakhri, Georges; Ouyang, Jinsong

2017-05-01

Dynamic PET myocardial perfusion imaging (MPI) used in conjunction with tracer kinetic modeling enables the quantification of absolute myocardial blood flow (MBF). However, MBF maps computed using the traditional indirect method (i.e. post-reconstruction voxel-wise fitting of kinetic model to PET time-activity-curves-TACs) suffer from poor signal-to-noise ratio (SNR). Direct reconstruction of kinetic parameters from raw PET projection data has been shown to offer parametric images with higher SNR compared to the indirect method. The aim of this study was to extend and evaluate the performance of a direct parametric reconstruction method using in vivo dynamic PET MPI data for the purpose of quantifying MBF. Dynamic PET MPI studies were performed on two healthy pigs using a Siemens Biograph mMR scanner. List-mode PET data for each animal were acquired following a bolus injection of ~7-8 mCi of 18F-flurpiridaz, a myocardial perfusion agent. Fully-3D dynamic PET sinograms were obtained by sorting the coincidence events into 16 temporal frames covering ~5 min after radiotracer administration. Additionally, eight independent noise realizations of both scans—each containing 1/8th of the total number of events—were generated from the original list-mode data. Dynamic sinograms were then used to compute parametric maps using the conventional indirect method and the proposed direct method. For both methods, a one-tissue compartment model accounting for spillover from the left and right ventricle blood-pools was used to describe the kinetics of 18F-flurpiridaz. An image-derived arterial input function obtained from a TAC taken in the left ventricle cavity was used for tracer kinetic analysis. For the indirect method, frame-by-frame images were estimated using two fully-3D reconstruction techniques: the standard ordered subset expectation maximization (OSEM) reconstruction algorithm on one side, and the one-step late maximum a posteriori (OSL-MAP) algorithm on the other side, which incorporates a quadratic penalty function. The parametric images were then calculated using voxel-wise weighted least-square fitting of the reconstructed myocardial PET TACs. For the direct method, parametric images were estimated directly from the dynamic PET sinograms using a maximum a posteriori (MAP) parametric reconstruction algorithm which optimizes an objective function comprised of the Poisson log-likelihood term, the kinetic model and a quadratic penalty function. Maximization of the objective function with respect to each set of parameters was achieved using a preconditioned conjugate gradient algorithm with a specifically developed pre-conditioner. The performance of the direct method was evaluated by comparing voxel- and segment-wise estimates of {{K}1} , the tracer transport rate (ml · min-1 · ml-1), to those obtained using the indirect method applied to both OSEM and OSL-MAP dynamic reconstructions. The proposed direct reconstruction method produced {{K}1} maps with visibly lower noise than the indirect method based on OSEM and OSL-MAP reconstructions. At normal count levels, the direct method was shown to outperform the indirect method based on OSL-MAP in the sense that at matched level of bias, reduced regional noise levels were obtained. At lower count levels, the direct method produced {{K}1} estimates with significantly lower standard deviation across noise realizations than the indirect method based on OSL-MAP at matched bias level. In all cases, the direct method yielded lower noise and standard deviation than the indirect method based on OSEM. Overall, the proposed direct reconstruction offered a better bias-variance tradeoff than the indirect method applied to either OSEM and OSL-MAP. Direct parametric reconstruction as applied to in vivo dynamic PET MPI data is therefore a promising method for producing MBF maps with lower variance.

Incomplete projection reconstruction of computed tomography based on the modified discrete algebraic reconstruction technique

NASA Astrophysics Data System (ADS)

Yang, Fuqiang; Zhang, Dinghua; Huang, Kuidong; Gao, Zongzhao; Yang, YaFei

2018-02-01

Based on the discrete algebraic reconstruction technique (DART), this study aims to address and test a new improved algorithm applied to incomplete projection data to generate a high quality reconstruction image by reducing the artifacts and noise in computed tomography. For the incomplete projections, an augmented Lagrangian based on compressed sensing is first used in the initial reconstruction for segmentation of the DART to get higher contrast graphics for boundary and non-boundary pixels. Then, the block matching 3D filtering operator was used to suppress the noise and to improve the gray distribution of the reconstructed image. Finally, simulation studies on the polychromatic spectrum were performed to test the performance of the new algorithm. Study results show a significant improvement in the signal-to-noise ratios (SNRs) and average gradients (AGs) of the images reconstructed from incomplete data. The SNRs and AGs of the new images reconstructed by DART-ALBM were on average 30%-40% and 10% higher than the images reconstructed by DART algorithms. Since the improved DART-ALBM algorithm has a better robustness to limited-view reconstruction, which not only makes the edge of the image clear but also makes the gray distribution of non-boundary pixels better, it has the potential to improve image quality from incomplete projections or sparse projections.

Embedded, real-time UAV control for improved, image-based 3D scene reconstruction

Treesearch

Jean Liénard; Andre Vogs; Demetrios Gatziolis; Nikolay Strigul

2016-01-01

Unmanned Aerial Vehicles (UAVs) are already broadly employed for 3D modeling of large objects such as trees and monuments via photogrammetry. The usual workflow includes two distinct steps: image acquisition with UAV and computationally demanding postflight image processing. Insufficient feature overlaps across images is a common shortcoming in post-flight image...

Statistical virtual eye model based on wavefront aberration

PubMed Central

Wang, Jie-Mei; Liu, Chun-Ling; Luo, Yi-Ning; Liu, Yi-Guang; Hu, Bing-Jie

2012-01-01

Wavefront aberration affects the quality of retinal image directly. This paper reviews the representation and reconstruction of wavefront aberration, as well as the construction of virtual eye model based on Zernike polynomial coefficients. In addition, the promising prospect of virtual eye model is emphasized. PMID:23173112

Maximising information recovery from rank-order codes

NASA Astrophysics Data System (ADS)

Sen, B.; Furber, S.

2007-04-01

The central nervous system encodes information in sequences of asynchronously generated voltage spikes, but the precise details of this encoding are not well understood. Thorpe proposed rank-order codes as an explanation of the observed speed of information processing in the human visual system. The work described in this paper is inspired by the performance of SpikeNET, a biologically inspired neural architecture using rank-order codes for information processing, and is based on the retinal model developed by VanRullen and Thorpe. This model mimics retinal information processing by passing an input image through a bank of Difference of Gaussian (DoG) filters and then encoding the resulting coefficients in rank-order. To test the effectiveness of this encoding in capturing the information content of an image, the rank-order representation is decoded to reconstruct an image that can be compared with the original. The reconstruction uses a look-up table to infer the filter coefficients from their rank in the encoded image. Since the DoG filters are approximately orthogonal functions, they are treated as their own inverses in the reconstruction process. We obtained a quantitative measure of the perceptually important information retained in the reconstructed image relative to the original using a slightly modified version of an objective metric proposed by Petrovic. It is observed that around 75% of the perceptually important information is retained in the reconstruction. In the present work we reconstruct the input using a pseudo-inverse of the DoG filter-bank with the aim of improving the reconstruction and thereby extracting more information from the rank-order encoded stimulus. We observe that there is an increase of 10 - 15% in the information retrieved from a reconstructed stimulus as a result of inverting the filter-bank.

GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration.

PubMed

Sharp, G C; Kandasamy, N; Singh, H; Folkert, M

2007-10-07

This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model. We describe data-parallel designs for the Feldkamp, Davis and Kress (FDK) reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit. The streaming versions of these algorithms are implemented using the Brook programming environment and executed on an NVidia 8800 GPU. Performance results using CT data of a preserved swine lung indicate that the GPU-based implementations of the FDK and demons algorithms achieve a substantial speedup--up to 80 times for FDK and 70 times for demons when compared to an optimized reference implementation on a 2.8 GHz Intel processor. In addition, the accuracy of the GPU-based implementations was found to be excellent. Compared with CPU-based implementations, the RMS differences were less than 0.1 Hounsfield unit for reconstruction and less than 0.1 mm for deformable registration.

Investigating the performance of reconstruction methods used in structured illumination microscopy as a function of the illumination pattern's modulation frequency

NASA Astrophysics Data System (ADS)

Shabani, H.; Sánchez-Ortiga, E.; Preza, C.

2016-03-01

Surpassing the resolution of optical microscopy defined by the Abbe diffraction limit, while simultaneously achieving optical sectioning, is a challenging problem particularly for live cell imaging of thick samples. Among a few developing techniques, structured illumination microscopy (SIM) addresses this challenge by imposing higher frequency information into the observable frequency band confined by the optical transfer function (OTF) of a conventional microscope either doubling the spatial resolution or filling the missing cone based on the spatial frequency of the pattern when the patterned illumination is two-dimensional. Standard reconstruction methods for SIM decompose the low and high frequency components from the recorded low-resolution images and then combine them to reach a high-resolution image. In contrast, model-based approaches rely on iterative optimization approaches to minimize the error between estimated and forward images. In this paper, we study the performance of both groups of methods by simulating fluorescence microscopy images from different type of objects (ranging from simulated two-point sources to extended objects). These simulations are used to investigate the methods' effectiveness on restoring objects with various types of power spectrum when modulation frequency of the patterned illumination is changing from zero to the incoherent cut-off frequency of the imaging system. Our results show that increasing the amount of imposed information by using a higher modulation frequency of the illumination pattern does not always yield a better restoration performance, which was found to be depended on the underlying object. Results from model-based restoration show performance improvement, quantified by an up to 62% drop in the mean square error compared to standard reconstruction, with increasing modulation frequency. However, we found cases for which results obtained with standard reconstruction methods do not always follow the same trend.

Comprehensive quantification of signal-to-noise ratio and g-factor for image-based and k-space-based parallel imaging reconstructions.

PubMed

Robson, Philip M; Grant, Aaron K; Madhuranthakam, Ananth J; Lattanzi, Riccardo; Sodickson, Daniel K; McKenzie, Charles A

2008-10-01

Parallel imaging reconstructions result in spatially varying noise amplification characterized by the g-factor, precluding conventional measurements of noise from the final image. A simple Monte Carlo based method is proposed for all linear image reconstruction algorithms, which allows measurement of signal-to-noise ratio and g-factor and is demonstrated for SENSE and GRAPPA reconstructions for accelerated acquisitions that have not previously been amenable to such assessment. Only a simple "prescan" measurement of noise amplitude and correlation in the phased-array receiver, and a single accelerated image acquisition are required, allowing robust assessment of signal-to-noise ratio and g-factor. The "pseudo multiple replica" method has been rigorously validated in phantoms and in vivo, showing excellent agreement with true multiple replica and analytical methods. This method is universally applicable to the parallel imaging reconstruction techniques used in clinical applications and will allow pixel-by-pixel image noise measurements for all parallel imaging strategies, allowing quantitative comparison between arbitrary k-space trajectories, image reconstruction, or noise conditioning techniques. (c) 2008 Wiley-Liss, Inc.

PET image reconstruction: a robust state space approach.

PubMed

Liu, Huafeng; Tian, Yi; Shi, Pengcheng

2005-01-01

Statistical iterative reconstruction algorithms have shown improved image quality over conventional nonstatistical methods in PET by using accurate system response models and measurement noise models. Strictly speaking, however, PET measurements, pre-corrected for accidental coincidences, are neither Poisson nor Gaussian distributed and thus do not meet basic assumptions of these algorithms. In addition, the difficulty in determining the proper system response model also greatly affects the quality of the reconstructed images. In this paper, we explore the usage of state space principles for the estimation of activity map in tomographic PET imaging. The proposed strategy formulates the organ activity distribution through tracer kinetics models, and the photon-counting measurements through observation equations, thus makes it possible to unify the dynamic reconstruction problem and static reconstruction problem into a general framework. Further, it coherently treats the uncertainties of the statistical model of the imaging system and the noisy nature of measurement data. Since H(infinity) filter seeks minimummaximum-error estimates without any assumptions on the system and data noise statistics, it is particular suited for PET image reconstruction where the statistical properties of measurement data and the system model are very complicated. The performance of the proposed framework is evaluated using Shepp-Logan simulated phantom data and real phantom data with favorable results.

3D delivered dose assessment using a 4DCT-based motion model

PubMed Central

Cai, Weixing; Hurwitz, Martina H.; Williams, Christopher L.; Dhou, Salam; Berbeco, Ross I.; Seco, Joao; Mishra, Pankaj; Lewis, John H.

2015-01-01

Purpose: The purpose of this work is to develop a clinically feasible method of calculating actual delivered dose distributions for patients who have significant respiratory motion during the course of stereotactic body radiation therapy (SBRT). Methods: A novel approach was proposed to calculate the actual delivered dose distribution for SBRT lung treatment. This approach can be specified in three steps. (1) At the treatment planning stage, a patient-specific motion model is created from planning 4DCT data. This model assumes that the displacement vector field (DVF) of any respiratory motion deformation can be described as a linear combination of some basis DVFs. (2) During the treatment procedure, 2D time-varying projection images (either kV or MV projections) are acquired, from which time-varying “fluoroscopic” 3D images of the patient are reconstructed using the motion model. The DVF of each timepoint in the time-varying reconstruction is an optimized linear combination of basis DVFs such that the 2D projection of the 3D volume at this timepoint matches the projection image. (3) 3D dose distribution is computed for each timepoint in the set of 3D reconstructed fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach was first validated using two modified digital extended cardio-torso (XCAT) phantoms with lung tumors and different respiratory motions. The estimated doses were compared to the dose that would be calculated for routine 4DCT-based planning and to the actual delivered dose that was calculated using “ground truth” XCAT phantoms at all timepoints. The approach was also tested using one set of patient data, which demonstrated the application of our method in a clinical scenario. Results: For the first XCAT phantom that has a mostly regular breathing pattern, the errors in 95% volume dose (D95) are 0.11% and 0.83%, respectively for 3D fluoroscopic images reconstructed from kV and MV projections compared to the ground truth, which is clinically comparable to 4DCT (0.093%). For the second XCAT phantom that has an irregular breathing pattern, the errors are 0.81% and 1.75% for kV and MV reconstructions, both of which are better than that of 4DCT (4.01%). In the case of real patient, although it is impossible to obtain the actual delivered dose, the dose estimation is clinically reasonable and demonstrates differences between 4DCT and MV reconstruction-based dose estimates. Conclusions: With the availability of kV or MV projection images, the proposed approach is able to assess delivered doses for all respiratory phases during treatment. Compared to the planning dose based on 4DCT, the dose estimation using reconstructed 3D fluoroscopic images was as good as 4DCT for regular respiratory pattern and was a better dose estimation for the irregular respiratory pattern. PMID:26127043

3D delivered dose assessment using a 4DCT-based motion model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cai, Weixing; Hurwitz, Martina H.; Williams, Christopher L.

Purpose: The purpose of this work is to develop a clinically feasible method of calculating actual delivered dose distributions for patients who have significant respiratory motion during the course of stereotactic body radiation therapy (SBRT). Methods: A novel approach was proposed to calculate the actual delivered dose distribution for SBRT lung treatment. This approach can be specified in three steps. (1) At the treatment planning stage, a patient-specific motion model is created from planning 4DCT data. This model assumes that the displacement vector field (DVF) of any respiratory motion deformation can be described as a linear combination of some basismore » DVFs. (2) During the treatment procedure, 2D time-varying projection images (either kV or MV projections) are acquired, from which time-varying “fluoroscopic” 3D images of the patient are reconstructed using the motion model. The DVF of each timepoint in the time-varying reconstruction is an optimized linear combination of basis DVFs such that the 2D projection of the 3D volume at this timepoint matches the projection image. (3) 3D dose distribution is computed for each timepoint in the set of 3D reconstructed fluoroscopic images, from which the total effective 3D delivered dose is calculated by accumulating deformed dose distributions. This approach was first validated using two modified digital extended cardio-torso (XCAT) phantoms with lung tumors and different respiratory motions. The estimated doses were compared to the dose that would be calculated for routine 4DCT-based planning and to the actual delivered dose that was calculated using “ground truth” XCAT phantoms at all timepoints. The approach was also tested using one set of patient data, which demonstrated the application of our method in a clinical scenario. Results: For the first XCAT phantom that has a mostly regular breathing pattern, the errors in 95% volume dose (D95) are 0.11% and 0.83%, respectively for 3D fluoroscopic images reconstructed from kV and MV projections compared to the ground truth, which is clinically comparable to 4DCT (0.093%). For the second XCAT phantom that has an irregular breathing pattern, the errors are 0.81% and 1.75% for kV and MV reconstructions, both of which are better than that of 4DCT (4.01%). In the case of real patient, although it is impossible to obtain the actual delivered dose, the dose estimation is clinically reasonable and demonstrates differences between 4DCT and MV reconstruction-based dose estimates. Conclusions: With the availability of kV or MV projection images, the proposed approach is able to assess delivered doses for all respiratory phases during treatment. Compared to the planning dose based on 4DCT, the dose estimation using reconstructed 3D fluoroscopic images was as good as 4DCT for regular respiratory pattern and was a better dose estimation for the irregular respiratory pattern.« less

A heuristic statistical stopping rule for iterative reconstruction in emission tomography.

PubMed

Ben Bouallègue, F; Crouzet, J F; Mariano-Goulart, D

2013-01-01

We propose a statistical stopping criterion for iterative reconstruction in emission tomography based on a heuristic statistical description of the reconstruction process. The method was assessed for MLEM reconstruction. Based on Monte-Carlo numerical simulations and using a perfectly modeled system matrix, our method was compared with classical iterative reconstruction followed by low-pass filtering in terms of Euclidian distance to the exact object, noise, and resolution. The stopping criterion was then evaluated with realistic PET data of a Hoffman brain phantom produced using the GATE platform for different count levels. The numerical experiments showed that compared with the classical method, our technique yielded significant improvement of the noise-resolution tradeoff for a wide range of counting statistics compatible with routine clinical settings. When working with realistic data, the stopping rule allowed a qualitatively and quantitatively efficient determination of the optimal image. Our method appears to give a reliable estimation of the optimal stopping point for iterative reconstruction. It should thus be of practical interest as it produces images with similar or better quality than classical post-filtered iterative reconstruction with a mastered computation time.

Imaging metallic samples using electrical capacitance tomography: forward modelling and reconstruction algorithms

NASA Astrophysics Data System (ADS)

Hosani, E. Al; Zhang, M.; Abascal, J. F. P. J.; Soleimani, M.

2016-11-01

Electrical capacitance tomography (ECT) is an imaging technology used to reconstruct the permittivity distribution within the sensing region. So far, ECT has been primarily used to image non-conductive media only, since if the conductivity of the imaged object is high, the capacitance measuring circuit will be almost shortened by the conductivity path and a clear image cannot be produced using the standard image reconstruction approaches. This paper tackles the problem of imaging metallic samples using conventional ECT systems by investigating the two main aspects of image reconstruction algorithms, namely the forward problem and the inverse problem. For the forward problem, two different methods to model the region of high conductivity in ECT is presented. On the other hand, for the inverse problem, three different algorithms to reconstruct the high contrast images are examined. The first two methods are the linear single step Tikhonov method and the iterative total variation regularization method, and use two sets of ECT data to reconstruct the image in time difference mode. The third method, namely the level set method, uses absolute ECT measurements and was developed using a metallic forward model. The results indicate that the applications of conventional ECT systems can be extended to metal samples using the suggested algorithms and forward model, especially using a level set algorithm to find the boundary of the metal.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery.

PubMed

Hashemi, SayedMasoud; Song, William Y; Sahgal, Arjun; Lee, Young; Huynh, Christopher; Grouza, Vladimir; Nordström, Håkan; Eriksson, Markus; Dorenlot, Antoine; Régis, Jean Marie; Mainprize, James G; Ruschin, Mark

2017-04-07

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm -1 which was increased to 1.2 mm -1 by SDIR, at half maximum.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery

NASA Astrophysics Data System (ADS)

Hashemi, SayedMasoud; Song, William Y.; Sahgal, Arjun; Lee, Young; Huynh, Christopher; Grouza, Vladimir; Nordström, Håkan; Eriksson, Markus; Dorenlot, Antoine; Régis, Jean Marie; Mainprize, James G.; Ruschin, Mark

2017-04-01

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm-1 which was increased to 1.2 mm-1 by SDIR, at half maximum.

Tomographic PIV: particles versus blobs

NASA Astrophysics Data System (ADS)

Champagnat, Frédéric; Cornic, Philippe; Cheminet, Adam; Leclaire, Benjamin; Le Besnerais, Guy; Plyer, Aurélien

2014-08-01

We present an alternative approach to tomographic particle image velocimetry (tomo-PIV) that seeks to recover nearly single voxel particles rather than blobs of extended size. The baseline of our approach is a particle-based representation of image data. An appropriate discretization of this representation yields an original linear forward model with a weight matrix built with specific samples of the system’s point spread function (PSF). Such an approach requires only a few voxels to explain the image appearance, therefore it favors much more sparsely reconstructed volumes than classic tomo-PIV. The proposed forward model is general and flexible and can be embedded in a classical multiplicative algebraic reconstruction technique (MART) or a simultaneous multiplicative algebraic reconstruction technique (SMART) inversion procedure. We show, using synthetic PIV images and by way of a large exploration of the generating conditions and a variety of performance metrics, that the model leads to better results than the classical tomo-PIV approach, in particular in the case of seeding densities greater than 0.06 particles per pixel and of PSFs characterized by a standard deviation larger than 0.8 pixels.

Bayesian nonparametric dictionary learning for compressed sensing MRI.

PubMed

Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Multichannel High Resolution Wide Swath SAR Imaging for Hypersonic Air Vehicle with Curved Trajectory.

PubMed

Zhou, Rui; Sun, Jinping; Hu, Yuxin; Qi, Yaolong

2018-01-31

Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm.

Multichannel High Resolution Wide Swath SAR Imaging for Hypersonic Air Vehicle with Curved Trajectory

PubMed Central

Zhou, Rui; Hu, Yuxin; Qi, Yaolong

2018-01-01

Synthetic aperture radar (SAR) equipped on the hypersonic air vehicle in near space has many advantages over the conventional airborne SAR. However, its high-speed maneuvering characteristics with curved trajectory result in serious range migration, and exacerbate the contradiction between the high resolution and wide swath. To solve this problem, this paper establishes the imaging geometrical model matched with the flight trajectory of the hypersonic platform and the multichannel azimuth sampling model based on the displaced phase center antenna (DPCA) technology. Furthermore, based on the multichannel signal reconstruction theory, a more efficient spectrum reconstruction model using discrete Fourier transform is proposed to obtain the azimuth uniform sampling data. Due to the high complexity of the slant range model, it is difficult to deduce the processing algorithm for SAR imaging. Thus, an approximate range model is derived based on the minimax criterion, and the optimal second-order approximate coefficients of cosine function are obtained using the two-population coevolutionary algorithm. On this basis, aiming at the problem that the traditional Omega-K algorithm cannot compensate the residual phase with the difficulty of Stolt mapping along the range frequency axis, this paper proposes an Exact Transfer Function (ETF) algorithm for SAR imaging, and presents a method of range division to achieve wide swath imaging. Simulation results verify the effectiveness of the ETF imaging algorithm. PMID:29385059

Linear information retrieval method in X-ray grating-based phase contrast imaging and its interchangeability with tomographic reconstruction

NASA Astrophysics Data System (ADS)

Wu, Z.; Gao, K.; Wang, Z. L.; Shao, Q. G.; Hu, R. F.; Wei, C. X.; Zan, G. B.; Wali, F.; Luo, R. H.; Zhu, P. P.; Tian, Y. C.

2017-06-01

In X-ray grating-based phase contrast imaging, information retrieval is necessary for quantitative research, especially for phase tomography. However, numerous and repetitive processes have to be performed for tomographic reconstruction. In this paper, we report a novel information retrieval method, which enables retrieving phase and absorption information by means of a linear combination of two mutually conjugate images. Thanks to the distributive law of the multiplication as well as the commutative law and associative law of the addition, the information retrieval can be performed after tomographic reconstruction, thus simplifying the information retrieval procedure dramatically. The theoretical model of this method is established in both parallel beam geometry for Talbot interferometer and fan beam geometry for Talbot-Lau interferometer. Numerical experiments are also performed to confirm the feasibility and validity of the proposed method. In addition, we discuss its possibility in cone beam geometry and its advantages compared with other methods. Moreover, this method can also be employed in other differential phase contrast imaging methods, such as diffraction enhanced imaging, non-interferometric imaging, and edge illumination.

Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction

DOE PAGES

Chen, Zhangxing; Huang, Tianyu; Shao, Yimin; ...

2018-03-15

Predicting the mechanical behavior of the chopped carbon fiber Sheet Molding Compound (SMC) due to spatial variations in local material properties is critical for the structural performance analysis but is computationally challenging. Such spatial variations are induced by the material flow in the compression molding process. In this work, a new multiscale SMC modeling framework and the associated computational techniques are developed to provide accurate and efficient predictions of SMC mechanical performance. The proposed multiscale modeling framework contains three modules. First, a stochastic algorithm for 3D chip-packing reconstruction is developed to efficiently generate the SMC mesoscale Representative Volume Element (RVE)more » model for Finite Element Analysis (FEA). A new fiber orientation tensor recovery function is embedded in the reconstruction algorithm to match reconstructions with the target characteristics of fiber orientation distribution. Second, a metamodeling module is established to improve the computational efficiency by creating the surrogates of mesoscale analyses. Third, the macroscale behaviors are predicted by an efficient multiscale model, in which the spatially varying material properties are obtained based on the local fiber orientation tensors. Our approach is further validated through experiments at both meso- and macro-scales, such as tensile tests assisted by Digital Image Correlation (DIC) and mesostructure imaging.« less

Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Zhangxing; Huang, Tianyu; Shao, Yimin

Predicting the mechanical behavior of the chopped carbon fiber Sheet Molding Compound (SMC) due to spatial variations in local material properties is critical for the structural performance analysis but is computationally challenging. Such spatial variations are induced by the material flow in the compression molding process. In this work, a new multiscale SMC modeling framework and the associated computational techniques are developed to provide accurate and efficient predictions of SMC mechanical performance. The proposed multiscale modeling framework contains three modules. First, a stochastic algorithm for 3D chip-packing reconstruction is developed to efficiently generate the SMC mesoscale Representative Volume Element (RVE)more » model for Finite Element Analysis (FEA). A new fiber orientation tensor recovery function is embedded in the reconstruction algorithm to match reconstructions with the target characteristics of fiber orientation distribution. Second, a metamodeling module is established to improve the computational efficiency by creating the surrogates of mesoscale analyses. Third, the macroscale behaviors are predicted by an efficient multiscale model, in which the spatially varying material properties are obtained based on the local fiber orientation tensors. Our approach is further validated through experiments at both meso- and macro-scales, such as tensile tests assisted by Digital Image Correlation (DIC) and mesostructure imaging.« less

A general framework for face reconstruction using single still image based on 2D-to-3D transformation kernel.

PubMed

Fooprateepsiri, Rerkchai; Kurutach, Werasak

2014-03-01

Face authentication is a biometric classification method that verifies the identity of a user based on image of their face. Accuracy of the authentication is reduced when the pose, illumination and expression of the training face images are different than the testing image. The methods in this paper are designed to improve the accuracy of a features-based face recognition system when the pose between the input images and training images are different. First, an efficient 2D-to-3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Second, realistic virtual faces with different poses are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; and (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex pose, illumination and expression. From the experimental results, we conclude that the proposed method improves the accuracy of face recognition by varying the pose, illumination and expression. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

High-resolution dynamic 31 P-MRSI using a low-rank tensor model.

PubMed

Ma, Chao; Clifford, Bryan; Liu, Yuchi; Gu, Yuning; Lam, Fan; Yu, Xin; Liang, Zhi-Pei

2017-08-01

To develop a rapid 31 P-MRSI method with high spatiospectral resolution using low-rank tensor-based data acquisition and image reconstruction. The multidimensional image function of 31 P-MRSI is represented by a low-rank tensor to capture the spatial-spectral-temporal correlations of data. A hybrid data acquisition scheme is used for sparse sampling, which consists of a set of "training" data with limited k-space coverage to capture the subspace structure of the image function, and a set of sparsely sampled "imaging" data for high-resolution image reconstruction. An explicit subspace pursuit approach is used for image reconstruction, which estimates the bases of the subspace from the "training" data and then reconstructs a high-resolution image function from the "imaging" data. We have validated the feasibility of the proposed method using phantom and in vivo studies on a 3T whole-body scanner and a 9.4T preclinical scanner. The proposed method produced high-resolution static 31 P-MRSI images (i.e., 6.9 × 6.9 × 10 mm 3 nominal resolution in a 15-min acquisition at 3T) and high-resolution, high-frame-rate dynamic 31 P-MRSI images (i.e., 1.5 × 1.5 × 1.6 mm 3 nominal resolution, 30 s/frame at 9.4T). Dynamic spatiospectral variations of 31 P-MRSI signals can be efficiently represented by a low-rank tensor. Exploiting this mathematical structure for data acquisition and image reconstruction can lead to fast 31 P-MRSI with high resolution, frame-rate, and SNR. Magn Reson Med 78:419-428, 2017. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Photogrammetric 3d Building Reconstruction from Thermal Images

NASA Astrophysics Data System (ADS)

Maset, E.; Fusiello, A.; Crosilla, F.; Toldo, R.; Zorzetto, D.

2017-08-01

This paper addresses the problem of 3D building reconstruction from thermal infrared (TIR) images. We show that a commercial Computer Vision software can be used to automatically orient sequences of TIR images taken from an Unmanned Aerial Vehicle (UAV) and to generate 3D point clouds, without requiring any GNSS/INS data about position and attitude of the images nor camera calibration parameters. Moreover, we propose a procedure based on Iterative Closest Point (ICP) algorithm to create a model that combines high resolution and geometric accuracy of RGB images with the thermal information deriving from TIR images. The process can be carried out entirely by the aforesaid software in a simple and efficient way.

Source Plane Reconstruction of the Bright Lensed Galaxy RCSGA 032727-132609

NASA Technical Reports Server (NTRS)

Sharon, Keren; Gladders, Michael D.; Rigby, Jane R.; Wuyts, Eva; Koester, Benjamin P.; Bayliss, Matthew B.; Barrientos, L. Felipe

2011-01-01

We present new HST/WFC3 imaging data of RCS2 032727-132609, a bright lensed galaxy at z=1.7 that is magnified and stretched by the lensing cluster RCS2 032727-132623. Using this new high-resolution imaging, we modify our previous lens model (which was based on ground-based data) to fully understand the lensing geometry, and use it to reconstruct the lensed galaxy in the source plane. This giant arc represents a unique opportunity to peer into 100-pc scale structures in a high redshift galaxy. This new source reconstruction will be crucial for a future analysis of the spatially-resolved rest-UV and rest-optical spectra of the brightest parts of the arc.

Toward a dose reduction strategy using model-based reconstruction with limited-angle tomosynthesis

NASA Astrophysics Data System (ADS)

Haneda, Eri; Tkaczyk, J. E.; Palma, Giovanni; Iordache, Rǎzvan; Zelakiewicz, Scott; Muller, Serge; De Man, Bruno

2014-03-01

Model-based iterative reconstruction (MBIR) is an emerging technique for several imaging modalities and appli- cations including medical CT, security CT, PET, and microscopy. Its success derives from an ability to preserve image resolution and perceived diagnostic quality under impressively reduced signal level. MBIR typically uses a cost optimization framework that models system geometry, photon statistics, and prior knowledge of the recon- structed volume. The challenge of tomosynthetic geometries is that the inverse problem becomes more ill-posed due to the limited angles, meaning the volumetric image solution is not uniquely determined by the incom- pletely sampled projection data. Furthermore, low signal level conditions introduce additional challenges due to noise. A fundamental strength of MBIR for limited-views and limited-angle is that it provides a framework for constraining the solution consistent with prior knowledge of expected image characteristics. In this study, we analyze through simulation the capability of MBIR with respect to prior modeling components for limited-views, limited-angle digital breast tomosynthesis (DBT) under low dose conditions. A comparison to ground truth phantoms shows that MBIR with regularization achieves a higher level of fidelity and lower level of blurring and streaking artifacts compared to other state of the art iterative reconstructions, especially for high contrast objects. The benefit of contrast preservation along with less artifacts may lead to detectability improvement of microcalcification for more accurate cancer diagnosis.

Fast backprojection-based reconstruction of spectral-spatial EPR images from projections with the constant sweep of a magnetic field.

PubMed

Komarov, Denis A; Hirata, Hiroshi

2017-08-01

In this paper, we introduce a procedure for the reconstruction of spectral-spatial EPR images using projections acquired with the constant sweep of a magnetic field. The application of a constant field-sweep and a predetermined data sampling rate simplifies the requirements for EPR imaging instrumentation and facilitates the backprojection-based reconstruction of spectral-spatial images. The proposed approach was applied to the reconstruction of a four-dimensional numerical phantom and to actual spectral-spatial EPR measurements. Image reconstruction using projections with a constant field-sweep was three times faster than the conventional approach with the application of a pseudo-angle and a scan range that depends on the applied field gradient. Spectral-spatial EPR imaging with a constant field-sweep for data acquisition only slightly reduces the signal-to-noise ratio or functional resolution of the resultant images and can be applied together with any common backprojection-based reconstruction algorithm. Copyright © 2017 Elsevier Inc. All rights reserved.

Patient-Adaptive Reconstruction and Acquisition in Dynamic Imaging with Sensitivity Encoding (PARADISE)

PubMed Central

Sharif, Behzad; Derbyshire, J. Andrew; Faranesh, Anthony Z.; Bresler, Yoram

2010-01-01

MR imaging of the human heart without explicit cardiac synchronization promises to extend the applicability of cardiac MR to a larger patient population and potentially expand its diagnostic capabilities. However, conventional non-gated imaging techniques typically suffer from low image quality or inadequate spatio-temporal resolution and fidelity. Patient-Adaptive Reconstruction and Acquisition in Dynamic Imaging with Sensitivity Encoding (PARADISE) is a highly-accelerated non-gated dynamic imaging method that enables artifact-free imaging with high spatio-temporal resolutions by utilizing novel computational techniques to optimize the imaging process. In addition to using parallel imaging, the method gains acceleration from a physiologically-driven spatio-temporal support model; hence, it is doubly accelerated. The support model is patient-adaptive, i.e., its geometry depends on dynamics of the imaged slice, e.g., subject’s heart-rate and heart location within the slice. The proposed method is also doubly adaptive as it adapts both the acquisition and reconstruction schemes. Based on the theory of time-sequential sampling, the proposed framework explicitly accounts for speed limitations of gradient encoding and provides performance guarantees on achievable image quality. The presented in-vivo results demonstrate the effectiveness and feasibility of the PARADISE method for high resolution non-gated cardiac MRI during a short breath-hold. PMID:20665794

Ophthalmologic diagnostic tool using MR images for biomechanically-based muscle volume deformation

NASA Astrophysics Data System (ADS)

Buchberger, Michael; Kaltofen, Thomas

2003-05-01

We would like to give a work-in-progress report on our ophthalmologic diagnostic software system which performs biomechanically-based muscle volume deformations using MR images. For reconstructing a three-dimensional representation of an extraocular eye muscle, a sufficient amount of high resolution MR images is used, each representing a slice of the muscle. In addition, threshold values are given, which restrict the amount of data used from the MR images. The Marching Cube algorithm is applied to the polygons, resulting in a 3D representation of the muscle, which can efficiently be rendered. A transformation to a dynamic, deformable model is applied by calculating the center of gravity of each muscle slice, approximating the muscle path and subsequently adding Hermite splines through the centers of gravity of all slices. Then, a radius function is defined for each slice, completing the transformation of the static 3D polygon model. Finally, this paper describes future extensions to our system. One of these extensions is the support for additional calculations and measurements within the reconstructed 3D muscle representation. Globe translation, localization of muscle pulleys by analyzing the 3D reconstruction in two different gaze positions and other diagnostic measurements will be available.

A Monte Carlo simulation study for the gamma-ray/neutron dual-particle imager using rotational modulation collimator (RMC).

PubMed

Kim, Hyun Suk; Choi, Hong Yeop; Lee, Gyemin; Ye, Sung-Joon; Smith, Martin B; Kim, Geehyun

2018-03-01

The aim of this work is to develop a gamma-ray/neutron dual-particle imager, based on rotational modulation collimators (RMCs) and pulse shape discrimination (PSD)-capable scintillators, for possible applications for radioactivity monitoring as well as nuclear security and safeguards. A Monte Carlo simulation study was performed to design an RMC system for the dual-particle imaging, and modulation patterns were obtained for gamma-ray and neutron sources in various configurations. We applied an image reconstruction algorithm utilizing the maximum-likelihood expectation-maximization method based on the analytical modeling of source-detector configurations, to the Monte Carlo simulation results. Both gamma-ray and neutron source distributions were reconstructed and evaluated in terms of signal-to-noise ratio, showing the viability of developing an RMC-based gamma-ray/neutron dual-particle imager using PSD-capable scintillators.

A pseudo-discrete algebraic reconstruction technique (PDART) prior image-based suppression of high density artifacts in computed tomography

NASA Astrophysics Data System (ADS)

Pua, Rizza; Park, Miran; Wi, Sunhee; Cho, Seungryong

2016-12-01

We propose a hybrid metal artifact reduction (MAR) approach for computed tomography (CT) that is computationally more efficient than a fully iterative reconstruction method, but at the same time achieves superior image quality to the interpolation-based in-painting techniques. Our proposed MAR method, an image-based artifact subtraction approach, utilizes an intermediate prior image reconstructed via PDART to recover the background information underlying the high density objects. For comparison, prior images generated by total-variation minimization (TVM) algorithm, as a realization of fully iterative approach, were also utilized as intermediate images. From the simulation and real experimental results, it has been shown that PDART drastically accelerates the reconstruction to an acceptable quality of prior images. Incorporating PDART-reconstructed prior images in the proposed MAR scheme achieved higher quality images than those by a conventional in-painting method. Furthermore, the results were comparable to the fully iterative MAR that uses high-quality TVM prior images.

Virtual reconstruction of very large skull defects featuring partly and completely missing midsagittal planes.

PubMed

Senck, Sascha; Coquerelle, Michael; Weber, Gerhard W; Benazzi, Stefano

2013-05-01

Despite the development of computer-based methods, cranial reconstruction of very large skull defects remains a challenge particularly if the damage affects the midsagittal region hampering the usage of mirror imaging techniques. This pilot study aims to deliver a new method that goes beyond mirror imaging, giving the possibility to reconstruct crania characterized by large missing areas, which might be useful in the fields of paleoanthropology, bioarcheology, and forensics. We test the accuracy of digital reconstructions in cases where two-thirds or more of a human cranium were missing. A three-dimensional (3D) virtual model of a human cranium was virtually damaged twice to compare two destruction-reconstruction scenarios. In the first case, a small fraction of the midsagittal region was still preserved, allowing the application of mirror imaging techniques. In the second case, the damage affected the complete midsagittal region, which demands a new approach to estimate the position of the midsagittal plane. Reconstructions were carried out using CT scans from a sample of modern humans (12 males and 13 females), to which 3D digital modeling techniques and geometric morphometric methods were applied. As expected, the second simulation showed a larger variability than the first one, which underlines the fact that the individual midsagittal plane is of course preferable in order to minimize the reconstruction error. However, in both simulations the Procrustes mean shape was an effective reference for the reconstruction of the entire cranium, producing models that showed a remarkably low error of about 3 mm, given the extent of missing data. Copyright © 2013 Wiley Periodicals, Inc.

Split Bregman multicoil accelerated reconstruction technique: A new framework for rapid reconstruction of cardiac perfusion MRI

PubMed Central

Kamesh Iyer, Srikant; Tasdizen, Tolga; Likhite, Devavrat; DiBella, Edward

2016-01-01

Purpose: Rapid reconstruction of undersampled multicoil MRI data with iterative constrained reconstruction method is a challenge. The authors sought to develop a new substitution based variable splitting algorithm for faster reconstruction of multicoil cardiac perfusion MRI data. Methods: The new method, split Bregman multicoil accelerated reconstruction technique (SMART), uses a combination of split Bregman based variable splitting and iterative reweighting techniques to achieve fast convergence. Total variation constraints are used along the spatial and temporal dimensions. The method is tested on nine ECG-gated dog perfusion datasets, acquired with a 30-ray golden ratio radial sampling pattern and ten ungated human perfusion datasets, acquired with a 24-ray golden ratio radial sampling pattern. Image quality and reconstruction speed are evaluated and compared to a gradient descent (GD) implementation and to multicoil k-t SLR, a reconstruction technique that uses a combination of sparsity and low rank constraints. Results: Comparisons based on blur metric and visual inspection showed that SMART images had lower blur and better texture as compared to the GD implementation. On average, the GD based images had an ∼18% higher blur metric as compared to SMART images. Reconstruction of dynamic contrast enhanced (DCE) cardiac perfusion images using the SMART method was ∼6 times faster than standard gradient descent methods. k-t SLR and SMART produced images with comparable image quality, though SMART was ∼6.8 times faster than k-t SLR. Conclusions: The SMART method is a promising approach to reconstruct good quality multicoil images from undersampled DCE cardiac perfusion data rapidly. PMID:27036592

Linear Reconstruction of Non-Stationary Image Ensembles Incorporating Blur and Noise Models

DTIC Science & Technology

1998-03-01

for phase distortions due to noise which leads to less deblurring as noise increases [41]. In contrast, the vector Wiener filter incorporates some a...AFIT/DS/ENG/98- 06 Linear Reconstruction of Non-Stationary Image Ensembles Incorporating Blur and Noise Models DISSERTATION Stephen D. Ford Captain...Dissertation 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS LINEAR RECONSTRUCTION OF NON-STATIONARY IMAGE ENSEMBLES INCORPORATING BLUR AND NOISE MODELS 6. AUTHOR(S

Computational tissue volume reconstruction of a peripheral nerve using high-resolution light-microscopy and reconstruct.

PubMed

Gierthmuehlen, Mortimer; Freiman, Thomas M; Haastert-Talini, Kirsten; Mueller, Alexandra; Kaminsky, Jan; Stieglitz, Thomas; Plachta, Dennis T T

2013-01-01

The development of neural cuff-electrodes requires several in vivo studies and revisions of the electrode design before the electrode is completely adapted to its target nerve. It is therefore favorable to simulate many of the steps involved in this process to reduce costs and animal testing. As the restoration of motor function is one of the most interesting applications of cuff-electrodes, the position and trajectories of myelinated fibers in the simulated nerve are important. In this paper, we investigate a method for building a precise neuroanatomical model of myelinated fibers in a peripheral nerve based on images obtained using high-resolution light microscopy. This anatomical model describes the first aim of our "Virtual workbench" project to establish a method for creating realistic neural simulation models based on image datasets. The imaging, processing, segmentation and technical limitations are described, and the steps involved in the transition into a simulation model are presented. The results showed that the position and trajectories of the myelinated axons were traced and virtualized using our technique, and small nerves could be reliably modeled based on of light microscopy images using low-cost OpenSource software and standard hardware. The anatomical model will be released to the scientific community.

Computational Tissue Volume Reconstruction of a Peripheral Nerve Using High-Resolution Light-Microscopy and Reconstruct

PubMed Central

Gierthmuehlen, Mortimer; Freiman, Thomas M.; Haastert-Talini, Kirsten; Mueller, Alexandra; Kaminsky, Jan; Stieglitz, Thomas; Plachta, Dennis T. T.

2013-01-01

The development of neural cuff-electrodes requires several in vivo studies and revisions of the electrode design before the electrode is completely adapted to its target nerve. It is therefore favorable to simulate many of the steps involved in this process to reduce costs and animal testing. As the restoration of motor function is one of the most interesting applications of cuff-electrodes, the position and trajectories of myelinated fibers in the simulated nerve are important. In this paper, we investigate a method for building a precise neuroanatomical model of myelinated fibers in a peripheral nerve based on images obtained using high-resolution light microscopy. This anatomical model describes the first aim of our “Virtual workbench” project to establish a method for creating realistic neural simulation models based on image datasets. The imaging, processing, segmentation and technical limitations are described, and the steps involved in the transition into a simulation model are presented. The results showed that the position and trajectories of the myelinated axons were traced and virtualized using our technique, and small nerves could be reliably modeled based on of light microscopy images using low-cost OpenSource software and standard hardware. The anatomical model will be released to the scientific community. PMID:23785485

Partial fourier and parallel MR image reconstruction with integrated gradient nonlinearity correction.

PubMed

Tao, Shengzhen; Trzasko, Joshua D; Shu, Yunhong; Weavers, Paul T; Huston, John; Gray, Erin M; Bernstein, Matt A

2016-06-01

To describe how integrated gradient nonlinearity (GNL) correction can be used within noniterative partial Fourier (homodyne) and parallel (SENSE and GRAPPA) MR image reconstruction strategies, and demonstrate that performing GNL correction during, rather than after, these routines mitigates the image blurring and resolution loss caused by postreconstruction image domain based GNL correction. Starting from partial Fourier and parallel magnetic resonance imaging signal models that explicitly account for GNL, noniterative image reconstruction strategies for each accelerated acquisition technique are derived under the same core mathematical assumptions as their standard counterparts. A series of phantom and in vivo experiments on retrospectively undersampled data were performed to investigate the spatial resolution benefit of integrated GNL correction over conventional postreconstruction correction. Phantom and in vivo results demonstrate that the integrated GNL correction reduces the image blurring introduced by the conventional GNL correction, while still correcting GNL-induced coarse-scale geometrical distortion. Images generated from undersampled data using the proposed integrated GNL strategies offer superior depiction of fine image detail, for example, phantom resolution inserts and anatomical tissue boundaries. Noniterative partial Fourier and parallel imaging reconstruction methods with integrated GNL correction reduce the resolution loss that occurs during conventional postreconstruction GNL correction while preserving the computational efficiency of standard reconstruction techniques. Magn Reson Med 75:2534-2544, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Park, C; Zhang, H; Chen, Y

Purpose: Recently, compressed sensing (CS) based iterative reconstruction (IR) method is receiving attentions to reconstruct high quality cone beam computed tomography (CBCT) images using sparsely sampled or noisy projections. The aim of this study is to develop a novel baseline algorithm called Mask Guided Image Reconstruction (MGIR), which can provide superior image quality for both low-dose 3DCBCT and 4DCBCT under single mathematical framework. Methods: In MGIR, the unknown CBCT volume was mathematically modeled as a combination of two regions where anatomical structures are 1) within the priori-defined mask and 2) outside the mask. Then we update each part of imagesmore » alternatively thorough solving minimization problems based on CS type IR. For low-dose 3DCBCT, the former region is defined as the anatomically complex region where it is focused to preserve edge information while latter region is defined as contrast uniform, and hence aggressively updated to remove noise/artifact. In 4DCBCT, the regions are separated as the common static part and moving part. Then, static volume and moving volumes were updated with global and phase sorted projection respectively, to optimize the image quality of both moving and static part simultaneously. Results: Examination of MGIR algorithm showed that high quality of both low-dose 3DCBCT and 4DCBCT images can be reconstructed without compromising the image resolution and imaging dose or scanning time respectively. For low-dose 3DCBCT, a clinical viable and high resolution head-and-neck image can be obtained while cutting the dose by 83%. In 4DCBCT, excellent quality 4DCBCT images could be reconstructed while requiring no more projection data and imaging dose than a typical clinical 3DCBCT scan. Conclusion: The results shown that the image quality of MGIR was superior compared to other published CS based IR algorithms for both 4DCBCT and low-dose 3DCBCT. This makes our MGIR algorithm potentially useful in various on-line clinical applications. Provisional Patent: UF#15476; WGS Ref. No. U1198.70067US00.« less

Differential Binary Encoding Method for Calibrating Image Sensors Based on IOFBs

PubMed Central

Fernández, Pedro R.; Lázaro-Galilea, José Luis; Gardel, Alfredo; Espinosa, Felipe; Bravo, Ignacio; Cano, Ángel

2012-01-01

Image transmission using incoherent optical fiber bundles (IOFBs) requires prior calibration to obtain the spatial in-out fiber correspondence necessary to reconstruct the image captured by the pseudo-sensor. This information is recorded in a Look-Up Table called the Reconstruction Table (RT), used later for reordering the fiber positions and reconstructing the original image. This paper presents a very fast method based on image-scanning using spaces encoded by a weighted binary code to obtain the in-out correspondence. The results demonstrate that this technique yields a remarkable reduction in processing time and the image reconstruction quality is very good compared to previous techniques based on spot or line scanning, for example. PMID:22666023

Comparison Study of Three Different Image Reconstruction Algorithms for MAT-MI

PubMed Central

Xia, Rongmin; Li, Xu

2010-01-01

We report a theoretical study on magnetoacoustic tomography with magnetic induction (MAT-MI). According to the description of signal generation mechanism using Green’s function, the acoustic dipole model was proposed to describe acoustic source excited by the Lorentz force. Using Green’s function, three kinds of reconstruction algorithms based on different models of acoustic source (potential energy, vectored acoustic pressure, and divergence of Lorenz force) are deduced and compared, and corresponding numerical simulations were conducted to compare these three kinds of reconstruction algorithms. The computer simulation results indicate that the potential energy method and vectored pressure method can directly reconstruct the Lorentz force distribution and give a more accurate reconstruction of electrical conductivity. PMID:19846363

Cone beam x-ray luminescence computed tomography reconstruction with a priori anatomical information

NASA Astrophysics Data System (ADS)

Lo, Pei-An; Lin, Meng-Lung; Jin, Shih-Chun; Chen, Jyh-Cheng; Lin, Syue-Liang; Chang, C. Allen; Chiang, Huihua Kenny

2014-09-01

X-ray luminescence computed tomography (XLCT) is a novel molecular imaging modality that reconstructs the optical distribution of x-ray-excited phosphor particles with prior informational of anatomical CT image. The prior information improves the accuracy of image reconstruction. The system can also present anatomical CT image. The optical system based on a high sensitive charge coupled device (CCD) is perpendicular with a CT system. In the XLCT system, the xray was adopted to excite the phosphor of the sample and CCD camera was utilized to acquire luminescence emitted from the sample in 360 degrees projection free-space. In this study, the fluorescence diffuse optical tomography (FDOT)-like algorithm was used for image reconstruction, the structural prior information was incorporated in the reconstruction by adding a penalty term to the minimization function. The phosphor used in this study is Gd2O2S:Tb. For the simulation and experiments, the data was collected from 16 projections. The cylinder phantom was 40 mm in diameter and contains 8 mm diameter inclusion; the phosphor in the in vivo study was 5 mm in diameter at a depth of 3 mm. Both the errors were no more than 5%. Based on the results from these simulation and experimental studies, the novel XLCT method has demonstrated the feasibility for in vivo animal model studies.

A 3D terrain reconstruction method of stereo vision based quadruped robot navigation system

NASA Astrophysics Data System (ADS)

Ge, Zhuo; Zhu, Ying; Liang, Guanhao

2017-01-01

To provide 3D environment information for the quadruped robot autonomous navigation system during walking through rough terrain, based on the stereo vision, a novel 3D terrain reconstruction method is presented. In order to solve the problem that images collected by stereo sensors have large regions with similar grayscale and the problem that image matching is poor at real-time performance, watershed algorithm and fuzzy c-means clustering algorithm are combined for contour extraction. Aiming at the problem of error matching, duel constraint with region matching and pixel matching is established for matching optimization. Using the stereo matching edge pixel pairs, the 3D coordinate algorithm is estimated according to the binocular stereo vision imaging model. Experimental results show that the proposed method can yield high stereo matching ratio and reconstruct 3D scene quickly and efficiently.

Three-dimensional self-gated cardiac MR imaging for the evaluation of myocardial infarction in mouse model on a 3T clinical MR system.

PubMed

Zhang, Xiaoyong; Qiu, Bensheng; Wei, Zijun; Yan, Fei; Shi, Caiyun; Su, Shi; Liu, Xin; Ji, Jim X; Xie, Guoxi

2017-01-01

To develop and assess a three-dimensional (3D) self-gated technique for the evaluation of myocardial infarction (MI) in mouse model without the use of external electrocardiogram (ECG) trigger and respiratory motion sensor on a 3T clinical MR system. A 3D T1-weighted GRE sequence with stack-of-stars sampling trajectories was developed and performed on six mice with MIs that were injected with a gadolinium-based contrast agent at a 3T clinical MR system. Respiratory and cardiac self-gating signals were derived from the Cartesian mapping of the k-space center along the partition encoding direction by bandpass filtering in image domain. The data were then realigned according to the predetermined self-gating signals for the following image reconstruction. In order to accelerate the data acquisition, image reconstruction was based on compressed sensing (CS) theory by exploiting temporal sparsity of the reconstructed images. In addition, images were also reconstructed from the same realigned data by conventional regridding method for demonstrating the advantageous of the proposed reconstruction method. Furthermore, the accuracy of detecting MI by the proposed method was assessed using histological analysis as the standard reference. Linear regression and Bland-Altman analysis were used to assess the agreement between the proposed method and the histological analysis. Compared to the conventional regridding method, the proposed CS method reconstructed images with much less streaking artifact, as well as a better contrast-to-noise ratio (CNR) between the blood and myocardium (4.1 ± 2.1 vs. 2.9 ± 1.1, p = 0.031). Linear regression and Bland-Altman analysis demonstrated that excellent correlation was obtained between infarct sizes derived from the proposed method and histology analysis. A 3D T1-weighted self-gating technique for mouse cardiac imaging was developed, which has potential for accurately evaluating MIs in mice at 3T clinical MR system without the use of external ECG trigger and respiratory motion sensor.

Accurate tissue characterization in low-dose CT imaging with pure iterative reconstruction.

PubMed

Murphy, Kevin P; McLaughlin, Patrick D; Twomey, Maria; Chan, Vincent E; Moloney, Fiachra; Fung, Adrian J; Chan, Faimee E; Kao, Tafline; O'Neill, Siobhan B; Watson, Benjamin; O'Connor, Owen J; Maher, Michael M

2017-04-01

We assess the ability of low-dose hybrid iterative reconstruction (IR) and 'pure' model-based IR (MBIR) images to maintain accurate Hounsfield unit (HU)-determined tissue characterization. Standard-protocol (SP) and low-dose modified-protocol (MP) CTs were contemporaneously acquired in 34 Crohn's disease patients referred for CT. SP image reconstruction was via the manufacturer's recommendations (60% FBP, filtered back projection; 40% ASiR, Adaptive Statistical iterative Reconstruction; SP-ASiR40). MP data sets underwent four reconstructions (100% FBP; 40% ASiR; 70% ASiR; MBIR). Three observers measured tissue volumes using HU thresholds for fat, soft tissue and bone/contrast on each data set. Analysis was via SPSS. Inter-observer agreement was strong for 1530 datapoints (rs > 0.9). MP-MBIR tissue volume measurement was superior to other MP reconstructions and closely correlated with the reference SP-ASiR40 images for all tissue types. MP-MBIR superiority was most marked for fat volume calculation - close SP-ASiR40 and MP-MBIR Bland-Altman plot correlation was seen with the lowest average difference (336 cm 3 ) when compared with other MP reconstructions. Hounsfield unit-determined tissue volume calculations from MP-MBIR images resulted in values comparable to SP-ASiR40 calculations and values that are superior to MP-ASiR images. Accuracy of estimation of volume of tissues (e.g. fat) using segmentation software on low-dose CT images appears optimal when reconstructed with pure IR. © 2016 The Royal Australian and New Zealand College of Radiologists.

Comparison of breathing gated CT images generated using a 5DCT technique and a commercial clinical protocol in a porcine model

PubMed Central

O’Connell, Dylan P.; Thomas, David H.; Dou, Tai H.; Lamb, James M.; Feingold, Franklin; Low, Daniel A.; Fuld, Matthew K.; Sieren, Jered P.; Sloan, Chelsea M.; Shirk, Melissa A.; Hoffman, Eric A.; Hofmann, Christian

2015-01-01

Purpose: To demonstrate that a “5DCT” technique which utilizes fast helical acquisition yields the same respiratory-gated images as a commercial technique for regular, mechanically produced breathing cycles. Methods: Respiratory-gated images of an anesthetized, mechanically ventilated pig were generated using a Siemens low-pitch helical protocol and 5DCT for a range of breathing rates and amplitudes and with standard and low dose imaging protocols. 5DCT reconstructions were independently evaluated by measuring the distances between tissue positions predicted by a 5D motion model and those measured using deformable registration, as well by reconstructing the originally acquired scans. Discrepancies between the 5DCT and commercial reconstructions were measured using landmark correspondences. Results: The mean distance between model predicted tissue positions and deformably registered tissue positions over the nine datasets was 0.65 ± 0.28 mm. Reconstructions of the original scans were on average accurate to 0.78 ± 0.57 mm. Mean landmark displacement between the commercial and 5DCT images was 1.76 ± 1.25 mm while the maximum lung tissue motion over the breathing cycle had a mean value of 27.2 ± 4.6 mm. An image composed of the average of 30 deformably registered images acquired with a low dose protocol had 6 HU image noise (single standard deviation) in the heart versus 31 HU for the commercial images. Conclusions: An end to end evaluation of the 5DCT technique was conducted through landmark based comparison to breathing gated images acquired with a commercial protocol under highly regular ventilation. The techniques were found to agree to within 2 mm for most respiratory phases and most points in the lung. PMID:26133604

3D widefield light microscope image reconstruction without dyes

NASA Astrophysics Data System (ADS)

Larkin, S.; Larson, J.; Holmes, C.; Vaicik, M.; Turturro, M.; Jurkevich, A.; Sinha, S.; Ezashi, T.; Papavasiliou, G.; Brey, E.; Holmes, T.

2015-03-01

3D image reconstruction using light microscope modalities without exogenous contrast agents is proposed and investigated as an approach to produce 3D images of biological samples for live imaging applications. Multimodality and multispectral imaging, used in concert with this 3D optical sectioning approach is also proposed as a way to further produce contrast that could be specific to components in the sample. The methods avoid usage of contrast agents. Contrast agents, such as fluorescent or absorbing dyes, can be toxic to cells or alter cell behavior. Current modes of producing 3D image sets from a light microscope, such as 3D deconvolution algorithms and confocal microscopy generally require contrast agents. Zernike phase contrast (ZPC), transmitted light brightfield (TLB), darkfield microscopy and others can produce contrast without dyes. Some of these modalities have not previously benefitted from 3D image reconstruction algorithms, however. The 3D image reconstruction algorithm is based on an underlying physical model of scattering potential, expressed as the sample's 3D absorption and phase quantities. The algorithm is based upon optimizing an objective function - the I-divergence - while solving for the 3D absorption and phase quantities. Unlike typical deconvolution algorithms, each microscope modality, such as ZPC or TLB, produces two output image sets instead of one. Contrast in the displayed image and 3D renderings is further enabled by treating the multispectral/multimodal data as a feature set in a mathematical formulation that uses the principal component method of statistics.

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

PubMed Central

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.; Pan, Xiaochuan

2010-01-01

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack–Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories. PMID:20175463

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT.

PubMed

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A; Pan, Xiaochuan

2010-01-01

Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. The proposed approach comprises of two reconstruction steps. In the first step, a chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredback-projection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.

Impact of reconstruction parameters on quantitative I-131 SPECT

NASA Astrophysics Data System (ADS)

van Gils, C. A. J.; Beijst, C.; van Rooij, R.; de Jong, H. W. A. M.

2016-07-01

Radioiodine therapy using I-131 is widely used for treatment of thyroid disease or neuroendocrine tumors. Monitoring treatment by accurate dosimetry requires quantitative imaging. The high energy photons however render quantitative SPECT reconstruction challenging, potentially requiring accurate correction for scatter and collimator effects. The goal of this work is to assess the effectiveness of various correction methods on these effects using phantom studies. A SPECT/CT acquisition of the NEMA IEC body phantom was performed. Images were reconstructed using the following parameters: (1) without scatter correction, (2) with triple energy window (TEW) scatter correction and (3) with Monte Carlo-based scatter correction. For modelling the collimator-detector response (CDR), both (a) geometric Gaussian CDRs as well as (b) Monte Carlo simulated CDRs were compared. Quantitative accuracy, contrast to noise ratios and recovery coefficients were calculated, as well as the background variability and the residual count error in the lung insert. The Monte Carlo scatter corrected reconstruction method was shown to be intrinsically quantitative, requiring no experimentally acquired calibration factor. It resulted in a more accurate quantification of the background compartment activity density compared with TEW or no scatter correction. The quantification error relative to a dose calibrator derived measurement was found to be  <1%,-26% and 33%, respectively. The adverse effects of partial volume were significantly smaller with the Monte Carlo simulated CDR correction compared with geometric Gaussian or no CDR modelling. Scatter correction showed a small effect on quantification of small volumes. When using a weighting factor, TEW correction was comparable to Monte Carlo reconstruction in all measured parameters, although this approach is clinically impractical since this factor may be patient dependent. Monte Carlo based scatter correction including accurately simulated CDR modelling is the most robust and reliable method to reconstruct accurate quantitative iodine-131 SPECT images.

SPIRiT: Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space

PubMed Central

Lustig, Michael; Pauly, John M.

2010-01-01

A new approach to autocalibrating, coil-by-coil parallel imaging reconstruction is presented. It is a generalized reconstruction framework based on self consistency. The reconstruction problem is formulated as an optimization that yields the most consistent solution with the calibration and acquisition data. The approach is general and can accurately reconstruct images from arbitrary k-space sampling patterns. The formulation can flexibly incorporate additional image priors such as off-resonance correction and regularization terms that appear in compressed sensing. Several iterative strategies to solve the posed reconstruction problem in both image and k-space domain are presented. These are based on a projection over convex sets (POCS) and a conjugate gradient (CG) algorithms. Phantom and in-vivo studies demonstrate efficient reconstructions from undersampled Cartesian and spiral trajectories. Reconstructions that include off-resonance correction and nonlinear ℓ1-wavelet regularization are also demonstrated. PMID:20665790

SU-G-IeP1-13: Sub-Nyquist Dynamic MRI Via Prior Rank, Intensity and Sparsity Model (PRISM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, B; Gao, H

Purpose: Accelerated dynamic MRI is important for MRI guided radiotherapy. Inspired by compressive sensing (CS), sub-Nyquist dynamic MRI has been an active research area, i.e., sparse sampling in k-t space for accelerated dynamic MRI. This work is to investigate sub-Nyquist dynamic MRI via a previously developed CS model, namely Prior Rank, Intensity and Sparsity Model (PRISM). Methods: The proposed method utilizes PRISM with rank minimization and incoherent sampling patterns for sub-Nyquist reconstruction. In PRISM, the low-rank background image, which is automatically calculated by rank minimization, is excluded from the L1 minimization step of the CS reconstruction to further sparsify themore » residual image, thus allowing for higher acceleration rates. Furthermore, the sampling pattern in k-t space is made more incoherent by sampling a different set of k-space points at different temporal frames. Results: Reconstruction results from L1-sparsity method and PRISM method with 30% undersampled data and 15% undersampled data are compared to demonstrate the power of PRISM for dynamic MRI. Conclusion: A sub- Nyquist MRI reconstruction method based on PRISM is developed with improved image quality from the L1-sparsity method.« less

A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography.

PubMed

Cai, C; Rodet, T; Legoupil, S; Mohammad-Djafari, A

2013-11-01

Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images. This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed. The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions. The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.

Penrose high-dynamic-range imaging

NASA Astrophysics Data System (ADS)

Li, Jia; Bai, Chenyan; Lin, Zhouchen; Yu, Jian

2016-05-01

High-dynamic-range (HDR) imaging is becoming increasingly popular and widespread. The most common multishot HDR approach, based on multiple low-dynamic-range images captured with different exposures, has difficulties in handling camera and object movements. The spatially varying exposures (SVE) technology provides a solution to overcome this limitation by obtaining multiple exposures of the scene in only one shot but suffers from a loss in spatial resolution of the captured image. While aperiodic assignment of exposures has been shown to be advantageous during reconstruction in alleviating resolution loss, almost all the existing imaging sensors use the square pixel layout, which is a periodic tiling of square pixels. We propose the Penrose pixel layout, using pixels in aperiodic rhombus Penrose tiling, for HDR imaging. With the SVE technology, Penrose pixel layout has both exposure and pixel aperiodicities. To investigate its performance, we have to reconstruct HDR images in square pixel layout from Penrose raw images with SVE. Since the two pixel layouts are different, the traditional HDR reconstruction methods are not applicable. We develop a reconstruction method for Penrose pixel layout using a Gaussian mixture model for regularization. Both quantitative and qualitative results show the superiority of Penrose pixel layout over square pixel layout.

Direct biomechanical modeling of trabecular bone using a nonlinear manifold-based volumetric representation

NASA Astrophysics Data System (ADS)

Jin, Dakai; Lu, Jia; Zhang, Xiaoliu; Chen, Cheng; Bai, ErWei; Saha, Punam K.

2017-03-01

Osteoporosis is associated with increased fracture risk. Recent advancement in the area of in vivo imaging allows segmentation of trabecular bone (TB) microstructures, which is a known key determinant of bone strength and fracture risk. An accurate biomechanical modelling of TB micro-architecture provides a comprehensive summary measure of bone strength and fracture risk. In this paper, a new direct TB biomechanical modelling method using nonlinear manifold-based volumetric reconstruction of trabecular network is presented. It is accomplished in two sequential modules. The first module reconstructs a nonlinear manifold-based volumetric representation of TB networks from three-dimensional digital images. Specifically, it starts with the fuzzy digital segmentation of a TB network, and computes its surface and curve skeletons. An individual trabecula is identified as a topological segment in the curve skeleton. Using geometric analysis, smoothing and optimization techniques, the algorithm generates smooth, curved, and continuous representations of individual trabeculae glued at their junctions. Also, the method generates a geometrically consistent TB volume at junctions. In the second module, a direct computational biomechanical stress-strain analysis is applied on the reconstructed TB volume to predict mechanical measures. The accuracy of the method was examined using micro-CT imaging of cadaveric distal tibia specimens (N = 12). A high linear correlation (r = 0.95) between TB volume computed using the new manifold-modelling algorithm and that directly derived from the voxel-based micro-CT images was observed. Young's modulus (YM) was computed using direct mechanical analysis on the TB manifold-model over a cubical volume of interest (VOI), and its correlation with the YM, computed using micro-CT based conventional finite-element analysis over the same VOI, was examined. A moderate linear correlation (r = 0.77) was observed between the two YM measures. This preliminary results show the accuracy of the new nonlinear manifold modelling algorithm for TB, and demonstrate the feasibility of a new direct mechanical strain-strain analysis on a nonlinear manifold model of a highly complex biological structure.

Right ventricular volumes assessed by echocardiographic three-dimensional knowledge-based reconstruction compared with magnetic resonance imaging in a clinical setting.

PubMed

Neukamm, Christian; Try, Kirsti; Norgård, Gunnar; Brun, Henrik

2014-01-01

A technique that uses two-dimensional images to create a knowledge-based, three-dimensional model was tested and compared to magnetic resonance imaging. Measurement of right ventricular volumes and function is important in the follow-up of patients after pulmonary valve replacement. Magnetic resonance imaging is the gold standard for volumetric assessment. Echocardiographic methods have been validated and are attractive alternatives. Thirty patients with tetralogy of Fallot (25 ± 14 years) after pulmonary valve replacement were examined. Magnetic resonance imaging volumetric measurements and echocardiography-based three-dimensional reconstruction were performed. End-diastolic volume, end-systolic volume, and ejection fraction were measured, and the results were compared. Magnetic resonance imaging measurements gave coefficient of variation in the intraobserver study of 3.5, 4.6, and 5.3 and in the interobserver study of 3.6, 5.9, and 6.7 for end-diastolic volume, end-systolic volume, and ejection fraction, respectively. Echocardiographic three-dimensional reconstruction was highly feasible (97%). In the intraobserver study, the corresponding values were 6.0, 7.0, and 8.9 and in the interobserver study 7.4, 10.8, and 13.4. In comparison of the methods, correlations with magnetic resonance imaging were r = 0.91, 0.91, and 0.38, and the corresponding coefficient of variations were 9.4, 10.8, and 14.7. Echocardiography derived volumes (mL/m(2)) were significantly higher than magnetic resonance imaging volumes in end-diastolic volume 13.7 ± 25.6 and in end-systolic volume 9.1 ± 17.0 (both P < .05). The knowledge-based three-dimensional right ventricular volume method was highly feasible. Intra and interobserver variabilities were satisfactory. Agreement with magnetic resonance imaging measurements for volumes was reasonable but unsatisfactory for ejection fraction. Knowledge-based reconstruction may replace magnetic resonance imaging measurements for serial follow-up, whereas magnetic resonance imaging should be used for surgical decision making.

Image fusion in craniofacial virtual reality modeling based on CT and 3dMD photogrammetry.

PubMed

Xin, Pengfei; Yu, Hongbo; Cheng, Huanchong; Shen, Shunyao; Shen, Steve G F

2013-09-01

The aim of this study was to demonstrate the feasibility of building a craniofacial virtual reality model by image fusion of 3-dimensional (3D) CT models and 3 dMD stereophotogrammetric facial surface. A CT scan and stereophotography were performed. The 3D CT models were reconstructed by Materialise Mimics software, and the stereophotogrammetric facial surface was reconstructed by 3 dMD patient software. All 3D CT models were exported as Stereo Lithography file format, and the 3 dMD model was exported as Virtual Reality Modeling Language file format. Image registration and fusion were performed in Mimics software. Genetic algorithm was used for precise image fusion alignment with minimum error. The 3D CT models and the 3 dMD stereophotogrammetric facial surface were finally merged into a single file and displayed using Deep Exploration software. Errors between the CT soft tissue model and 3 dMD facial surface were also analyzed. Virtual model based on CT-3 dMD image fusion clearly showed the photorealistic face and bone structures. Image registration errors in virtual face are mainly located in bilateral cheeks and eyeballs, and the errors are more than 1.5 mm. However, the image fusion of whole point cloud sets of CT and 3 dMD is acceptable with a minimum error that is less than 1 mm. The ease of use and high reliability of CT-3 dMD image fusion allows the 3D virtual head to be an accurate, realistic, and widespread tool, and has a great benefit to virtual face model.

A Reconstruction Algorithm of Magnetoacoustic Tomography with Magnetic Induction for Acoustically Inhomogeneous Tissue

PubMed Central

Zhou, Lian; Zhu, Shanan

2014-01-01

Magnetoacoustic tomography with Magnetic Induction (MAT-MI) is a noninvasive electrical conductivity imaging approach that measures ultrasound wave induced by magnetic stimulation, for reconstructing the distribution of electrical impedance in biological tissue. Existing reconstruction algorithms for MAT-MI are based on the assumption that the acoustic properties in the tissue are homogeneous. However, the tissue in most parts of human body, has heterogeneous acoustic properties, which leads to potential distortion and blurring of small buried objects in the impedance images. In the present study, we proposed a new algorithm for MAT-MI to image the impedance distribution in tissues with inhomogeneous acoustic speed distributions. With a computer head model constructed from MR images of a human subject, a series of numerical simulation experiments were conducted. The present results indicate that the inhomogeneous acoustic properties of tissues in terms of speed variation can be incorporated in MAT-MI imaging. PMID:24845284

An interior-point method for total variation regularized positron emission tomography image reconstruction

NASA Astrophysics Data System (ADS)

Bai, Bing

2012-03-01

There has been a lot of work on total variation (TV) regularized tomographic image reconstruction recently. Many of them use gradient-based optimization algorithms with a differentiable approximation of the TV functional. In this paper we apply TV regularization in Positron Emission Tomography (PET) image reconstruction. We reconstruct the PET image in a Bayesian framework, using Poisson noise model and TV prior functional. The original optimization problem is transformed to an equivalent problem with inequality constraints by adding auxiliary variables. Then we use an interior point method with logarithmic barrier functions to solve the constrained optimization problem. In this method, a series of points approaching the solution from inside the feasible region are found by solving a sequence of subproblems characterized by an increasing positive parameter. We use preconditioned conjugate gradient (PCG) algorithm to solve the subproblems directly. The nonnegativity constraint is enforced by bend line search. The exact expression of the TV functional is used in our calculations. Simulation results show that the algorithm converges fast and the convergence is insensitive to the values of the regularization and reconstruction parameters.

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data.

PubMed

Kotasidis, F A; Mehranian, A; Zaidi, H

2016-05-07

Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image reconstruction can substantially prevent kinetic parameter error propagation either from erroneous kinetic modelling, inter-frame motion or emission/transmission mismatch. Furthermore, we demonstrate the benefits of TOF in parameter estimation when conventional post-reconstruction (3D) methods are used and compare the potential improvements to direct 4D methods. Further improvements could possibly be achieved in the future by combining TOF direct 4D image reconstruction with adaptive kinetic models and inter-frame motion correction schemes.

Impact of time-of-flight on indirect 3D and direct 4D parametric image reconstruction in the presence of inconsistent dynamic PET data

NASA Astrophysics Data System (ADS)

Kotasidis, F. A.; Mehranian, A.; Zaidi, H.

2016-05-01

Kinetic parameter estimation in dynamic PET suffers from reduced accuracy and precision when parametric maps are estimated using kinetic modelling following image reconstruction of the dynamic data. Direct approaches to parameter estimation attempt to directly estimate the kinetic parameters from the measured dynamic data within a unified framework. Such image reconstruction methods have been shown to generate parametric maps of improved precision and accuracy in dynamic PET. However, due to the interleaving between the tomographic and kinetic modelling steps, any tomographic or kinetic modelling errors in certain regions or frames, tend to spatially or temporally propagate. This results in biased kinetic parameters and thus limits the benefits of such direct methods. Kinetic modelling errors originate from the inability to construct a common single kinetic model for the entire field-of-view, and such errors in erroneously modelled regions could spatially propagate. Adaptive models have been used within 4D image reconstruction to mitigate the problem, though they are complex and difficult to optimize. Tomographic errors in dynamic imaging on the other hand, can originate from involuntary patient motion between dynamic frames, as well as from emission/transmission mismatch. Motion correction schemes can be used, however, if residual errors exist or motion correction is not included in the study protocol, errors in the affected dynamic frames could potentially propagate either temporally, to other frames during the kinetic modelling step or spatially, during the tomographic step. In this work, we demonstrate a new strategy to minimize such error propagation in direct 4D image reconstruction, focusing on the tomographic step rather than the kinetic modelling step, by incorporating time-of-flight (TOF) within a direct 4D reconstruction framework. Using ever improving TOF resolutions (580 ps, 440 ps, 300 ps and 160 ps), we demonstrate that direct 4D TOF image reconstruction can substantially prevent kinetic parameter error propagation either from erroneous kinetic modelling, inter-frame motion or emission/transmission mismatch. Furthermore, we demonstrate the benefits of TOF in parameter estimation when conventional post-reconstruction (3D) methods are used and compare the potential improvements to direct 4D methods. Further improvements could possibly be achieved in the future by combining TOF direct 4D image reconstruction with adaptive kinetic models and inter-frame motion correction schemes.

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

NASA Astrophysics Data System (ADS)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

Recovering the 3d Pose and Shape of Vehicles from Stereo Images

NASA Astrophysics Data System (ADS)

Coenen, M.; Rottensteiner, F.; Heipke, C.

2018-05-01

The precise reconstruction and pose estimation of vehicles plays an important role, e.g. for autonomous driving. We tackle this problem on the basis of street level stereo images obtained from a moving vehicle. Starting from initial vehicle detections, we use a deformable vehicle shape prior learned from CAD vehicle data to fully reconstruct the vehicles in 3D and to recover their 3D pose and shape. To fit a deformable vehicle model to each detection by inferring the optimal parameters for pose and shape, we define an energy function leveraging reconstructed 3D data, image information, the vehicle model and derived scene knowledge. To minimise the energy function, we apply a robust model fitting procedure based on iterative Monte Carlo model particle sampling. We evaluate our approach using the object detection and orientation estimation benchmark of the KITTI dataset (Geiger et al., 2012). Our approach can deal with very coarse pose initialisations and we achieve encouraging results with up to 82 % correct pose estimations. Moreover, we are able to deliver very precise orientation estimation results with an average absolute error smaller than 4°.

SU-E-T-428: Feasibility Study of 4D Image Reconstruction by Organ Motion Vector Extension Based On Portal Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, J; Jung, J; Yeo, I

2015-06-15

Purpose: To develop and to test a method to generate a new 4D CT images of the treatment day from the old 4D CT and the portal images of the day when the motion extent exceeded from that represented by plan CTs. Methods: A motion vector of a moving tumor in a patient may be extended to reconstruct the tumor position when the motion extent exceeded from that represented by plan CTs. To test this, 1. a phantom that consists of a polystyrene cylinder (tumor) embedded in cork (lung) was placed on a moving platform with 4 sec/cycle and amplitudesmore » of 1 cm and 2 cm, and was 4D-scanned. 2. A 6MV photon beam was irradiated on the moving phantoms and cineEPID images were obtained. 3. A motion vector of the tumor was acquired from 4D CT images of the phantom with 1 cm amplitude. 4. From cine EPID images of the phantom with the 2 cm amplitude, various motion extents (0.3 cm, 0.5 cm, etc) were acquired and programmed into the motion vector, producing CT images at each position. 5. The reconstructed CT images were then compared with pre-acquired “reference” 4D CT images at each position (i.e. phase). Results: The CT image was reconstructed and compared with the reference image, showing a slight mismatch in the transition direction limited by voxel size (slice thickness) in CT image. Due to the rigid nature of the phantom studied, the modeling the displacement of the center of object was sufficient. When deformable tumors are to be modeled, more complex scheme is necessary, which utilize cine EPID and 4D CT images. Conclusion: The new idea of CT image reconstruction was demonstrated. Deformable tumor movements need to be considered in the future.« less

Impact of knowledge-based iterative model reconstruction on myocardial late iodine enhancement in computed tomography and comparison with cardiac magnetic resonance.

PubMed

Tanabe, Yuki; Kido, Teruhito; Kurata, Akira; Fukuyama, Naoki; Yokoi, Takahiro; Kido, Tomoyuki; Uetani, Teruyoshi; Vembar, Mani; Dhanantwari, Amar; Tokuyasu, Shinichi; Yamashita, Natsumi; Mochizuki, Teruhito

2017-10-01

We evaluated the image quality and diagnostic performance of late iodine enhancement computed tomography (LIE-CT) with knowledge-based iterative model reconstruction (IMR) for the detection of myocardial infarction (MI) in comparison with late gadolinium enhancement magnetic resonance imaging (LGE-MRI). The study investigated 35 patients who underwent a comprehensive cardiac CT protocol and LGE-MRI for the assessment of coronary artery disease. The CT protocol consisted of stress dynamic myocardial CT perfusion, coronary CT angiography (CTA) and LIE-CT using 256-slice CT. LIE-CT scans were acquired 5 min after CTA without additional contrast medium and reconstructed with filtered back projection (FBP), a hybrid iterative reconstruction (HIR), and IMR. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were assessed. Sensitivity and specificity of LIE-CT for detecting MI were assessed according to the 16-segment model. Image quality scores, and diagnostic performance were compared among LIE-CT with FBP, HIR and IMR. Among the 35 patients, 139 of 560 segments showed MI in LGE-MRI. On LIE-CT with FBP, HIR, and IMR, the median SNRs were 2.1, 2.9, and 6.1; and the median CNRs were 1.7, 2.2, and 4.7, respectively. Sensitivity and specificity were 56 and 93% for FBP, 62 and 91% for HIR, and 80 and 91% for IMR. LIE-CT with IMR showed the highest image quality and sensitivity (p < 0.05). The use of IMR enables significant improvement of image quality and diagnostic performance of LIE-CT for detecting MI in comparison with FBP and HIR.

Hyperspectral image reconstruction for x-ray fluorescence tomography

DOE PAGES

Gürsoy, Doǧa; Biçer, Tekin; Lanzirotti, Antonio; ...

2015-01-01

A penalized maximum-likelihood estimation is proposed to perform hyperspectral (spatio-spectral) image reconstruction for X-ray fluorescence tomography. The approach minimizes a Poisson-based negative log-likelihood of the observed photon counts, and uses a penalty term that has the effect of encouraging local continuity of model parameter estimates in both spatial and spectral dimensions simultaneously. The performance of the reconstruction method is demonstrated with experimental data acquired from a seed of arabidopsis thaliana collected at the 13-ID-E microprobe beamline at the Advanced Photon Source. The resulting element distribution estimates with the proposed approach show significantly better reconstruction quality than the conventional analytical inversionmore » approaches, and allows for a high data compression factor which can reduce data acquisition times remarkably. In particular, this technique provides the capability to tomographically reconstruct full energy dispersive spectra without compromising reconstruction artifacts that impact the interpretation of results.« less

Exploiting sparsity and low-rank structure for the recovery of multi-slice breast MRIs with reduced sampling error.

PubMed

Yin, X X; Ng, B W-H; Ramamohanarao, K; Baghai-Wadji, A; Abbott, D

2012-09-01

It has been shown that, magnetic resonance images (MRIs) with sparsity representation in a transformed domain, e.g. spatial finite-differences (FD), or discrete cosine transform (DCT), can be restored from undersampled k-space via applying current compressive sampling theory. The paper presents a model-based method for the restoration of MRIs. The reduced-order model, in which a full-system-response is projected onto a subspace of lower dimensionality, has been used to accelerate image reconstruction by reducing the size of the involved linear system. In this paper, the singular value threshold (SVT) technique is applied as a denoising scheme to reduce and select the model order of the inverse Fourier transform image, and to restore multi-slice breast MRIs that have been compressively sampled in k-space. The restored MRIs with SVT for denoising show reduced sampling errors compared to the direct MRI restoration methods via spatial FD, or DCT. Compressive sampling is a technique for finding sparse solutions to underdetermined linear systems. The sparsity that is implicit in MRIs is to explore the solution to MRI reconstruction after transformation from significantly undersampled k-space. The challenge, however, is that, since some incoherent artifacts result from the random undersampling, noise-like interference is added to the image with sparse representation. These recovery algorithms in the literature are not capable of fully removing the artifacts. It is necessary to introduce a denoising procedure to improve the quality of image recovery. This paper applies a singular value threshold algorithm to reduce the model order of image basis functions, which allows further improvement of the quality of image reconstruction with removal of noise artifacts. The principle of the denoising scheme is to reconstruct the sparse MRI matrices optimally with a lower rank via selecting smaller number of dominant singular values. The singular value threshold algorithm is performed by minimizing the nuclear norm of difference between the sampled image and the recovered image. It has been illustrated that this algorithm improves the ability of previous image reconstruction algorithms to remove noise artifacts while significantly improving the quality of MRI recovery.

Advanced prior modeling for 3D bright field electron tomography

NASA Astrophysics Data System (ADS)

Sreehari, Suhas; Venkatakrishnan, S. V.; Drummy, Lawrence F.; Simmons, Jeffrey P.; Bouman, Charles A.

2015-03-01

Many important imaging problems in material science involve reconstruction of images containing repetitive non-local structures. Model-based iterative reconstruction (MBIR) could in principle exploit such redundancies through the selection of a log prior probability term. However, in practice, determining such a log prior term that accounts for the similarity between distant structures in the image is quite challenging. Much progress has been made in the development of denoising algorithms like non-local means and BM3D, and these are known to successfully capture non-local redundancies in images. But the fact that these denoising operations are not explicitly formulated as cost functions makes it unclear as to how to incorporate them in the MBIR framework. In this paper, we formulate a solution to bright field electron tomography by augmenting the existing bright field MBIR method to incorporate any non-local denoising operator as a prior model. We accomplish this using a framework we call plug-and-play priors that decouples the log likelihood and the log prior probability terms in the MBIR cost function. We specifically use 3D non-local means (NLM) as the prior model in the plug-and-play framework, and showcase high quality tomographic reconstructions of a simulated aluminum spheres dataset, and two real datasets of aluminum spheres and ferritin structures. We observe that streak and smear artifacts are visibly suppressed, and that edges are preserved. Also, we report lower RMSE values compared to the conventional MBIR reconstruction using qGGMRF as the prior model.

3D reconstruction based on light field images

NASA Astrophysics Data System (ADS)

Zhu, Dong; Wu, Chunhong; Liu, Yunluo; Fu, Dongmei

2018-04-01

This paper proposed a method of reconstructing three-dimensional (3D) scene from two light field images capture by Lytro illium. The work was carried out by first extracting the sub-aperture images from light field images and using the scale-invariant feature transform (SIFT) for feature registration on the selected sub-aperture images. Structure from motion (SFM) algorithm is further used on the registration completed sub-aperture images to reconstruct the three-dimensional scene. 3D sparse point cloud was obtained in the end. The method shows that the 3D reconstruction can be implemented by only two light field camera captures, rather than at least a dozen times captures by traditional cameras. This can effectively solve the time-consuming, laborious issues for 3D reconstruction based on traditional digital cameras, to achieve a more rapid, convenient and accurate reconstruction.

CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction.

PubMed

Ichikawa, Yasutaka; Kitagawa, Kakuya; Nagasawa, Naoki; Murashima, Shuichi; Sakuma, Hajime

2013-08-09

The recently developed model-based iterative reconstruction (MBIR) enables significant reduction of image noise and artifacts, compared with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP). The purpose of this study was to evaluate lesion detectability of low-dose chest computed tomography (CT) with MBIR in comparison with ASIR and FBP. Chest CT was acquired with 64-slice CT (Discovery CT750HD) with standard-dose (5.7 ± 2.3 mSv) and low-dose (1.6 ± 0.8 mSv) conditions in 55 patients (aged 72 ± 7 years) who were suspected of lung disease on chest radiograms. Low-dose CT images were reconstructed with MBIR, ASIR 50% and FBP, and standard-dose CT images were reconstructed with FBP, using a reconstructed slice thickness of 0.625 mm. Two observers evaluated the image quality of abnormal lung and mediastinal structures on a 5-point scale (Score 5 = excellent and score 1 = non-diagnostic). The objective image noise was also measured as the standard deviation of CT intensity in the descending aorta. The image quality score of enlarged mediastinal lymph nodes on low-dose MBIR CT (4.7 ± 0.5) was significantly improved in comparison with low-dose FBP and ASIR CT (3.0 ± 0.5, p = 0.004; 4.0 ± 0.5, p = 0.02, respectively), and was nearly identical to the score of standard-dose FBP image (4.8 ± 0.4, p = 0.66). Concerning decreased lung attenuation (bulla, emphysema, or cyst), the image quality score on low-dose MBIR CT (4.9 ± 0.2) was slightly better compared to low-dose FBP and ASIR CT (4.5 ± 0.6, p = 0.01; 4.6 ± 0.5, p = 0.01, respectively). There were no significant differences in image quality scores of visualization of consolidation or mass, ground-glass attenuation, or reticular opacity among low- and standard-dose CT series. Image noise with low-dose MBIR CT (11.6 ± 1.0 Hounsfield units (HU)) were significantly lower than with low-dose ASIR (21.1 ± 2.6 HU, p < 0.0005), low-dose FBP CT (30.9 ± 3.9 HU, p < 0.0005), and standard-dose FBP CT (16.6 ± 2.3 HU, p < 0.0005). MBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT without compromising image quality. With radiation dose reduction of >70%, MBIR can provide equivalent lesion detectability of standard-dose FBP CT.

Investigation into image quality difference between total variation and nonlinear sparsifying transform based compressed sensing

NASA Astrophysics Data System (ADS)

Dong, Jian; Kudo, Hiroyuki

2017-03-01

Compressed sensing (CS) is attracting growing concerns in sparse-view computed tomography (CT) image reconstruction. The most standard approach of CS is total variation (TV) minimization. However, images reconstructed by TV usually suffer from distortions, especially in reconstruction of practical CT images, in forms of patchy artifacts, improper serrate edges and loss of image textures. Most existing CS approaches including TV achieve image quality improvement by applying linear transforms to object image, but linear transforms usually fail to take discontinuities into account, such as edges and image textures, which is considered to be the key reason for image distortions. Actually, discussions on nonlinear filter based image processing has a long history, leading us to clarify that the nonlinear filters yield better results compared to linear filters in image processing task such as denoising. Median root prior was first utilized by Alenius as nonlinear transform in CT image reconstruction, with significant gains obtained. Subsequently, Zhang developed the application of nonlocal means-based CS. A fact is gradually becoming clear that the nonlinear transform based CS has superiority in improving image quality compared with the linear transform based CS. However, it has not been clearly concluded in any previous paper within the scope of our knowledge. In this work, we investigated the image quality differences between the conventional TV minimization and nonlinear sparsifying transform based CS, as well as image quality differences among different nonlinear sparisying transform based CSs in sparse-view CT image reconstruction. Additionally, we accelerated the implementation of nonlinear sparsifying transform based CS algorithm.

Registration of 3D fetal neurosonography and MRI☆

PubMed Central

Kuklisova-Murgasova, Maria; Cifor, Amalia; Napolitano, Raffaele; Papageorghiou, Aris; Quaghebeur, Gerardine; Rutherford, Mary A.; Hajnal, Joseph V.; Noble, J. Alison; Schnabel, Julia A.

2013-01-01

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image. PMID:23969169

Reconstruction of electrical impedance tomography (EIT) images based on the expectation maximum (EM) method.

PubMed

Wang, Qi; Wang, Huaxiang; Cui, Ziqiang; Yang, Chengyi

2012-11-01

Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Test of 3D CT reconstructions by EM + TV algorithm from undersampled data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Evseev, Ivan; Ahmann, Francielle; Silva, Hamilton P. da

2013-05-06

Computerized tomography (CT) plays an important role in medical imaging for diagnosis and therapy. However, CT imaging is connected with ionization radiation exposure of patients. Therefore, the dose reduction is an essential issue in CT. In 2011, the Expectation Maximization and Total Variation Based Model for CT Reconstruction (EM+TV) was proposed. This method can reconstruct a better image using less CT projections in comparison with the usual filtered back projection (FBP) technique. Thus, it could significantly reduce the overall dose of radiation in CT. This work reports the results of an independent numerical simulation for cone beam CT geometry withmore » alternative virtual phantoms. As in the original report, the 3D CT images of 128 Multiplication-Sign 128 Multiplication-Sign 128 virtual phantoms were reconstructed. It was not possible to implement phantoms with lager dimensions because of the slowness of code execution even by the CORE i7 CPU.« less

Simultaneous reconstruction of emission activity and attenuation coefficient distribution from TOF data, acquired with external transmission source

NASA Astrophysics Data System (ADS)

Panin, V. Y.; Aykac, M.; Casey, M. E.

2013-06-01

The simultaneous PET data reconstruction of emission activity and attenuation coefficient distribution is presented, where the attenuation image is constrained by exploiting an external transmission source. Data are acquired in time-of-flight (TOF) mode, allowing in principle for separation of emission and transmission data. Nevertheless, here all data are reconstructed at once, eliminating the need to trace the position of the transmission source in sinogram space. Contamination of emission data by the transmission source and vice versa is naturally modeled. Attenuated emission activity data also provide additional information about object attenuation coefficient values. The algorithm alternates between attenuation and emission activity image updates. We also proposed a method of estimation of spatial scatter distribution from the transmission source by incorporating knowledge about the expected range of attenuation map values. The reconstruction of experimental data from the Siemens mCT scanner suggests that simultaneous reconstruction improves attenuation map image quality, as compared to when data are separated. In the presented example, the attenuation map image noise was reduced and non-uniformity artifacts that occurred due to scatter estimation were suppressed. On the other hand, the use of transmission data stabilizes attenuation coefficient distribution reconstruction from TOF emission data alone. The example of improving emission images by refining a CT-based patient attenuation map is presented, revealing potential benefits of simultaneous CT and PET data reconstruction.

Reconstruction of fluorescence molecular tomography with a cosinoidal level set method.

PubMed

Zhang, Xuanxuan; Cao, Xu; Zhu, Shouping

2017-06-27

Implicit shape-based reconstruction method in fluorescence molecular tomography (FMT) is capable of achieving higher image clarity than image-based reconstruction method. However, the implicit shape method suffers from a low convergence speed and performs unstably due to the utilization of gradient-based optimization methods. Moreover, the implicit shape method requires priori information about the number of targets. A shape-based reconstruction scheme of FMT with a cosinoidal level set method is proposed in this paper. The Heaviside function in the classical implicit shape method is replaced with a cosine function, and then the reconstruction can be accomplished with the Levenberg-Marquardt method rather than gradient-based methods. As a result, the priori information about the number of targets is not required anymore and the choice of step length is avoided. Numerical simulations and phantom experiments were carried out to validate the proposed method. Results of the proposed method show higher contrast to noise ratios and Pearson correlations than the implicit shape method and image-based reconstruction method. Moreover, the number of iterations required in the proposed method is much less than the implicit shape method. The proposed method performs more stably, provides a faster convergence speed than the implicit shape method, and achieves higher image clarity than the image-based reconstruction method.

MR image denoising method for brain surface 3D modeling

NASA Astrophysics Data System (ADS)

Zhao, De-xin; Liu, Peng-jie; Zhang, De-gan

2014-11-01

Three-dimensional (3D) modeling of medical images is a critical part of surgical simulation. In this paper, we focus on the magnetic resonance (MR) images denoising for brain modeling reconstruction, and exploit a practical solution. We attempt to remove the noise existing in the MR imaging signal and preserve the image characteristics. A wavelet-based adaptive curve shrinkage function is presented in spherical coordinates system. The comparative experiments show that the denoising method can preserve better image details and enhance the coefficients of contours. Using these denoised images, the brain 3D visualization is given through surface triangle mesh model, which demonstrates the effectiveness of the proposed method.

Radiation dose reduction in abdominal computed tomography during the late hepatic arterial phase using a model-based iterative reconstruction algorithm: how low can we go?

PubMed

Husarik, Daniela B; Marin, Daniele; Samei, Ehsan; Richard, Samuel; Chen, Baiyu; Jaffe, Tracy A; Bashir, Mustafa R; Nelson, Rendon C

2012-08-01

The aim of this study was to compare the image quality of abdominal computed tomography scans in an anthropomorphic phantom acquired at different radiation dose levels where each raw data set is reconstructed with both a standard convolution filtered back projection (FBP) and a full model-based iterative reconstruction (MBIR) algorithm. An anthropomorphic phantom in 3 sizes was used with a custom-built liver insert simulating late hepatic arterial enhancement and containing hypervascular liver lesions of various sizes. Imaging was performed on a 64-section multidetector-row computed tomography scanner (Discovery CT750 HD; GE Healthcare, Waukesha, WI) at 3 different tube voltages for each patient size and 5 incrementally decreasing tube current-time products for each tube voltage. Quantitative analysis consisted of contrast-to-noise ratio calculations and image noise assessment. Qualitative image analysis was performed by 3 independent radiologists rating subjective image quality and lesion conspicuity. Contrast-to-noise ratio was significantly higher and mean image noise was significantly lower on MBIR images than on FBP images in all patient sizes, at all tube voltage settings, and all radiation dose levels (P < 0.05). Overall image quality and lesion conspicuity were rated higher for MBIR images compared with FBP images at all radiation dose levels. Image quality and lesion conspicuity on 25% to 50% dose MBIR images were rated equal to full-dose FBP images. This phantom study suggests that depending on patient size, clinically acceptable image quality of the liver in the late hepatic arterial phase can be achieved with MBIR at approximately 50% lower radiation dose compared with FBP.

Higher order total variation regularization for EIT reconstruction.

PubMed

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Zhang, Fan; Mueller-Lisse, Ullrich; Moeller, Knut

2018-01-08

Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images. Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.

MO-FG-204-08: Optimization-Based Image Reconstruction From Unevenly Distributed Sparse Projection Views

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Huiqiao; Yang, Yi; Tang, Xiangyang

2015-06-15

Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, whichmore » are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality for advanced clinical applications wherein only unevenly distributed sparse views are available. Research Grants: W81XWH-12-1-0138 (DoD), Sinovision Technologies.« less

Low-Contrast and Low-Radiation Dose Protocol in Cardiac Computed Tomography: Usefulness of Low Tube Voltage and Knowledge-Based Iterative Model Reconstruction Algorithm.

PubMed

Iyama, Yuji; Nakaura, Takeshi; Yokoyama, Koichi; Kidoh, Masafumi; Harada, Kazunori; Oda, Seitaro; Tokuyasu, Shinichi; Yamashita, Yasuyuki

This study aimed to evaluate the feasibility of a low contrast, low-radiation dose protocol of 80-peak kilovoltage (kVp) with prospective electrocardiography-gated cardiac computed tomography (CT) using knowledge-based iterative model reconstruction (IMR). Thirty patients underwent an 80-kVp prospective electrocardiography-gated cardiac CT with low-contrast agent (222-mg iodine per kilogram of body weight) dose. We also enrolled 30 consecutive patients who were scanned with a 120-kVp cardiac CT with filtered back projection using the standard contrast agent dose (370-mg iodine per kilogram of body weight) as a historical control group. We evaluated the radiation dose for the 2 groups. The 80-kVp images were reconstructed with filtered back projection (protocol A), hybrid iterative reconstruction (HIR, protocol B), and IMR (protocol C). We compared CT numbers, image noise, and contrast-to-noise ratio among 120-kVp protocol, protocol A, protocol B, and protocol C. In addition, we compared the noise reduction rate between HIR and IMR. Two independent readers compared image contrast, image noise, image sharpness, unfamiliar image texture, and overall image quality among the 4 protocols. The estimated effective dose (ED) of the 80-kVp protocol was 74% lower than that of the 120-kVp protocol (1.4 vs 5.4 mSv). The contrast-to-noise ratio of protocol C was significantly higher than that of protocol A. The noise reduction rate of IMR was significantly higher than that of HIR (P < 0.01). There was no significant difference in almost all qualitative image quality between 120-kVp protocol and protocol C except for image contrast. A 80-kVp protocol with IMR yields higher image quality with 74% decreased radiation dose and 40% decreased contrast agent dose as compared with a 120-kVp protocol, while decreasing more image noise compared with the 80-kVp protocol with HIR.

Students' Ideas about Prismatic Images: Teaching Experiments for an Image-Based Approach

ERIC Educational Resources Information Center

Grusche, Sascha

2017-01-01

Prismatic refraction is a classic topic in science education. To investigate how undergraduate students think about prismatic dispersion, and to see how they change their thinking when observing dispersed images, five teaching experiments were done and analysed according to the Model of Educational Reconstruction. For projection through a prism,…

WE-FG-207B-05: Iterative Reconstruction Via Prior Image Constrained Total Generalized Variation for Spectral CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Niu, S; Zhang, Y; Ma, J

Purpose: To investigate iterative reconstruction via prior image constrained total generalized variation (PICTGV) for spectral computed tomography (CT) using fewer projections while achieving greater image quality. Methods: The proposed PICTGV method is formulated as an optimization problem, which balances the data fidelity and prior image constrained total generalized variation of reconstructed images in one framework. The PICTGV method is based on structure correlations among images in the energy domain and high-quality images to guide the reconstruction of energy-specific images. In PICTGV method, the high-quality image is reconstructed from all detector-collected X-ray signals and is referred as the broad-spectrum image. Distinctmore » from the existing reconstruction methods applied on the images with first order derivative, the higher order derivative of the images is incorporated into the PICTGV method. An alternating optimization algorithm is used to minimize the PICTGV objective function. We evaluate the performance of PICTGV on noise and artifacts suppressing using phantom studies and compare the method with the conventional filtered back-projection method as well as TGV based method without prior image. Results: On the digital phantom, the proposed method outperforms the existing TGV method in terms of the noise reduction, artifacts suppression, and edge detail preservation. Compared to that obtained by the TGV based method without prior image, the relative root mean square error in the images reconstructed by the proposed method is reduced by over 20%. Conclusion: The authors propose an iterative reconstruction via prior image constrained total generalize variation for spectral CT. Also, we have developed an alternating optimization algorithm and numerically demonstrated the merits of our approach. Results show that the proposed PICTGV method outperforms the TGV method for spectral CT.« less

Evaluation of Abdominal CT Image Quality Using a New Version of Vendor-Specific Model-Based Iterative Reconstruction

PubMed Central

Jensen, Corey T.; Telesmanich, Morgan E.; Wagner-Bartak, Nicolaus A.; Liu, Xinming; Rong, John; Szklaruk, Janio; Qayyum, Aliya; Wei, Wei; Chandler, Adam G.; Tamm, Eric P.

2016-01-01

Purpose To qualitatively and quantitatively compare abdominal CT images reconstructed with a new version of model-based iterative reconstruction (Veo 3.0; GE Healthcare) to those created with Veo 2.0. Materials & Methods This retrospective study was approved by our IRB and was HIPPA compliant. The raw data from 29 consecutive patients who had undergone CT abdomen scanning was used to reconstruct 4 sets of 3.75mm axial images: Veo 2.0, Veo 3.0 standard, Veo 3.0 5% resolution preference and Veo 3.0 20% resolution preference. A slice thickness optimization of 3.75 mm and texture feature was selected for Veo 3.0 reconstructions. The images were reviewed by three independent readers in a blinded, randomized fashion using a 5-point Likert scale and 5-point comparative scale. Multiple 2D circular regions of interest were defined for noise and contrast-to-noise ratio (CNR) measurements. Line profiles were drawn across the 7 lp/cm bar pattern of the CatPhan 600 phantom for spatial resolution evaluation. Results The Veo 3.0 standard image set was scored better than Veo 2.0 in terms of artifacts (mean difference 0.43, 95% CI 0.25-0.6, P<0.0001), overall image quality (mean difference 0.87, 95% CI 0.62-1.13, P<0.0001) and qualitative resolution (mean difference 0.9, 95% CI 0.69-1.1, P<0.0001). While the Veo 3.0 standard and RP05 presets were preferred across most categories, the Veo 3.0 RP20 series ranked best for bone detail. Image noise and spatial resolution increased along a spectrum with Veo 2.0 the lowest and RP20 the highest. Conclusion Veo 3.0 enhances imaging evaluation relative to Veo 2.0; readers preferred Veo 3.0 image appearance despite the associated mild increases in image noise. These results provide suggested parameters to be used clinically and as a basis for future evaluations such as focal lesion detection in the oncology setting. PMID:27529683

Evaluation of Abdominal Computed Tomography Image Quality Using a New Version of Vendor-Specific Model-Based Iterative Reconstruction.

PubMed

Jensen, Corey T; Telesmanich, Morgan E; Wagner-Bartak, Nicolaus A; Liu, Xinming; Rong, John; Szklaruk, Janio; Qayyum, Aliya; Wei, Wei; Chandler, Adam G; Tamm, Eric P

2017-01-01

To qualitatively and quantitatively compare abdominal computed tomography (CT) images reconstructed with a new version of model-based iterative reconstruction (Veo 3.0; GE Healthcare) to those created with Veo 2.0. This retrospective study was approved by our institutional review board and was Health Insurance Portability and Accountability Act compliant. The raw data from 29 consecutive patients who had undergone CT abdomen scanning was used to reconstruct 4 sets of 3.75-mm axial images: Veo 2.0, Veo 3.0 standard, Veo 3.0 5% resolution preference (RP), and Veo 3.0 20% RP. A slice thickness optimization of 3.75 mm and texture feature was selected for Veo 3.0 reconstructions.The images were reviewed by 3 independent readers in a blinded, randomized fashion using a 5-point Likert scale and 5-point comparative scale.Multiple 2-dimensional circular regions of interest were defined for noise and contrast-to-noise ratio measurements. Line profiles were drawn across the 7 lp/cm bar pattern of the CatPhan 600 phantom for spatial resolution evaluation. The Veo 3.0 standard image set was scored better than Veo 2.0 in terms of artifacts (mean difference, 0.43; 95% confidence interval [95% CI], 0.25-0.6; P < 0.0001), overall image quality (mean difference, 0.87; 95% CI, 0.62-1.13; P < 0.0001) and qualitative resolution (mean difference, 0.9; 95% CI, 0.69-1.1; P < 0.0001). Although the Veo 3.0 standard and RP05 presets were preferred across most categories, the Veo 3.0 RP20 series ranked best for bone detail. Image noise and spatial resolution increased along a spectrum with Veo 2.0 the lowest and RP20 the highest. Veo 3.0 enhances imaging evaluation relative to Veo 2.0; readers preferred Veo 3.0 image appearance despite the associated mild increases in image noise. These results provide suggested parameters to be used clinically and as a basis for future evaluations, such as focal lesion detection, in the oncology setting.

Time-of-flight camera via a single-pixel correlation image sensor

NASA Astrophysics Data System (ADS)

Mao, Tianyi; Chen, Qian; He, Weiji; Dai, Huidong; Ye, Ling; Gu, Guohua

2018-04-01

A time-of-flight imager based on single-pixel correlation image sensors is proposed for noise-free depth map acquisition in presence of ambient light. Digital micro-mirror device and time-modulated IR-laser provide spatial and temporal illumination on the unknown object. Compressed sensing and ‘four bucket principle’ method are combined to reconstruct the depth map from a sequence of measurements at a low sampling rate. Second-order correlation transform is also introduced to reduce the noise from the detector itself and direct ambient light. Computer simulations are presented to validate the computational models and improvement of reconstructions.

A simultaneous beta and coincidence-gamma imaging system for plant leaves

NASA Astrophysics Data System (ADS)

Ranjbar, Homayoon; Wen, Jie; Mathews, Aswin J.; Komarov, Sergey; Wang, Qiang; Li, Ke; O'Sullivan, Joseph A.; Tai, Yuan-Chuan

2016-05-01

Positron emitting isotopes, such as 11C, 13N, and 18F, can be used to label molecules. The tracers, such as 11CO2, are delivered to plants to study their biological processes, particularly metabolism and photosynthesis, which may contribute to the development of plants that have a higher yield of crops and biomass. Measurements and resulting images from PET scanners are not quantitative in young plant structures or in plant leaves due to poor positron annihilation in thin objects. To address this problem we have designed, assembled, modeled, and tested a nuclear imaging system (simultaneous beta-gamma imager). The imager can simultaneously detect positrons ({β+} ) and coincidence-gamma rays (γ). The imaging system employs two planar detectors; one is a regular gamma detector which has a LYSO crystal array, and the other is a phoswich detector which has an additional BC-404 plastic scintillator for beta detection. A forward model for positrons is proposed along with a joint image reconstruction formulation to utilize the beta and coincidence-gamma measurements for estimating radioactivity distribution in plant leaves. The joint reconstruction algorithm first reconstructs beta and gamma images independently to estimate the thickness component of the beta forward model and afterward jointly estimates the radioactivity distribution in the object. We have validated the physics model and reconstruction framework through a phantom imaging study and imaging a tomato leaf that has absorbed 11CO2. The results demonstrate that the simultaneously acquired beta and coincidence-gamma data, combined with our proposed joint reconstruction algorithm, improved the quantitative accuracy of estimating radioactivity distribution in thin objects such as leaves. We used the structural similarity (SSIM) index for comparing the leaf images from the simultaneous beta-gamma imager with the ground truth image. The jointly reconstructed images yield SSIM indices of 0.69 and 0.63, whereas the separately reconstructed beta alone and gamma alone images had indices of 0.33 and 0.52, respectively.

A simultaneous beta and coincidence-gamma imaging system for plant leaves.

PubMed

Ranjbar, Homayoon; Wen, Jie; Mathews, Aswin J; Komarov, Sergey; Wang, Qiang; Li, Ke; O'Sullivan, Joseph A; Tai, Yuan-Chuan

2016-05-07

Positron emitting isotopes, such as (11)C, (13)N, and (18)F, can be used to label molecules. The tracers, such as (11)CO2, are delivered to plants to study their biological processes, particularly metabolism and photosynthesis, which may contribute to the development of plants that have a higher yield of crops and biomass. Measurements and resulting images from PET scanners are not quantitative in young plant structures or in plant leaves due to poor positron annihilation in thin objects. To address this problem we have designed, assembled, modeled, and tested a nuclear imaging system (simultaneous beta-gamma imager). The imager can simultaneously detect positrons ([Formula: see text]) and coincidence-gamma rays (γ). The imaging system employs two planar detectors; one is a regular gamma detector which has a LYSO crystal array, and the other is a phoswich detector which has an additional BC-404 plastic scintillator for beta detection. A forward model for positrons is proposed along with a joint image reconstruction formulation to utilize the beta and coincidence-gamma measurements for estimating radioactivity distribution in plant leaves. The joint reconstruction algorithm first reconstructs beta and gamma images independently to estimate the thickness component of the beta forward model and afterward jointly estimates the radioactivity distribution in the object. We have validated the physics model and reconstruction framework through a phantom imaging study and imaging a tomato leaf that has absorbed (11)CO2. The results demonstrate that the simultaneously acquired beta and coincidence-gamma data, combined with our proposed joint reconstruction algorithm, improved the quantitative accuracy of estimating radioactivity distribution in thin objects such as leaves. We used the structural similarity (SSIM) index for comparing the leaf images from the simultaneous beta-gamma imager with the ground truth image. The jointly reconstructed images yield SSIM indices of 0.69 and 0.63, whereas the separately reconstructed beta alone and gamma alone images had indices of 0.33 and 0.52, respectively.

Simultaneous determination of sample thickness, tilt, and electron mean free path using tomographic tilt images based on Beer-Lambert law

PubMed Central

Yan, Rui; Edwards, Thomas J.; Pankratz, Logan M.; Kuhn, Richard J.; Lanman, Jason K.; Liu, Jun; Jiang, Wen

2015-01-01

Cryo-electron tomography (cryo-ET) is an emerging technique that can elucidate the architecture of macromolecular complexes and cellular ultrastructure in a near-native state. Some important sample parameters, such as thickness and tilt, are needed for 3-D reconstruction. However, these parameters can currently only be determined using trial 3-D reconstructions. Accurate electron mean free path plays a significant role in modeling image formation process essential for simulation of electron microscopy images and model-based iterative 3-D reconstruction methods; however, their values are voltage and sample dependent and have only been experimentally measured for a limited number of sample conditions. Here, we report a computational method, tomoThickness, based on the Beer-Lambert law, to simultaneously determine the sample thickness, tilt and electron inelastic mean free path by solving an overdetermined nonlinear least square optimization problem utilizing the strong constraints of tilt relationships. The method has been extensively tested with both stained and cryo datasets. The fitted electron mean free paths are consistent with reported experimental measurements. The accurate thickness estimation eliminates the need for a generous assignment of Z-dimension size of the tomogram. Interestingly, we have also found that nearly all samples are a few degrees tilted relative to the electron beam. Compensation of the intrinsic sample tilt can result in horizontal structure and reduced Z-dimension of tomograms. Our fast, pre-reconstruction method can thus provide important sample parameters that can help improve performance of tomographic reconstruction of a wide range of samples. PMID:26433027

Simultaneous determination of sample thickness, tilt, and electron mean free path using tomographic tilt images based on Beer-Lambert law.

PubMed

Yan, Rui; Edwards, Thomas J; Pankratz, Logan M; Kuhn, Richard J; Lanman, Jason K; Liu, Jun; Jiang, Wen

2015-11-01

Cryo-electron tomography (cryo-ET) is an emerging technique that can elucidate the architecture of macromolecular complexes and cellular ultrastructure in a near-native state. Some important sample parameters, such as thickness and tilt, are needed for 3-D reconstruction. However, these parameters can currently only be determined using trial 3-D reconstructions. Accurate electron mean free path plays a significant role in modeling image formation process essential for simulation of electron microscopy images and model-based iterative 3-D reconstruction methods; however, their values are voltage and sample dependent and have only been experimentally measured for a limited number of sample conditions. Here, we report a computational method, tomoThickness, based on the Beer-Lambert law, to simultaneously determine the sample thickness, tilt and electron inelastic mean free path by solving an overdetermined nonlinear least square optimization problem utilizing the strong constraints of tilt relationships. The method has been extensively tested with both stained and cryo datasets. The fitted electron mean free paths are consistent with reported experimental measurements. The accurate thickness estimation eliminates the need for a generous assignment of Z-dimension size of the tomogram. Interestingly, we have also found that nearly all samples are a few degrees tilted relative to the electron beam. Compensation of the intrinsic sample tilt can result in horizontal structure and reduced Z-dimension of tomograms. Our fast, pre-reconstruction method can thus provide important sample parameters that can help improve performance of tomographic reconstruction of a wide range of samples. Copyright © 2015 Elsevier Inc. All rights reserved.

Development and clinical translation of a cone-beam CT scanner for high-quality imaging of intracranial hemorrhage

NASA Astrophysics Data System (ADS)

Sisniega, A.; Xu, J.; Dang, H.; Zbijewski, W.; Stayman, J. W.; Mow, M.; Koliatsos, V. E.; Aygun, N.; Wang, X.; Foos, D. H.; Siewerdsen, J. H.

2017-03-01

Purpose: Prompt, reliable detection of intracranial hemorrhage (ICH) is essential for treatment of stroke and traumatic brain injury, and would benefit from availability of imaging directly at the point-of-care. This work reports the performance evaluation of a clinical prototype of a cone-beam CT (CBCT) system for ICH imaging and introduces novel algorithms for model-based reconstruction with compensation for data truncation and patient motion. Methods: The tradeoffs in dose and image quality were investigated as a function of analytical (FBP) and model-based iterative reconstruction (PWLS) algorithm parameters using phantoms with ICH-mimicking inserts. Image quality in clinical applications was evaluated in a human cadaver imaged with simulated ICH. Objects outside of the field of view (FOV), such as the head-holder, were found to introduce challenging truncation artifacts in PWLS that were mitigated with a novel multi-resolution reconstruction strategy. Following phantom and cadaver studies, the scanner was translated to a clinical pilot study. Initial clinical experience indicates the presence of motion in some patient scans, and an image-based motion estimation method that does not require fiducial tracking or prior patient information was implemented and evaluated. Results: The weighted CTDI for a nominal scan technique was 22.8 mGy. The high-resolution FBP reconstruction protocol achieved < 0.9 mm full width at half maximum (FWHM) of the point spread function (PSF). The PWLS soft-tissue reconstruction showed <1.2 mm PSF FWHM and lower noise than FBP at the same resolution. Effects of truncation in PWLS were mitigated with the multi-resolution approach, resulting in 60% reduction in root mean squared error compared to conventional PWLS. Cadaver images showed clear visualization of anatomical landmarks (ventricles and sulci), and ICH was conspicuous. The motion compensation method was shown in clinical studies to restore visibility of fine bone structures, such as the subtle fracture, cranial sutures, and the cochlea as well as subtle low-contrast structures in the brain parenchyma. Conclusion: The imaging performance of the prototype suggests sufficient quality for ICH imaging and motivates continued clinical studies to assess the diagnosis utility of the CBCT system in realistic clinical scenarios at the point of care.

GPU-Based Real-Time Volumetric Ultrasound Image Reconstruction for a Ring Array

PubMed Central

Choe, Jung Woo; Nikoozadeh, Amin; Oralkan, Ömer; Khuri-Yakub, Butrus T.

2014-01-01

Synthetic phased array (SPA) beamforming with Hadamard coding and aperture weighting is an optimal option for real-time volumetric imaging with a ring array, a particularly attractive geometry in intracardiac and intravascular applications. However, the imaging frame rate of this method is limited by the immense computational load required in synthetic beamforming. For fast imaging with a ring array, we developed graphics processing unit (GPU)-based, real-time image reconstruction software that exploits massive data-level parallelism in beamforming operations. The GPU-based software reconstructs and displays three cross-sectional images at 45 frames per second (fps). This frame rate is 4.5 times higher than that for our previously-developed multi-core CPU-based software. In an alternative imaging mode, it shows one B-mode image rotating about the axis and its maximum intensity projection (MIP), processed at a rate of 104 fps. This paper describes the image reconstruction procedure on the GPU platform and presents the experimental images obtained using this software. PMID:23529080

GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.

PubMed

Wen, Tiexiang; Li, Ling; Zhu, Qingsong; Qin, Wenjian; Gu, Jia; Yang, Feng; Xie, Yaoqin

2017-07-01

Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor-based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of [Formula: see text] and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.

Dynamic dual-tracer PET reconstruction.

PubMed

Gao, Fei; Liu, Huafeng; Jian, Yiqiang; Shi, Pengcheng

2009-01-01

Although of important medical implications, simultaneous dual-tracer positron emission tomography reconstruction remains a challenging problem, primarily because the photon measurements from dual tracers are overlapped. In this paper, we propose a simultaneous dynamic dual-tracer reconstruction of tissue activity maps based on guidance from tracer kinetics. The dual-tracer reconstruction problem is formulated in a state-space representation, where parallel compartment models serve as continuous-time system equation describing the tracer kinetic processes of dual tracers, and the imaging data is expressed as discrete sampling of the system states in measurement equation. The image reconstruction problem has therefore become a state estimation problem in a continuous-discrete hybrid paradigm, and H infinity filtering is adopted as the estimation strategy. As H infinity filtering makes no assumptions on the system and measurement statistics, robust reconstruction results can be obtained for the dual-tracer PET imaging system where the statistical properties of measurement data and system uncertainty are not available a priori, even when there are disturbances in the kinetic parameters. Experimental results on digital phantoms, Monte Carlo simulations and physical phantoms have demonstrated the superior performance.

Interleaved EPI diffusion imaging using SPIRiT-based reconstruction with virtual coil compression.

PubMed

Dong, Zijing; Wang, Fuyixue; Ma, Xiaodong; Zhang, Zhe; Dai, Erpeng; Yuan, Chun; Guo, Hua

2018-03-01

To develop a novel diffusion imaging reconstruction framework based on iterative self-consistent parallel imaging reconstruction (SPIRiT) for multishot interleaved echo planar imaging (iEPI), with computation acceleration by virtual coil compression. As a general approach for autocalibrating parallel imaging, SPIRiT improves the performance of traditional generalized autocalibrating partially parallel acquisitions (GRAPPA) methods in that the formulation with self-consistency is better conditioned, suggesting SPIRiT to be a better candidate in k-space-based reconstruction. In this study, a general SPIRiT framework is adopted to incorporate both coil sensitivity and phase variation information as virtual coils and then is applied to 2D navigated iEPI diffusion imaging. To reduce the reconstruction time when using a large number of coils and shots, a novel shot-coil compression method is proposed for computation acceleration in Cartesian sampling. Simulations and in vivo experiments were conducted to evaluate the performance of the proposed method. Compared with the conventional coil compression, the shot-coil compression achieved higher compression rates with reduced errors. The simulation and in vivo experiments demonstrate that the SPIRiT-based reconstruction outperformed the existing method, realigned GRAPPA, and provided superior images with reduced artifacts. The SPIRiT-based reconstruction with virtual coil compression is a reliable method for high-resolution iEPI diffusion imaging. Magn Reson Med 79:1525-1531, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Patch-based image reconstruction for PET using prior-image derived dictionaries

NASA Astrophysics Data System (ADS)

Tahaei, Marzieh S.; Reader, Andrew J.

2016-09-01

In PET image reconstruction, regularization is often needed to reduce the noise in the resulting images. Patch-based image processing techniques have recently been successfully used for regularization in medical image reconstruction through a penalized likelihood framework. Re-parameterization within reconstruction is another powerful regularization technique in which the object in the scanner is re-parameterized using coefficients for spatially-extensive basis vectors. In this work, a method for extracting patch-based basis vectors from the subject’s MR image is proposed. The coefficients for these basis vectors are then estimated using the conventional MLEM algorithm. Furthermore, using the alternating direction method of multipliers, an algorithm for optimizing the Poisson log-likelihood while imposing sparsity on the parameters is also proposed. This novel method is then utilized to find sparse coefficients for the patch-based basis vectors extracted from the MR image. The results indicate the superiority of the proposed methods to patch-based regularization using the penalized likelihood framework.

An Efficient Framework for Compressed Sensing Reconstruction of Highly Accelerated Dynamic Cardiac MRI

NASA Astrophysics Data System (ADS)

Ting, Samuel T.

The research presented in this work seeks to develop, validate, and deploy practical techniques for improving diagnosis of cardiovascular disease. In the philosophy of biomedical engineering, we seek to identify an existing medical problem having significant societal and economic effects and address this problem using engineering approaches. Cardiovascular disease is the leading cause of mortality in the United States, accounting for more deaths than any other major cause of death in every year since 1900 with the exception of the year 1918. Cardiovascular disease is estimated to account for almost one-third of all deaths in the United States, with more than 2150 deaths each day, or roughly 1 death every 40 seconds. In the past several decades, a growing array of imaging modalities have proven useful in aiding the diagnosis and evaluation of cardiovascular disease, including computed tomography, single photon emission computed tomography, and echocardiography. In particular, cardiac magnetic resonance imaging is an excellent diagnostic tool that can provide within a single exam a high quality evaluation of cardiac function, blood flow, perfusion, viability, and edema without the use of ionizing radiation. The scope of this work focuses on the application of engineering techniques for improving imaging using cardiac magnetic resonance with the goal of improving the utility of this powerful imaging modality. Dynamic cine imaging, or the capturing of movies of a single slice or volume within the heart or great vessel region, is used in nearly every cardiac magnetic resonance imaging exam, and adequate evaluation of cardiac function and morphology for diagnosis and evaluation of cardiovascular disease depends heavily on both the spatial and temporal resolution as well as the image quality of the reconstruction cine images. This work focuses primarily on image reconstruction techniques utilized in cine imaging; however, the techniques discussed are also relevant to other dynamic and static imaging techniques based on cardiac magnetic resonance. Conventional segmented techniques for cardiac cine imaging require breath-holding as well as regular cardiac rhythm, and can be time-consuming to acquire. Inadequate breath-holding or irregular cardiac rhythm can result in completely non-diagnostic images, limiting the utility of these techniques in a significant patient population. Real-time single-shot cardiac cine imaging enables free-breathing acquisition with significantly shortened imaging time and promises to significantly improve the utility of cine imaging for diagnosis and evaluation of cardiovascular disease. However, utility of real-time cine images depends heavily on the successful reconstruction of final cine images from undersampled data. Successful reconstruction of images from more highly undersampled data results directly in images exhibiting finer spatial and temporal resolution provided that image quality is sufficient. This work focuses primarily on the development, validation, and deployment of practical techniques for enabling the reconstruction of real-time cardiac cine images at the spatial and temporal resolutions and image quality needed for diagnostic utility. Particular emphasis is placed on the development of reconstruction approaches resulting in with short computation times that can be used in the clinical environment. Specifically, the use of compressed sensing signal recovery techniques is considered; such techniques show great promise in allowing successful reconstruction of highly undersampled data. The scope of this work concerns two primary topics related to signal recovery using compressed sensing: (1) long reconstruction times of these techniques, and (2) improved sparsity models for signal recovery from more highly undersampled data. Both of these aspects are relevant to the practical application of compressed sensing techniques in the context of improving image reconstruction of real-time cardiac cine images. First, algorithmic and implementational approaches are proposed for reducing the computational time for a compressed sensing reconstruction framework. Specific optimization algorithms based on the fast iterative/shrinkage algorithm (FISTA) are applied in the context of real-time cine image reconstruction to achieve efficient per-iteration computation time. Implementation within a code framework utilizing commercially available graphics processing units (GPUs) allows for practical and efficient implementation directly within the clinical environment. Second, patch-based sparsity models are proposed to enable compressed sensing signal recovery from highly undersampled data. Numerical studies demonstrate that this approach can help improve image quality at higher undersampling ratios, enabling real-time cine imaging at higher acceleration rates. In this work, it is shown that these techniques yield a holistic framework for achieving efficient reconstruction of real-time cine images with spatial and temporal resolution sufficient for use in the clinical environment. A thorough description of these techniques from both a theoretical and practical view is provided - both of which may be of interest to the reader in terms of future work.

Combined multi-spectrum and orthogonal Laplacianfaces for fast CB-XLCT imaging with single-view data

NASA Astrophysics Data System (ADS)

Zhang, Haibo; Geng, Guohua; Chen, Yanrong; Qu, Xuan; Zhao, Fengjun; Hou, Yuqing; Yi, Huangjian; He, Xiaowei

2017-12-01

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, which has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. Single-view data reconstruction as a key issue of CB-XLCT imaging promotes the effective study of dynamic XLCT imaging. However, it suffers from serious ill-posedness in the inverse problem. In this paper, a multi-spectrum strategy is adopted to relieve the ill-posedness of reconstruction. The strategy is based on the third-order simplified spherical harmonic approximation model. Then, an orthogonal Laplacianfaces-based method is proposed to reduce the large computational burden without degrading the imaging quality. Both simulated data and in vivo experimental data were used to evaluate the efficiency and robustness of the proposed method. The results are satisfactory in terms of both location and quantitative recovering with computational efficiency, indicating that the proposed method is practical and promising for single-view CB-XLCT imaging.

Task-driven optimization of CT tube current modulation and regularization in model-based iterative reconstruction

NASA Astrophysics Data System (ADS)

Gang, Grace J.; Siewerdsen, Jeffrey H.; Webster Stayman, J.

2017-06-01

Tube current modulation (TCM) is routinely adopted on diagnostic CT scanners for dose reduction. Conventional TCM strategies are generally designed for filtered-backprojection (FBP) reconstruction to satisfy simple image quality requirements based on noise. This work investigates TCM designs for model-based iterative reconstruction (MBIR) to achieve optimal imaging performance as determined by a task-based image quality metric. Additionally, regularization is an important aspect of MBIR that is jointly optimized with TCM, and includes both the regularization strength that controls overall smoothness as well as directional weights that permits control of the isotropy/anisotropy of the local noise and resolution properties. Initial investigations focus on a known imaging task at a single location in the image volume. The framework adopts Fourier and analytical approximations for fast estimation of the local noise power spectrum (NPS) and modulation transfer function (MTF)—each carrying dependencies on TCM and regularization. For the single location optimization, the local detectability index (d‧) of the specific task was directly adopted as the objective function. A covariance matrix adaptation evolution strategy (CMA-ES) algorithm was employed to identify the optimal combination of imaging parameters. Evaluations of both conventional and task-driven approaches were performed in an abdomen phantom for a mid-frequency discrimination task in the kidney. Among the conventional strategies, the TCM pattern optimal for FBP using a minimum variance criterion yielded a worse task-based performance compared to an unmodulated strategy when applied to MBIR. Moreover, task-driven TCM designs for MBIR were found to have the opposite behavior from conventional designs for FBP, with greater fluence assigned to the less attenuating views of the abdomen and less fluence to the more attenuating lateral views. Such TCM patterns exaggerate the intrinsic anisotropy of the MTF and NPS as a result of the data weighting in MBIR. Directional penalty design was found to reinforce the same trend. The task-driven approaches outperform conventional approaches, with the maximum improvement in d‧ of 13% given by the joint optimization of TCM and regularization. This work demonstrates that the TCM optimal for MBIR is distinct from conventional strategies proposed for FBP reconstruction and strategies optimal for FBP are suboptimal and may even reduce performance when applied to MBIR. The task-driven imaging framework offers a promising approach for optimizing acquisition and reconstruction for MBIR that can improve imaging performance and/or dose utilization beyond conventional imaging strategies.

Detailed Primitive-Based 3d Modeling of Architectural Elements

NASA Astrophysics Data System (ADS)

Remondino, F.; Lo Buglio, D.; Nony, N.; De Luca, L.

2012-07-01

The article describes a pipeline, based on image-data, for the 3D reconstruction of building façades or architectural elements and the successive modeling using geometric primitives. The approach overcome some existing problems in modeling architectural elements and deliver efficient-in-size reality-based textured 3D models useful for metric applications. For the 3D reconstruction, an opensource pipeline developed within the TAPENADE project is employed. In the successive modeling steps, the user manually selects an area containing an architectural element (capital, column, bas-relief, window tympanum, etc.) and then the procedure fits geometric primitives and computes disparity and displacement maps in order to tie visual and geometric information together in a light but detailed 3D model. Examples are reported and commented.

Kernel Regression Estimation of Fiber Orientation Mixtures in Diffusion MRI

PubMed Central

Cabeen, Ryan P.; Bastin, Mark E.; Laidlaw, David H.

2016-01-01

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework’s data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III. PMID:26691524

TH-EF-207A-05: Feasibility of Applying SMEIR Method On Small Animal 4D Cone Beam CT Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhong, Y; Zhang, Y; Shao, Y

Purpose: Small animal cone beam CT imaging has been widely used in preclinical research. Due to the higher respiratory rate and heat beats of small animals, motion blurring is inevitable and needs to be corrected in the reconstruction. Simultaneous motion estimation and image reconstruction (SMEIR) method, which uses projection images of all phases, proved to be effective in motion model estimation and able to reconstruct motion-compensated images. We demonstrate the application of SMEIR for small animal 4D cone beam CT imaging by computer simulations on a digital rat model. Methods: The small animal CBCT imaging system was simulated with themore » source-to-detector distance of 300 mm and the source-to-object distance of 200 mm. A sequence of rat phantom were generated with 0.4 mm{sup 3} voxel size. The respiratory cycle was taken as 1.0 second and the motions were simulated with a diaphragm motion of 2.4mm and an anterior-posterior expansion of 1.6 mm. The projection images were calculated using a ray-tracing method, and 4D-CBCT were reconstructed using SMEIR and FDK methods. The SMEIR method iterates over two alternating steps: 1) motion-compensated iterative image reconstruction by using projections from all respiration phases and 2) motion model estimation from projections directly through a 2D-3D deformable registration of the image obtained in the first step to projection images of other phases. Results: The images reconstructed using SMEIR method reproduced the features in the original phantom. Projections from the same phase were also reconstructed using FDK method. Compared with the FDK results, the images from SMEIR method substantially improve the image quality with minimum artifacts. Conclusion: We demonstrate that it is viable to apply SMEIR method to reconstruct small animal 4D-CBCT images.« less

Artifact reduction in short-scan CBCT by use of optimization-based reconstruction

PubMed Central

Zhang, Zheng; Han, Xiao; Pearson, Erik; Pelizzari, Charles; Sidky, Emil Y; Pan, Xiaochuan

2017-01-01

Increasing interest in optimization-based reconstruction in research on, and applications of, cone-beam computed tomography (CBCT) exists because it has been shown to have to potential to reduce artifacts observed in reconstructions obtained with the Feldkamp–Davis–Kress (FDK) algorithm (or its variants), which is used extensively for image reconstruction in current CBCT applications. In this work, we carried out a study on optimization-based reconstruction for possible reduction of artifacts in FDK reconstruction specifically from short-scan CBCT data. The investigation includes a set of optimization programs such as the image-total-variation (TV)-constrained data-divergency minimization, data-weighting matrices such as the Parker weighting matrix, and objects of practical interest for demonstrating and assessing the degree of artifact reduction. Results of investigative work reveal that appropriately designed optimization-based reconstruction, including the image-TV-constrained reconstruction, can reduce significant artifacts observed in FDK reconstruction in CBCT with a short-scan configuration. PMID:27046218

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

PubMed Central

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-01-01

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

PubMed

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-02-07

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution

PubMed Central

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-01-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images. PMID:29062159

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution.

PubMed

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-03-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.

Direct Extraction of Tumor Response Based on Ensemble Empirical Mode Decomposition for Image Reconstruction of Early Breast Cancer Detection by UWB.

PubMed

Li, Qinwei; Xiao, Xia; Wang, Liang; Song, Hang; Kono, Hayato; Liu, Peifang; Lu, Hong; Kikkawa, Takamaro

2015-10-01

A direct extraction method of tumor response based on ensemble empirical mode decomposition (EEMD) is proposed for early breast cancer detection by ultra-wide band (UWB) microwave imaging. With this approach, the image reconstruction for the tumor detection can be realized with only extracted signals from as-detected waveforms. The calibration process executed in the previous research for obtaining reference waveforms which stand for signals detected from the tumor-free model is not required. The correctness of the method is testified by successfully detecting a 4 mm tumor located inside the glandular region in one breast model and by the model located at the interface between the gland and the fat, respectively. The reliability of the method is checked by distinguishing a tumor buried in the glandular tissue whose dielectric constant is 35. The feasibility of the method is confirmed by showing the correct tumor information in both simulation results and experimental results for the realistic 3-D printed breast phantom.

Task-Driven Optimization of Fluence Field and Regularization for Model-Based Iterative Reconstruction in Computed Tomography.

PubMed

Gang, Grace J; Siewerdsen, Jeffrey H; Stayman, J Webster

2017-12-01

This paper presents a joint optimization of dynamic fluence field modulation (FFM) and regularization in quadratic penalized-likelihood reconstruction that maximizes a task-based imaging performance metric. We adopted a task-driven imaging framework for prospective designs of the imaging parameters. A maxi-min objective function was adopted to maximize the minimum detectability index ( ) throughout the image. The optimization algorithm alternates between FFM (represented by low-dimensional basis functions) and local regularization (including the regularization strength and directional penalty weights). The task-driven approach was compared with three FFM strategies commonly proposed for FBP reconstruction (as well as a task-driven TCM strategy) for a discrimination task in an abdomen phantom. The task-driven FFM assigned more fluence to less attenuating anteroposterior views and yielded approximately constant fluence behind the object. The optimal regularization was almost uniform throughout image. Furthermore, the task-driven FFM strategy redistribute fluence across detector elements in order to prescribe more fluence to the more attenuating central region of the phantom. Compared with all strategies, the task-driven FFM strategy not only improved minimum by at least 17.8%, but yielded higher over a large area inside the object. The optimal FFM was highly dependent on the amount of regularization, indicating the importance of a joint optimization. Sample reconstructions of simulated data generally support the performance estimates based on computed . The improvements in detectability show the potential of the task-driven imaging framework to improve imaging performance at a fixed dose, or, equivalently, to provide a similar level of performance at reduced dose.

An Automatic Procedure for Combining Digital Images and Laser Scanner Data

NASA Astrophysics Data System (ADS)

Moussa, W.; Abdel-Wahab, M.; Fritsch, D.

2012-07-01

Besides improving both the geometry and the visual quality of the model, the integration of close-range photogrammetry and terrestrial laser scanning techniques directs at filling gaps in laser scanner point clouds to avoid modeling errors, reconstructing more details in higher resolution and recovering simple structures with less geometric details. Thus, within this paper a flexible approach for the automatic combination of digital images and laser scanner data is presented. Our approach comprises two methods for data fusion. The first method starts by a marker-free registration of digital images based on a point-based environment model (PEM) of a scene which stores the 3D laser scanner point clouds associated with intensity and RGB values. The PEM allows the extraction of accurate control information for the direct computation of absolute camera orientations with redundant information by means of accurate space resection methods. In order to use the computed relations between the digital images and the laser scanner data, an extended Helmert (seven-parameter) transformation is introduced and its parameters are estimated. Precedent to that, in the second method, the local relative orientation parameters of the camera images are calculated by means of an optimized Structure and Motion (SaM) reconstruction method. Then, using the determined transformation parameters results in having absolute oriented images in relation to the laser scanner data. With the resulting absolute orientations we have employed robust dense image reconstruction algorithms to create oriented dense image point clouds, which are automatically combined with the laser scanner data to form a complete detailed representation of a scene. Examples of different data sets are shown and experimental results demonstrate the effectiveness of the presented procedures.

A quantitative comparison of noise reduction across five commercial (hybrid and model-based) iterative reconstruction techniques: an anthropomorphic phantom study.

PubMed

Patino, Manuel; Fuentes, Jorge M; Hayano, Koichi; Kambadakone, Avinash R; Uyeda, Jennifer W; Sahani, Dushyant V

2015-02-01

OBJECTIVE. The objective of our study was to compare the performance of three hybrid iterative reconstruction techniques (IRTs) (ASiR, iDose4, SAFIRE) and their respective strengths for image noise reduction on low-dose CT examinations using filtered back projection (FBP) as the standard reference. Also, we compared the performance of these three hybrid IRTs with two model-based IRTs (Veo and IMR) for image noise reduction on low-dose examinations. MATERIALS AND METHODS. An anthropomorphic abdomen phantom was scanned at 100 and 120 kVp and different tube current-exposure time products (25-100 mAs) on three CT systems (for ASiR and Veo, Discovery CT750 HD; for iDose4 and IMR, Brilliance iCT; and for SAFIRE, Somatom Definition Flash). Images were reconstructed using FBP and using IRTs at various strengths. Nine noise measurements (mean ROI size, 423 mm(2)) on extracolonic fat for the different strengths of IRTs were recorded and compared with FBP using ANOVA. Radiation dose, which was measured as the volume CT dose index and dose-length product, was also compared. RESULTS. There were no significant differences in radiation dose and image noise among the scanners when FBP was used (p > 0.05). Gradual image noise reduction was observed with each increasing increment of hybrid IRT strength, with a maximum noise suppression of approximately 50% (48.2-53.9%). Similar noise reduction was achieved on the scanners by applying specific hybrid IRT strengths. Maximum noise reduction was higher on model-based IRTs (68.3-81.1%) than hybrid IRTs (48.2-53.9%) (p < 0.05). CONCLUSION. When constant scanning parameters are used, radiation dose and image noise on FBP are similar for CT scanners made by different manufacturers. Significant image noise reduction is achieved on low-dose CT examinations rendered with IRTs. The image noise on various scanners can be matched by applying specific hybrid IRT strengths. Model-based IRTs attain substantially higher noise reduction than hybrid IRTs irrespective of the radiation dose.

Estimation of a super-resolved PSF for the data reduction of undersampled stellar observations. Deriving an accurate model for fitting photometry with Corot space telescope

NASA Astrophysics Data System (ADS)

Pinheiro da Silva, L.; Auvergne, M.; Toublanc, D.; Rowe, J.; Kuschnig, R.; Matthews, J.

2006-06-01

Context: .Fitting photometry algorithms can be very effective provided that an accurate model of the instrumental point spread function (PSF) is available. When high-precision time-resolved photometry is required, however, the use of point-source star images as empirical PSF models can be unsatisfactory, due to the limits in their spatial resolution. Theoretically-derived models, on the other hand, are limited by the unavoidable assumption of simplifying hypothesis, while the use of analytical approximations is restricted to regularly-shaped PSFs. Aims: .This work investigates an innovative technique for space-based fitting photometry, based on the reconstruction of an empirical but properly-resolved PSF. The aim is the exploitation of arbitrary star images, including those produced under intentional defocus. The cases of both MOST and COROT, the first space telescopes dedicated to time-resolved stellar photometry, are considered in the evaluation of the effectiveness and performances of the proposed methodology. Methods: .PSF reconstruction is based on a set of star images, periodically acquired and presenting relative subpixel displacements due to motion of the acquisition system, in this case the jitter of the satellite attitude. Higher resolution is achieved through the solution of the inverse problem. The approach can be regarded as a special application of super-resolution techniques, though a specialised procedure is proposed to better meet the PSF determination problem specificities. The application of such a model to fitting photometry is illustrated by numerical simulations for COROT and on a complete set of observations from MOST. Results: .We verify that, in both scenarios, significantly better resolved PSFs can be estimated, leading to corresponding improvements in photometric results. For COROT, indeed, subpixel reconstruction enabled the successful use of fitting algorithms despite its rather complex PSF profile, which could hardly be modeled otherwise. For MOST, whose direct-imaging PSF is closer to the ordinary, comparison to other models or photometry techniques were carried out and confirmed the potential of PSF reconstruction in real observational conditions.

Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

NASA Astrophysics Data System (ADS)

Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang

2018-02-01

Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106 frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang's related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image's edge blurs.

Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression

NASA Astrophysics Data System (ADS)

Zou, Zhenzhen; Luo, Xinghong; Yu, Qiang

2018-05-01

Microgravity and containerless conditions, which are produced via electrostatic levitation combined with a drop tube, are important when studying the intrinsic properties of new metastable materials. Generally, temperature and image sensors can be used to measure the changes of sample temperature, morphology and volume. Then, the specific heat, surface tension, viscosity changes and sample density can be obtained. Considering that the falling speed of the material sample droplet is approximately 31.3 m/s when it reaches the bottom of a 50-meter-high drop tube, a high-speed camera with a collection rate of up to 106 frames/s is required to image the falling droplet. However, at the high-speed mode, very few pixels, approximately 48-120, will be obtained in each exposure time, which results in low image quality. Super-resolution image reconstruction is an algorithm that provides finer details than the sampling grid of a given imaging device by increasing the number of pixels per unit area in the image. In this work, we demonstrate the application of single image-resolution reconstruction in the microgravity and electrostatic levitation for the first time. Here, using the image super-resolution method based on sparse representation, a low-resolution droplet image can be reconstructed. Employed Yang's related dictionary model, high- and low-resolution image patches were combined with dictionary training, and high- and low-resolution-related dictionaries were obtained. The online double-sparse dictionary training algorithm was used in the study of related dictionaries and overcome the shortcomings of the traditional training algorithm with small image patch. During the stage of image reconstruction, the algorithm of kernel regression is added, which effectively overcomes the shortcomings of the Yang image's edge blurs.

SU-E-T-398: Evaluation of Radiobiological Parameters Using Serial Tumor Imaging During Radiotherapy as An Inverse Ill-Posed Problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chvetsov, A; Sandison, G; Schwartz, J

Purpose: Combination of serial tumor imaging with radiobiological modeling can provide more accurate information on the nature of treatment response and what underlies resistance. The purpose of this article is to improve the algorithms related to imaging-based radiobilogical modeling of tumor response. Methods: Serial imaging of tumor response to radiation therapy represents a sum of tumor cell sensitivity, tumor growth rates, and the rate of cell loss which are not separated explicitly. Accurate treatment response assessment would require separation of these radiobiological determinants of treatment response because they define tumor control probability. We show that the problem of reconstruction ofmore » radiobiological parameters from serial imaging data can be considered as inverse ill-posed problem described by the Fredholm integral equation of the first kind because it is governed by a sum of several exponential processes. Therefore, the parameter reconstruction can be solved using regularization methods. Results: To study the reconstruction problem, we used a set of serial CT imaging data for the head and neck cancer and a two-level cell population model of tumor response which separates the entire tumor cell population in two subpopulations of viable and lethally damage cells. The reconstruction was done using a least squared objective function and a simulated annealing algorithm. Using in vitro data for radiobiological parameters as reference data, we shown that the reconstructed values of cell surviving fractions and potential doubling time exhibit non-physical fluctuations if no stabilization algorithms are applied. The variational regularization allowed us to obtain statistical distribution for cell surviving fractions and cell number doubling times comparable to in vitro data. Conclusion: Our results indicate that using variational regularization can increase the number of free parameters in the model and open the way to development of more advanced algorithms which take into account tumor heterogeneity, for example, related to hypoxia.« less

Adaptive tight frame based medical image reconstruction: a proof-of-concept study for computed tomography

NASA Astrophysics Data System (ADS)

Zhou, Weifeng; Cai, Jian-Feng; Gao, Hao

2013-12-01

A popular approach for medical image reconstruction has been through the sparsity regularization, assuming the targeted image can be well approximated by sparse coefficients under some properly designed system. The wavelet tight frame is such a widely used system due to its capability for sparsely approximating piecewise-smooth functions, such as medical images. However, using a fixed system may not always be optimal for reconstructing a variety of diversified images. Recently, the method based on the adaptive over-complete dictionary that is specific to structures of the targeted images has demonstrated its superiority for image processing. This work is to develop the adaptive wavelet tight frame method image reconstruction. The proposed scheme first constructs the adaptive wavelet tight frame that is task specific, and then reconstructs the image of interest by solving an l1-regularized minimization problem using the constructed adaptive tight frame system. The proof-of-concept study is performed for computed tomography (CT), and the simulation results suggest that the adaptive tight frame method improves the reconstructed CT image quality from the traditional tight frame method.

TH-AB-207A-05: A Fully-Automated Pipeline for Generating CT Images Across a Range of Doses and Reconstruction Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Young, S; Lo, P; Hoffman, J

Purpose: To evaluate the robustness of CAD or Quantitative Imaging methods, they should be tested on a variety of cases and under a variety of image acquisition and reconstruction conditions that represent the heterogeneity encountered in clinical practice. The purpose of this work was to develop a fully-automated pipeline for generating CT images that represent a wide range of dose and reconstruction conditions. Methods: The pipeline consists of three main modules: reduced-dose simulation, image reconstruction, and quantitative analysis. The first two modules of the pipeline can be operated in a completely automated fashion, using configuration files and running the modulesmore » in a batch queue. The input to the pipeline is raw projection CT data; this data is used to simulate different levels of dose reduction using a previously-published algorithm. Filtered-backprojection reconstructions are then performed using FreeCT-wFBP, a freely-available reconstruction software for helical CT. We also added support for an in-house, model-based iterative reconstruction algorithm using iterative coordinate-descent optimization, which may be run in tandem with the more conventional recon methods. The reduced-dose simulations and image reconstructions are controlled automatically by a single script, and they can be run in parallel on our research cluster. The pipeline was tested on phantom and lung screening datasets from a clinical scanner (Definition AS, Siemens Healthcare). Results: The images generated from our test datasets appeared to represent a realistic range of acquisition and reconstruction conditions that we would expect to find clinically. The time to generate images was approximately 30 minutes per dose/reconstruction combination on a hybrid CPU/GPU architecture. Conclusion: The automated research pipeline promises to be a useful tool for either training or evaluating performance of quantitative imaging software such as classifiers and CAD algorithms across the range of acquisition and reconstruction parameters present in the clinical environment. Funding support: NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.« less

Segmentation-free statistical image reconstruction for polyenergetic x-ray computed tomography with experimental validation.

PubMed

Idris A, Elbakri; Fessler, Jeffrey A

2003-08-07

This paper describes a statistical image reconstruction method for x-ray CT that is based on a physical model that accounts for the polyenergetic x-ray source spectrum and the measurement nonlinearities caused by energy-dependent attenuation. Unlike our earlier work, the proposed algorithm does not require pre-segmentation of the object into the various tissue classes (e.g., bone and soft tissue) and allows mixed pixels. The attenuation coefficient of each voxel is modelled as the product of its unknown density and a weighted sum of energy-dependent mass attenuation coefficients. We formulate a penalized-likelihood function for this polyenergetic model and develop an iterative algorithm for estimating the unknown density of each voxel. Applying this method to simulated x-ray CT measurements of objects containing both bone and soft tissue yields images with significantly reduced beam hardening artefacts relative to conventional beam hardening correction methods. We also apply the method to real data acquired from a phantom containing various concentrations of potassium phosphate solution. The algorithm reconstructs an image with accurate density values for the different concentrations, demonstrating its potential for quantitative CT applications.

Imaging reconstruction based on improved wavelet denoising combined with parallel-beam filtered back-projection algorithm

NASA Astrophysics Data System (ADS)

Ren, Zhong; Liu, Guodong; Huang, Zhen

2012-11-01

The image reconstruction is a key step in medical imaging (MI) and its algorithm's performance determinates the quality and resolution of reconstructed image. Although some algorithms have been used, filter back-projection (FBP) algorithm is still the classical and commonly-used algorithm in clinical MI. In FBP algorithm, filtering of original projection data is a key step in order to overcome artifact of the reconstructed image. Since simple using of classical filters, such as Shepp-Logan (SL), Ram-Lak (RL) filter have some drawbacks and limitations in practice, especially for the projection data polluted by non-stationary random noises. So, an improved wavelet denoising combined with parallel-beam FBP algorithm is used to enhance the quality of reconstructed image in this paper. In the experiments, the reconstructed effects were compared between the improved wavelet denoising and others (directly FBP, mean filter combined FBP and median filter combined FBP method). To determine the optimum reconstruction effect, different algorithms, and different wavelet bases combined with three filters were respectively test. Experimental results show the reconstruction effect of improved FBP algorithm is better than that of others. Comparing the results of different algorithms based on two evaluation standards i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR), it was found that the reconstructed effects of the improved FBP based on db2 and Hanning filter at decomposition scale 2 was best, its MSE value was less and the PSNR value was higher than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.

Can state-of-the-art HVS-based objective image quality criteria be used for image reconstruction techniques based on ROI analysis?

NASA Astrophysics Data System (ADS)

Dostal, P.; Krasula, L.; Klima, M.

2012-06-01

Various image processing techniques in multimedia technology are optimized using visual attention feature of the human visual system. Spatial non-uniformity causes that different locations in an image are of different importance in terms of perception of the image. In other words, the perceived image quality depends mainly on the quality of important locations known as regions of interest. The performance of such techniques is measured by subjective evaluation or objective image quality criteria. Many state-of-the-art objective metrics are based on HVS properties; SSIM, MS-SSIM based on image structural information, VIF based on the information that human brain can ideally gain from the reference image or FSIM utilizing the low-level features to assign the different importance to each location in the image. But still none of these objective metrics utilize the analysis of regions of interest. We solve the question if these objective metrics can be used for effective evaluation of images reconstructed by processing techniques based on ROI analysis utilizing high-level features. In this paper authors show that the state-of-the-art objective metrics do not correlate well with subjective evaluation while the demosaicing based on ROI analysis is used for reconstruction. The ROI were computed from "ground truth" visual attention data. The algorithm combining two known demosaicing techniques on the basis of ROI location is proposed to reconstruct the ROI in fine quality while the rest of image is reconstructed with low quality. The color image reconstructed by this ROI approach was compared with selected demosaicing techniques by objective criteria and subjective testing. The qualitative comparison of the objective and subjective results indicates that the state-of-the-art objective metrics are still not suitable for evaluation image processing techniques based on ROI analysis and new criteria is demanded.

Applications of the line-of-response probability density function resolution model in PET list mode reconstruction.

PubMed

Jian, Y; Yao, R; Mulnix, T; Jin, X; Carson, R E

2015-01-07

Resolution degradation in PET image reconstruction can be caused by inaccurate modeling of the physical factors in the acquisition process. Resolution modeling (RM) is a common technique that takes into account the resolution degrading factors in the system matrix. Our previous work has introduced a probability density function (PDF) method of deriving the resolution kernels from Monte Carlo simulation and parameterizing the LORs to reduce the number of kernels needed for image reconstruction. In addition, LOR-PDF allows different PDFs to be applied to LORs from different crystal layer pairs of the HRRT. In this study, a thorough test was performed with this new model (LOR-PDF) applied to two PET scanners-the HRRT and Focus-220. A more uniform resolution distribution was observed in point source reconstructions by replacing the spatially-invariant kernels with the spatially-variant LOR-PDF. Specifically, from the center to the edge of radial field of view (FOV) of the HRRT, the measured in-plane FWHMs of point sources in a warm background varied slightly from 1.7 mm to 1.9 mm in LOR-PDF reconstructions. In Minihot and contrast phantom reconstructions, LOR-PDF resulted in up to 9% higher contrast at any given noise level than image-space resolution model. LOR-PDF also has the advantage in performing crystal-layer-dependent resolution modeling. The contrast improvement by using LOR-PDF was verified statistically by replicate reconstructions. In addition, [(11)C]AFM rats imaged on the HRRT and [(11)C]PHNO rats imaged on the Focus-220 were utilized to demonstrated the advantage of the new model. Higher contrast between high-uptake regions of only a few millimeter diameter and the background was observed in LOR-PDF reconstruction than in other methods.

Applications of the line-of-response probability density function resolution model in PET list mode reconstruction

PubMed Central

Jian, Y; Yao, R; Mulnix, T; Jin, X; Carson, R E

2016-01-01

Resolution degradation in PET image reconstruction can be caused by inaccurate modeling of the physical factors in the acquisition process. Resolution modeling (RM) is a common technique that takes into account the resolution degrading factors in the system matrix. Our previous work has introduced a probability density function (PDF) method of deriving the resolution kernels from Monte Carlo simulation and parameterizing the LORs to reduce the number of kernels needed for image reconstruction. In addition, LOR-PDF allows different PDFs to be applied to LORs from different crystal layer pairs of the HRRT. In this study, a thorough test was performed with this new model (LOR-PDF) applied to two PET scanners - the HRRT and Focus-220. A more uniform resolution distribution was observed in point source reconstructions by replacing the spatially-invariant kernels with the spatially-variant LOR-PDF. Specifically, from the center to the edge of radial field of view (FOV) of the HRRT, the measured in-plane FWHMs of point sources in a warm background varied slightly from 1.7 mm to 1.9 mm in LOR-PDF reconstructions. In Minihot and contrast phantom reconstructions, LOR-PDF resulted in up to 9% higher contrast at any given noise level than image-space resolution model. LOR-PDF also has the advantage in performing crystal-layer-dependent resolution modeling. The contrast improvement by using LOR-PDF was verified statistically by replicate reconstructions. In addition, [11C]AFM rats imaged on the HRRT and [11C]PHNO rats imaged on the Focus-220 were utilized to demonstrated the advantage of the new model. Higher contrast between high-uptake regions of only a few millimeter diameter and the background was observed in LOR-PDF reconstruction than in other methods. PMID:25490063

Performance measurement of PSF modeling reconstruction (True X) on Siemens Biograph TruePoint TrueV PET/CT.

PubMed

Lee, Young Sub; Kim, Jin Su; Kim, Kyeong Min; Kang, Joo Hyun; Lim, Sang Moo; Kim, Hee-Joung

2014-05-01

The Siemens Biograph TruePoint TrueV (B-TPTV) positron emission tomography (PET) scanner performs 3D PET reconstruction using a system matrix with point spread function (PSF) modeling (called the True X reconstruction). PET resolution was dramatically improved with the True X method. In this study, we assessed the spatial resolution and image quality on a B-TPTV PET scanner. In addition, we assessed the feasibility of animal imaging with a B-TPTV PET and compared it with a microPET R4 scanner. Spatial resolution was measured at center and at 8 cm offset from the center in transverse plane with warm background activity. True X, ordered subset expectation maximization (OSEM) without PSF modeling, and filtered back-projection (FBP) reconstruction methods were used. Percent contrast (% contrast) and percent background variability (% BV) were assessed according to NEMA NU2-2007. The recovery coefficient (RC), non-uniformity, spill-over ratio (SOR), and PET imaging of the Micro Deluxe Phantom were assessed to compare image quality of B-TPTV PET with that of the microPET R4. When True X reconstruction was used, spatial resolution was <3.65 mm with warm background activity. % contrast and % BV with True X reconstruction were higher than those with the OSEM reconstruction algorithm without PSF modeling. In addition, the RC with True X reconstruction was higher than that with the FBP method and the OSEM without PSF modeling method on the microPET R4. The non-uniformity with True X reconstruction was higher than that with FBP and OSEM without PSF modeling on microPET R4. SOR with True X reconstruction was better than that with FBP or OSEM without PSF modeling on the microPET R4. This study assessed the performance of the True X reconstruction. Spatial resolution with True X reconstruction was improved by 45 % and its % contrast was significantly improved compared to those with the conventional OSEM without PSF modeling reconstruction algorithm. The noise level was higher than that with the other reconstruction algorithm. Therefore, True X reconstruction should be used with caution when quantifying PET data.

Spectrotemporal CT data acquisition and reconstruction at low dose

PubMed Central

Clark, Darin P.; Lee, Chang-Lung; Kirsch, David G.; Badea, Cristian T.

2015-01-01

Purpose: X-ray computed tomography (CT) is widely used, both clinically and preclinically, for fast, high-resolution anatomic imaging; however, compelling opportunities exist to expand its use in functional imaging applications. For instance, spectral information combined with nanoparticle contrast agents enables quantification of tissue perfusion levels, while temporal information details cardiac and respiratory dynamics. The authors propose and demonstrate a projection acquisition and reconstruction strategy for 5D CT (3D + dual energy + time) which recovers spectral and temporal information without substantially increasing radiation dose or sampling time relative to anatomic imaging protocols. Methods: The authors approach the 5D reconstruction problem within the framework of low-rank and sparse matrix decomposition. Unlike previous work on rank-sparsity constrained CT reconstruction, the authors establish an explicit rank-sparse signal model to describe the spectral and temporal dimensions. The spectral dimension is represented as a well-sampled time and energy averaged image plus regularly undersampled principal components describing the spectral contrast. The temporal dimension is represented as the same time and energy averaged reconstruction plus contiguous, spatially sparse, and irregularly sampled temporal contrast images. Using a nonlinear, image domain filtration approach, the authors refer to as rank-sparse kernel regression, the authors transfer image structure from the well-sampled time and energy averaged reconstruction to the spectral and temporal contrast images. This regularization strategy strictly constrains the reconstruction problem while approximately separating the temporal and spectral dimensions. Separability results in a highly compressed representation for the 5D data in which projections are shared between the temporal and spectral reconstruction subproblems, enabling substantial undersampling. The authors solved the 5D reconstruction problem using the split Bregman method and GPU-based implementations of backprojection, reprojection, and kernel regression. Using a preclinical mouse model, the authors apply the proposed algorithm to study myocardial injury following radiation treatment of breast cancer. Results: Quantitative 5D simulations are performed using the MOBY mouse phantom. Twenty data sets (ten cardiac phases, two energies) are reconstructed with 88 μm, isotropic voxels from 450 total projections acquired over a single 360° rotation. In vivo 5D myocardial injury data sets acquired in two mice injected with gold and iodine nanoparticles are also reconstructed with 20 data sets per mouse using the same acquisition parameters (dose: ∼60 mGy). For both the simulations and the in vivo data, the reconstruction quality is sufficient to perform material decomposition into gold and iodine maps to localize the extent of myocardial injury (gold accumulation) and to measure cardiac functional metrics (vascular iodine). Their 5D CT imaging protocol represents a 95% reduction in radiation dose per cardiac phase and energy and a 40-fold decrease in projection sampling time relative to their standard imaging protocol. Conclusions: Their 5D CT data acquisition and reconstruction protocol efficiently exploits the rank-sparse nature of spectral and temporal CT data to provide high-fidelity reconstruction results without increased radiation dose or sampling time. PMID:26520724

Subpixel based defocused points removal in photon-limited volumetric dataset

NASA Astrophysics Data System (ADS)

Muniraj, Inbarasan; Guo, Changliang; Malallah, Ra'ed; Maraka, Harsha Vardhan R.; Ryle, James P.; Sheridan, John T.

2017-03-01

The asymptotic property of the maximum likelihood estimator (MLE) has been utilized to reconstruct three-dimensional (3D) sectional images in the photon counting imaging (PCI) regime. At first, multiple 2D intensity images, known as Elemental images (EI), are captured. Then the geometric ray-tracing method is employed to reconstruct the 3D sectional images at various depth cues. We note that a 3D sectional image consists of both focused and defocused regions, depending on the reconstructed depth position. The defocused portion is redundant and should be removed in order to facilitate image analysis e.g., 3D object tracking, recognition, classification and navigation. In this paper, we present a subpixel level three-step based technique (i.e. involving adaptive thresholding, boundary detection and entropy based segmentation) to discard the defocused sparse-samples from the reconstructed photon-limited 3D sectional images. Simulation results are presented demonstrating the feasibility and efficiency of the proposed method.

Calibrationless parallel magnetic resonance imaging: a joint sparsity model.

PubMed

Majumdar, Angshul; Chaudhury, Kunal Narayan; Ward, Rabab

2013-12-05

State-of-the-art parallel MRI techniques either explicitly or implicitly require certain parameters to be estimated, e.g., the sensitivity map for SENSE, SMASH and interpolation weights for GRAPPA, SPIRiT. Thus all these techniques are sensitive to the calibration (parameter estimation) stage. In this work, we have proposed a parallel MRI technique that does not require any calibration but yields reconstruction results that are at par with (or even better than) state-of-the-art methods in parallel MRI. Our proposed method required solving non-convex analysis and synthesis prior joint-sparsity problems. This work also derives the algorithms for solving them. Experimental validation was carried out on two datasets-eight channel brain and eight channel Shepp-Logan phantom. Two sampling methods were used-Variable Density Random sampling and non-Cartesian Radial sampling. For the brain data, acceleration factor of 4 was used and for the other an acceleration factor of 6 was used. The reconstruction results were quantitatively evaluated based on the Normalised Mean Squared Error between the reconstructed image and the originals. The qualitative evaluation was based on the actual reconstructed images. We compared our work with four state-of-the-art parallel imaging techniques; two calibrated methods-CS SENSE and l1SPIRiT and two calibration free techniques-Distributed CS and SAKE. Our method yields better reconstruction results than all of them.

An endoscopic diffuse optical tomographic method with high resolution based on the improved FOCUSS method

NASA Astrophysics Data System (ADS)

Qin, Zhuanping; Ma, Wenjuan; Ren, Shuyan; Geng, Liqing; Li, Jing; Yang, Ying; Qin, Yingmei

2017-02-01

Endoscopic DOT has the potential to apply to cancer-related imaging in tubular organs. Although the DOT has relatively large tissue penetration depth, the endoscopic DOT is limited by the narrow space of the internal tubular tissue, so as to the relatively small penetration depth. Because some adenocarcinomas including cervical adenocarcinoma are located in deep canal, it is necessary to improve the imaging resolution under the limited measurement condition. To improve the resolution, a new FOCUSS algorithm along with the image reconstruction algorithm based on the effective detection range (EDR) is developed. This algorithm is based on the region of interest (ROI) to reduce the dimensions of the matrix. The shrinking method cuts down the computation burden. To reduce the computational complexity, double conjugate gradient method is used in the matrix inversion. For a typical inner size and optical properties of the cervix-like tubular tissue, reconstructed images from the simulation data demonstrate that the proposed method achieves equivalent image quality to that obtained from the method based on EDR when the target is close the inner boundary of the model, and with higher spatial resolution and quantitative ratio when the targets are far from the inner boundary of the model. The quantitative ratio of reconstructed absorption and reduced scattering coefficient can be up to 70% and 80% under 5mm depth, respectively. Furthermore, the two close targets with different depths can be separated from each other. The proposed method will be useful to the development of endoscopic DOT technologies in tubular organs.

A novel neural network based image reconstruction model with scale and rotation invariance for target identification and classification for Active millimetre wave imaging

NASA Astrophysics Data System (ADS)

Agarwal, Smriti; Bisht, Amit Singh; Singh, Dharmendra; Pathak, Nagendra Prasad

2014-12-01

Millimetre wave imaging (MMW) is gaining tremendous interest among researchers, which has potential applications for security check, standoff personal screening, automotive collision-avoidance, and lot more. Current state-of-art imaging techniques viz. microwave and X-ray imaging suffers from lower resolution and harmful ionizing radiation, respectively. In contrast, MMW imaging operates at lower power and is non-ionizing, hence, medically safe. Despite these favourable attributes, MMW imaging encounters various challenges as; still it is very less explored area and lacks suitable imaging methodology for extracting complete target information. Keeping in view of these challenges, a MMW active imaging radar system at 60 GHz was designed for standoff imaging application. A C-scan (horizontal and vertical scanning) methodology was developed that provides cross-range resolution of 8.59 mm. The paper further details a suitable target identification and classification methodology. For identification of regular shape targets: mean-standard deviation based segmentation technique was formulated and further validated using a different target shape. For classification: probability density function based target material discrimination methodology was proposed and further validated on different dataset. Lastly, a novel artificial neural network based scale and rotation invariant, image reconstruction methodology has been proposed to counter the distortions in the image caused due to noise, rotation or scale variations. The designed neural network once trained with sample images, automatically takes care of these deformations and successfully reconstructs the corrected image for the test targets. Techniques developed in this paper are tested and validated using four different regular shapes viz. rectangle, square, triangle and circle.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Siman, W.; Mikell, J. K.; Kappadath, S. C., E-mail

Purpose: To develop a practical background compensation (BC) technique to improve quantitative {sup 90}Y-bremsstrahlung single-photon emission computed tomography (SPECT)/computed tomography (CT) using a commercially available imaging system. Methods: All images were acquired using medium-energy collimation in six energy windows (EWs), ranging from 70 to 410 keV. The EWs were determined based on the signal-to-background ratio in planar images of an acrylic phantom of different thicknesses (2–16 cm) positioned below a {sup 90}Y source and set at different distances (15–35 cm) from a gamma camera. The authors adapted the widely used EW-based scatter-correction technique by modeling the BC as scaled images.more » The BC EW was determined empirically in SPECT/CT studies using an IEC phantom based on the sphere activity recovery and residual activity in the cold lung insert. The scaling factor was calculated from 20 clinical planar {sup 90}Y images. Reconstruction parameters were optimized in the same SPECT images for improved image quantification and contrast. A count-to-activity calibration factor was calculated from 30 clinical {sup 90}Y images. Results: The authors found that the most appropriate imaging EW range was 90–125 keV. BC was modeled as 0.53× images in the EW of 310–410 keV. The background-compensated clinical images had higher image contrast than uncompensated images. The maximum deviation of their SPECT calibration in clinical studies was lowest (<10%) for SPECT with attenuation correction (AC) and SPECT with AC + BC. Using the proposed SPECT-with-AC + BC reconstruction protocol, the authors found that the recovery coefficient of a 37-mm sphere (in a 10-mm volume of interest) increased from 39% to 90% and that the residual activity in the lung insert decreased from 44% to 14% over that of SPECT images with AC alone. Conclusions: The proposed EW-based BC model was developed for {sup 90}Y bremsstrahlung imaging. SPECT with AC + BC gave improved lesion detectability and activity quantification compared to SPECT with AC only. The proposed methodology can readily be used to tailor {sup 90}Y SPECT/CT acquisition and reconstruction protocols with different SPECT/CT systems for quantification and improved image quality in clinical settings.« less

A BPF-FBP tandem algorithm for image reconstruction in reverse helical cone-beam CT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cho, Seungryong; Xia, Dan; Pellizzari, Charles A.

2010-01-15

Purpose: Reverse helical cone-beam computed tomography (CBCT) is a scanning configuration for potential applications in image-guided radiation therapy in which an accurate anatomic image of the patient is needed for image-guidance procedures. The authors previously developed an algorithm for image reconstruction from nontruncated data of an object that is completely within the reverse helix. The purpose of this work is to develop an image reconstruction approach for reverse helical CBCT of a long object that extends out of the reverse helix and therefore constitutes data truncation. Methods: The proposed approach comprises of two reconstruction steps. In the first step, amore » chord-based backprojection-filtration (BPF) algorithm reconstructs a volumetric image of an object from the original cone-beam data. Because there exists a chordless region in the middle of the reverse helix, the image obtained in the first step contains an unreconstructed central-gap region. In the second step, the gap region is reconstructed by use of a Pack-Noo-formula-based filteredbackprojection (FBP) algorithm from the modified cone-beam data obtained by subtracting from the original cone-beam data the reprojection of the image reconstructed in the first step. Results: The authors have performed numerical studies to validate the proposed approach in image reconstruction from reverse helical cone-beam data. The results confirm that the proposed approach can reconstruct accurate images of a long object without suffering from data-truncation artifacts or cone-angle artifacts. Conclusions: They developed and validated a BPF-FBP tandem algorithm to reconstruct images of a long object from reverse helical cone-beam data. The chord-based BPF algorithm was utilized for converting the long-object problem into a short-object problem. The proposed approach is applicable to other scanning configurations such as reduced circular sinusoidal trajectories.« less

SU-F-BRCD-09: Total Variation (TV) Based Fast Convergent Iterative CBCT Reconstruction with GPU Acceleration.

PubMed

Xu, Q; Yang, D; Tan, J; Anastasio, M

2012-06-01

To improve image quality and reduce imaging dose in CBCT for radiation therapy applications and to realize near real-time image reconstruction based on use of a fast convergence iterative algorithm and acceleration by multi-GPUs. An iterative image reconstruction that sought to minimize a weighted least squares cost function that employed total variation (TV) regularization was employed to mitigate projection data incompleteness and noise. To achieve rapid 3D image reconstruction (< 1 min), a highly optimized multiple-GPU implementation of the algorithm was developed. The convergence rate and reconstruction accuracy were evaluated using a modified 3D Shepp-Logan digital phantom and a Catphan-600 physical phantom. The reconstructed images were compared with the clinical FDK reconstruction results. Digital phantom studies showed that only 15 iterations and 60 iterations are needed to achieve algorithm convergence for 360-view and 60-view cases, respectively. The RMSE was reduced to 10-4 and 10-2, respectively, by using 15 iterations for each case. Our algorithm required 5.4s to complete one iteration for the 60-view case using one Tesla C2075 GPU. The few-view study indicated that our iterative algorithm has great potential to reduce the imaging dose and preserve good image quality. For the physical Catphan studies, the images obtained from the iterative algorithm possessed better spatial resolution and higher SNRs than those obtained from by use of a clinical FDK reconstruction algorithm. We have developed a fast convergence iterative algorithm for CBCT image reconstruction. The developed algorithm yielded images with better spatial resolution and higher SNR than those produced by a commercial FDK tool. In addition, from the few-view study, the iterative algorithm has shown great potential for significantly reducing imaging dose. We expect that the developed reconstruction approach will facilitate applications including IGART and patient daily CBCT-based treatment localization. © 2012 American Association of Physicists in Medicine.

Kalman filter techniques for accelerated Cartesian dynamic cardiac imaging.

PubMed

Feng, Xue; Salerno, Michael; Kramer, Christopher M; Meyer, Craig H

2013-05-01

In dynamic MRI, spatial and temporal parallel imaging can be exploited to reduce scan time. Real-time reconstruction enables immediate visualization during the scan. Commonly used view-sharing techniques suffer from limited temporal resolution, and many of the more advanced reconstruction methods are either retrospective, time-consuming, or both. A Kalman filter model capable of real-time reconstruction can be used to increase the spatial and temporal resolution in dynamic MRI reconstruction. The original study describing the use of the Kalman filter in dynamic MRI was limited to non-Cartesian trajectories because of a limitation intrinsic to the dynamic model used in that study. Here the limitation is overcome, and the model is applied to the more commonly used Cartesian trajectory with fast reconstruction. Furthermore, a combination of the Kalman filter model with Cartesian parallel imaging is presented to further increase the spatial and temporal resolution and signal-to-noise ratio. Simulations and experiments were conducted to demonstrate that the Kalman filter model can increase the temporal resolution of the image series compared with view-sharing techniques and decrease the spatial aliasing compared with TGRAPPA. The method requires relatively little computation, and thus is suitable for real-time reconstruction. Copyright © 2012 Wiley Periodicals, Inc.

Kalman Filter Techniques for Accelerated Cartesian Dynamic Cardiac Imaging

PubMed Central

Feng, Xue; Salerno, Michael; Kramer, Christopher M.; Meyer, Craig H.

2012-01-01

In dynamic MRI, spatial and temporal parallel imaging can be exploited to reduce scan time. Real-time reconstruction enables immediate visualization during the scan. Commonly used view-sharing techniques suffer from limited temporal resolution, and many of the more advanced reconstruction methods are either retrospective, time-consuming, or both. A Kalman filter model capable of real-time reconstruction can be used to increase the spatial and temporal resolution in dynamic MRI reconstruction. The original study describing the use of the Kalman filter in dynamic MRI was limited to non-Cartesian trajectories, because of a limitation intrinsic to the dynamic model used in that study. Here the limitation is overcome and the model is applied to the more commonly used Cartesian trajectory with fast reconstruction. Furthermore, a combination of the Kalman filter model with Cartesian parallel imaging is presented to further increase the spatial and temporal resolution and SNR. Simulations and experiments were conducted to demonstrate that the Kalman filter model can increase the temporal resolution of the image series compared with view sharing techniques and decrease the spatial aliasing compared with TGRAPPA. The method requires relatively little computation, and thus is suitable for real-time reconstruction. PMID:22926804

Dental cone-beam CT reconstruction from limited-angle view data based on compressed-sensing (CS) theory for fast, low-dose X-ray imaging

NASA Astrophysics Data System (ADS)

Je, Uikyu; Cho, Hyosung; Lee, Minsik; Oh, Jieun; Park, Yeonok; Hong, Daeki; Park, Cheulkyu; Cho, Heemoon; Choi, Sungil; Koo, Yangseo

2014-06-01

Recently, reducing radiation doses has become an issue of critical importance in the broader radiological community. As a possible technical approach, especially, in dental cone-beam computed tomography (CBCT), reconstruction from limited-angle view data (< 360°) would enable fast scanning with reduced doses to the patient. In this study, we investigated and implemented an efficient reconstruction algorithm based on compressed-sensing (CS) theory for the scan geometry and performed systematic simulation works to investigate the image characteristics. We also performed experimental works by applying the algorithm to a commercially-available dental CBCT system to demonstrate its effectiveness for image reconstruction in incomplete data problems. We successfully reconstructed CBCT images with incomplete projections acquired at selected scan angles of 120, 150, 180, and 200° with a fixed angle step of 1.2° and evaluated the reconstruction quality quantitatively. Both simulation and experimental demonstrations of the CS-based reconstruction from limited-angle view data show that the algorithm can be applied directly to current dental CBCT systems for reducing the imaging doses and further improving the image quality.

Image-Based Modeling Techniques for Architectural Heritage 3d Digitalization: Limits and Potentialities

NASA Astrophysics Data System (ADS)

Santagati, C.; Inzerillo, L.; Di Paola, F.

2013-07-01

3D reconstruction from images has undergone a revolution in the last few years. Computer vision techniques use photographs from data set collection to rapidly build detailed 3D models. The simultaneous applications of different algorithms (MVS), the different techniques of image matching, feature extracting and mesh optimization are inside an active field of research in computer vision. The results are promising: the obtained models are beginning to challenge the precision of laser-based reconstructions. Among all the possibilities we can mainly distinguish desktop and web-based packages. Those last ones offer the opportunity to exploit the power of cloud computing in order to carry out a semi-automatic data processing, thus allowing the user to fulfill other tasks on its computer; whereas desktop systems employ too much processing time and hard heavy approaches. Computer vision researchers have explored many applications to verify the visual accuracy of 3D model but the approaches to verify metric accuracy are few and no one is on Autodesk 123D Catch applied on Architectural Heritage Documentation. Our approach to this challenging problem is to compare the 3Dmodels by Autodesk 123D Catch and 3D models by terrestrial LIDAR considering different object size, from the detail (capitals, moldings, bases) to large scale buildings for practitioner purpose.