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

Advancements to the planogram frequency–distance rebinning algorithm

PubMed Central

Champley, Kyle M; Raylman, Raymond R; Kinahan, Paul E

2010-01-01

In this paper we consider the task of image reconstruction in positron emission tomography (PET) with the planogram frequency–distance rebinning (PFDR) algorithm. The PFDR algorithm is a rebinning algorithm for PET systems with panel detectors. The algorithm is derived in the planogram coordinate system which is a native data format for PET systems with panel detectors. A rebinning algorithm averages over the redundant four-dimensional set of PET data to produce a three-dimensional set of data. Images can be reconstructed from this rebinned three-dimensional set of data. This process enables one to reconstruct PET images more quickly than reconstructing directly from the four-dimensional PET data. The PFDR algorithm is an approximate rebinning algorithm. We show that implementing the PFDR algorithm followed by the (ramp) filtered backprojection (FBP) algorithm in linogram coordinates from multiple views reconstructs a filtered version of our image. We develop an explicit formula for this filter which can be used to achieve exact reconstruction by means of a modified FBP algorithm applied to the stack of rebinned linograms and can also be used to quantify the errors introduced by the PFDR algorithm. This filter is similar to the filter in the planogram filtered backprojection algorithm derived by Brasse et al. The planogram filtered backprojection and exact reconstruction with the PFDR algorithm require complete projections which can be completed with a reprojection algorithm. The PFDR algorithm is similar to the rebinning algorithm developed by Kao et al. By expressing the PFDR algorithm in detector coordinates, we provide a comparative analysis between the two algorithms. Numerical experiments using both simulated data and measured data from a positron emission mammography/tomography (PEM/PET) system are performed. Images are reconstructed by PFDR+FBP (PFDR followed by 2D FBP reconstruction), PFDRX (PFDR followed by the modified FBP algorithm for exact

A Survey of the Use of Iterative Reconstruction Algorithms in Electron Microscopy

PubMed Central

Otón, J.; Vilas, J. L.; Kazemi, M.; Melero, R.; del Caño, L.; Cuenca, J.; Conesa, P.; Gómez-Blanco, J.; Marabini, R.; Carazo, J. M.

2017-01-01

One of the key steps in Electron Microscopy is the tomographic reconstruction of a three-dimensional (3D) map of the specimen being studied from a set of two-dimensional (2D) projections acquired at the microscope. This tomographic reconstruction may be performed with different reconstruction algorithms that can be grouped into several large families: direct Fourier inversion methods, back-projection methods, Radon methods, or iterative algorithms. In this review, we focus on the latter family of algorithms, explaining the mathematical rationale behind the different algorithms in this family as they have been introduced in the field of Electron Microscopy. We cover their use in Single Particle Analysis (SPA) as well as in Electron Tomography (ET). PMID:29312997

SU-F-SPS-06: Implementation of a Back-Projection Algorithm for 2D in Vivo Dosimetry with An EPID System

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

Hernandez Reyes, B; Rodriguez Perez, E; Sosa Aquino, M

Purpose: To implement a back-projection algorithm for 2D dose reconstructions for in vivo dosimetry in radiation therapy using an Electronic Portal Imaging Device (EPID) based on amorphous silicon. Methods: An EPID system was used to calculate dose-response function, pixel sensitivity map, exponential scatter kernels and beam hardenig correction for the back-projection algorithm. All measurements were done with a 6 MV beam. A 2D dose reconstruction for an irradiated water phantom (30×30×30 cm{sup 3}) was done to verify the algorithm implementation. Gamma index evaluation between the 2D reconstructed dose and the calculated with a treatment planning system (TPS) was done. Results:more » A linear fit was found for the dose-response function. The pixel sensitivity map has a radial symmetry and was calculated with a profile of the pixel sensitivity variation. The parameters for the scatter kernels were determined only for a 6 MV beam. The primary dose was estimated applying the scatter kernel within EPID and scatter kernel within the patient. The beam hardening coefficient is σBH= 3.788×10{sup −4} cm{sup 2} and the effective linear attenuation coefficient is µAC= 0.06084 cm{sup −1}. The 95% of points evaluated had γ values not longer than the unity, with gamma criteria of ΔD = 3% and Δd = 3 mm, and within the 50% isodose surface. Conclusion: The use of EPID systems proved to be a fast tool for in vivo dosimetry, but the implementation is more complex that the elaborated for pre-treatment dose verification, therefore, a simplest method must be investigated. The accuracy of this method should be improved modifying the algorithm in order to compare lower isodose curves.« less

A back-projection algorithm in the presence of an extra attenuating medium: towards EPID dosimetry for the MR-Linac

NASA Astrophysics Data System (ADS)

Torres-Xirau, I.; Olaciregui-Ruiz, I.; Rozendaal, R. A.; González, P.; Mijnheer, B. J.; Sonke, J.-J.; van der Heide, U. A.; Mans, A.

2017-08-01

In external beam radiotherapy, electronic portal imaging devices (EPIDs) are frequently used for pre-treatment and for in vivo dose verification. Currently, various MR-guided radiotherapy systems are being developed and clinically implemented. Independent dosimetric verification is highly desirable. For this purpose we adapted our EPID-based dose verification system for use with the MR-Linac combination developed by Elekta in cooperation with UMC Utrecht and Philips. In this study we extended our back-projection method to cope with the presence of an extra attenuating medium between the patient and the EPID. Experiments were performed at a conventional linac, using an aluminum mock-up of the MRI scanner housing between the phantom and the EPID. For a 10 cm square field, the attenuation by the mock-up was 72%, while 16% of the remaining EPID signal resulted from scattered radiation. 58 IMRT fields were delivered to a 20 cm slab phantom with and without the mock-up. EPID reconstructed dose distributions were compared to planned dose distributions using the γ -evaluation method (global, 3%, 3 mm). In our adapted back-projection algorithm the averaged {γmean} was 0.27+/- 0.06 , while in the conventional it was 0.28+/- 0.06 . Dose profiles of several square fields reconstructed with our adapted algorithm showed excellent agreement when compared to TPS.

Feature selection and back-projection algorithms for nonline-of-sight laser-gated viewing

NASA Astrophysics Data System (ADS)

Laurenzis, Martin; Velten, Andreas

2014-11-01

We discuss new approaches to analyze laser-gated viewing data for nonline-of-sight vision with a frame-to-frame back-projection as well as feature selection algorithms. Although first back-projection approaches use time transients for each pixel, our method has the ability to calculate the projection of imaging data on the voxel space for each frame. Further, different data analysis algorithms and their sequential application were studied with the aim of identifying and selecting signals from different target positions. A slight modification of commonly used filters leads to a powerful selection of local maximum values. It is demonstrated that the choice of the filter has an impact on the selectivity i.e., multiple target detection as well as on the localization precision.

Precise Aperture-Dependent Motion Compensation with Frequency Domain Fast Back-Projection Algorithm.

PubMed

Zhang, Man; Wang, Guanyong; Zhang, Lei

2017-10-26

Precise azimuth-variant motion compensation (MOCO) is an essential and difficult task for high-resolution synthetic aperture radar (SAR) imagery. In conventional post-filtering approaches, residual azimuth-variant motion errors are generally compensated through a set of spatial post-filters, where the coarse-focused image is segmented into overlapped blocks concerning the azimuth-dependent residual errors. However, image domain post-filtering approaches, such as precise topography- and aperture-dependent motion compensation algorithm (PTA), have difficulty of robustness in declining, when strong motion errors are involved in the coarse-focused image. In this case, in order to capture the complete motion blurring function within each image block, both the block size and the overlapped part need necessary extension leading to degeneration of efficiency and robustness inevitably. Herein, a frequency domain fast back-projection algorithm (FDFBPA) is introduced to deal with strong azimuth-variant motion errors. FDFBPA disposes of the azimuth-variant motion errors based on a precise azimuth spectrum expression in the azimuth wavenumber domain. First, a wavenumber domain sub-aperture processing strategy is introduced to accelerate computation. After that, the azimuth wavenumber spectrum is partitioned into a set of wavenumber blocks, and each block is formed into a sub-aperture coarse resolution image via the back-projection integral. Then, the sub-aperture images are straightforwardly fused together in azimuth wavenumber domain to obtain a full resolution image. Moreover, chirp-Z transform (CZT) is also introduced to implement the sub-aperture back-projection integral, increasing the efficiency of the algorithm. By disusing the image domain post-filtering strategy, robustness of the proposed algorithm is improved. Both simulation and real-measured data experiments demonstrate the effectiveness and superiority of the proposal.

A model for filtered backprojection reconstruction artifacts due to time-varying attenuation values in perfusion C-arm CT.

PubMed

Fieselmann, Andreas; Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim

2011-06-21

Filtered backprojection is the basis for many CT reconstruction tasks. It assumes constant attenuation values of the object during the acquisition of the projection data. Reconstruction artifacts can arise if this assumption is violated. For example, contrast flow in perfusion imaging with C-arm CT systems, which have acquisition times of several seconds per C-arm rotation, can cause this violation. In this paper, we derived and validated a novel spatio-temporal model to describe these kinds of artifacts. The model separates the temporal dynamics due to contrast flow from the scan and reconstruction parameters. We introduced derivative-weighted point spread functions to describe the spatial spread of the artifacts. The model allows prediction of reconstruction artifacts for given temporal dynamics of the attenuation values. Furthermore, it can be used to systematically investigate the influence of different reconstruction parameters on the artifacts. We have shown that with optimized redundancy weighting function parameters the spatial spread of the artifacts around a typical arterial vessel can be reduced by about 70%. Finally, an inversion of our model could be used as the basis for novel dynamic reconstruction algorithms that further minimize these artifacts.

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.

CT image reconstruction with half precision floating-point values.

PubMed

Maaß, Clemens; Baer, Matthias; Kachelrieß, Marc

2011-07-01

Analytic CT image reconstruction is a computationally demanding task. Currently, the even more demanding iterative reconstruction algorithms find their way into clinical routine because their image quality is superior to analytic image reconstruction. The authors thoroughly analyze a so far unconsidered but valuable tool of tomorrow's reconstruction hardware (CPU and GPU) that allows implementing the forward projection and backprojection steps, which are the computationally most demanding parts of any reconstruction algorithm, much more efficiently. Instead of the standard 32 bit floating-point values (float), a recently standardized floating-point value with 16 bit (half) is adopted for data representation in image domain and in rawdata domain. The reduction in the total data amount reduces the traffic on the memory bus, which is the bottleneck of today's high-performance algorithms, by 50%. In CT simulations and CT measurements, float reconstructions (gold standard) and half reconstructions are visually compared via difference images and by quantitative image quality evaluation. This is done for analytical reconstruction (filtered backprojection) and iterative reconstruction (ordered subset SART). The magnitude of quantization noise, which is caused by a reduction in the data precision of both rawdata and image data during image reconstruction, is negligible. This is clearly shown for filtered backprojection and iterative ordered subset SART reconstruction. In filtered backprojection, the implementation of the backprojection should be optimized for low data precision if the image data are represented in half format. In ordered subset SART image reconstruction, no adaptations are necessary and the convergence speed remains unchanged. Half precision floating-point values allow to speed up CT image reconstruction without compromising image quality.

Use of the Hotelling observer to optimize image reconstruction in digital breast tomosynthesis

PubMed Central

Sánchez, Adrian A.; Sidky, Emil Y.; Pan, Xiaochuan

2015-01-01

Abstract. We propose an implementation of the Hotelling observer that can be applied to the optimization of linear image reconstruction algorithms in digital breast tomosynthesis. The method is based on considering information within a specific region of interest, and it is applied to the optimization of algorithms for detectability of microcalcifications. Several linear algorithms are considered: simple back-projection, filtered back-projection, back-projection filtration, and Λ-tomography. The optimized algorithms are then evaluated through the reconstruction of phantom data. The method appears robust across algorithms and parameters and leads to the generation of algorithm implementations which subjectively appear optimized for the task of interest. PMID:26702408

Spatial-impulse-response-dependent back-projection using the non-stationary convolution in optoacoustic mesoscopy

NASA Astrophysics Data System (ADS)

Lu, Tong; Wang, Yihan; Gao, Feng; Zhao, Huijuan; Ntziachristos, Vasilis; Li, Jiao

2018-02-01

Photoacoustic mesoscopy (PAMe), offering high-resolution (sub-100-μm) and high optical contrast imaging at the depth of 1-10 mm, generally obtains massive collection data using a high-frequency focused ultrasonic transducer. The spatial impulse response (SIR) of this focused transducer causes the distortion of measured signals in both duration and amplitude. Thus, the reconstruction method considering the SIR needs to be investigated in the computation-economic way for PAMe. Here, we present a modified back-projection algorithm, by introducing a SIR-dependent calibration process using a non-satationary convolution method. The proposed method is performed on numerical simulations and phantom experiments of microspheres with diameter of both 50 μm and 100 μm, and the improvement of image fidelity of this method is proved to be evident by methodology parameters. The results demonstrate that, the images reconstructed when the SIR of transducer is accounted for have higher contrast-to-noise ratio and more reasonable spatial resolution, compared to the common back-projection algorithm.

Tomographic image reconstruction using the cell broadband engine (CBE) general purpose hardware

NASA Astrophysics Data System (ADS)

Knaup, Michael; Steckmann, Sven; Bockenbach, Olivier; Kachelrieß, Marc

2007-02-01

Tomographic image reconstruction, such as the reconstruction of CT projection values, of tomosynthesis data, PET or SPECT events, is computational very demanding. In filtered backprojection as well as in iterative reconstruction schemes, the most time-consuming steps are forward- and backprojection which are often limited by the memory bandwidth. Recently, a novel general purpose architecture optimized for distributed computing became available: the Cell Broadband Engine (CBE). Its eight synergistic processing elements (SPEs) currently allow for a theoretical performance of 192 GFlops (3 GHz, 8 units, 4 floats per vector, 2 instructions, multiply and add, per clock). To maximize image reconstruction speed we modified our parallel-beam and perspective backprojection algorithms which are highly optimized for standard PCs, and optimized the code for the CBE processor. 1-3 In addition, we implemented an optimized perspective forwardprojection on the CBE which allows us to perform statistical image reconstructions like the ordered subset convex (OSC) algorithm. 4 Performance was measured using simulated data with 512 projections per rotation and 5122 detector elements. The data were backprojected into an image of 512 3 voxels using our PC-based approaches and the new CBE- based algorithms. Both the PC and the CBE timings were scaled to a 3 GHz clock frequency. On the CBE, we obtain total reconstruction times of 4.04 s for the parallel backprojection, 13.6 s for the perspective backprojection and 192 s for a complete OSC reconstruction, consisting of one initial Feldkamp reconstruction, followed by 4 OSC iterations.

A biological phantom for evaluation of CT image reconstruction algorithms

NASA Astrophysics Data System (ADS)

Cammin, J.; Fung, G. S. K.; Fishman, E. K.; Siewerdsen, J. H.; Stayman, J. W.; Taguchi, K.

2014-03-01

In recent years, iterative algorithms have become popular in diagnostic CT imaging to reduce noise or radiation dose to the patient. The non-linear nature of these algorithms leads to non-linearities in the imaging chain. However, the methods to assess the performance of CT imaging systems were developed assuming the linear process of filtered backprojection (FBP). Those methods may not be suitable any longer when applied to non-linear systems. In order to evaluate the imaging performance, a phantom is typically scanned and the image quality is measured using various indices. For reasons of practicality, cost, and durability, those phantoms often consist of simple water containers with uniform cylinder inserts. However, these phantoms do not represent the rich structure and patterns of real tissue accurately. As a result, the measured image quality or detectability performance for lesions may not reflect the performance on clinical images. The discrepancy between estimated and real performance may be even larger for iterative methods which sometimes produce "plastic-like", patchy images with homogeneous patterns. Consequently, more realistic phantoms should be used to assess the performance of iterative algorithms. We designed and constructed a biological phantom consisting of porcine organs and tissue that models a human abdomen, including liver lesions. We scanned the phantom on a clinical CT scanner and compared basic image quality indices between filtered backprojection and an iterative reconstruction algorithm.

Exact BPF and FBP algorithms for nonstandard saddle curves.

PubMed

Yu, Hengyong; Zhao, Shiying; Ye, Yangbo; Wang, Ge

2005-11-01

A hot topic in cone-beam CT research is exact cone-beam reconstruction from a general scanning trajectory. Particularly, a nonstandard saddle curve attracts attention, as this construct allows the continuous periodic scanning of a volume-of-interest (VOI). Here we evaluate two algorithms for reconstruction from data collected along a nonstandard saddle curve, which are in the filtered backprojection (FBP) and backprojection filtration (BPF) formats, respectively. Both the algorithms are implemented in a chord-based coordinate system. Then, a rebinning procedure is utilized to transform the reconstructed results into the natural coordinate system. The simulation results demonstrate that the FBP algorithm produces better image quality than the BPF algorithm, while both the algorithms exhibit similar noise characteristics.

Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm

NASA Astrophysics Data System (ADS)

Gilat Schmidt, Taly; Barber, Rina F.; Sidky, Emil Y.

2017-03-01

Metal objects cause artifacts in computed tomography (CT) images. This work investigated the feasibility of a spectral CT method to reduce metal artifacts. Spectral CT acquisition combined with optimization-based reconstruction is proposed to reduce artifacts by modeling the physical effects that cause metal artifacts and by providing the flexibility to selectively remove corrupted spectral measurements in the spectral-sinogram space. The proposed Constrained `One-Step' Spectral CT Image Reconstruction (cOSSCIR) algorithm directly estimates the basis material maps while enforcing convex constraints. The incorporation of constraints on the reconstructed basis material maps is expected to mitigate undersampling effects that occur when corrupted data is excluded from reconstruction. The feasibility of the cOSSCIR algorithm to reduce metal artifacts was investigated through simulations of a pelvis phantom. The cOSSCIR algorithm was investigated with and without the use of a third basis material representing metal. The effects of excluding data corrupted by metal were also investigated. The results demonstrated that the proposed cOSSCIR algorithm reduced metal artifacts and improved CT number accuracy. For example, CT number error in a bright shading artifact region was reduced from 403 HU in the reference filtered backprojection reconstruction to 33 HU using the proposed algorithm in simulation. In the dark shading regions, the error was reduced from 1141 HU to 25 HU. Of the investigated approaches, decomposing the data into three basis material maps and excluding the corrupted data demonstrated the greatest reduction in metal artifacts.

Objective performance assessment of five computed tomography iterative reconstruction algorithms.

PubMed

Omotayo, Azeez; Elbakri, Idris

2016-11-22

Iterative algorithms are gaining clinical acceptance in CT. We performed objective phantom-based image quality evaluation of five commercial iterative reconstruction algorithms available on four different multi-detector CT (MDCT) scanners at different dose levels as well as the conventional filtered back-projection (FBP) reconstruction. Using the Catphan500 phantom, we evaluated image noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF) and noise-power spectrum (NPS). The algorithms were evaluated over a CTDIvol range of 0.75-18.7 mGy on four major MDCT scanners: GE DiscoveryCT750HD (algorithms: ASIR™ and VEO™); Siemens Somatom Definition AS+ (algorithm: SAFIRE™); Toshiba Aquilion64 (algorithm: AIDR3D™); and Philips Ingenuity iCT256 (algorithm: iDose4™). Images were reconstructed using FBP and the respective iterative algorithms on the four scanners. Use of iterative algorithms decreased image noise and increased CNR, relative to FBP. In the dose range of 1.3-1.5 mGy, noise reduction using iterative algorithms was in the range of 11%-51% on GE DiscoveryCT750HD, 10%-52% on Siemens Somatom Definition AS+, 49%-62% on Toshiba Aquilion64, and 13%-44% on Philips Ingenuity iCT256. The corresponding CNR increase was in the range 11%-105% on GE, 11%-106% on Siemens, 85%-145% on Toshiba and 13%-77% on Philips respectively. Most algorithms did not affect the MTF, except for VEO™ which produced an increase in the limiting resolution of up to 30%. A shift in the peak of the NPS curve towards lower frequencies and a decrease in NPS amplitude were obtained with all iterative algorithms. VEO™ required long reconstruction times, while all other algorithms produced reconstructions in real time. Compared to FBP, iterative algorithms reduced image noise and increased CNR. The iterative algorithms available on different scanners achieved different levels of noise reduction and CNR increase while spatial resolution improvements were obtained only with

Exact BPF and FBP algorithms for nonstandard saddle curves

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

Yu Hengyong; Zhao Shiying; Ye Yangbo

2005-11-15

A hot topic in cone-beam CT research is exact cone-beam reconstruction from a general scanning trajectory. Particularly, a nonstandard saddle curve attracts attention, as this construct allows the continuous periodic scanning of a volume-of-interest (VOI). Here we evaluate two algorithms for reconstruction from data collected along a nonstandard saddle curve, which are in the filtered backprojection (FBP) and backprojection filtration (BPF) formats, respectively. Both the algorithms are implemented in a chord-based coordinate system. Then, a rebinning procedure is utilized to transform the reconstructed results into the natural coordinate system. The simulation results demonstrate that the FBP algorithm produces better imagemore » quality than the BPF algorithm, while both the algorithms exhibit similar noise characteristics.« less

CUDA-based high-performance computing of the S-BPF algorithm with no-waiting pipelining

NASA Astrophysics Data System (ADS)

Deng, Lin; Yan, Bin; Chang, Qingmei; Han, Yu; Zhang, Xiang; Xi, Xiaoqi; Li, Lei

2015-10-01

The backprojection-filtration (BPF) algorithm has become a good solution for local reconstruction in cone-beam computed tomography (CBCT). However, the reconstruction speed of BPF is a severe limitation for clinical applications. The selective-backprojection filtration (S-BPF) algorithm is developed to improve the parallel performance of BPF by selective backprojection. Furthermore, the general-purpose graphics processing unit (GP-GPU) is a popular tool for accelerating the reconstruction. Much work has been performed aiming for the optimization of the cone-beam back-projection. As the cone-beam back-projection process becomes faster, the data transportation holds a much bigger time proportion in the reconstruction than before. This paper focuses on minimizing the total time in the reconstruction with the S-BPF algorithm by hiding the data transportation among hard disk, CPU and GPU. And based on the analysis of the S-BPF algorithm, some strategies are implemented: (1) the asynchronous calls are used to overlap the implemention of CPU and GPU, (2) an innovative strategy is applied to obtain the DBP image to hide the transport time effectively, (3) two streams for data transportation and calculation are synchronized by the cudaEvent in the inverse of finite Hilbert transform on GPU. Our main contribution is a smart reconstruction of the S-BPF algorithm with GPU's continuous calculation and no data transportation time cost. a 5123 volume is reconstructed in less than 0.7 second on a single Tesla-based K20 GPU from 182 views projection with 5122 pixel per projection. The time cost of our implementation is about a half of that without the overlap behavior.

Multi-GPU Acceleration of Branchless Distance Driven Projection and Backprojection for Clinical Helical CT.

PubMed

Mitra, Ayan; Politte, David G; Whiting, Bruce R; Williamson, Jeffrey F; O'Sullivan, Joseph A

2017-01-01

Model-based image reconstruction (MBIR) techniques have the potential to generate high quality images from noisy measurements and a small number of projections which can reduce the x-ray dose in patients. These MBIR techniques rely on projection and backprojection to refine an image estimate. One of the widely used projectors for these modern MBIR based technique is called branchless distance driven (DD) projection and backprojection. While this method produces superior quality images, the computational cost of iterative updates keeps it from being ubiquitous in clinical applications. In this paper, we provide several new parallelization ideas for concurrent execution of the DD projectors in multi-GPU systems using CUDA programming tools. We have introduced some novel schemes for dividing the projection data and image voxels over multiple GPUs to avoid runtime overhead and inter-device synchronization issues. We have also reduced the complexity of overlap calculation of the algorithm by eliminating the common projection plane and directly projecting the detector boundaries onto image voxel boundaries. To reduce the time required for calculating the overlap between the detector edges and image voxel boundaries, we have proposed a pre-accumulation technique to accumulate image intensities in perpendicular 2D image slabs (from a 3D image) before projection and after backprojection to ensure our DD kernels run faster in parallel GPU threads. For the implementation of our iterative MBIR technique we use a parallel multi-GPU version of the alternating minimization (AM) algorithm with penalized likelihood update. The time performance using our proposed reconstruction method with Siemens Sensation 16 patient scan data shows an average of 24 times speedup using a single TITAN X GPU and 74 times speedup using 3 TITAN X GPUs in parallel for combined projection and backprojection.

Broadband Tomography System: Direct Time-Space Reconstruction Algorithm

NASA Astrophysics Data System (ADS)

Biagi, E.; Capineri, Lorenzo; Castellini, Guido; Masotti, Leonardo F.; Rocchi, Santina

1989-10-01

In this paper a new ultrasound tomographic image algorithm is presented. A complete laboratory system is built up to test the algorithm in experimental conditions. The proposed system is based on a physical model consisting of a bidimensional distribution of single scattering elements. Multiple scattering is neglected, so Born approximation is assumed. This tomographic technique only requires two orthogonal scanning sections. For each rotational position of the object, data are collected by means of the complete data set method in transmission mode. After a numeric envelope detection, the received signals are back-projected in the space-domain through a scalar function. The reconstruction of each scattering element is accomplished by correlating the ultrasound time of flight and attenuation with the points' loci given by the possible positions of the scattering element. The points' locus is represented by an ellipse with the focuses located on the transmitter and receiver positions. In the image matrix the ellipses' contributions are coherently summed in the position of the scattering element. Computer simulations of cylindrical-shaped objects have pointed out the performances of the reconstruction algorithm. Preliminary experimental results show the laboratory system features. On the basis of these results an experimental procedure to test the confidence and repeatability of ultrasonic measurements on human carotid vessel is proposed.

Optimization, evaluation, and comparison of standard algorithms for image reconstruction with the VIP-PET.

PubMed

Mikhaylova, E; Kolstein, M; De Lorenzo, G; Chmeissani, M

2014-07-01

A novel positron emission tomography (PET) scanner design based on a room-temperature pixelated CdTe solid-state detector is being developed within the framework of the Voxel Imaging PET (VIP) Pathfinder project [1]. The simulation results show a great potential of the VIP to produce high-resolution images even in extremely challenging conditions such as the screening of a human head [2]. With unprecedented high channel density (450 channels/cm 3 ) image reconstruction is a challenge. Therefore optimization is needed to find the best algorithm in order to exploit correctly the promising detector potential. The following reconstruction algorithms are evaluated: 2-D Filtered Backprojection (FBP), Ordered Subset Expectation Maximization (OSEM), List-Mode OSEM (LM-OSEM), and the Origin Ensemble (OE) algorithm. The evaluation is based on the comparison of a true image phantom with a set of reconstructed images obtained by each algorithm. This is achieved by calculation of image quality merit parameters such as the bias, the variance and the mean square error (MSE). A systematic optimization of each algorithm is performed by varying the reconstruction parameters, such as the cutoff frequency of the noise filters and the number of iterations. The region of interest (ROI) analysis of the reconstructed phantom is also performed for each algorithm and the results are compared. Additionally, the performance of the image reconstruction methods is compared by calculating the modulation transfer function (MTF). The reconstruction time is also taken into account to choose the optimal algorithm. The analysis is based on GAMOS [3] simulation including the expected CdTe and electronic specifics.

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.

Neural network Hilbert transform based filtered backprojection for fast inline x-ray inspection

NASA Astrophysics Data System (ADS)

Janssens, Eline; De Beenhouwer, Jan; Van Dael, Mattias; De Schryver, Thomas; Van Hoorebeke, Luc; Verboven, Pieter; Nicolai, Bart; Sijbers, Jan

2018-03-01

X-ray imaging is an important tool for quality control since it allows to inspect the interior of products in a non-destructive way. Conventional x-ray imaging, however, is slow and expensive. Inline x-ray inspection, on the other hand, can pave the way towards fast and individual quality control, provided that a sufficiently high throughput can be achieved at a minimal cost. To meet these criteria, an inline inspection acquisition geometry is proposed where the object moves and rotates on a conveyor belt while it passes a fixed source and detector. Moreover, for this acquisition geometry, a new neural-network-based reconstruction algorithm is introduced: the neural network Hilbert transform based filtered backprojection. The proposed algorithm is evaluated both on simulated and real inline x-ray data and has shown to generate high quality reconstructions of 400  ×  400 reconstruction pixels within 200 ms, thereby meeting the high throughput criteria.

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

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

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.

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

Matched Backprojection Operator for Combined Scanning Transmission Electron Microscopy Tilt- and Focal Series.

PubMed

Dahmen, Tim; Kohr, Holger; de Jonge, Niels; Slusallek, Philipp

2015-06-01

Combined tilt- and focal series scanning transmission electron microscopy is a recently developed method to obtain nanoscale three-dimensional (3D) information of thin specimens. In this study, we formulate the forward projection in this acquisition scheme as a linear operator and prove that it is a generalization of the Ray transform for parallel illumination. We analytically derive the corresponding backprojection operator as the adjoint of the forward projection. We further demonstrate that the matched backprojection operator drastically improves the convergence rate of iterative 3D reconstruction compared to the case where a backprojection based on heuristic weighting is used. In addition, we show that the 3D reconstruction is of better quality.

A rapid parallelization of cone-beam projection and back-projection operator based on texture fetching interpolation

NASA Astrophysics Data System (ADS)

Xie, Lizhe; Hu, Yining; Chen, Yang; Shi, Luyao

2015-03-01

Projection and back-projection are the most computational consuming parts in Computed Tomography (CT) reconstruction. Parallelization strategies using GPU computing techniques have been introduced. We in this paper present a new parallelization scheme for both projection and back-projection. The proposed method is based on CUDA technology carried out by NVIDIA Corporation. Instead of build complex model, we aimed on optimizing the existing algorithm and make it suitable for CUDA implementation so as to gain fast computation speed. Besides making use of texture fetching operation which helps gain faster interpolation speed, we fixed sampling numbers in the computation of projection, to ensure the synchronization of blocks and threads, thus prevents the latency caused by inconsistent computation complexity. Experiment results have proven the computational efficiency and imaging quality of the proposed method.

Applications of nonlocal means algorithm in low-dose X-ray CT image processing and reconstruction: a review

PubMed Central

Zhang, Hao; Zeng, Dong; Zhang, Hua; Wang, Jing; Liang, Zhengrong

2017-01-01

Low-dose X-ray computed tomography (LDCT) imaging is highly recommended for use in the clinic because of growing concerns over excessive radiation exposure. However, the CT images reconstructed by the conventional filtered back-projection (FBP) method from low-dose acquisitions may be severely degraded with noise and streak artifacts due to excessive X-ray quantum noise, or with view-aliasing artifacts due to insufficient angular sampling. In 2005, the nonlocal means (NLM) algorithm was introduced as a non-iterative edge-preserving filter to denoise natural images corrupted by additive Gaussian noise, and showed superior performance. It has since been adapted and applied to many other image types and various inverse problems. This paper specifically reviews the applications of the NLM algorithm in LDCT image processing and reconstruction, and explicitly demonstrates its improving effects on the reconstructed CT image quality from low-dose acquisitions. The effectiveness of these applications on LDCT and their relative performance are described in detail. PMID:28303644

BPF-type region-of-interest reconstruction for parallel translational computed tomography.

PubMed

Wu, Weiwen; Yu, Hengyong; Wang, Shaoyu; Liu, Fenglin

2017-01-01

The objective of this study is to present and test a new ultra-low-cost linear scan based tomography architecture. Similar to linear tomosynthesis, the source and detector are translated in opposite directions and the data acquisition system targets on a region-of-interest (ROI) to acquire data for image reconstruction. This kind of tomographic architecture was named parallel translational computed tomography (PTCT). In previous studies, filtered backprojection (FBP)-type algorithms were developed to reconstruct images from PTCT. However, the reconstructed ROI images from truncated projections have severe truncation artefact. In order to overcome this limitation, we in this study proposed two backprojection filtering (BPF)-type algorithms named MP-BPF and MZ-BPF to reconstruct ROI images from truncated PTCT data. A weight function is constructed to deal with data redundancy for multi-linear translations modes. Extensive numerical simulations are performed to evaluate the proposed MP-BPF and MZ-BPF algorithms for PTCT in fan-beam geometry. Qualitative and quantitative results demonstrate that the proposed BPF-type algorithms cannot only more accurately reconstruct ROI images from truncated projections but also generate high-quality images for the entire image support in some circumstances.

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.

Optimization of view weighting in tilted-plane-based reconstruction algorithms to minimize helical artifacts in multi-slice helical CT

NASA Astrophysics Data System (ADS)

Tang, Xiangyang

2003-05-01

In multi-slice helical CT, the single-tilted-plane-based reconstruction algorithm has been proposed to combat helical and cone beam artifacts by tilting a reconstruction plane to fit a helical source trajectory optimally. Furthermore, to improve the noise characteristics or dose efficiency of the single-tilted-plane-based reconstruction algorithm, the multi-tilted-plane-based reconstruction algorithm has been proposed, in which the reconstruction plane deviates from the pose globally optimized due to an extra rotation along the 3rd axis. As a result, the capability of suppressing helical and cone beam artifacts in the multi-tilted-plane-based reconstruction algorithm is compromised. An optomized tilted-plane-based reconstruction algorithm is proposed in this paper, in which a matched view weighting strategy is proposed to optimize the capability of suppressing helical and cone beam artifacts and noise characteristics. A helical body phantom is employed to quantitatively evaluate the imaging performance of the matched view weighting approach by tabulating artifact index and noise characteristics, showing that the matched view weighting improves both the helical artifact suppression and noise characteristics or dose efficiency significantly in comparison to the case in which non-matched view weighting is applied. Finally, it is believed that the matched view weighting approach is of practical importance in the development of multi-slive helical CT, because it maintains the computational structure of fan beam filtered backprojection and demands no extra computational services.

Exact Fan-Beam Reconstruction With Arbitrary Object Translations and Truncated Projections

NASA Astrophysics Data System (ADS)

Hoskovec, Jan; Clackdoyle, Rolf; Desbat, Laurent; Rit, Simon

2016-06-01

This article proposes a new method for reconstructing two-dimensional (2D) computed tomography (CT) images from truncated and motion contaminated sinograms. The type of motion considered here is a sequence of rigid translations which are assumed to be known. The algorithm first identifies the sufficiency of angular coverage in each 2D point of the CT image to calculate the Hilbert transform from the local “virtual” trajectory which accounts for the motion and the truncation. By taking advantage of data redundancy in the full circular scan, our method expands the reconstructible region beyond the one obtained with chord-based methods. The proposed direct reconstruction algorithm is based on the Differentiated Back-Projection with Hilbert filtering (DBP-H). The motion is taken into account during backprojection which is the first step of our direct reconstruction, before taking the derivatives and inverting the finite Hilbert transform. The algorithm has been tested in a proof-of-concept study on Shepp-Logan phantom simulations with several motion cases and detector sizes.

High-resolution reconstruction for terahertz imaging.

PubMed

Xu, Li-Min; Fan, Wen-Hui; Liu, Jia

2014-11-20

We present a high-resolution (HR) reconstruction model and algorithms for terahertz imaging, taking advantage of super-resolution methodology and algorithms. The algorithms used include projection onto a convex sets approach, iterative backprojection approach, Lucy-Richardson iteration, and 2D wavelet decomposition reconstruction. Using the first two HR reconstruction methods, we successfully obtain HR terahertz images with improved definition and lower noise from four low-resolution (LR) 22×24 terahertz images taken from our homemade THz-TDS system at the same experimental conditions with 1.0 mm pixel. Using the last two HR reconstruction methods, we transform one relatively LR terahertz image to a HR terahertz image with decreased noise. This indicates potential application of HR reconstruction methods in terahertz imaging with pulsed and continuous wave terahertz sources.

Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms.

PubMed

Kim, Ye-seul; Park, Hye-suk; Lee, Haeng-Hwa; Choi, Young-Wook; Choi, Jae-Gu; Kim, Hak Hee; Kim, Hee-Joung

2016-02-01

Digital breast tomosynthesis (DBT) is a recently developed system for three-dimensional imaging that offers the potential to reduce the false positives of mammography by preventing tissue overlap. Many qualitative evaluations of digital breast tomosynthesis were previously performed by using a phantom with an unrealistic model and with heterogeneous background and noise, which is not representative of real breasts. The purpose of the present work was to compare reconstruction algorithms for DBT by using various breast phantoms; validation was also performed by using patient images. DBT was performed by using a prototype unit that was optimized for very low exposures and rapid readout. Three algorithms were compared: a back-projection (BP) algorithm, a filtered BP (FBP) algorithm, and an iterative expectation maximization (EM) algorithm. To compare the algorithms, three types of breast phantoms (homogeneous background phantom, heterogeneous background phantom, and anthropomorphic breast phantom) were evaluated, and clinical images were also reconstructed by using the different reconstruction algorithms. The in-plane image quality was evaluated based on the line profile and the contrast-to-noise ratio (CNR), and out-of-plane artifacts were evaluated by means of the artifact spread function (ASF). Parenchymal texture features of contrast and homogeneity were computed based on reconstructed images of an anthropomorphic breast phantom. The clinical images were studied to validate the effect of reconstruction algorithms. The results showed that the CNRs of masses reconstructed by using the EM algorithm were slightly higher than those obtained by using the BP algorithm, whereas the FBP algorithm yielded much lower CNR due to its high fluctuations of background noise. The FBP algorithm provides the best conspicuity for larger calcifications by enhancing their contrast and sharpness more than the other algorithms; however, in the case of small-size and low

Axial 3D region of interest reconstruction using weighted cone beam BPF/DBPF algorithm cascaded with adequately oriented orthogonal butterfly filtering

NASA Astrophysics Data System (ADS)

Tang, Shaojie; Tang, Xiangyang

2016-03-01

Axial cone beam (CB) computed tomography (CT) reconstruction is still the most desirable in clinical applications. As the potential candidates with analytic form for the task, the back projection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical and axial reconstruction from CB and fan beam projection data, respectively. These two algorithms have been heuristically extended for axial CB reconstruction via adoption of virtual PI-line segments. Unfortunately, however, streak artifacts are induced along the Hilbert filtering direction, since these algorithms are no longer accurate on the virtual PI-line segments. We have proposed to cascade the extended BPF/DBPF algorithm with orthogonal butterfly filtering for image reconstruction (namely axial CB-BPP/DBPF cascaded with orthogonal butterfly filtering), in which the orientation-specific artifacts caused by post-BP Hilbert transform can be eliminated, at a possible expense of losing the BPF/DBPF's capability of dealing with projection data truncation. Our preliminary results have shown that this is not the case in practice. Hence, in this work, we carry out an algorithmic analysis and experimental study to investigate the performance of the axial CB-BPP/DBPF cascaded with adequately oriented orthogonal butterfly filtering for three-dimensional (3D) reconstruction in region of interest (ROI).

Axial Cone-Beam Reconstruction by Weighted BPF/DBPF and Orthogonal Butterfly Filtering.

PubMed

Tang, Shaojie; Tang, Xiangyang

2016-09-01

The backprojection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical reconstruction from cone-beam (CB) scan data and axial reconstruction from fan beam data, respectively. These two algorithms can be heuristically extended for image reconstruction from axial CB scan data, but induce severe artifacts in images located away from the central plane, determined by the circular source trajectory. We propose an algorithmic solution herein to eliminate the artifacts. The solution is an integration of three-dimensional (3-D) weighted axial CB-BPF/DBPF algorithm with orthogonal butterfly filtering, namely axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering. Using the computer simulated Forbild head and thoracic phantoms that are rigorous in inspecting the reconstruction accuracy, and an anthropomorphic thoracic phantom with projection data acquired by a CT scanner, we evaluate the performance of the proposed algorithm. Preliminary results show that the orthogonal butterfly filtering can eliminate the severe streak artifacts existing in the images reconstructed by the 3-D weighted axial CB-BPF/DBPF algorithm located at off-central planes. Integrated with orthogonal butterfly filtering, the 3-D weighted CB-BPF/DBPF algorithm can perform at least as well as the 3-D weighted CB-FBP algorithm in image reconstruction from axial CB scan data. The proposed 3-D weighted axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering can be an algorithmic solution for CT imaging in extensive clinical and preclinical applications.

Axial Cone Beam Reconstruction by Weighted BPF/DBPF and Orthogonal Butterfly Filtering

PubMed Central

Tang, Shaojie; Tang, Xiangyang

2016-01-01

Goal The backprojection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical reconstruction from cone beam (CB) scan data and axial reconstruction from fan beam data, respectively. These two algorithms can be heuristically extended for image reconstruction from axial CB scan data, but induce severe artifacts in images located away from the central plane determined by the circular source trajectory. We propose an algorithmic solution herein to eliminate the artifacts. Methods The solution is an integration of three-dimensional (3D) weighted axial CB-BPF/ DBPF algorithm with orthogonal butterfly filtering, namely axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering. Using the computer simulated Forbild head and thoracic phantoms that are rigorous in inspecting reconstruction accuracy and an anthropomorphic thoracic phantom with projection data acquired by a CT scanner, we evaluate performance of the proposed algorithm. Results Preliminary results show that the orthogonal butterfly filtering can eliminate the severe streak artifacts existing in the images reconstructed by the 3D weighted axial CB-BPF/DBPF algorithm located at off-central planes. Conclusion Integrated with orthogonal butterfly filtering, the 3D weighted CB-BPF/DBPF algorithm can perform at least as well as the 3D weighted CB-FBP algorithm in image reconstruction from axial CB scan data. Significance The proposed 3D weighted axial CB-BPF/DBPF cascaded with orthogonal butterfly filtering can be an algorithmic solution for CT imaging in extensive clinical and preclinical applications. PMID:26660512

Filtered back-projection algorithm for Compton telescopes

DOEpatents

Gunter, Donald L [Lisle, IL

2008-03-18

A method for the conversion of Compton camera data into a 2D image of the incident-radiation flux on the celestial sphere includes detecting coincident gamma radiation flux arriving from various directions of a 2-sphere. These events are mapped by back-projection onto the 2-sphere to produce a convolution integral that is subsequently stereographically projected onto a 2-plane to produce a second convolution integral which is deconvolved by the Fourier method to produce an image that is then projected onto the 2-sphere.

Optimization of image quality and acquisition time for lab-based X-ray microtomography using an iterative reconstruction algorithm

NASA Astrophysics Data System (ADS)

Lin, Qingyang; Andrew, Matthew; Thompson, William; Blunt, Martin J.; Bijeljic, Branko

2018-05-01

Non-invasive laboratory-based X-ray microtomography has been widely applied in many industrial and research disciplines. However, the main barrier to the use of laboratory systems compared to a synchrotron beamline is its much longer image acquisition time (hours per scan compared to seconds to minutes at a synchrotron), which results in limited application for dynamic in situ processes. Therefore, the majority of existing laboratory X-ray microtomography is limited to static imaging; relatively fast imaging (tens of minutes per scan) can only be achieved by sacrificing imaging quality, e.g. reducing exposure time or number of projections. To alleviate this barrier, we introduce an optimized implementation of a well-known iterative reconstruction algorithm that allows users to reconstruct tomographic images with reasonable image quality, but requires lower X-ray signal counts and fewer projections than conventional methods. Quantitative analysis and comparison between the iterative and the conventional filtered back-projection reconstruction algorithm was performed using a sandstone rock sample with and without liquid phases in the pore space. Overall, by implementing the iterative reconstruction algorithm, the required image acquisition time for samples such as this, with sparse object structure, can be reduced by a factor of up to 4 without measurable loss of sharpness or signal to noise ratio.

Evaluation of corrective reconstruction methods using a 3D cardiac-torso phantom and bull's-eye plots

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

Zhao, X.D.; Tsui, B.M.W.; Gregoriou, G.K.

The goal of the investigation was to study the effectiveness of the corrective reconstruction methods in cardiac SPECT using a realistic phantom and to qualitatively and quantitatively evaluate the reconstructed images using bull's-eye plots. A 3D mathematical phantom which realistically models the anatomical structures of the cardiac-torso region of patients was used. The phantom allows simulation of both the attenuation distribution and the uptake of radiopharmaceuticals in different organs. Also, the phantom can be easily modified to simulate different genders and variations in patient anatomy. Two-dimensional projection data were generated from the phantom and included the effects of attenuation andmore » detector response blurring. The reconstruction methods used in the study included the conventional filtered backprojection (FBP) with no attenuation compensation, and the first-order Chang algorithm, an iterative filtered backprojection algorithm (IFBP), the weighted least square conjugate gradient algorithm and the ML-EM algorithm with non-uniform attenuation compensation. The transaxial reconstructed images were rearranged into short-axis slices from which bull's-eye plots of the count density distribution in the myocardium were generated.« less

Image reconstruction in cone-beam CT with a spherical detector using the BPF algorithm

NASA Astrophysics Data System (ADS)

Zuo, Nianming; Zou, Yu; Jiang, Tianzi; Pan, Xiaochuan

2006-03-01

Both flat-panel detectors and cylindrical detectors have been used in CT systems for data acquisition. The cylindrical detector generally offers a sampling of a transverse image plane more uniformly than does a flat-panel detector. However, in the longitudinal dimension, the cylindrical and flat-panel detectors offer similar sampling of the image space. In this work, we investigate a detector of spherical shape, which can yield uniform sampling of the 3D image space because the solid angle subtended by each individual detector bin remains unchanged. We have extended the backprojection-filtration (BPF) algorithm, which we have developed previously for cone-beam CT, to reconstruct images in cone-beam CT with a spherical detector. We also conduct computer-simulation studies to validate the extended BPF algorithm. Quantitative results in these numerical studies indicate that accurate images can be obtained from data acquired with a spherical detector by use of our extended BPF cone-beam algorithms.

Reconstruction of internal density distributions in porous bodies from laser ultrasonic data

NASA Technical Reports Server (NTRS)

Lu, Yichi; Goldman, Jeffrey A.; Wadley, Haydn N. G.

1992-01-01

It is presently shown that, for density-reconstruction problems in which information about the inhomogeneity is known a priori, the nonlinear least-squares algorithm yields satisfactory results on the basis of limited projection data. The back-projection algorithm, which obviates assumptions about the objective function to be reconstructed, does not recover the boundary of the inhomogeneity when the number of projections is limited and ray-bending is ignored.

Chromotomography for a rotating-prism instrument using backprojection, then filtering.

PubMed

Deming, Ross W

2006-08-01

A simple closed-form solution is derived for reconstructing a 3D spatial-chromatic image cube from a set of chromatically dispersed 2D image frames. The algorithm is tailored for a particular instrument in which the dispersion element is a matching set of mechanically rotated direct vision prisms positioned between a lens and a focal plane array. By using a linear operator formalism to derive the Tikhonov-regularized pseudoinverse operator, it is found that the unique minimum-norm solution is obtained by applying the adjoint operator, followed by 1D filtering with respect to the chromatic variable. Thus the filtering and backprojection (adjoint) steps are applied in reverse order relative to an existing method. Computational efficiency is provided by use of the fast Fourier transform in the filtering step.

Analysis of an Optimized MLOS Tomographic Reconstruction Algorithm and Comparison to the MART Reconstruction Algorithm

NASA Astrophysics Data System (ADS)

La Foy, Roderick; Vlachos, Pavlos

2011-11-01

An optimally designed MLOS tomographic reconstruction algorithm for use in 3D PIV and PTV applications is analyzed. Using a set of optimized reconstruction parameters, the reconstructions produced by the MLOS algorithm are shown to be comparable to reconstructions produced by the MART algorithm for a range of camera geometries, camera numbers, and particle seeding densities. The resultant velocity field error calculated using PIV and PTV algorithms is further minimized by applying both pre and post processing to the reconstructed data sets.

PI-line-based image reconstruction in helical cone-beam computed tomography with a variable pitch.

PubMed

Zou, Yu; Pan, Xiaochuan; Xia, Dan; Wang, Ge

2005-08-01

Current applications of helical cone-beam computed tomography (CT) involve primarily a constant pitch where the translating speed of the table and the rotation speed of the source-detector remain constant. However, situations do exist where it may be more desirable to use a helical scan with a variable translating speed of the table, leading a variable pitch. One of such applications could arise in helical cone-beam CT fluoroscopy for the determination of vascular structures through real-time imaging of contrast bolus arrival. Most of the existing reconstruction algorithms have been developed only for helical cone-beam CT with constant pitch, including the backprojection-filtration (BPF) and filtered-backprojection (FBP) algorithms that we proposed previously. It is possible to generalize some of these algorithms to reconstruct images exactly for helical cone-beam CT with a variable pitch. In this work, we generalize our BPF and FBP algorithms to reconstruct images directly from data acquired in helical cone-beam CT with a variable pitch. We have also performed a preliminary numerical study to demonstrate and verify the generalization of the two algorithms. The results of the study confirm that our generalized BPF and FBP algorithms can yield exact reconstruction in helical cone-beam CT with a variable pitch. It should be pointed out that our generalized BPF algorithm is the only algorithm that is capable of reconstructing exactly region-of-interest image from data containing transverse truncations.

A Comparison of Four-Image Reconstruction Algorithms for 3-D PET Imaging of MDAPET Camera Using Phantom Data

NASA Astrophysics Data System (ADS)

Baghaei, H.; Wong, Wai-Hoi; Uribe, J.; Li, Hongdi; Wang, Yu; Liu, Yaqiang; Xing, Tao; Ramirez, R.; Xie, Shuping; Kim, Soonseok

2004-10-01

We compared two fully three-dimensional (3-D) image reconstruction algorithms and two 3-D rebinning algorithms followed by reconstruction with a two-dimensional (2-D) filtered-backprojection algorithm for 3-D positron emission tomography (PET) imaging. The two 3-D image reconstruction algorithms were ordered-subsets expectation-maximization (3D-OSEM) and 3-D reprojection (3DRP) algorithms. The two rebinning algorithms were Fourier rebinning (FORE) and single slice rebinning (SSRB). The 3-D projection data used for this work were acquired with a high-resolution PET scanner (MDAPET) with an intrinsic transaxial resolution of 2.8 mm. The scanner has 14 detector rings covering an axial field-of-view of 38.5 mm. We scanned three phantoms: 1) a uniform cylindrical phantom with inner diameter of 21.5 cm; 2) a uniform 11.5-cm cylindrical phantom with four embedded small hot lesions with diameters of 3, 4, 5, and 6 mm; and 3) the 3-D Hoffman brain phantom with three embedded small hot lesion phantoms with diameters of 3, 5, and 8.6 mm in a warm background. Lesions were placed at different radial and axial distances. We evaluated the different reconstruction methods for MDAPET camera by comparing the noise level of images, contrast recovery, and hot lesion detection, and visually compared images. We found that overall the 3D-OSEM algorithm, especially when images post filtered with the Metz filter, produced the best results in terms of contrast-noise tradeoff, and detection of hot spots, and reproduction of brain phantom structures. Even though the MDAPET camera has a relatively small maximum axial acceptance (/spl plusmn/5 deg), images produced with the 3DRP algorithm had slightly better contrast recovery and reproduced the structures of the brain phantom slightly better than the faster 2-D rebinning methods.

Computed tomography imaging with the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm: dependence of image quality on the blending level of reconstruction.

PubMed

Barca, Patrizio; Giannelli, Marco; Fantacci, Maria Evelina; Caramella, Davide

2018-06-01

Computed tomography (CT) is a useful and widely employed imaging technique, which represents the largest source of population exposure to ionizing radiation in industrialized countries. Adaptive Statistical Iterative Reconstruction (ASIR) is an iterative reconstruction algorithm with the potential to allow reduction of radiation exposure while preserving diagnostic information. The aim of this phantom study was to assess the performance of ASIR, in terms of a number of image quality indices, when different reconstruction blending levels are employed. CT images of the Catphan-504 phantom were reconstructed using conventional filtered back-projection (FBP) and ASIR with reconstruction blending levels of 20, 40, 60, 80, and 100%. Noise, noise power spectrum (NPS), contrast-to-noise ratio (CNR) and modulation transfer function (MTF) were estimated for different scanning parameters and contrast objects. Noise decreased and CNR increased non-linearly up to 50 and 100%, respectively, with increasing blending level of reconstruction. Also, ASIR has proven to modify the NPS curve shape. The MTF of ASIR reconstructed images depended on tube load/contrast and decreased with increasing blending level of reconstruction. In particular, for low radiation exposure and low contrast acquisitions, ASIR showed lower performance than FBP, in terms of spatial resolution for all blending levels of reconstruction. CT image quality varies substantially with the blending level of reconstruction. ASIR has the potential to reduce noise whilst maintaining diagnostic information in low radiation exposure CT imaging. Given the opposite variation of CNR and spatial resolution with the blending level of reconstruction, it is recommended to use an optimal value of this parameter for each specific clinical application.

GPU-Accelerated Forward and Back-Projections with Spatially Varying Kernels for 3D DIRECT TOF PET Reconstruction.

PubMed

Ha, S; Matej, S; Ispiryan, M; Mueller, K

2013-02-01

We describe a GPU-accelerated framework that efficiently models spatially (shift) variant system response kernels and performs forward- and back-projection operations with these kernels for the DIRECT (Direct Image Reconstruction for TOF) iterative reconstruction approach. Inherent challenges arise from the poor memory cache performance at non-axis aligned TOF directions. Focusing on the GPU memory access patterns, we utilize different kinds of GPU memory according to these patterns in order to maximize the memory cache performance. We also exploit the GPU instruction-level parallelism to efficiently hide long latencies from the memory operations. Our experiments indicate that our GPU implementation of the projection operators has slightly faster or approximately comparable time performance than FFT-based approaches using state-of-the-art FFTW routines. However, most importantly, our GPU framework can also efficiently handle any generic system response kernels, such as spatially symmetric and shift-variant as well as spatially asymmetric and shift-variant, both of which an FFT-based approach cannot cope with.

GPU-Accelerated Forward and Back-Projections With Spatially Varying Kernels for 3D DIRECT TOF PET Reconstruction

NASA Astrophysics Data System (ADS)

Ha, S.; Matej, S.; Ispiryan, M.; Mueller, K.

2013-02-01

We describe a GPU-accelerated framework that efficiently models spatially (shift) variant system response kernels and performs forward- and back-projection operations with these kernels for the DIRECT (Direct Image Reconstruction for TOF) iterative reconstruction approach. Inherent challenges arise from the poor memory cache performance at non-axis aligned TOF directions. Focusing on the GPU memory access patterns, we utilize different kinds of GPU memory according to these patterns in order to maximize the memory cache performance. We also exploit the GPU instruction-level parallelism to efficiently hide long latencies from the memory operations. Our experiments indicate that our GPU implementation of the projection operators has slightly faster or approximately comparable time performance than FFT-based approaches using state-of-the-art FFTW routines. However, most importantly, our GPU framework can also efficiently handle any generic system response kernels, such as spatially symmetric and shift-variant as well as spatially asymmetric and shift-variant, both of which an FFT-based approach cannot cope with.

Frequency-radial duality based photoacoustic image reconstruction.

PubMed

Akramus Salehin, S M; Abhayapala, Thushara D

2012-07-01

Photoacoustic image reconstruction algorithms are usually slow due to the large sizes of data that are processed. This paper proposes a method for exact photoacoustic reconstruction for the spherical geometry in the limiting case of a continuous aperture and infinite measurement bandwidth that is faster than existing methods namely (1) backprojection method and (2) the Norton-Linzer method [S. J. Norton and M. Linzer, "Ultrasonic reflectivity imaging in three dimensions: Exact inverse scattering solution for plane, cylindrical and spherical apertures," Biomedical Engineering, IEEE Trans. BME 28, 202-220 (1981)]. The initial pressure distribution is expanded using a spherical Fourier Bessel series. The proposed method estimates the Fourier Bessel coefficients and subsequently recovers the pressure distribution. A concept of frequency-radial duality is introduced that separates the information from the different radial basis functions by using frequencies corresponding to the Bessel zeros. This approach provides a means to analyze the information obtained given a measurement bandwidth. Using order analysis and numerical experiments, the proposed method is shown to be faster than both the backprojection and the Norton-Linzer methods. Further, the reconstructed images using the proposed methodology were of similar quality to the Norton-Linzer method and were better than the approximate backprojection method.

Suppression of motion-induced streak artifacts along chords in fan-beam BPF-reconstructions of motion-contaminated projection data

NASA Astrophysics Data System (ADS)

King, Martin; Xia, Dan; Yu, Lifeng; Pan, Xiaochuan; Giger, Maryellen

2006-03-01

Usage of the backprojection filtration (BPF) algorithm for reconstructing images from motion-contaminated fan-beam data may result in motion-induced streak artifacts, which appear in the direction of the chords on which images are reconstructed. These streak artifacts, which are most pronounced along chords tangent to the edges of the moving object, may be suppressed by use of the weighted BPF (WBPF) algorithm, which can exploit the inherent redundancies in fan-beam data. More specifically, reconstructions using full-scan and short-scan data can allow for substantial suppression of these streaks, whereas those using reduced-scan data can allow for partial suppression. Since multiple different reconstructions of the same chord can be obtained by varying the amount of redundant data used, we have laid the groundwork for a possible method to characterize the amount of motion encoded within the data used for reconstructing an image on a particular chord. Furthermore, since motion artifacts in WBPF reconstructions using full-scan and short-scan data appear similar to those in corresponding fan-beam filtered backprojection (FFBP) reconstructions for the cases performed in this study, the BPF and WBPF algorithms potentially may be used to arrive at a more fundamental characterization of how motion artifacts appear in FFBP reconstructions.

Median prior constrained TV algorithm for sparse view low-dose CT reconstruction.

PubMed

Liu, Yi; Shangguan, Hong; Zhang, Quan; Zhu, Hongqing; Shu, Huazhong; Gui, Zhiguo

2015-05-01

It is known that lowering the X-ray tube current (mAs) or tube voltage (kVp) and simultaneously reducing the total number of X-ray views (sparse view) is an effective means to achieve low-dose in computed tomography (CT) scan. However, the associated image quality by the conventional filtered back-projection (FBP) usually degrades due to the excessive quantum noise. Although sparse-view CT reconstruction algorithm via total variation (TV), in the scanning protocol of reducing X-ray tube current, has been demonstrated to be able to result in significant radiation dose reduction while maintain image quality, noticeable patchy artifacts still exist in reconstructed images. In this study, to address the problem of patchy artifacts, we proposed a median prior constrained TV regularization to retain the image quality by introducing an auxiliary vector m in register with the object. Specifically, the approximate action of m is to draw, in each iteration, an object voxel toward its own local median, aiming to improve low-dose image quality with sparse-view projection measurements. Subsequently, an alternating optimization algorithm is adopted to optimize the associative objective function. We refer to the median prior constrained TV regularization as "TV_MP" for simplicity. Experimental results on digital phantoms and clinical phantom demonstrated that the proposed TV_MP with appropriate control parameters can not only ensure a higher signal to noise ratio (SNR) of the reconstructed image, but also its resolution compared with the original TV method. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Feasibility of using two-dimensional array dosimeter for in vivo dose reconstruction via transit dosimetry.

PubMed

Chung, Heeteak; Li, Jonathan; Samant, Sanjiv

2011-04-08

Two-dimensional array dosimeters are commonly used to perform pretreatment quality assurance procedures, which makes them highly desirable for measuring transit fluences for in vivo dose reconstruction. The purpose of this study was to determine if an in vivo dose reconstruction via transit dosimetry using a 2D array dosimeter was possible. To test the accuracy of measuring transit dose distribution using a 2D array dosimeter, we evaluated it against the measurements made using ionization chamber and radiochromic film (RCF) profiles for various air gap distances (distance from the exit side of the solid water slabs to the detector distance; 0 cm, 30 cm, 40 cm, 50 cm, and 60 cm) and solid water slab thicknesses (10 cm and 20 cm). The backprojection dose reconstruction algorithm was described and evaluated. The agreement between the ionization chamber and RCF profiles for the transit dose distribution measurements ranged from -0.2% ~ 4.0% (average 1.79%). Using the backprojection dose reconstruction algorithm, we found that, of the six conformal fields, four had a 100% gamma index passing rate (3%/3 mm gamma index criteria), and two had gamma index passing rates of 99.4% and 99.6%. Of the five IMRT fields, three had a 100% gamma index passing rate, and two had gamma index passing rates of 99.6% and 98.8%. It was found that a 2D array dosimeter could be used for backprojection dose reconstruction for in vivo dosimetry.

Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm*

PubMed Central

Bian, Junguo; Xia, Dan; Sidky, Emil Y; Pan, Xiaochuan

2010-01-01

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement. PMID:20617122

Region of Interest Imaging for a General Trajectory with the Rebinned BPF Algorithm.

PubMed

Bian, Junguo; Xia, Dan; Sidky, Emil Y; Pan, Xiaochuan

2010-02-01

The back-projection-filtration (BPF) algorithm has been applied to image reconstruction for cone-beam configurations with general source trajectories. The BPF algorithm can reconstruct 3-D region-of-interest (ROI) images from data containing truncations. However, like many other existing algorithms for cone-beam configurations, the BPF algorithm involves a back-projection with a spatially varying weighting factor, which can result in the non-uniform noise levels in reconstructed images and increased computation time. In this work, we propose a BPF algorithm to eliminate the spatially varying weighting factor by using a rebinned geometry for a general scanning trajectory. This proposed BPF algorithm has an improved noise property, while retaining the advantages of the original BPF algorithm such as minimum data requirement.

Local reconstruction in computed tomography of diffraction enhanced imaging

NASA Astrophysics Data System (ADS)

Huang, Zhi-Feng; Zhang, Li; Kang, Ke-Jun; Chen, Zhi-Qiang; Zhu, Pei-Ping; Yuan, Qing-Xi; Huang, Wan-Xia

2007-07-01

Computed tomography of diffraction enhanced imaging (DEI-CT) based on synchrotron radiation source has extremely high sensitivity of weakly absorbing low-Z samples in medical and biological fields. The authors propose a modified backprojection filtration(BPF)-type algorithm based on PI-line segments to reconstruct region of interest from truncated refraction-angle projection data in DEI-CT. The distribution of refractive index decrement in the sample can be directly estimated from its reconstruction images, which has been proved by experiments at the Beijing Synchrotron Radiation Facility. The algorithm paves the way for local reconstruction of large-size samples by the use of DEI-CT with small field of view based on synchrotron radiation source.

Backprojection of volcanic tremor

USGS Publications Warehouse

Haney, Matthew M.

2014-01-01

Backprojection has become a powerful tool for imaging the rupture process of global earthquakes. We demonstrate the ability of backprojection to illuminate and track volcanic sources as well. We apply the method to the seismic network from Okmok Volcano, Alaska, at the time of an escalation in tremor during the 2008 eruption. Although we are able to focus the wavefield close to the location of the active cone, the network array response lacks sufficient resolution to reveal kilometer-scale changes in tremor location. By deconvolving the response in successive backprojection images, we enhance resolution and find that the tremor source moved toward an intracaldera lake prior to its escalation. The increased tremor therefore resulted from magma-water interaction, in agreement with the overall phreatomagmatic character of the eruption. Imaging of eruption tremor shows that time reversal methods, such as backprojection, can provide new insights into the temporal evolution of volcanic sources.

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

An electron tomography algorithm for reconstructing 3D morphology using surface tangents of projected scattering interfaces

NASA Astrophysics Data System (ADS)

Petersen, T. C.; Ringer, S. P.

2010-03-01

range. The algorithm does not solve the tomographic back-projection problem but rather reconstructs the local 3D morphology of surfaces defined by varied scattering densities. Solution method: Reconstruction using differential geometry applied to image analysis computations. Restrictions: The code has only been tested with square images and has been developed for only single-axis tilting. Running time: For high quality reconstruction, 5-15 min

Feasibility of using two‐dimensional array dosimeter for in vivo dose reconstruction via transit dosimetry

PubMed Central

Li, Jonathan; Samant, Sanjiv

2011-01-01

Two‐dimensional array dosimeters are commonly used to perform pretreatment quality assurance procedures, which makes them highly desirable for measuring transit fluences for in vivo dose reconstruction. The purpose of this study was to determine if an in vivo dose reconstruction via transit dosimetry using a 2D array dosimeter was possible. To test the accuracy of measuring transit dose distribution using a 2D array dosimeter, we evaluated it against the measurements made using ionization chamber and radiochromic film (RCF) profiles for various air gap distances (distance from the exit side of the solid water slabs to the detector distance; 0 cm, 30 cm, 40 cm, 50 cm, and 60 cm) and solid water slab thicknesses (10 cm and 20 cm). The backprojection dose reconstruction algorithm was described and evaluated. The agreement between the ionization chamber and RCF profiles for the transit dose distribution measurements ranged from ‐0.2%~ 4.0% (average 1.79%). Using the backprojection dose reconstruction algorithm, we found that, of the six conformal fields, four had a 100% gamma index passing rate (3%/3 mm gamma index criteria), and two had gamma index passing rates of 99.4% and 99.6%. Of the five IMRT fields, three had a 100% gamma index passing rate, and two had gamma index passing rates of 99.6% and 98.8%. It was found that a 2D array dosimeter could be used for backprojection dose reconstruction for in vivo dosimetry. PACS number: 87.55.N‐

A BPF-type algorithm for CT with a curved PI detector.

PubMed

Tang, Jie; Zhang, Li; Chen, Zhiqiang; Xing, Yuxiang; Cheng, Jianping

2006-08-21

Helical cone-beam CT is used widely nowadays because of its rapid scan speed and efficient utilization of x-ray dose. Recently, an exact reconstruction algorithm for helical cone-beam CT was proposed (Zou and Pan 2004a Phys. Med. Biol. 49 941-59). The algorithm is referred to as a backprojection-filtering (BPF) algorithm. This BPF algorithm for a helical cone-beam CT with a flat-panel detector (FPD-HCBCT) requires minimum data within the Tam-Danielsson window and can naturally address the problem of ROI reconstruction from data truncated in both longitudinal and transversal directions. In practical CT systems, detectors are expensive and always take a very important position in the total cost. Hence, we work on an exact reconstruction algorithm for a CT system with a detector of the smallest size, i.e., a curved PI detector fitting the Tam-Danielsson window. The reconstruction algorithm is derived following the framework of the BPF algorithm. Numerical simulations are done to validate our algorithm in this study.

Evaluation of Origin Ensemble algorithm for image reconstruction for pixelated solid-state detectors with large number of channels

NASA Astrophysics Data System (ADS)

Kolstein, M.; De Lorenzo, G.; Mikhaylova, E.; Chmeissani, M.; Ariño, G.; Calderón, Y.; Ozsahin, I.; Uzun, D.

2013-04-01

The Voxel Imaging PET (VIP) Pathfinder project intends to show the advantages of using pixelated solid-state technology for nuclear medicine applications. It proposes designs for Positron Emission Tomography (PET), Positron Emission Mammography (PEM) and Compton gamma camera detectors with a large number of signal channels (of the order of 106). For PET scanners, conventional algorithms like Filtered Back-Projection (FBP) and Ordered Subset Expectation Maximization (OSEM) are straightforward to use and give good results. However, FBP presents difficulties for detectors with limited angular coverage like PEM and Compton gamma cameras, whereas OSEM has an impractically large time and memory consumption for a Compton gamma camera with a large number of channels. In this article, the Origin Ensemble (OE) algorithm is evaluated as an alternative algorithm for image reconstruction. Monte Carlo simulations of the PET design are used to compare the performance of OE, FBP and OSEM in terms of the bias, variance and average mean squared error (MSE) image quality metrics. For the PEM and Compton camera designs, results obtained with OE are presented.

Evaluation of hybrid SART  +  OS  +  TV iterative reconstruction algorithm for optical-CT gel dosimeter imaging

NASA Astrophysics Data System (ADS)

Du, Yi; Wang, Xiangang; Xiang, Xincheng; Wei, Zhouping

2016-12-01

Optical computed tomography (optical-CT) is a high-resolution, fast, and easily accessible readout modality for gel dosimeters. This paper evaluates a hybrid iterative image reconstruction algorithm for optical-CT gel dosimeter imaging, namely, the simultaneous algebraic reconstruction technique (SART) integrated with ordered subsets (OS) iteration and total variation (TV) minimization regularization. The mathematical theory and implementation workflow of the algorithm are detailed. Experiments on two different optical-CT scanners were performed for cross-platform validation. For algorithm evaluation, the iterative convergence is first shown, and peak-to-noise-ratio (PNR) and contrast-to-noise ratio (CNR) results are given with the cone-beam filtered backprojection (FDK) algorithm and the FDK results followed by median filtering (mFDK) as reference. The effect on spatial gradients and reconstruction artefacts is also investigated. The PNR curve illustrates that the results of SART  +  OS  +  TV finally converges to that of FDK but with less noise, which implies that the dose-OD calibration method for FDK is also applicable to the proposed algorithm. The CNR in selected regions-of-interest (ROIs) of SART  +  OS  +  TV results is almost double that of FDK and 50% higher than that of mFDK. The artefacts in SART  +  OS  +  TV results are still visible, but have been much suppressed with little spatial gradient loss. Based on the assessment, we can conclude that this hybrid SART  +  OS  +  TV algorithm outperforms both FDK and mFDK in denoising, preserving spatial dose gradients and reducing artefacts, and its effectiveness and efficiency are platform independent.

Extended volume coverage in helical cone-beam CT by using PI-line based BPF algorithm

NASA Astrophysics Data System (ADS)

Cho, Seungryong; Pan, Xiaochuan

2007-03-01

We compared data requirements of filtered-backprojection (FBP) and backprojection-filtration (BPF) algorithms based on PI-lines in helical cone-beam CT. Since the filtration process in FBP algorithm needs all the projection data of PI-lines for each view, the required detector size should be bigger than the size that can cover Tam-Danielsson (T-D) window to avoid data truncation. BPF algorithm, however, requires the projection data only within the T-D window, which means smaller detector size can be used to reconstruct the same image than that in FBP. In other words, a longer helical pitch can be obtained by using BPF algorithm without any truncation artifacts when a fixed detector size is given. The purpose of the work is to demonstrate numerically that extended volume coverage in helical cone-beam CT by using PI-line-based BPF algorithm can be achieved.

NOTE: A BPF-type algorithm for CT with a curved PI detector

NASA Astrophysics Data System (ADS)

Tang, Jie; Zhang, Li; Chen, Zhiqiang; Xing, Yuxiang; Cheng, Jianping

2006-08-01

Helical cone-beam CT is used widely nowadays because of its rapid scan speed and efficient utilization of x-ray dose. Recently, an exact reconstruction algorithm for helical cone-beam CT was proposed (Zou and Pan 2004a Phys. Med. Biol. 49 941 59). The algorithm is referred to as a backprojection-filtering (BPF) algorithm. This BPF algorithm for a helical cone-beam CT with a flat-panel detector (FPD-HCBCT) requires minimum data within the Tam Danielsson window and can naturally address the problem of ROI reconstruction from data truncated in both longitudinal and transversal directions. In practical CT systems, detectors are expensive and always take a very important position in the total cost. Hence, we work on an exact reconstruction algorithm for a CT system with a detector of the smallest size, i.e., a curved PI detector fitting the Tam Danielsson window. The reconstruction algorithm is derived following the framework of the BPF algorithm. Numerical simulations are done to validate our algorithm in this study.

Time-of-flight PET image reconstruction using origin ensembles.

PubMed

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-07

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.

Time-of-flight PET image reconstruction using origin ensembles

NASA Astrophysics Data System (ADS)

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-01

The origin ensemble (OE) algorithm is a novel statistical method for minimum-mean-square-error (MMSE) reconstruction of emission tomography data. This method allows one to perform reconstruction entirely in the image domain, i.e. without the use of forward and backprojection operations. We have investigated the OE algorithm in the context of list-mode (LM) time-of-flight (TOF) PET reconstruction. In this paper, we provide a general introduction to MMSE reconstruction, and a statistically rigorous derivation of the OE algorithm. We show how to efficiently incorporate TOF information into the reconstruction process, and how to correct for random coincidences and scattered events. To examine the feasibility of LM-TOF MMSE reconstruction with the OE algorithm, we applied MMSE-OE and standard maximum-likelihood expectation-maximization (ML-EM) reconstruction to LM-TOF phantom data with a count number typically registered in clinical PET examinations. We analyzed the convergence behavior of the OE algorithm, and compared reconstruction time and image quality to that of the EM algorithm. In summary, during the reconstruction process, MMSE-OE contrast recovery (CRV) remained approximately the same, while background variability (BV) gradually decreased with an increasing number of OE iterations. The final MMSE-OE images exhibited lower BV and a slightly lower CRV than the corresponding ML-EM images. The reconstruction time of the OE algorithm was approximately 1.3 times longer. At the same time, the OE algorithm can inherently provide a comprehensive statistical characterization of the acquired data. This characterization can be utilized for further data processing, e.g. in kinetic analysis and image registration, making the OE algorithm a promising approach in a variety of applications.

FBP and BPF reconstruction methods for circular X-ray tomography with off-center detector

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

Schaefer, Dirk; Grass, Michael; Haar, Peter van de

2011-05-15

Purpose: Circular scanning with an off-center planar detector is an acquisition scheme that allows to save detector area while keeping a large field of view (FOV). Several filtered back-projection (FBP) algorithms have been proposed earlier. The purpose of this work is to present two newly developed back-projection filtration (BPF) variants and evaluate the image quality of these methods compared to the existing state-of-the-art FBP methods. Methods: The first new BPF algorithm applies redundancy weighting of overlapping opposite projections before differentiation in a single projection. The second one uses the Katsevich-type differentiation involving two neighboring projections followed by redundancy weighting andmore » back-projection. An averaging scheme is presented to mitigate streak artifacts inherent to circular BPF algorithms along the Hilbert filter lines in the off-center transaxial slices of the reconstructions. The image quality is assessed visually on reconstructed slices of simulated and clinical data. Quantitative evaluation studies are performed with the Forbild head phantom by calculating root-mean-squared-deviations (RMSDs) to the voxelized phantom for different detector overlap settings and by investigating the noise resolution trade-off with a wire phantom in the full detector and off-center scenario. Results: The noise-resolution behavior of all off-center reconstruction methods corresponds to their full detector performance with the best resolution for the FDK based methods with the given imaging geometry. With respect to RMSD and visual inspection, the proposed BPF with Katsevich-type differentiation outperforms all other methods for the smallest chosen detector overlap of about 15 mm. The best FBP method is the algorithm that is also based on the Katsevich-type differentiation and subsequent redundancy weighting. For wider overlap of about 40-50 mm, these two algorithms produce similar results outperforming the other three methods. The

FBP and BPF reconstruction methods for circular X-ray tomography with off-center detector.

PubMed

Schäfer, Dirk; Grass, Michael; van de Haar, Peter

2011-07-01

Circular scanning with an off-center planar detector is an acquisition scheme that allows to save detector area while keeping a large field of view (FOV). Several filtered back-projection (FBP) algorithms have been proposed earlier. The purpose of this work is to present two newly developed back-projection filtration (BPF) variants and evaluate the image quality of these methods compared to the existing state-of-the-art FBP methods. The first new BPF algorithm applies redundancy weighting of overlapping opposite projections before differentiation in a single projection. The second one uses the Katsevich-type differentiation involving two neighboring projections followed by redundancy weighting and back-projection. An averaging scheme is presented to mitigate streak artifacts inherent to circular BPF algorithms along the Hilbert filter lines in the off-center transaxial slices of the reconstructions. The image quality is assessed visually on reconstructed slices of simulated and clinical data. Quantitative evaluation studies are performed with the Forbild head phantom by calculating root-mean-squared-deviations (RMSDs) to the voxelized phantom for different detector overlap settings and by investigating the noise resolution trade-off with a wire phantom in the full detector and off-center scenario. The noise-resolution behavior of all off-center reconstruction methods corresponds to their full detector performance with the best resolution for the FDK based methods with the given imaging geometry. With respect to RMSD and visual inspection, the proposed BPF with Katsevich-type differentiation outperforms all other methods for the smallest chosen detector overlap of about 15 mm. The best FBP method is the algorithm that is also based on the Katsevich-type differentiation and subsequent redundancy weighting. For wider overlap of about 40-50 mm, these two algorithms produce similar results outperforming the other three methods. The clinical case with a detector overlap

Impact of Reconstruction Algorithms on CT Radiomic Features of Pulmonary Tumors: Analysis of Intra- and Inter-Reader Variability and Inter-Reconstruction Algorithm Variability.

PubMed

Kim, Hyungjin; Park, Chang Min; Lee, Myunghee; Park, Sang Joon; Song, Yong Sub; Lee, Jong Hyuk; Hwang, Eui Jin; Goo, Jin Mo

2016-01-01

To identify the impact of reconstruction algorithms on CT radiomic features of pulmonary tumors and to reveal and compare the intra- and inter-reader and inter-reconstruction algorithm variability of each feature. Forty-two patients (M:F = 19:23; mean age, 60.43±10.56 years) with 42 pulmonary tumors (22.56±8.51mm) underwent contrast-enhanced CT scans, which were reconstructed with filtered back projection and commercial iterative reconstruction algorithm (level 3 and 5). Two readers independently segmented the whole tumor volume. Fifteen radiomic features were extracted and compared among reconstruction algorithms. Intra- and inter-reader variability and inter-reconstruction algorithm variability were calculated using coefficients of variation (CVs) and then compared. Among the 15 features, 5 first-order tumor intensity features and 4 gray level co-occurrence matrix (GLCM)-based features showed significant differences (p<0.05) among reconstruction algorithms. As for the variability, effective diameter, sphericity, entropy, and GLCM entropy were the most robust features (CV≤5%). Inter-reader variability was larger than intra-reader or inter-reconstruction algorithm variability in 9 features. However, for entropy, homogeneity, and 4 GLCM-based features, inter-reconstruction algorithm variability was significantly greater than inter-reader variability (p<0.013). Most of the radiomic features were significantly affected by the reconstruction algorithms. Inter-reconstruction algorithm variability was greater than inter-reader variability for entropy, homogeneity, and GLCM-based features.

Reconstruction of 2D PET data with Monte Carlo generated system matrix for generalized natural pixels

NASA Astrophysics Data System (ADS)

Vandenberghe, Stefaan; Staelens, Steven; Byrne, Charles L.; Soares, Edward J.; Lemahieu, Ignace; Glick, Stephen J.

2006-06-01

In discrete detector PET, natural pixels are image basis functions calculated from the response of detector pairs. By using reconstruction with natural pixel basis functions, the discretization of the object into a predefined grid can be avoided. Here, we propose to use generalized natural pixel reconstruction. Using this approach, the basis functions are not the detector sensitivity functions as in the natural pixel case but uniform parallel strips. The backprojection of the strip coefficients results in the reconstructed image. This paper proposes an easy and efficient way to generate the matrix M directly by Monte Carlo simulation. Elements of the generalized natural pixel system matrix are formed by calculating the intersection of a parallel strip with the detector sensitivity function. These generalized natural pixels are easier to use than conventional natural pixels because the final step from solution to a square pixel representation is done by simple backprojection. Due to rotational symmetry in the PET scanner, the matrix M is block circulant and only the first blockrow needs to be stored. Data were generated using a fast Monte Carlo simulator using ray tracing. The proposed method was compared to a listmode MLEM algorithm, which used ray tracing for doing forward and backprojection. Comparison of the algorithms with different phantoms showed that an improved resolution can be obtained using generalized natural pixel reconstruction with accurate system modelling. In addition, it was noted that for the same resolution a lower noise level is present in this reconstruction. A numerical observer study showed the proposed method exhibited increased performance as compared to a standard listmode EM algorithm. In another study, more realistic data were generated using the GATE Monte Carlo simulator. For these data, a more uniform contrast recovery and a better contrast-to-noise performance were observed. It was observed that major improvements in contrast

Preliminary Study of Image Reconstruction Algorithm on a Digital Signal Processor

DTIC Science & Technology

2014-03-01

5.2 Comparison of CPU-GPU, CPU-FPGA, and CPU-DSP Designs The work for implementing VHDL description of the back-projection algorithm on a physical...FPGA was not complete. Hence, the DSP implementation results are compared with the simulated results for the VHDL design. Simulating VHDL provides an...rather than at the software level. Depending on an application’s characteristics, FPGA implementations can provide a significant performance

UV Reconstruction Algorithm And Diurnal Cycle Variability

NASA Astrophysics Data System (ADS)

Curylo, Aleksander; Litynska, Zenobia; Krzyscin, Janusz; Bogdanska, Barbara

2009-03-01

UV reconstruction is a method of estimation of surface UV with the use of available actinometrical and aerological measurements. UV reconstruction is necessary for the study of long-term UV change. A typical series of UV measurements is not longer than 15 years, which is too short for trend estimation. The essential problem in the reconstruction algorithm is the good parameterization of clouds. In our previous algorithm we used an empirical relation between Cloud Modification Factor (CMF) in global radiation and CMF in UV. The CMF is defined as the ratio between measured and modelled irradiances. Clear sky irradiance was calculated with a solar radiative transfer model. In the proposed algorithm, the time variability of global radiation during the diurnal cycle is used as an additional source of information. For elaborating an improved reconstruction algorithm relevant data from Legionowo [52.4 N, 21.0 E, 96 m a.s.l], Poland were collected with the following instruments: NILU-UV multi channel radiometer, Kipp&Zonen pyranometer, radiosonde profiles of ozone, humidity and temperature. The proposed algorithm has been used for reconstruction of UV at four Polish sites: Mikolajki, Kolobrzeg, Warszawa-Bielany and Zakopane since the early 1960s. Krzyscin's reconstruction of total ozone has been used in the calculations.

Image-guided filtering for improving photoacoustic tomographic image reconstruction.

PubMed

Awasthi, Navchetan; Kalva, Sandeep Kumar; Pramanik, Manojit; Yalavarthy, Phaneendra K

2018-06-01

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

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.

Filtered refocusing: a volumetric reconstruction algorithm for plenoptic-PIV

NASA Astrophysics Data System (ADS)

Fahringer, Timothy W.; Thurow, Brian S.

2016-09-01

A new algorithm for reconstruction of 3D particle fields from plenoptic image data is presented. The algorithm is based on the technique of computational refocusing with the addition of a post reconstruction filter to remove the out of focus particles. This new algorithm is tested in terms of reconstruction quality on synthetic particle fields as well as a synthetically generated 3D Gaussian ring vortex. Preliminary results indicate that the new algorithm performs as well as the MART algorithm (used in previous work) in terms of the reconstructed particle position accuracy, but produces more elongated particles. The major advantage to the new algorithm is the dramatic reduction in the computational cost required to reconstruct a volume. It is shown that the new algorithm takes 1/9th the time to reconstruct the same volume as MART while using minimal resources. Experimental results are presented in the form of the wake behind a cylinder at a Reynolds number of 185.

Wind reconstruction algorithm for Viking Lander 1

NASA Astrophysics Data System (ADS)

Kynkäänniemi, Tuomas; Kemppinen, Osku; Harri, Ari-Matti; Schmidt, Walter

2017-06-01

The wind measurement sensors of Viking Lander 1 (VL1) were only fully operational for the first 45 sols of the mission. We have developed an algorithm for reconstructing the wind measurement data after the wind measurement sensor failures. The algorithm for wind reconstruction enables the processing of wind data during the complete VL1 mission. The heater element of the quadrant sensor, which provided auxiliary measurement for wind direction, failed during the 45th sol of the VL1 mission. Additionally, one of the wind sensors of VL1 broke down during sol 378. Regardless of the failures, it was still possible to reconstruct the wind measurement data, because the failed components of the sensors did not prevent the determination of the wind direction and speed, as some of the components of the wind measurement setup remained intact for the complete mission. This article concentrates on presenting the wind reconstruction algorithm and methods for validating the operation of the algorithm. The algorithm enables the reconstruction of wind measurements for the complete VL1 mission. The amount of available sols is extended from 350 to 2245 sols.

A reconstruction method for cone-beam differential x-ray phase-contrast computed tomography.

PubMed

Fu, Jian; Velroyen, Astrid; Tan, Renbo; Zhang, Junwei; Chen, Liyuan; Tapfer, Arne; Bech, Martin; Pfeiffer, Franz

2012-09-10

Most existing differential phase-contrast computed tomography (DPC-CT) approaches are based on three kinds of scanning geometries, described by parallel-beam, fan-beam and cone-beam. Due to the potential of compact imaging systems with magnified spatial resolution, cone-beam DPC-CT has attracted significant interest. In this paper, we report a reconstruction method based on a back-projection filtration (BPF) algorithm for cone-beam DPC-CT. Due to the differential nature of phase contrast projections, the algorithm restrains from differentiation of the projection data prior to back-projection, unlike BPF algorithms commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a micro-focus x-ray tube source. Moreover, the numerical simulation and experimental results demonstrate that the proposed method can deal with several classes of truncated cone-beam datasets. We believe that this feature is of particular interest for future medical cone-beam phase-contrast CT imaging applications.

Zero cylinder coordinate system approach to image reconstruction in fan beam ICT

NASA Astrophysics Data System (ADS)

Yan, Yan-Chun; Xian, Wu; Hall, Ernest L.

1992-11-01

The state-of-the-art of the transform algorithms has allowed the newest versions to produce excellent and efficient reconstructed images in most applications, especially in medical CT and industrial CT etc. Based on the Zero Cylinder Coordinate system (ZCC) presented in this paper, a new transform algorithm of image reconstruction in fan beam industrial CT is suggested. It greatly reduces the amount of computation of the backprojection, which requires only two INC instructions to calculate the weighted factor and the subcoordinate. A new backprojector is designed, which simplifies its assembly-line mechanism based on the ZCC method. Finally, a simulation results on microcomputer is given out, which proves this method is effective and practical.

The algorithm of central axis in surface reconstruction

NASA Astrophysics Data System (ADS)

Zhao, Bao Ping; Zhang, Zheng Mei; Cai Li, Ji; Sun, Da Ming; Cao, Hui Ying; Xing, Bao Liang

2017-09-01

Reverse engineering is an important technique means of product imitation and new product development. Its core technology -- surface reconstruction is the current research for scholars. In the various algorithms of surface reconstruction, using axis reconstruction is a kind of important method. For the various reconstruction, using medial axis algorithm was summarized, pointed out the problems existed in various methods, as well as the place needs to be improved. Also discussed the later surface reconstruction and development of axial direction.

Research on compressive sensing reconstruction algorithm based on total variation model

NASA Astrophysics Data System (ADS)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

Analytic image reconstruction from partial data for a single-scan cone-beam CT with scatter correction

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

Min, Jonghwan; Pua, Rizza; Cho, Seungryong, E-mail: scho@kaist.ac.kr

Purpose: A beam-blocker composed of multiple strips is a useful gadget for scatter correction and/or for dose reduction in cone-beam CT (CBCT). However, the use of such a beam-blocker would yield cone-beam data that can be challenging for accurate image reconstruction from a single scan in the filtered-backprojection framework. The focus of the work was to develop an analytic image reconstruction method for CBCT that can be directly applied to partially blocked cone-beam data in conjunction with the scatter correction. Methods: The authors developed a rebinned backprojection-filteration (BPF) algorithm for reconstructing images from the partially blocked cone-beam data in amore » circular scan. The authors also proposed a beam-blocking geometry considering data redundancy such that an efficient scatter estimate can be acquired and sufficient data for BPF image reconstruction can be secured at the same time from a single scan without using any blocker motion. Additionally, scatter correction method and noise reduction scheme have been developed. The authors have performed both simulation and experimental studies to validate the rebinned BPF algorithm for image reconstruction from partially blocked cone-beam data. Quantitative evaluations of the reconstructed image quality were performed in the experimental studies. Results: The simulation study revealed that the developed reconstruction algorithm successfully reconstructs the images from the partial cone-beam data. In the experimental study, the proposed method effectively corrected for the scatter in each projection and reconstructed scatter-corrected images from a single scan. Reduction of cupping artifacts and an enhancement of the image contrast have been demonstrated. The image contrast has increased by a factor of about 2, and the image accuracy in terms of root-mean-square-error with respect to the fan-beam CT image has increased by more than 30%. Conclusions: The authors have successfully demonstrated that

Investigation of Backprojection Uncertainties With M6 Earthquakes

NASA Astrophysics Data System (ADS)

Fan, Wenyuan; Shearer, Peter M.

2017-10-01

We investigate possible biasing effects of inaccurate timing corrections on teleseismic P wave backprojection imaging of large earthquake ruptures. These errors occur because empirically estimated time shifts based on aligning P wave first arrivals are exact only at the hypocenter and provide approximate corrections for other parts of the rupture. Using the Japan subduction zone as a test region, we analyze 46 M6-M7 earthquakes over a 10 year period, including many aftershocks of the 2011 M9 Tohoku earthquake, performing waveform cross correlation of their initial P wave arrivals to obtain hypocenter timing corrections to global seismic stations. We then compare backprojection images for each earthquake using its own timing corrections with those obtained using the time corrections from other earthquakes. This provides a measure of how well subevents can be resolved with backprojection of a large rupture as a function of distance from the hypocenter. Our results show that backprojection is generally very robust and that the median subevent location error is about 25 km across the entire study region (˜700 km). The backprojection coherence loss and location errors do not noticeably converge to zero even when the event pairs are very close (<20 km). This indicates that most of the timing differences are due to 3-D structure close to each of the hypocenter regions, which limits the effectiveness of attempts to refine backprojection images using aftershock calibration, at least in this region.

Evaluation of accelerated iterative x-ray CT image reconstruction using floating point graphics hardware.

PubMed

Kole, J S; Beekman, F J

2006-02-21

Statistical reconstruction methods offer possibilities to improve image quality as compared with analytical methods, but current reconstruction times prohibit routine application in clinical and micro-CT. In particular, for cone-beam x-ray CT, the use of graphics hardware has been proposed to accelerate the forward and back-projection operations, in order to reduce reconstruction times. In the past, wide application of this texture hardware mapping approach was hampered owing to limited intrinsic accuracy. Recently, however, floating point precision has become available in the latest generation commodity graphics cards. In this paper, we utilize this feature to construct a graphics hardware accelerated version of the ordered subset convex reconstruction algorithm. The aims of this paper are (i) to study the impact of using graphics hardware acceleration for statistical reconstruction on the reconstructed image accuracy and (ii) to measure the speed increase one can obtain by using graphics hardware acceleration. We compare the unaccelerated algorithm with the graphics hardware accelerated version, and for the latter we consider two different interpolation techniques. A simulation study of a micro-CT scanner with a mathematical phantom shows that at almost preserved reconstructed image accuracy, speed-ups of a factor 40 to 222 can be achieved, compared with the unaccelerated algorithm, and depending on the phantom and detector sizes. Reconstruction from physical phantom data reconfirms the usability of the accelerated algorithm for practical cases.

WE-D-18A-04: How Iterative Reconstruction Algorithms Affect the MTFs of Variable-Contrast Targets in CT Images

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

Dodge, C.T.; Rong, J.; Dodge, C.W.

2014-06-15

Purpose: To determine how filtered back-projection (FBP), adaptive statistical (ASiR), and model based (MBIR) iterative reconstruction algorithms affect the measured modulation transfer functions (MTFs) of variable-contrast targets over a wide range of clinically applicable dose levels. Methods: The Catphan 600 CTP401 module, surrounded by an oval, fat-equivalent ring to mimic patient size/shape, was scanned on a GE HD750 CT scanner at 1, 2, 3, 6, 12 and 24 mGy CTDIvol levels with typical patient scan parameters: 120kVp, 0.8s, 40mm beam width, large SFOV, 2.5mm thickness, 0.984 pitch. The images were reconstructed using GE's Standard kernel with FBP; 20%, 40% andmore » 70% ASiR; and MBIR. A task-based MTF (MTFtask) was computed for six cylindrical targets: 2 low-contrast (Polystyrene, LDPE), 2 medium-contrast (Delrin, PMP), and 2 high-contrast (Teflon, air). MTFtask was used to compare the performance of reconstruction algorithms with decreasing CTDIvol from 24mGy, which is currently used in the clinic. Results: For the air target and 75% dose savings (6 mGy), MBIR MTFtask at 5 lp/cm measured 0.24, compared to 0.20 for 70% ASiR and 0.11 for FBP. Overall, for both high-contrast targets, MBIR MTFtask improved with increasing CTDIvol and consistently outperformed ASiR and FBP near the system's Nyquist frequency. Conversely, for Polystyrene at 6 mGy, MBIR (0.10) and 70% ASiR (0.07) MTFtask was lower than for FBP (0.18). For medium and low-contrast targets, FBP remains the best overall algorithm for improved resolution at low CTDIvol (1–6 mGy) levels, whereas MBIR is comparable at higher dose levels (12–24 mGy). Conclusion: MBIR improved the MTF of small, high-contrast targets compared to FBP and ASiR at doses of 50%–12.5% of those currently used in the clinic. However, for imaging low- and mediumcontrast targets, FBP performed the best across all dose levels. For assessing MTF from different reconstruction algorithms, task-based MTF measurements are necessary.« less

CT reconstruction from portal images acquired during volumetric-modulated arc therapy

NASA Astrophysics Data System (ADS)

Poludniowski, G.; Thomas, M. D. R.; Evans, P. M.; Webb, S.

2010-10-01

Volumetric-modulated arc therapy (VMAT), a form of intensity-modulated arc therapy (IMAT), has become a topic of research and clinical activity in recent years. As a form of arc therapy, portal images acquired during the treatment fraction form a (partial) Radon transform of the patient. We show that these portal images, when used in a modified global cone-beam filtered backprojection (FBP) algorithm, allow a surprisingly recognizable CT-volume to be reconstructed. The possibility of distinguishing anatomy in such VMAT-CT reconstructions suggests that this could prove to be a valuable treatment position-verification tool. Further, some potential for local-tomography techniques to improve image quality is shown.

Low dose reconstruction algorithm for differential phase contrast imaging.

PubMed

Wang, Zhentian; Huang, Zhifeng; Zhang, Li; Chen, Zhiqiang; Kang, Kejun; Yin, Hongxia; Wang, Zhenchang; Marco, Stampanoni

2011-01-01

Differential phase contrast imaging computed tomography (DPCI-CT) is a novel x-ray inspection method to reconstruct the distribution of refraction index rather than the attenuation coefficient in weakly absorbing samples. In this paper, we propose an iterative reconstruction algorithm for DPCI-CT which benefits from the new compressed sensing theory. We first realize a differential algebraic reconstruction technique (DART) by discretizing the projection process of the differential phase contrast imaging into a linear partial derivative matrix. In this way the compressed sensing reconstruction problem of DPCI reconstruction can be transformed to a resolved problem in the transmission imaging CT. Our algorithm has the potential to reconstruct the refraction index distribution of the sample from highly undersampled projection data. Thus it can significantly reduce the dose and inspection time. The proposed algorithm has been validated by numerical simulations and actual experiments.

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.

Simultaneous signal reconstruction from both superficial and deep tissue for fNIRS using depth-selective filtering method

NASA Astrophysics Data System (ADS)

Fujii, M.

2017-07-01

Two variations of a depth-selective back-projection filter for functional near-infrared spectroscopy (fNIRS) systems are introduced. The filter comprises a depth-selective algorithm that uses inverse problems applied to an optically diffusive multilayer medium. In this study, simultaneous signal reconstruction of both superficial and deep tissue from fNIRS experiments of the human forehead using a prototype of a CW-NIRS system is demonstrated.

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.

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.

Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework

PubMed Central

Matej, Samuel; Daube-Witherspoon, Margaret E.; Karp, Joel S.

2016-01-01

Iterative reconstruction algorithms are routinely used for clinical practice; however, analytic algorithms are relevant candidates for quantitative research studies due to their linear behavior. While iterative algorithms also benefit from the inclusion of accurate data and noise models the widespread use of TOF scanners with less sensitivity to noise and data imperfections make analytic algorithms even more promising. In our previous work we have developed a novel iterative reconstruction approach (Direct Image Reconstruction for TOF) providing convenient TOF data partitioning framework and leading to very efficient reconstructions. In this work we have expanded DIRECT to include an analytic TOF algorithm with confidence weighting incorporating models of both TOF and spatial resolution kernels. Feasibility studies using simulated and measured data demonstrate that analytic-DIRECT with appropriate resolution and regularization filters is able to provide matched bias vs. variance performance to iterative TOF reconstruction with a matched resolution model. PMID:27032968

Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework

NASA Astrophysics Data System (ADS)

Matej, Samuel; Daube-Witherspoon, Margaret E.; Karp, Joel S.

2016-05-01

Iterative reconstruction algorithms are routinely used for clinical practice; however, analytic algorithms are relevant candidates for quantitative research studies due to their linear behavior. While iterative algorithms also benefit from the inclusion of accurate data and noise models the widespread use of time-of-flight (TOF) scanners with less sensitivity to noise and data imperfections make analytic algorithms even more promising. In our previous work we have developed a novel iterative reconstruction approach (DIRECT: direct image reconstruction for TOF) providing convenient TOF data partitioning framework and leading to very efficient reconstructions. In this work we have expanded DIRECT to include an analytic TOF algorithm with confidence weighting incorporating models of both TOF and spatial resolution kernels. Feasibility studies using simulated and measured data demonstrate that analytic-DIRECT with appropriate resolution and regularization filters is able to provide matched bias versus variance performance to iterative TOF reconstruction with a matched resolution model.

Image reconstruction through thin scattering media by simulated annealing algorithm

NASA Astrophysics Data System (ADS)

Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua

2018-07-01

An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.

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.

MO-FG-204-04: How Iterative Reconstruction Algorithms Affect the NPS of CT Images

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

Li, G; Liu, X; Dodge, C

2015-06-15

Purpose: To evaluate how the third generation model based iterative reconstruction (MBIR) compares with filtered back-projection (FBP), adaptive statistical iterative reconstruction (ASiR), and the second generation MBIR based on noise power spectrum (NPS) analysis over a wide range of clinically applicable dose levels. Methods: The Catphan 600 CTP515 module, surrounded by an oval, fat-equivalent ring to mimic patient size/shape, was scanned on a GE HD750 CT scanner at 1, 2, 3, 6, 12 and 19mGy CTDIvol levels with typical patient scan parameters: 120kVp, 0.8s, 40mm beam width, large SFOV, 0.984 pitch and reconstructed thickness 2.5mm (VEO3.0: Abd/Pelvis with Texture andmore » NR05). At each CTDIvol level, 10 repeated scans were acquired for achieving sufficient data sampling. The images were reconstructed using Standard kernel with FBP; 20%, 40% and 70% ASiR; and two versions of MBIR (VEO2.0 and 3.0). For evaluating the effect of the ROI spatial location to the Result of NPS, 4 ROI groups were categorized based on their distances from the center of the phantom. Results: VEO3.0 performed inferiorly comparing to VEO2.0 over all dose levels. On the other hand, at low dose levels (less than 3 mGy), it clearly outperformed ASiR and FBP, in NPS values. Therefore, the lower the dose level, the relative performance of MBIR improves. However, the shapes of the NPS show substantial differences in horizontal and vertical sampling dimensions. These differences may determine the characteristics of the noise/texture features in images, and hence, play an important role in image interpretation. Conclusion: The third generation MBIR did not improve over the second generation MBIR in term of NPS analysis. The overall performance of both versions of MBIR improved as compared to other reconstruction algorithms when dose was reduced. The shapes of the NPS curves provided additional value for future characterization of the image noise/texture features.« less

A reconstruction algorithm for helical CT imaging on PI-planes.

PubMed

Liang, Hongzhu; Zhang, Cishen; Yan, Ming

2006-01-01

In this paper, a Feldkamp type approximate reconstruction algorithm is presented for helical cone-beam Computed Tomography. To effectively suppress artifacts due to large cone angle scanning, it is proposed to reconstruct the object point-wisely on unique customized tilted PI-planes which are close to the data collecting helices of the corresponding points. Such a reconstruction scheme can considerably suppress the artifacts in the cone-angle scanning. Computer simulations show that the proposed algorithm can provide improved imaging performance compared with the existing approximate cone-beam reconstruction algorithms.

Region-of-interest image reconstruction in circular cone-beam microCT

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

Cho, Seungryong; Bian, Junguo; Pelizzari, Charles A.

2007-12-15

Cone-beam microcomputed tomography (microCT) is one of the most popular choices for small animal imaging which is becoming an important tool for studying animal models with transplanted diseases. Region-of-interest (ROI) imaging techniques in CT, which can reconstruct an ROI image from the projection data set of the ROI, can be used not only for reducing imaging-radiation exposure to the subject and scatters to the detector but also for potentially increasing spatial resolution of the reconstructed images. Increasing spatial resolution in microCT images can facilitate improved accuracy in many assessment tasks. A method proposed previously for increasing CT image spatial resolutionmore » entails the exploitation of the geometric magnification in cone-beam CT. Due to finite detector size, however, this method can lead to data truncation for a large geometric magnification. The Feldkamp-Davis-Kress (FDK) algorithm yields images with artifacts when truncated data are used, whereas the recently developed backprojection filtration (BPF) algorithm is capable of reconstructing ROI images without truncation artifacts from truncated cone-beam data. We apply the BPF algorithm to reconstructing ROI images from truncated data of three different objects acquired by our circular cone-beam microCT system. Reconstructed images by use of the FDK and BPF algorithms from both truncated and nontruncated cone-beam data are compared. The results of the experimental studies demonstrate that, from certain truncated data, the BPF algorithm can reconstruct ROI images with quality comparable to that reconstructed from nontruncated data. In contrast, the FDK algorithm yields ROI images with truncation artifacts. Therefore, an implication of the studies is that, when truncated data are acquired with a configuration of a large geometric magnification, the BPF algorithm can be used for effective enhancement of the spatial resolution of a ROI image.« less

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.

Method for position emission mammography image reconstruction

DOEpatents

Smith, Mark Frederick

2004-10-12

An image reconstruction method comprising accepting coincidence datat from either a data file or in real time from a pair of detector heads, culling event data that is outside a desired energy range, optionally saving the desired data for each detector position or for each pair of detector pixels on the two detector heads, and then reconstructing the image either by backprojection image reconstruction or by iterative image reconstruction. In the backprojection image reconstruction mode, rays are traced between centers of lines of response (LOR's), counts are then either allocated by nearest pixel interpolation or allocated by an overlap method and then corrected for geometric effects and attenuation and the data file updated. If the iterative image reconstruction option is selected, one implementation is to compute a grid Siddon retracing, and to perform maximum likelihood expectation maiximization (MLEM) computed by either: a) tracing parallel rays between subpixels on opposite detector heads; or b) tracing rays between randomized endpoint locations on opposite detector heads.

Investigation of Back-Projection Uncertainties with M6 Earthquakes

NASA Astrophysics Data System (ADS)

Fan, W.; Shearer, P. M.

2017-12-01

We investigate possible biasing effects of inaccurate timing corrections on teleseismic P-wave back-projection imaging of large earthquake ruptures. These errors occur because empirically-estimated time shifts based on aligning P-wave first arrivals are exact only at the hypocenter and provide approximate corrections for other parts of the rupture. Using the Japan subduction zone as a test region, we analyze 46 M6-7 earthquakes over a ten-year period, including many aftershocks of the 2011 M9 Tohoku earthquake, performing waveform cross-correlation of their initial P-wave arrivals to obtain hypocenter timing corrections to global seismic stations. We then compare back-projection images for each earthquake using its own timing corrections with those obtained using the time corrections for other earthquakes. This provides a measure of how well sub-events can be resolved with back-projection of a large rupture as a function of distance from the hypocenter. Our results show that back-projection is generally very robust and that sub-event location errors average about 20 km across the entire study region ( 700 km). The back-projection coherence loss and location errors do not noticeably converge to zero even when the event pairs are very close (<20 km). This indicates that most of the timing differences are due to 3D structure close to each of the hypocenter regions, which limits the effectiveness of attempts to refine back-projection images using aftershock calibration, at least in this region.

DART: a practical reconstruction algorithm for discrete tomography.

PubMed

Batenburg, Kees Joost; Sijbers, Jan

2011-09-01

In this paper, we present an iterative reconstruction algorithm for discrete tomography, called discrete algebraic reconstruction technique (DART). DART can be applied if the scanned object is known to consist of only a few different compositions, each corresponding to a constant gray value in the reconstruction. Prior knowledge of the gray values for each of the compositions is exploited to steer the current reconstruction towards a reconstruction that contains only these gray values. Based on experiments with both simulated CT data and experimental μCT data, it is shown that DART is capable of computing more accurate reconstructions from a small number of projection images, or from a small angular range, than alternative methods. It is also shown that DART can deal effectively with noisy projection data and that the algorithm is robust with respect to errors in the estimation of the gray values.

GPU-accelerated iterative reconstruction for limited-data tomography in CBCT systems.

PubMed

de Molina, Claudia; Serrano, Estefania; Garcia-Blas, Javier; Carretero, Jesus; Desco, Manuel; Abella, Monica

2018-05-15

Standard cone-beam computed tomography (CBCT) involves the acquisition of at least 360 projections rotating through 360 degrees. Nevertheless, there are cases in which only a few projections can be taken in a limited angular span, such as during surgery, where rotation of the source-detector pair is limited to less than 180 degrees. Reconstruction of limited data with the conventional method proposed by Feldkamp, Davis and Kress (FDK) results in severe artifacts. Iterative methods may compensate for the lack of data by including additional prior information, although they imply a high computational burden and memory consumption. We present an accelerated implementation of an iterative method for CBCT following the Split Bregman formulation, which reduces computational time through GPU-accelerated kernels. The implementation enables the reconstruction of large volumes (>1024 3 pixels) using partitioning strategies in forward- and back-projection operations. We evaluated the algorithm on small-animal data for different scenarios with different numbers of projections, angular span, and projection size. Reconstruction time varied linearly with the number of projections and quadratically with projection size but remained almost unchanged with angular span. Forward- and back-projection operations represent 60% of the total computational burden. Efficient implementation using parallel processing and large-memory management strategies together with GPU kernels enables the use of advanced reconstruction approaches which are needed in limited-data scenarios. Our GPU implementation showed a significant time reduction (up to 48 ×) compared to a CPU-only implementation, resulting in a total reconstruction time from several hours to few minutes.

Comparison of low-contrast detectability between two CT reconstruction algorithms using voxel-based 3D printed textured phantoms.

PubMed

Solomon, Justin; Ba, Alexandre; Bochud, François; Samei, Ehsan

2016-12-01

To use novel voxel-based 3D printed textured phantoms in order to compare low-contrast detectability between two reconstruction algorithms, FBP (filtered-backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE. 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 CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A-C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low-contrast spherical signals (6 mm diameter at nominal contrast levels of ∼3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel-based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels

Markov prior-based block-matching algorithm for superdimension reconstruction of porous media

NASA Astrophysics Data System (ADS)

Li, Yang; He, Xiaohai; Teng, Qizhi; Feng, Junxi; Wu, Xiaohong

2018-04-01

A superdimension reconstruction algorithm is used for the reconstruction of three-dimensional (3D) structures of a porous medium based on a single two-dimensional image. The algorithm borrows the concepts of "blocks," "learning," and "dictionary" from learning-based superresolution reconstruction and applies them to the 3D reconstruction of a porous medium. In the neighborhood-matching process of the conventional superdimension reconstruction algorithm, the Euclidean distance is used as a criterion, although it may not really reflect the structural correlation between adjacent blocks in an actual situation. Hence, in this study, regular items are adopted as prior knowledge in the reconstruction process, and a Markov prior-based block-matching algorithm for superdimension reconstruction is developed for more accurate reconstruction. The algorithm simultaneously takes into consideration the probabilistic relationship between the already reconstructed blocks in three different perpendicular directions (x , y , and z ) and the block to be reconstructed, and the maximum value of the probability product of the blocks to be reconstructed (as found in the dictionary for the three directions) is adopted as the basis for the final block selection. Using this approach, the problem of an imprecise spatial structure caused by a point simulation can be overcome. The problem of artifacts in the reconstructed structure is also addressed through the addition of hard data and by neighborhood matching. To verify the improved reconstruction accuracy of the proposed method, the statistical and morphological features of the results from the proposed method and traditional superdimension reconstruction method are compared with those of the target system. The proposed superdimension reconstruction algorithm is confirmed to enable a more accurate reconstruction of the target system while also eliminating artifacts.

Measuring the performance of super-resolution reconstruction algorithms

NASA Astrophysics Data System (ADS)

Dijk, Judith; Schutte, Klamer; van Eekeren, Adam W. M.; Bijl, Piet

2012-06-01

For many military operations situational awareness is of great importance. This situational awareness and related tasks such as Target Acquisition can be acquired using cameras, of which the resolution is an important characteristic. Super resolution reconstruction algorithms can be used to improve the effective sensor resolution. In order to judge these algorithms and the conditions under which they operate best, performance evaluation methods are necessary. This evaluation, however, is not straightforward for several reasons. First of all, frequency-based evaluation techniques alone will not provide a correct answer, due to the fact that they are unable to discriminate between structure-related and noise-related effects. Secondly, most super-resolution packages perform additional image enhancement techniques such as noise reduction and edge enhancement. As these algorithms improve the results they cannot be evaluated separately. Thirdly, a single high-resolution ground truth is rarely available. Therefore, evaluation of the differences in high resolution between the estimated high resolution image and its ground truth is not that straightforward. Fourth, different artifacts can occur due to super-resolution reconstruction, which are not known on forehand and hence are difficult to evaluate. In this paper we present a set of new evaluation techniques to assess super-resolution reconstruction algorithms. Some of these evaluation techniques are derived from processing on dedicated (synthetic) imagery. Other evaluation techniques can be evaluated on both synthetic and natural images (real camera data). The result is a balanced set of evaluation algorithms that can be used to assess the performance of super-resolution reconstruction algorithms.

Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography

PubMed Central

Li, Yanqiu; Liu, Shi; Inaki, Schlaberg H.

2017-01-01

Accuracy and speed of algorithms play an important role in the reconstruction of temperature field measurements by acoustic tomography. Existing algorithms are based on static models which only consider the measurement information. A dynamic model of three-dimensional temperature reconstruction by acoustic tomography is established in this paper. A dynamic algorithm is proposed considering both acoustic measurement information and the dynamic evolution information of the temperature field. An objective function is built which fuses measurement information and the space constraint of the temperature field with its dynamic evolution information. Robust estimation is used to extend the objective function. The method combines a tunneling algorithm and a local minimization technique to solve the objective function. Numerical simulations show that the image quality and noise immunity of the dynamic reconstruction algorithm are better when compared with static algorithms such as least square method, algebraic reconstruction technique and standard Tikhonov regularization algorithms. An effective method is provided for temperature field reconstruction by acoustic tomography. PMID:28895930

CT cardiac imaging: evolution from 2D to 3D backprojection

NASA Astrophysics Data System (ADS)

Tang, Xiangyang; Pan, Tinsu; Sasaki, Kosuke

2004-04-01

The state-of-the-art multiple detector-row CT, which usually employs fan beam reconstruction algorithms by approximating a cone beam geometry into a fan beam geometry, has been well recognized as an important modality for cardiac imaging. At present, the multiple detector-row CT is evolving into volumetric CT, in which cone beam reconstruction algorithms are needed to combat cone beam artifacts caused by large cone angle. An ECG-gated cardiac cone beam reconstruction algorithm based upon the so-called semi-CB geometry is implemented in this study. To get the highest temporal resolution, only the projection data corresponding to 180° plus the cone angle are row-wise rebinned into the semi-CB geometry for three-dimensional reconstruction. Data extrapolation is utilized to extend the z-coverage of the ECG-gated cardiac cone beam reconstruction algorithm approaching the edge of a CT detector. A helical body phantom is used to evaluate the ECG-gated cone beam reconstruction algorithm"s z-coverage and capability of suppressing cone beam artifacts. Furthermore, two sets of cardiac data scanned by a multiple detector-row CT scanner at 16 x 1.25 (mm) and normalized pitch 0.275 and 0.3 respectively are used to evaluate the ECG-gated CB reconstruction algorithm"s imaging performance. As a reference, the images reconstructed by a fan beam reconstruction algorithm for multiple detector-row CT are also presented. The qualitative evaluation shows that, the ECG-gated cone beam reconstruction algorithm outperforms its fan beam counterpart from the perspective of cone beam artifact suppression and z-coverage while the temporal resolution is well maintained. Consequently, the scan speed can be increased to reduce the contrast agent amount and injection time, improve the patient comfort and x-ray dose efficiency. Based up on the comparison, it is believed that, with the transition of multiple detector-row CT into volumetric CT, ECG-gated cone beam reconstruction algorithms will

Three-dimensional Image Reconstruction in J-PET Using Filtered Back-projection Method

NASA Astrophysics Data System (ADS)

Shopa, R. Y.; Klimaszewski, K.; Kowalski, P.; Krzemień, W.; Raczyński, L.; Wiślicki, W.; Białas, P.; Curceanu, C.; Czerwiński, E.; Dulski, K.; Gajos, A.; Głowacz, B.; Gorgol, M.; Hiesmayr, B.; Jasińska, B.; Kisielewska-Kamińska, D.; Korcyl, G.; Kozik, T.; Krawczyk, N.; Kubicz, E.; Mohammed, M.; Pawlik-Niedźwiecka, M.; Niedźwiecki, S.; Pałka, M.; Rudy, Z.; Sharma, N. G.; Sharma, S.; Silarski, M.; Skurzok, M.; Wieczorek, A.; Zgardzińska, B.; Zieliński, M.; Moskal, P.

We present a method and preliminary results of the image reconstruction in the Jagiellonian PET tomograph. Using GATE (Geant4 Application for Tomographic Emission), interactions of the 511 keV photons with a cylindrical detector were generated. Pairs of such photons, flying back-to-back, originate from e+e- annihilations inside a 1-mm spherical source. Spatial and temporal coordinates of hits were smeared using experimental resolutions of the detector. We incorporated the algorithm of the 3D Filtered Back Projection, implemented in the STIR and TomoPy software packages, which differ in approximation methods. Consistent results for the Point Spread Functions of ~5/7,mm and ~9/20, mm were obtained, using STIR, for transverse and longitudinal directions, respectively, with no time of flight information included.

On the assessment of spatial resolution of PET systems with iterative image reconstruction

NASA Astrophysics Data System (ADS)

Gong, Kuang; Cherry, Simon R.; Qi, Jinyi

2016-03-01

Spatial resolution is an important metric for performance characterization in PET systems. Measuring spatial resolution is straightforward with a linear reconstruction algorithm, such as filtered backprojection, and can be performed by reconstructing a point source scan and calculating the full-width-at-half-maximum (FWHM) along the principal directions. With the widespread adoption of iterative reconstruction methods, it is desirable to quantify the spatial resolution using an iterative reconstruction algorithm. However, the task can be difficult because the reconstruction algorithms are nonlinear and the non-negativity constraint can artificially enhance the apparent spatial resolution if a point source image is reconstructed without any background. Thus, it was recommended that a background should be added to the point source data before reconstruction for resolution measurement. However, there has been no detailed study on the effect of the point source contrast on the measured spatial resolution. Here we use point source scans from a preclinical PET scanner to investigate the relationship between measured spatial resolution and the point source contrast. We also evaluate whether the reconstruction of an isolated point source is predictive of the ability of the system to resolve two adjacent point sources. Our results indicate that when the point source contrast is below a certain threshold, the measured FWHM remains stable. Once the contrast is above the threshold, the measured FWHM monotonically decreases with increasing point source contrast. In addition, the measured FWHM also monotonically decreases with iteration number for maximum likelihood estimate. Therefore, when measuring system resolution with an iterative reconstruction algorithm, we recommend using a low-contrast point source and a fixed number of iterations.

Residual motion compensation in ECG-gated interventional cardiac vasculature reconstruction

NASA Astrophysics Data System (ADS)

Schwemmer, C.; Rohkohl, C.; Lauritsch, G.; Müller, K.; Hornegger, J.

2013-06-01

Three-dimensional reconstruction of cardiac vasculature from angiographic C-arm CT (rotational angiography) data is a major challenge. Motion artefacts corrupt image quality, reducing usability for diagnosis and guidance. Many state-of-the-art approaches depend on retrospective ECG-gating of projection data for image reconstruction. A trade-off has to be made regarding the size of the ECG-gating window. A large temporal window is desirable to avoid undersampling. However, residual motion will occur in a large window, causing motion artefacts. We present an algorithm to correct for residual motion. Our approach is based on a deformable 2D-2D registration between the forward projection of an initial, ECG-gated reconstruction, and the original projection data. The approach is fully automatic and does not require any complex segmentation of vasculature, or landmarks. The estimated motion is compensated for during the backprojection step of a subsequent reconstruction. We evaluated the method using the publicly available CAVAREV platform and on six human clinical datasets. We found a better visibility of structure, reduced motion artefacts, and increased sharpness of the vessels in the compensated reconstructions compared to the initial reconstructions. At the time of writing, our algorithm outperforms the leading result of the CAVAREV ranking list. For the clinical datasets, we found an average reduction of motion artefacts by 13 ± 6%. Vessel sharpness was improved by 25 ± 12% on average.

Limited angle C-arm tomosynthesis reconstruction algorithms

NASA Astrophysics Data System (ADS)

Malalla, Nuhad A. Y.; Xu, Shiyu; Chen, Ying

2015-03-01

In this paper, C-arm tomosynthesis with digital detector was investigated as a novel three dimensional (3D) imaging technique. Digital tomosythses is an imaging technique to provide 3D information of the object by reconstructing slices passing through the object, based on a series of angular projection views with respect to the object. C-arm tomosynthesis provides two dimensional (2D) X-ray projection images with rotation (-/+20 angular range) of both X-ray source and detector. In this paper, four representative reconstruction algorithms including point by point back projection (BP), filtered back projection (FBP), simultaneous algebraic reconstruction technique (SART) and maximum likelihood expectation maximization (MLEM) were investigated. Dataset of 25 projection views of 3D spherical object that located at center of C-arm imaging space was simulated from 25 angular locations over a total view angle of 40 degrees. With reconstructed images, 3D mesh plot and 2D line profile of normalized pixel intensities on focus reconstruction plane crossing the center of the object were studied with each reconstruction algorithm. Results demonstrated the capability to generate 3D information from limited angle C-arm tomosynthesis. Since C-arm tomosynthesis is relatively compact, portable and can avoid moving patients, it has been investigated for different clinical applications ranging from tumor surgery to interventional radiology. It is very important to evaluate C-arm tomosynthesis for valuable applications.

Ionospheric-thermospheric UV tomography: 1. Image space reconstruction algorithms

NASA Astrophysics Data System (ADS)

Dymond, K. F.; Budzien, S. A.; Hei, M. A.

2017-03-01

We present and discuss two algorithms of the class known as Image Space Reconstruction Algorithms (ISRAs) that we are applying to the solution of large-scale ionospheric tomography problems. ISRAs have several desirable features that make them useful for ionospheric tomography. In addition to producing nonnegative solutions, ISRAs are amenable to sparse-matrix formulations and are fast, stable, and robust. We present the results of our studies of two types of ISRA: the Least Squares Positive Definite and the Richardson-Lucy algorithms. We compare their performance to the Multiplicative Algebraic Reconstruction and Conjugate Gradient Least Squares algorithms. We then discuss the use of regularization in these algorithms and present our new approach based on regularization to a partial differential equation.

Optical diffraction tomography: accuracy of an off-axis reconstruction

NASA Astrophysics Data System (ADS)

Kostencka, Julianna; Kozacki, Tomasz

2014-05-01

Optical diffraction tomography is an increasingly popular method that allows for reconstruction of three-dimensional refractive index distribution of semi-transparent samples using multiple measurements of an optical field transmitted through the sample for various illumination directions. The process of assembly of the angular measurements is usually performed with one of two methods: filtered backprojection (FBPJ) or filtered backpropagation (FBPP) tomographic reconstruction algorithm. The former approach, although conceptually very simple, provides an accurate reconstruction for the object regions located close to the plane of focus. However, since FBPJ ignores diffraction, its use for spatially extended structures is arguable. According to the theory of scattering, more precise restoration of a 3D structure shall be achieved with the FBPP algorithm, which unlike the former approach incorporates diffraction. It is believed that with this method one is allowed to obtain a high accuracy reconstruction in a large measurement volume exceeding depth of focus of an imaging system. However, some studies have suggested that a considerable improvement of the FBPP results can be achieved with prior propagation of the transmitted fields back to the centre of the object. This, supposedly, enables reduction of errors due to approximated diffraction formulas used in FBPP. In our view this finding casts doubt on quality of the FBPP reconstruction in the regions far from the rotation axis. The objective of this paper is to investigate limitation of the FBPP algorithm in terms of an off-axis reconstruction and compare its performance with the FBPJ approach. Moreover, in this work we propose some modifications to the FBPP algorithm that allow for more precise restoration of a sample structure in off-axis locations. The research is based on extensive numerical simulations supported with wave-propagation method.

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.

Efficient iterative image reconstruction algorithm for dedicated breast CT

NASA Astrophysics Data System (ADS)

Antropova, Natalia; Sanchez, Adrian; Reiser, Ingrid S.; Sidky, Emil Y.; Boone, John; Pan, Xiaochuan

2016-03-01

Dedicated breast computed tomography (bCT) is currently being studied as a potential screening method for breast cancer. The X-ray exposure is set low to achieve an average glandular dose comparable to that of mammography, yielding projection data that contains high levels of noise. Iterative image reconstruction (IIR) algorithms may be well-suited for the system since they potentially reduce the effects of noise in the reconstructed images. However, IIR outcomes can be difficult to control since the algorithm parameters do not directly correspond to the image properties. Also, IIR algorithms are computationally demanding and have optimal parameter settings that depend on the size and shape of the breast and positioning of the patient. In this work, we design an efficient IIR algorithm with meaningful parameter specifications and that can be used on a large, diverse sample of bCT cases. The flexibility and efficiency of this method comes from having the final image produced by a linear combination of two separately reconstructed images - one containing gray level information and the other with enhanced high frequency components. Both of the images result from few iterations of separate IIR algorithms. The proposed algorithm depends on two parameters both of which have a well-defined impact on image quality. The algorithm is applied to numerous bCT cases from a dedicated bCT prototype system developed at University of California, Davis.

Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data

NASA Astrophysics Data System (ADS)

Martins, Fabio J. W. A.; Foucaut, Jean-Marc; Thomas, Lionel; Azevedo, Luis F. A.; Stanislas, Michel

2015-08-01

Tomographic PIV is a three-component volumetric velocity measurement technique based on the tomographic reconstruction of a particle distribution imaged by multiple camera views. In essence, the performance and accuracy of this technique is highly dependent on the parametric adjustment and the reconstruction algorithm used. Although synthetic data have been widely employed to optimize experiments, the resulting reconstructed volumes might not have optimal quality. The purpose of the present study is to offer quality indicators that can be applied to data samples in order to improve the quality of velocity results obtained by the tomo-PIV technique. The methodology proposed can potentially lead to significantly reduction in the time required to optimize a tomo-PIV reconstruction, also leading to better quality velocity results. Tomo-PIV data provided by a six-camera turbulent boundary-layer experiment were used to optimize the reconstruction algorithms according to this methodology. Velocity statistics measurements obtained by optimized BIMART, SMART and MART algorithms were compared with hot-wire anemometer data and velocity measurement uncertainties were computed. Results indicated that BIMART and SMART algorithms produced reconstructed volumes with equivalent quality as the standard MART with the benefit of reduced computational time.

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.

Preconditioned alternating projection algorithms for maximum a posteriori ECT reconstruction

NASA Astrophysics Data System (ADS)

Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng

2012-11-01

We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constraint involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the PAPA. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality.

Preconditioned Alternating Projection Algorithms for Maximum a Posteriori ECT Reconstruction

PubMed Central

Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng

2012-01-01

We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constrain involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the preconditioned alternating projection algorithm. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality. PMID:23271835

Parametric boundary reconstruction algorithm for industrial CT metrology application.

PubMed

Yin, Zhye; Khare, Kedar; De Man, Bruno

2009-01-01

High-energy X-ray computed tomography (CT) systems have been recently used to produce high-resolution images in various nondestructive testing and evaluation (NDT/NDE) applications. The accuracy of the dimensional information extracted from CT images is rapidly approaching the accuracy achieved with a coordinate measuring machine (CMM), the conventional approach to acquire the metrology information directly. On the other hand, CT systems generate the sinogram which is transformed mathematically to the pixel-based images. The dimensional information of the scanned object is extracted later by performing edge detection on reconstructed CT images. The dimensional accuracy of this approach is limited by the grid size of the pixel-based representation of CT images since the edge detection is performed on the pixel grid. Moreover, reconstructed CT images usually display various artifacts due to the underlying physical process and resulting object boundaries from the edge detection fail to represent the true boundaries of the scanned object. In this paper, a novel algorithm to reconstruct the boundaries of an object with uniform material composition and uniform density is presented. There are three major benefits in the proposed approach. First, since the boundary parameters are reconstructed instead of image pixels, the complexity of the reconstruction algorithm is significantly reduced. The iterative approach, which can be computationally intensive, will be practical with the parametric boundary reconstruction. Second, the object of interest in metrology can be represented more directly and accurately by the boundary parameters instead of the image pixels. By eliminating the extra edge detection step, the overall dimensional accuracy and process time can be improved. Third, since the parametric reconstruction approach shares the boundary representation with other conventional metrology modalities such as CMM, boundary information from other modalities can be directly

A Novel Image Compression Algorithm for High Resolution 3D Reconstruction

NASA Astrophysics Data System (ADS)

Siddeq, M. M.; Rodrigues, M. A.

2014-06-01

This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models.

Analytic reconstruction algorithms for triple-source CT with horizontal data truncation.

PubMed

Chen, Ming; Yu, Hengyong

2015-10-01

This paper explores a triple-source imaging method with horizontal data truncation to enlarge the field of view (FOV) for big objects. The study is conducted by using theoretical analysis, mathematical deduction, and numerical simulations. The proposed algorithms are implemented in c + + and matlab. While the basic platform is constructed in matlab, the computationally intensive segments are coded in c + +, which are linked via a mex interface. A triple-source circular scanning configuration with horizontal data truncation is developed, where three pairs of x-ray sources and detectors are unevenly distributed on the same circle to cover the whole imaging object. For this triple-source configuration, a fan-beam filtered backprojection-type algorithm is derived for truncated full-scan projections without data rebinning. The algorithm is also extended for horizontally truncated half-scan projections and cone-beam projections in a Feldkamp-type framework. Using their method, the FOV is enlarged twofold to threefold to scan bigger objects with high speed and quality. The numerical simulation results confirm the correctness and effectiveness of the developed algorithms. The triple-source scanning configuration with horizontal data truncation cannot only keep most of the advantages of a traditional multisource system but also cover a larger FOV for big imaging objects. In addition, because the filtering is shift-invariant, the proposed algorithms are very fast and easily parallelized on graphic processing units.

Microsurgical reconstruction of the maxilla: Algorithm and concepts.

PubMed

Costa, Horácio; Zenha, Horácio; Sequeira, Hugo; Coelho, Gustavo; Gomes, Nuno; Pinto, Cristina; Martins, João; Santos, Diana; Andresen, Carolina

2015-05-01

The main purpose of this article is to highlight free tissue transfers as the first-choice method for three-dimensional (3D) maxillary reconstruction, particularly in providing enough bone for palate and maxillary arch reconstruction and consequently an implant-retained prosthesis. To achieve this, the myosseous free iliac crest was selected whenever possible as the first choice inside the reconstructive algorithm and free flap armamentarium. A new maxillectomy classification and algorithm reconstruction are proposed. Technical modifications and improvements accomplished over time are discussed, considering palate, dental implants and prosthesis, nasal sidewall, cranial base and dura, as well as recipient vessels. We present functional and aesthetic outcomes of the senior author's past 24-year experience (H. C.) with complex midface reconstructions. The authors report and analyse a 24-year experience with 57 midface defects in 54 patients (30 males and 24 females). A total of 57 maxillary defects - classified as Class I (limited maxillectomy) = 12, Class II (subtotal maxillectomy) = 15, Class III (total maxillectomy) = 19 and Class IV (orbitomaxillectomy) = 11 - were analysed regarding sex, age, tumour recurrence, free flap, reconstruction and necrosis. In addition, functional outcomes were evaluated regarding diet, speech, globe position and vision, while aesthetic outcomes were evaluated by patient and surgeon scores. A total of 52 free flaps were performed in 47 patients; three patients were operated upon twice; and two other patients needed two sequentially linked flow-through flaps. The free flap survival was 96% with two total flap losses (4%). The other seven patients were fitted with a soft tissue-retained obturator prosthesis. Microsurgical vascularised osteomyocutaneous free flaps are actually the gold standard for reconstruction of complex defects following maxillectomy. This algorithm is based on the anatomofunctional defect of the maxilla and

WE-G-18A-08: Axial Cone Beam DBPF Reconstruction with Three-Dimensional Weighting and Butterfly Filtering

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

Tang, S; Wang, W; Tang, X

2014-06-15

Purpose: With the major benefit in dealing with data truncation for ROI reconstruction, the algorithm of differentiated backprojection followed by Hilbert filtering (DBPF) is originally derived for image reconstruction from parallel- or fan-beam data. To extend its application for axial CB scan, we proposed the integration of the DBPF algorithm with 3-D weighting. In this work, we further propose the incorporation of Butterfly filtering into the 3-D weighted axial CB-DBPF algorithm and conduct an evaluation to verify its performance. Methods: Given an axial scan, tomographic images are reconstructed by the DBPF algorithm with 3-D weighting, in which streak artifacts existmore » along the direction of Hilbert filtering. Recognizing this orientation-specific behavior, a pair of orthogonal Butterfly filtering is applied on the reconstructed images with the horizontal and vertical Hilbert filtering correspondingly. In addition, the Butterfly filtering can also be utilized for streak artifact suppression in the scenarios wherein only partial scan data with an angular range as small as 270° are available. Results: Preliminary data show that, with the correspondingly applied Butterfly filtering, the streak artifacts existing in the images reconstructed by the 3-D weighted DBPF algorithm can be suppressed to an unnoticeable level. Moreover, the Butterfly filtering also works at the scenarios of partial scan, though the 3-D weighting scheme may have to be dropped because of no sufficient projection data are available. Conclusion: As an algorithmic step, the incorporation of Butterfly filtering enables the DBPF algorithm for CB image reconstruction from data acquired along either a full or partial axial scan.« less

Filtered gradient reconstruction algorithm for compressive spectral imaging

NASA Astrophysics Data System (ADS)

Mejia, Yuri; Arguello, Henry

2017-04-01

Compressive sensing matrices are traditionally based on random Gaussian and Bernoulli entries. Nevertheless, they are subject to physical constraints, and their structure unusually follows a dense matrix distribution, such as the case of the matrix related to compressive spectral imaging (CSI). The CSI matrix represents the integration of coded and shifted versions of the spectral bands. A spectral image can be recovered from CSI measurements by using iterative algorithms for linear inverse problems that minimize an objective function including a quadratic error term combined with a sparsity regularization term. However, current algorithms are slow because they do not exploit the structure and sparse characteristics of the CSI matrices. A gradient-based CSI reconstruction algorithm, which introduces a filtering step in each iteration of a conventional CSI reconstruction algorithm that yields improved image quality, is proposed. Motivated by the structure of the CSI matrix, Φ, this algorithm modifies the iterative solution such that it is forced to converge to a filtered version of the residual ΦTy, where y is the compressive measurement vector. We show that the filtered-based algorithm converges to better quality performance results than the unfiltered version. Simulation results highlight the relative performance gain over the existing iterative algorithms.

Beard reconstruction: A surgical algorithm.

PubMed

Ninkovic, M; Heidekrueger, P I; Ehrl, D; von Spiegel, F; Broer, P N

2016-06-01

Facial defects with loss of hair-bearing regions can be caused by trauma, infection, tumor excision, or burn injury. The presented analysis evaluates a series of different surgical approaches with a focus on male beard reconstruction, emphasizing the role of tissue expansion of regional and free flaps. Locoregional and free flap reconstructions were performed in 11 male patients with 14 facial defects affecting the hair-bearing bucco-mandibular or perioral region. In order to minimize donor-site morbidity and obtain large amounts of thin, pliable, hair-bearing tissue, pre-expansion was performed in five of 14 patients. Eight of 14 patients were treated with locoregional flap reconstructions and six with free flap reconstructions. Algorithms regarding pre- and intraoperative decision making are discussed and long-term (mean follow-up 1.5 years) results analyzed. Major complications, including tissue expander infection with the need for removal or exchange, partial or full flap loss, occurred in 0% (0/8) of patients with locoregional flaps and in 17% (1/6) of patients undergoing free flap reconstructions. Secondary refinement surgery was performed in 25% (2/8) of locoregional flaps and in 67% (4/6) of free flaps. Both locoregional and distant tissue transfers play a role in beard reconstruction, while pre-expansion remains an invaluable tool. Paying attention to the presented principles and considering the significance of aesthetic facial subunits, range of motion, aesthetics, and patient satisfaction were improved long term in all our patients while minimizing donor-site morbidity. Copyright © 2016 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Improved sensitivity of computed tomography towards iodine and gold nanoparticle contrast agents via iterative reconstruction methods

PubMed Central

Bernstein, Ally Leigh; Dhanantwari, Amar; Jurcova, Martina; Cheheltani, Rabee; Naha, Pratap Chandra; Ivanc, Thomas; Shefer, Efrat; Cormode, David Peter

2016-01-01

Computed tomography is a widely used medical imaging technique that has high spatial and temporal resolution. Its weakness is its low sensitivity towards contrast media. Iterative reconstruction techniques (ITER) have recently become available, which provide reduced image noise compared with traditional filtered back-projection methods (FBP), which may allow the sensitivity of CT to be improved, however this effect has not been studied in detail. We scanned phantoms containing either an iodine contrast agent or gold nanoparticles. We used a range of tube voltages and currents. We performed reconstruction with FBP, ITER and a novel, iterative, modal-based reconstruction (IMR) algorithm. We found that noise decreased in an algorithm dependent manner (FBP > ITER > IMR) for every scan and that no differences were observed in attenuation rates of the agents. The contrast to noise ratio (CNR) of iodine was highest at 80 kV, whilst the CNR for gold was highest at 140 kV. The CNR of IMR images was almost tenfold higher than that of FBP images. Similar trends were found in dual energy images formed using these algorithms. In conclusion, IMR-based reconstruction techniques will allow contrast agents to be detected with greater sensitivity, and may allow lower contrast agent doses to be used. PMID:27185492

Theory and algorithms for image reconstruction on chords and within regions of interest

NASA Astrophysics Data System (ADS)

Zou, Yu; Pan, Xiaochuan; Sidky, Emilâ Y.

2005-11-01

We introduce a formula for image reconstruction on a chord of a general source trajectory. We subsequently develop three algorithms for exact image reconstruction on a chord from data acquired with the general trajectory. Interestingly, two of the developed algorithms can accommodate data containing transverse truncations. The widely used helical trajectory and other trajectories discussed in literature can be interpreted as special cases of the general trajectory, and the developed theory and algorithms are thus directly applicable to reconstructing images exactly from data acquired with these trajectories. For instance, chords on a helical trajectory are equivalent to the n-PI-line segments. In this situation, the proposed algorithms become the algorithms that we proposed previously for image reconstruction on PI-line segments. We have performed preliminary numerical studies, which include the study on image reconstruction on chords of two-circle trajectory, which is nonsmooth, and on n-PI lines of a helical trajectory, which is smooth. Quantitative results of these studies verify and demonstrate the proposed theory and algorithms.

Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment.

PubMed

Meng, Bowen; Pratx, Guillem; Xing, Lei

2011-12-01

Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT∕CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment. In this work, we accelerated the Feldcamp-Davis-Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT∕CT reconstruction algorithm. Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10(-7). Our study also proved that cloud computing with MapReduce is fault tolerant: the reconstruction completed

Ultrafast and scalable cone-beam CT reconstruction using MapReduce in a cloud computing environment

PubMed Central

Meng, Bowen; Pratx, Guillem; Xing, Lei

2011-01-01

Purpose: Four-dimensional CT (4DCT) and cone beam CT (CBCT) are widely used in radiation therapy for accurate tumor target definition and localization. However, high-resolution and dynamic image reconstruction is computationally demanding because of the large amount of data processed. Efficient use of these imaging techniques in the clinic requires high-performance computing. The purpose of this work is to develop a novel ultrafast, scalable and reliable image reconstruction technique for 4D CBCT/CT using a parallel computing framework called MapReduce. We show the utility of MapReduce for solving large-scale medical physics problems in a cloud computing environment. Methods: In this work, we accelerated the Feldcamp–Davis–Kress (FDK) algorithm by porting it to Hadoop, an open-source MapReduce implementation. Gated phases from a 4DCT scans were reconstructed independently. Following the MapReduce formalism, Map functions were used to filter and backproject subsets of projections, and Reduce function to aggregate those partial backprojection into the whole volume. MapReduce automatically parallelized the reconstruction process on a large cluster of computer nodes. As a validation, reconstruction of a digital phantom and an acquired CatPhan 600 phantom was performed on a commercial cloud computing environment using the proposed 4D CBCT/CT reconstruction algorithm. Results: Speedup of reconstruction time is found to be roughly linear with the number of nodes employed. For instance, greater than 10 times speedup was achieved using 200 nodes for all cases, compared to the same code executed on a single machine. Without modifying the code, faster reconstruction is readily achievable by allocating more nodes in the cloud computing environment. Root mean square error between the images obtained using MapReduce and a single-threaded reference implementation was on the order of 10−7. Our study also proved that cloud computing with MapReduce is fault tolerant: the

GPU-accelerated regularized iterative reconstruction for few-view cone beam CT

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

Matenine, Dmitri, E-mail: dmitri.matenine.1@ulaval.ca; Goussard, Yves, E-mail: yves.goussard@polymtl.ca; Després, Philippe, E-mail: philippe.despres@phy.ulaval.ca

2015-04-15

Purpose: The present work proposes an iterative reconstruction technique designed for x-ray transmission computed tomography (CT). The main objective is to provide a model-based solution to the cone-beam CT reconstruction problem, yielding accurate low-dose images via few-views acquisitions in clinically acceptable time frames. Methods: The proposed technique combines a modified ordered subsets convex (OSC) algorithm and the total variation minimization (TV) regularization technique and is called OSC-TV. The number of subsets of each OSC iteration follows a reduction pattern in order to ensure the best performance of the regularization method. Considering the high computational cost of the algorithm, it ismore » implemented on a graphics processing unit, using parallelization to accelerate computations. Results: The reconstructions were performed on computer-simulated as well as human pelvic cone-beam CT projection data and image quality was assessed. In terms of convergence and image quality, OSC-TV performs well in reconstruction of low-dose cone-beam CT data obtained via a few-view acquisition protocol. It compares favorably to the few-view TV-regularized projections onto convex sets (POCS-TV) algorithm. It also appears to be a viable alternative to full-dataset filtered backprojection. Execution times are of 1–2 min and are compatible with the typical clinical workflow for nonreal-time applications. Conclusions: Considering the image quality and execution times, this method may be useful for reconstruction of low-dose clinical acquisitions. It may be of particular benefit to patients who undergo multiple acquisitions by reducing the overall imaging radiation dose and associated risks.« less

A simplified surgical algorithm for flap reconstruction of eyebrow defects.

PubMed

Liu, Hai-Peng; Shao, Ying; Yu, Xiao-Jie; Zhang, Duo

2017-04-01

Partial or total eyebrow defects after trauma or tumor excisions have been repaired by several surgical technique and algorithms. However, these algorithms are often complicated and difficult to apply clinically. We therefore established a simplified surgical algorithm for the treatment of eyebrow defects using flap reconstruction. During the period between January 2009 and December 2015, a total of 21 Chinese patients (12 males, 9 females) with eyebrow defects were treated with eyebrow flap reconstruction. The ages ranged from 12 to 51 years. The patients included 13 cases located on the left and 8 cases on the right eyebrow. These defects were caused by trauma (5 patients) and tumor excision (16 patients). Among them, 6 patients were treated using superficial temporal artery island flap, while 15 patients were treated using the V-Y advancement pedicle flap based on the orbicularis oculi muscle. The minimum defect area was 0.8 × 1.0 cm and maximum area was 2.3 × 4.3 cm. All patients were followed up for 6 months to 5 years postoperatively. The clinical effects of eyebrow reconstruction were evaluated using a designated scoring system. All 21 flaps survived without significant complications and the shapes of the reconstructed eyebrows were continuous, symmetrical and with good integrity. According to the rating scale, there were 13 excellent, 8 good reconstructions among all patients. After an average of 9 months of follow-up, all patients had no recurrence of tumors and no infection or scarring. Based upon our experience with 21 patients who underwent eyebrow reconstruction for various eyebrow defects, we believe that our simplified surgical algorithm can serve as a model for the treatment of patients with eyebrow defects. Copyright © 2017 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

High-speed parallel implementation of a modified PBR algorithm on DSP-based EH topology

NASA Astrophysics Data System (ADS)

Rajan, K.; Patnaik, L. M.; Ramakrishna, J.

1997-08-01

Algebraic Reconstruction Technique (ART) is an age-old method used for solving the problem of three-dimensional (3-D) reconstruction from projections in electron microscopy and radiology. In medical applications, direct 3-D reconstruction is at the forefront of investigation. The simultaneous iterative reconstruction technique (SIRT) is an ART-type algorithm with the potential of generating in a few iterations tomographic images of a quality comparable to that of convolution backprojection (CBP) methods. Pixel-based reconstruction (PBR) is similar to SIRT reconstruction, and it has been shown that PBR algorithms give better quality pictures compared to those produced by SIRT algorithms. In this work, we propose a few modifications to the PBR algorithms. The modified algorithms are shown to give better quality pictures compared to PBR algorithms. The PBR algorithm and the modified PBR algorithms are highly compute intensive, Not many attempts have been made to reconstruct objects in the true 3-D sense because of the high computational overhead. In this study, we have developed parallel two-dimensional (2-D) and 3-D reconstruction algorithms based on modified PBR. We attempt to solve the two problems encountered by the PBR and modified PBR algorithms, i.e., the long computational time and the large memory requirements, by parallelizing the algorithm on a multiprocessor system. We investigate the possible task and data partitioning schemes by exploiting the potential parallelism in the PBR algorithm subject to minimizing the memory requirement. We have implemented an extended hypercube (EH) architecture for the high-speed execution of the 3-D reconstruction algorithm using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PEs) and dual-port random access memories (DPR) as channels between the PEs. We discuss and compare the performances of the PBR algorithm on an IBM 6000 RISC workstation, on a Silicon

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.

Fast algorithm for wavefront reconstruction in XAO/SCAO with pyramid wavefront sensor

NASA Astrophysics Data System (ADS)

Shatokhina, Iuliia; Obereder, Andreas; Ramlau, Ronny

2014-08-01

We present a fast wavefront reconstruction algorithm developed for an extreme adaptive optics system equipped with a pyramid wavefront sensor on a 42m telescope. The method is called the Preprocessed Cumulative Reconstructor with domain decomposition (P-CuReD). The algorithm is based on the theoretical relationship between pyramid and Shack-Hartmann wavefront sensor data. The algorithm consists of two consecutive steps - a data preprocessing, and an application of the CuReD algorithm, which is a fast method for wavefront reconstruction from Shack-Hartmann sensor data. The closed loop simulation results show that the P-CuReD method provides the same reconstruction quality and is significantly faster than an MVM.

Analytic reconstruction algorithms for triple-source CT with horizontal data truncation

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

Chen, Ming; Yu, Hengyong, E-mail: hengyong-yu@ieee.org

2015-10-15

Purpose: This paper explores a triple-source imaging method with horizontal data truncation to enlarge the field of view (FOV) for big objects. Methods: The study is conducted by using theoretical analysis, mathematical deduction, and numerical simulations. The proposed algorithms are implemented in c + + and MATLAB. While the basic platform is constructed in MATLAB, the computationally intensive segments are coded in c + +, which are linked via a MEX interface. Results: A triple-source circular scanning configuration with horizontal data truncation is developed, where three pairs of x-ray sources and detectors are unevenly distributed on the same circle tomore » cover the whole imaging object. For this triple-source configuration, a fan-beam filtered backprojection-type algorithm is derived for truncated full-scan projections without data rebinning. The algorithm is also extended for horizontally truncated half-scan projections and cone-beam projections in a Feldkamp-type framework. Using their method, the FOV is enlarged twofold to threefold to scan bigger objects with high speed and quality. The numerical simulation results confirm the correctness and effectiveness of the developed algorithms. Conclusions: The triple-source scanning configuration with horizontal data truncation cannot only keep most of the advantages of a traditional multisource system but also cover a larger FOV for big imaging objects. In addition, because the filtering is shift-invariant, the proposed algorithms are very fast and easily parallelized on graphic processing units.« less

Medical image reconstruction algorithm based on the geometric information between sensor detector and ROI

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu; Lee, Kangsan; Roh, Seungkuk

2016-05-01

In this paper, we propose a new image reconstruction algorithm considering the geometric information of acoustic sources and senor detector and review the two-step reconstruction algorithm which was previously proposed based on the geometrical information of ROI(region of interest) considering the finite size of acoustic sensor element. In a new image reconstruction algorithm, not only mathematical analysis is very simple but also its software implementation is very easy because we don't need to use the FFT. We verify the effectiveness of the proposed reconstruction algorithm by showing the simulation results by using Matlab k-wave toolkit.

High spatial resolution technique for SPECT using a fan-beam collimator

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

Ichihar, T.; Nambu, K.; Motomura, N.

1993-08-01

The physical characteristics of the collimator cause degradation of resolution with increasing distance from the collimator surface. A new convolutional backprojection algorithm has been derived for fanbeam SPECT data without rebinding into parallel beam geometry. The projections are filtered and then backprojected into the area within an isosceles triangle whose vertex is the focal point of the fan-beam and whose base is the fan-beam collimator face, and outside of the circle whose center is located midway between the focal point and the center of rotation and whose diameter is the distance between the focal point and the center of rotation.more » Consequently the backprojected area is close to the collimator surface. This algorithm has been implemented on a GCA-9300A SPECT system showing good results with both phantom and patient studies. The SPECT transaxial resolution was 4.6mm FWHM (reconstructed image matrix size of 256x256) at the center of SPECT FOV using UHR (ultra-high-resolution) fan beam collimators for brain study. Clinically, Tc-99m HMPAO and Tc-99m ECD brain data were reconstructed using this algorithm. The reconstruction results were compared with MRI images of the same slice position and showed significantly improved over results obtained with standard reconstruction algorithms.« less

Incorporating HYPR de-noising within iterative PET reconstruction (HYPR-OSEM)

NASA Astrophysics Data System (ADS)

(Kevin Cheng, Ju-Chieh; Matthews, Julian; Sossi, Vesna; Anton-Rodriguez, Jose; Salomon, André; Boellaard, Ronald

2017-08-01

HighlY constrained back-PRojection (HYPR) is a post-processing de-noising technique originally developed for time-resolved magnetic resonance imaging. It has been recently applied to dynamic imaging for positron emission tomography and shown promising results. In this work, we have developed an iterative reconstruction algorithm (HYPR-OSEM) which improves the signal-to-noise ratio (SNR) in static imaging (i.e. single frame reconstruction) by incorporating HYPR de-noising directly within the ordered subsets expectation maximization (OSEM) algorithm. The proposed HYPR operator in this work operates on the target image(s) from each subset of OSEM and uses the sum of the preceding subset images as the composite which is updated every iteration. Three strategies were used to apply the HYPR operator in OSEM: (i) within the image space modeling component of the system matrix in forward-projection only, (ii) within the image space modeling component in both forward-projection and back-projection, and (iii) on the image estimate after the OSEM update for each subset thus generating three forms: (i) HYPR-F-OSEM, (ii) HYPR-FB-OSEM, and (iii) HYPR-AU-OSEM. Resolution and contrast phantom simulations with various sizes of hot and cold regions as well as experimental phantom and patient data were used to evaluate the performance of the three forms of HYPR-OSEM, and the results were compared to OSEM with and without a post reconstruction filter. It was observed that the convergence in contrast recovery coefficients (CRC) obtained from all forms of HYPR-OSEM was slower than that obtained from OSEM. Nevertheless, HYPR-OSEM improved SNR without degrading accuracy in terms of resolution and contrast. It achieved better accuracy in CRC at equivalent noise level and better precision than OSEM and better accuracy than filtered OSEM in general. In addition, HYPR-AU-OSEM has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision based on the studies

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

Fast half-sibling population reconstruction: theory and algorithms.

PubMed

Dexter, Daniel; Brown, Daniel G

2013-07-12

Kinship inference is the task of identifying genealogically related individuals. Kinship information is important for determining mating structures, notably in endangered populations. Although many solutions exist for reconstructing full sibling relationships, few exist for half-siblings. We consider the problem of determining whether a proposed half-sibling population reconstruction is valid under Mendelian inheritance assumptions. We show that this problem is NP-complete and provide a 0/1 integer program that identifies the minimum number of individuals that must be removed from a population in order for the reconstruction to become valid. We also present SibJoin, a heuristic-based clustering approach based on Mendelian genetics, which is strikingly fast. The software is available at http://github.com/ddexter/SibJoin.git+. Our SibJoin algorithm is reasonably accurate and thousands of times faster than existing algorithms. The heuristic is used to infer a half-sibling structure for a population which was, until recently, too large to evaluate.

Objective evaluation of linear and nonlinear tomosynthetic reconstruction algorithms

NASA Astrophysics Data System (ADS)

Webber, Richard L.; Hemler, Paul F.; Lavery, John E.

2000-04-01

This investigation objectively tests five different tomosynthetic reconstruction methods involving three different digital sensors, each used in a different radiologic application: chest, breast, and pelvis, respectively. The common task was to simulate a specific representative projection for each application by summation of appropriately shifted tomosynthetically generated slices produced by using the five algorithms. These algorithms were, respectively, (1) conventional back projection, (2) iteratively deconvoluted back projection, (3) a nonlinear algorithm similar to back projection, except that the minimum value from all of the component projections for each pixel is computed instead of the average value, (4) a similar algorithm wherein the maximum value was computed instead of the minimum value, and (5) the same type of algorithm except that the median value was computed. Using these five algorithms, we obtained data from each sensor-tissue combination, yielding three factorially distributed series of contiguous tomosynthetic slices. The respective slice stacks then were aligned orthogonally and averaged to yield an approximation of a single orthogonal projection radiograph of the complete (unsliced) tissue thickness. Resulting images were histogram equalized, and actual projection control images were subtracted from their tomosynthetically synthesized counterparts. Standard deviations of the resulting histograms were recorded as inverse figures of merit (FOMs). Visual rankings of image differences by five human observers of a subset (breast data only) also were performed to determine whether their subjective observations correlated with homologous FOMs. Nonparametric statistical analysis of these data demonstrated significant differences (P > 0.05) between reconstruction algorithms. The nonlinear minimization reconstruction method nearly always outperformed the other methods tested. Observer rankings were similar to those measured objectively.

Validation of Ionosonde Electron Density Reconstruction Algorithms with IONOLAB-RAY in Central Europe

NASA Astrophysics Data System (ADS)

Gok, Gokhan; Mosna, Zbysek; Arikan, Feza; Arikan, Orhan; Erdem, Esra

2016-07-01

Ionospheric observation is essentially accomplished by specialized radar systems called ionosondes. The time delay between the transmitted and received signals versus frequency is measured by the ionosondes and the received signals are processed to generate ionogram plots, which show the time delay or reflection height of signals with respect to transmitted frequency. The critical frequencies of ionospheric layers and virtual heights, that provide useful information about ionospheric structurecan be extracted from ionograms . Ionograms also indicate the amount of variability or disturbances in the ionosphere. With special inversion algorithms and tomographical methods, electron density profiles can also be estimated from the ionograms. Although structural pictures of ionosphere in the vertical direction can be observed from ionosonde measurements, some errors may arise due to inaccuracies that arise from signal propagation, modeling, data processing and tomographic reconstruction algorithms. Recently IONOLAB group (www.ionolab.org) developed a new algorithm for effective and accurate extraction of ionospheric parameters and reconstruction of electron density profile from ionograms. The electron density reconstruction algorithm applies advanced optimization techniques to calculate parameters of any existing analytical function which defines electron density with respect to height using ionogram measurement data. The process of reconstructing electron density with respect to height is known as the ionogram scaling or true height analysis. IONOLAB-RAY algorithm is a tool to investigate the propagation path and parameters of HF wave in the ionosphere. The algorithm models the wave propagation using ray representation under geometrical optics approximation. In the algorithm , the structural ionospheric characteristics arerepresented as realistically as possible including anisotropicity, inhomogenity and time dependence in 3-D voxel structure. The algorithm is also used

The Evolution of Complex Microsurgical Midface Reconstruction: A Classification Scheme and Reconstructive Algorithm.

PubMed

Alam, Daniel; Ali, Yaseen; Klem, Christopher; Coventry, Daniel

2016-11-01

Orbito-malar reconstruction after oncological resection represents one of the most challenging facial reconstructive procedures. Until the last few decades, rehabilitation was typically prosthesis based with a limited role for surgery. The advent of microsurgical techniques allowed large-volume tissue reconstitution from a distant donor site, revolutionizing the potential approaches to these defects. The authors report a novel surgery-based algorithm and a classification scheme for complete midface reconstruction with a foundation in the Gillies principles of like-to-like reconstruction and with a significant role of computer-aided virtual planning. With this approach, the authors have been able to achieve significantly better patient outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

Intermediate view reconstruction using adaptive disparity search algorithm for real-time 3D processing

NASA Astrophysics Data System (ADS)

Bae, Kyung-hoon; Park, Changhan; Kim, Eun-soo

2008-03-01

In this paper, intermediate view reconstruction (IVR) using adaptive disparity search algorithm (ASDA) is for realtime 3-dimensional (3D) processing proposed. The proposed algorithm can reduce processing time of disparity estimation by selecting adaptive disparity search range. Also, the proposed algorithm can increase the quality of the 3D imaging. That is, by adaptively predicting the mutual correlation between stereo images pair using the proposed algorithm, the bandwidth of stereo input images pair can be compressed to the level of a conventional 2D image and a predicted image also can be effectively reconstructed using a reference image and disparity vectors. From some experiments, stereo sequences of 'Pot Plant' and 'IVO', it is shown that the proposed algorithm improves the PSNRs of a reconstructed image to about 4.8 dB by comparing with that of conventional algorithms, and reduces the Synthesizing time of a reconstructed image to about 7.02 sec by comparing with that of conventional algorithms.

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

In-line phase contrast micro-CT reconstruction for biomedical specimens.

PubMed

Fu, Jian; Tan, Renbo

2014-01-01

X-ray phase contrast micro computed tomography (micro-CT) can non-destructively provide the internal structure information of soft tissues and low atomic number materials. It has become an invaluable analysis tool for biomedical specimens. Here an in-line phase contrast micro-CT reconstruction technique is reported, which consists of a projection extraction method and the conventional filter back-projection (FBP) reconstruction algorithm. The projection extraction is implemented by applying the Fourier transform to the forward projections of in-line phase contrast micro-CT. This work comprises a numerical study of the method and its experimental verification using a biomedical specimen dataset measured at an X-ray tube source micro-CT setup. The numerical and experimental results demonstrate that the presented technique can improve the imaging contrast of biomedical specimens. It will be of interest for a wide range of in-line phase contrast micro-CT applications in medicine and biology.

SU-F-T-261: Reconstruction of Initial Photon Fluence Based On EPID Images

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

Seliger, T; Engenhart-Cabillic, R; Czarnecki, D

2016-06-15

Purpose: Verifying an algorithm to reconstruct relative initial photon fluence for clinical use. Clinical EPID and CT images were acquired to reconstruct an external photon radiation treatment field. The reconstructed initial photon fluence could be used to verify the treatment or calculate the applied dose to the patient. Methods: The acquired EPID images were corrected for scatter caused by the patient and the EPID with an iterative reconstruction algorithm. The transmitted photon fluence behind the patient was calculated subsequently. Based on the transmitted fluence the initial photon fluence was calculated using a back-projection algorithm which takes the patient geometry andmore » its energy dependent linear attenuation into account. This attenuation was gained from the acquired cone-beam CT or the planning CT by calculating a water-equivalent radiological thickness for each irradiation direction. To verify the algorithm an inhomogeneous phantom consisting of three inhomogeneities was irradiated by a static 6 MV photon field and compared to a reference flood field image. Results: The mean deviation between the reconstructed relative photon fluence for the inhomogeneous phantom and the flood field EPID image was 3% rising up to 7% for off-axis fluence. This was probably caused by the used clinical EPID calibration, which flattens the inhomogeneous fluence profile of the beam. Conclusion: In this clinical experiment the algorithm achieved good results in the center of the field while it showed high deviation of the lateral fluence. This could be reduced by optimizing the EPID calibration, considering the off-axis differential energy response. In further progress this and other aspects of the EPID, eg. field size dependency, CT and dose calibration have to be studied to realize a clinical acceptable accuracy of 2%.« less

4D Cone-beam CT reconstruction using a motion model based on principal component analysis

PubMed Central

Staub, David; Docef, Alen; Brock, Robert S.; Vaman, Constantin; Murphy, Martin J.

2011-01-01

Purpose: To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. Methods: The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. Results: The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. Conclusions: Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine. PMID:22149852

Optimizing convergence rates of alternating minimization reconstruction algorithms for real-time explosive detection applications

NASA Astrophysics Data System (ADS)

Bosch, Carl; Degirmenci, Soysal; Barlow, Jason; Mesika, Assaf; Politte, David G.; O'Sullivan, Joseph A.

2016-05-01

X-ray computed tomography reconstruction for medical, security and industrial applications has evolved through 40 years of experience with rotating gantry scanners using analytic reconstruction techniques such as filtered back projection (FBP). In parallel, research into statistical iterative reconstruction algorithms has evolved to apply to sparse view scanners in nuclear medicine, low data rate scanners in Positron Emission Tomography (PET) [5, 7, 10] and more recently to reduce exposure to ionizing radiation in conventional X-ray CT scanners. Multiple approaches to statistical iterative reconstruction have been developed based primarily on variations of expectation maximization (EM) algorithms. The primary benefit of EM algorithms is the guarantee of convergence that is maintained when iterative corrections are made within the limits of convergent algorithms. The primary disadvantage, however is that strict adherence to correction limits of convergent algorithms extends the number of iterations and ultimate timeline to complete a 3D volumetric reconstruction. Researchers have studied methods to accelerate convergence through more aggressive corrections [1], ordered subsets [1, 3, 4, 9] and spatially variant image updates. In this paper we describe the development of an AM reconstruction algorithm with accelerated convergence for use in a real-time explosive detection application for aviation security. By judiciously applying multiple acceleration techniques and advanced GPU processing architectures, we are able to perform 3D reconstruction of scanned passenger baggage at a rate of 75 slices per second. Analysis of the results on stream of commerce passenger bags demonstrates accelerated convergence by factors of 8 to 15, when comparing images from accelerated and strictly convergent algorithms.

A fast 4D cone beam CT reconstruction method based on the OSC-TV algorithm.

PubMed

Mascolo-Fortin, Julia; Matenine, Dmitri; Archambault, Louis; Després, Philippe

2018-01-01

Four-dimensional cone beam computed tomography allows for temporally resolved imaging with useful applications in radiotherapy, but raises particular challenges in terms of image quality and computation time. The purpose of this work is to develop a fast and accurate 4D algorithm by adapting a GPU-accelerated ordered subsets convex algorithm (OSC), combined with the total variation minimization regularization technique (TV). Different initialization schemes were studied to adapt the OSC-TV algorithm to 4D reconstruction: each respiratory phase was initialized either with a 3D reconstruction or a blank image. Reconstruction algorithms were tested on a dynamic numerical phantom and on a clinical dataset. 4D iterations were implemented for a cluster of 8 GPUs. All developed methods allowed for an adequate visualization of the respiratory movement and compared favorably to the McKinnon-Bates and adaptive steepest descent projection onto convex sets algorithms, while the 4D reconstructions initialized from a prior 3D reconstruction led to better overall image quality. The most suitable adaptation of OSC-TV to 4D CBCT was found to be a combination of a prior FDK reconstruction and a 4D OSC-TV reconstruction with a reconstruction time of 4.5 minutes. This relatively short reconstruction time could facilitate a clinical use.

Accurate 3D reconstruction by a new PDS-OSEM algorithm for HRRT

NASA Astrophysics Data System (ADS)

Chen, Tai-Been; Horng-Shing Lu, Henry; Kim, Hang-Keun; Son, Young-Don; Cho, Zang-Hee

2014-03-01

State-of-the-art high resolution research tomography (HRRT) provides high resolution PET images with full 3D human brain scanning. But, a short time frame in dynamic study causes many problems related to the low counts in the acquired data. The PDS-OSEM algorithm was proposed to reconstruct the HRRT image with a high signal-to-noise ratio that provides accurate information for dynamic data. The new algorithm was evaluated by simulated image, empirical phantoms, and real human brain data. Meanwhile, the time activity curve was adopted to validate a reconstructed performance of dynamic data between PDS-OSEM and OP-OSEM algorithms. According to simulated and empirical studies, the PDS-OSEM algorithm reconstructs images with higher quality, higher accuracy, less noise, and less average sum of square error than those of OP-OSEM. The presented algorithm is useful to provide quality images under the condition of low count rates in dynamic studies with a short scan time.

Gamma-ray momentum reconstruction from Compton electron trajectories by filtered back-projection

DOE PAGES

Haefner, A.; Gunter, D.; Plimley, B.; ...

2014-11-03

Gamma-ray imaging utilizing Compton scattering has traditionally relied on measuring coincident gamma-ray interactions to map directional information of the source distribution. This coincidence requirement makes it an inherently inefficient process. We present an approach to gamma-ray reconstruction from Compton scattering that requires only a single electron tracking detector, thus removing the coincidence requirement. From the Compton scattered electron momentum distribution, our algorithm analytically computes the incident photon's correlated direction and energy distributions. Because this method maps the source energy and location, it is useful in applications, where prior information about the source distribution is unknown. We demonstrate this method withmore » electron tracks measured in a scientific Si charge coupled device. While this method was demonstrated with electron tracks in a Si-based detector, it is applicable to any detector that can measure electron direction and energy, or equivalently the electron momentum. For example, it can increase the sensitivity to obtain energy and direction in gas-based systems that suffer from limited efficiency.« less

Validation of a method for in vivo 3D dose reconstruction for IMRT and VMAT treatments using on-treatment EPID images and a model-based forward-calculation algorithm

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

Van Uytven, Eric, E-mail: eric.vanuytven@cancercare.mb.ca; Van Beek, Timothy; McCowan, Peter M.

2015-12-15

Purpose: Radiation treatments are trending toward delivering higher doses per fraction under stereotactic radiosurgery and hypofractionated treatment regimens. There is a need for accurate 3D in vivo patient dose verification using electronic portal imaging device (EPID) measurements. This work presents a model-based technique to compute full three-dimensional patient dose reconstructed from on-treatment EPID portal images (i.e., transmission images). Methods: EPID dose is converted to incident fluence entering the patient using a series of steps which include converting measured EPID dose to fluence at the detector plane and then back-projecting the primary source component of the EPID fluence upstream of themore » patient. Incident fluence is then recombined with predicted extra-focal fluence and used to calculate 3D patient dose via a collapsed-cone convolution method. This method is implemented in an iterative manner, although in practice it provides accurate results in a single iteration. The robustness of the dose reconstruction technique is demonstrated with several simple slab phantom and nine anthropomorphic phantom cases. Prostate, head and neck, and lung treatments are all included as well as a range of delivery techniques including VMAT and dynamic intensity modulated radiation therapy (IMRT). Results: Results indicate that the patient dose reconstruction algorithm compares well with treatment planning system computed doses for controlled test situations. For simple phantom and square field tests, agreement was excellent with a 2%/2 mm 3D chi pass rate ≥98.9%. On anthropomorphic phantoms, the 2%/2 mm 3D chi pass rates ranged from 79.9% to 99.9% in the planning target volume (PTV) region and 96.5% to 100% in the low dose region (>20% of prescription, excluding PTV and skin build-up region). Conclusions: An algorithm to reconstruct delivered patient 3D doses from EPID exit dosimetry measurements was presented. The method was applied to phantom and

CORRIGENDUM: A new algorithm for the shape reconstruction of perfectly conducting objects A new algorithm for the shape reconstruction of perfectly conducting objects

NASA Astrophysics Data System (ADS)

Çayören, M.; Akduman, I.; Yapar, A.; Crocco, L.

2010-03-01

The reference list should have included the conference communications [1] and [2], wherein we introduced the algorithm described in this paper. Note that a less complete description of the algorithm was given in [1]. However, the example considering a bean-shaped target is the same in the two papers and it is reused in this paper by kind permission of the Applied Computational Electromagnetics Society. References [1] Crocco L, Akduman I, Çayören M and Yapar A 2007 A new method for shape reconstruction of perfectly conducting targets The 23rd Annual Review of Progress in Applied Computational Electromagnetics (Verona, Italy) [2] Çayören M, Akduman I, Yapar A and Crocco L 2007 A new algorithm for the shape reconstruction of perfectly conducting objects Progress in Electromagnetics Research Symposium (PIERS) (Beijing, PRC)

Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms

NASA Astrophysics Data System (ADS)

Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr

2017-12-01

There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.

Image reconstruction from cone-beam projections with attenuation correction

NASA Astrophysics Data System (ADS)

Weng, Yi

1997-07-01

In single photon emission computered tomography (SPECT) imaging, photon attenuation within the body is a major factor contributing to the quantitative inaccuracy in measuring the distribution of radioactivity. Cone-beam SPECT provides improved sensitivity for imaging small organs. This thesis extends the results for 2D parallel- beam and fan-beam geometry to 3D parallel-beam and cone- beam geometries in order to derive filtered backprojection reconstruction algorithms for the 3D exponential parallel-beam transform and for the exponential cone-beam transform with sampling on a sphere. An exact inversion formula for the 3D exponential parallel-beam transform is obtained and is extended to the 3D exponential cone-beam transform. Sampling on a sphere is not useful clinically and current cone-beam tomography, with the focal point traversing a planar orbit, does not acquire sufficient data to give an accurate reconstruction. Thus a data acquisition method that obtains complete data for cone-beam SPECT by simultaneously rotating the gamma camera and translating the patient bed, so that cone-beam projections can be obtained with the focal point traversing a helix that surrounds the patient was developed. First, an implementation of Grangeat's algorithm for helical cone- beam projections was developed without attenuation correction. A fast new rebinning scheme was developed that uses all of the detected data to reconstruct the image and properly normalizes any multiply scanned data. In the case of attenuation no theorem analogous to Tuy's has been proven. We hypothesized that an artifact-free reconstruction could be obtained even if the cone-beam data are attenuated, provided the imaging orbit satisfies Tuy's condition and the exact attenuation map is known. Cone-beam emission data were acquired by using a circle- and-line and a helix orbit on a clinical SPECT system. An iterative conjugate gradient reconstruction algorithm was used to reconstruct projection data with a

Event-by-event PET image reconstruction using list-mode origin ensembles algorithm

NASA Astrophysics Data System (ADS)

Andreyev, Andriy

2016-03-01

There is a great demand for real time or event-by-event (EBE) image reconstruction in emission tomography. Ideally, as soon as event has been detected by the acquisition electronics, it needs to be used in the image reconstruction software. This would greatly speed up the image reconstruction since most of the data will be processed and reconstructed while the patient is still undergoing the scan. Unfortunately, the current industry standard is that the reconstruction of the image would not start until all the data for the current image frame would be acquired. Implementing an EBE reconstruction for MLEM family of algorithms is possible, but not straightforward as multiple (computationally expensive) updates to the image estimate are required. In this work an alternative Origin Ensembles (OE) image reconstruction algorithm for PET imaging is converted to EBE mode and is investigated whether it is viable alternative for real-time image reconstruction. In OE algorithm all acquired events are seen as points that are located somewhere along the corresponding line-of-responses (LORs), together forming a point cloud. Iteratively, with a multitude of quasi-random shifts following the likelihood function the point cloud converges to a reflection of an actual radiotracer distribution with the degree of accuracy that is similar to MLEM. New data can be naturally added into the point cloud. Preliminary results with simulated data show little difference between regular reconstruction and EBE mode, proving the feasibility of the proposed approach.

A multiresolution approach to iterative reconstruction algorithms in X-ray computed tomography.

PubMed

De Witte, Yoni; Vlassenbroeck, Jelle; Van Hoorebeke, Luc

2010-09-01

In computed tomography, the application of iterative reconstruction methods in practical situations is impeded by their high computational demands. Especially in high resolution X-ray computed tomography, where reconstruction volumes contain a high number of volume elements (several giga voxels), this computational burden prevents their actual breakthrough. Besides the large amount of calculations, iterative algorithms require the entire volume to be kept in memory during reconstruction, which quickly becomes cumbersome for large data sets. To overcome this obstacle, we present a novel multiresolution reconstruction, which greatly reduces the required amount of memory without significantly affecting the reconstructed image quality. It is shown that, combined with an efficient implementation on a graphical processing unit, the multiresolution approach enables the application of iterative algorithms in the reconstruction of large volumes at an acceptable speed using only limited resources.

Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR).

PubMed

Chen, Baiyu; Barnhart, Huiman; Richard, Samuel; Robins, Marthony; Colsher, James; Samei, Ehsan

2013-11-01

Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables. Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision. Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A. The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms

The role of advanced reconstruction algorithms in cardiac CT

PubMed Central

Halliburton, Sandra S.; Tanabe, Yuki; Partovi, Sasan

2017-01-01

Non-linear iterative reconstruction (IR) algorithms have been increasingly incorporated into clinical cardiac CT protocols at institutions around the world. Multiple IR algorithms are available commercially from various vendors. IR algorithms decrease image noise and are primarily used to enable lower radiation dose protocols. IR can also be used to improve image quality for imaging of obese patients, coronary atherosclerotic plaques, coronary stents, and myocardial perfusion. In this article, we will review the various applications of IR algorithms in cardiac imaging and evaluate how they have changed practice. PMID:29255694

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

NASA Astrophysics Data System (ADS)

PourkamaliAnaraki, Maryam; Sadeghi, Mehdi

2016-03-01

Reconstructing haplotypes from SNP fragments is an important problem in computational biology. There have been a lot of interests in this field because haplotypes have been shown to contain promising data for disease association research. It is proved that haplotype reconstruction in Minimum Error Correction model is an NP-hard problem. Therefore, several methods such as clustering techniques, evolutionary algorithms, neural networks and swarm intelligence approaches have been proposed in order to solve this problem in appropriate time. In this paper, we have focused on various evolutionary clustering techniques and try to find an efficient technique for solving haplotype reconstruction problem. It can be referred from our experiments that the clustering methods relying on the behaviour of honey bee colony in nature, specifically bees algorithm and artificial bee colony methods, are expected to result in more efficient solutions. An application program of the methods is available at the following link. http://www.bioinf.cs.ipm.ir/software/haprs/

Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry.

PubMed

Jiang, Xiaolei; Zhang, Li; Zhang, Ran; Yin, Hongxia; Wang, Zhenchang

2015-01-01

X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm.

Explicit Filtering Based Low-Dose Differential Phase Reconstruction Algorithm with the Grating Interferometry

PubMed Central

Zhang, Li; Zhang, Ran; Yin, Hongxia; Wang, Zhenchang

2015-01-01

X-ray grating interferometry offers a novel framework for the study of weakly absorbing samples. Three kinds of information, that is, the attenuation, differential phase contrast (DPC), and dark-field images, can be obtained after a single scanning, providing additional and complementary information to the conventional attenuation image. Phase shifts of X-rays are measured by the DPC method; hence, DPC-CT reconstructs refraction indexes rather than attenuation coefficients. In this work, we propose an explicit filtering based low-dose differential phase reconstruction algorithm, which enables reconstruction from reduced scanning without artifacts. The algorithm adopts a differential algebraic reconstruction technique (DART) with the explicit filtering based sparse regularization rather than the commonly used total variation (TV) method. Both the numerical simulation and the biological sample experiment demonstrate the feasibility of the proposed algorithm. PMID:26089971

Scalp reconstruction: an algorithmic approach and systematic review.

PubMed

Desai, Shaun C; Sand, Jordan P; Sharon, Jeffrey D; Branham, Gregory; Nussenbaum, Brian

2015-01-01

Reconstruction of the scalp after acquired defects remains a common challenge for the reconstructive surgeon, especially in a patient with a history of radiation to the area. To review the current literature and describe a novel algorithm to help guide the reconstructive surgeon in determining the optimal reconstruction from a cosmetic and functional standpoint. Pertinent surgical anatomy, considerations for patient and technique selection, reconstructive goals, as well as the reconstructive ladder, are also discussed. A PubMed and Medline search was performed of the entire English literature with respect to scalp reconstruction. Priority of review was given to those studies with higher-quality levels of evidence. Size, location, radiation history, and potential for hairline distortion are important factors in determining the ideal reconstruction. The tighter and looser areas of the scalp play a major role in the potential for primary or local flap closure. Patients with medium to large defects and a history of radiation will likely benefit from free tissue transfer. Ideal reconstruction of scalp defects relies on a comprehensive understanding of scalp anatomy, a full consideration of the armamentarium of surgical techniques, and a detailed appraisal of patient factors and expectations. The simplest reconstruction should be used whenever possible to provide the most functional and aesthetic scalp reconstruction, with the least amount of complexity. NA.

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.

Low Statistics Reconstruction of the Compton Camera Point Spread Function in 3D Prompt-γ Imaging of Ion Beam Therapy

NASA Astrophysics Data System (ADS)

Lojacono, Xavier; Richard, Marie-Hélène; Ley, Jean-Luc; Testa, Etienne; Ray, Cédric; Freud, Nicolas; Létang, Jean Michel; Dauvergne, Denis; Maxim, Voichiţa; Prost, Rémy

2013-10-01

The Compton camera is a relevant imaging device for the detection of prompt photons produced by nuclear fragmentation in hadrontherapy. It may allow an improvement in detection efficiency compared to a standard gamma-camera but requires more sophisticated image reconstruction techniques. In this work, we simulate low statistics acquisitions from a point source having a broad energy spectrum compatible with hadrontherapy. We then reconstruct the image of the source with a recently developed filtered backprojection algorithm, a line-cone approach and an iterative List Mode Maximum Likelihood Expectation Maximization algorithm. Simulated data come from a Compton camera prototype designed for hadrontherapy online monitoring. Results indicate that the achievable resolution in directions parallel to the detector, that may include the beam direction, is compatible with the quality control requirements. With the prototype under study, the reconstructed image is elongated in the direction orthogonal to the detector. However this direction is of less interest in hadrontherapy where the first requirement is to determine the penetration depth of the beam in the patient. Additionally, the resolution may be recovered using a second camera.

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.

Reconstruction of a digital core containing clay minerals based on a clustering algorithm.

PubMed

He, Yanlong; Pu, Chunsheng; Jing, Cheng; Gu, Xiaoyu; Chen, Qingdong; Liu, Hongzhi; Khan, Nasir; Dong, Qiaoling

2017-10-01

It is difficult to obtain a core sample and information for digital core reconstruction of mature sandstone reservoirs around the world, especially for an unconsolidated sandstone reservoir. Meanwhile, reconstruction and division of clay minerals play a vital role in the reconstruction of the digital cores, although the two-dimensional data-based reconstruction methods are specifically applicable as the microstructure reservoir simulation methods for the sandstone reservoir. However, reconstruction of clay minerals is still challenging from a research viewpoint for the better reconstruction of various clay minerals in the digital cores. In the present work, the content of clay minerals was considered on the basis of two-dimensional information about the reservoir. After application of the hybrid method, and compared with the model reconstructed by the process-based method, the digital core containing clay clusters without the labels of the clusters' number, size, and texture were the output. The statistics and geometry of the reconstruction model were similar to the reference model. In addition, the Hoshen-Kopelman algorithm was used to label various connected unclassified clay clusters in the initial model and then the number and size of clay clusters were recorded. At the same time, the K-means clustering algorithm was applied to divide the labeled, large connecting clusters into smaller clusters on the basis of difference in the clusters' characteristics. According to the clay minerals' characteristics, such as types, textures, and distributions, the digital core containing clay minerals was reconstructed by means of the clustering algorithm and the clay clusters' structure judgment. The distributions and textures of the clay minerals of the digital core were reasonable. The clustering algorithm improved the digital core reconstruction and provided an alternative method for the simulation of different clay minerals in the digital cores.

Potency backprojection

NASA Astrophysics Data System (ADS)

Okuwaki, R.; Kasahara, A.; Yagi, Y.

2017-12-01

The backprojection (BP) method has been one of the powerful tools of tracking seismic-wave sources of the large/mega earthquakes. The BP method projects waveforms onto a possible source point by stacking them with the theoretical-travel-time shifts between the source point and the stations. Following the BP method, the hybrid backprojection (HBP) method was developed to enhance depth-resolution of projected images and mitigate the dummy imaging of the depth phases, which are shortcomings of the BP method, by stacking cross-correlation functions of the observed waveforms and theoretically calculated Green's functions (GFs). The signal-intensity of the BP/HBP image at a source point is related to how much of observed waveforms was radiated from that point. Since the amplitude of the GF associated with the slip-rate increases with depth as the rigidity increases with depth, the intensity of the BP/HBP image inherently has depth dependence. To make a direct comparison of the BP/HBP image with the corresponding slip distribution inferred from a waveform inversion, and discuss the rupture properties along the fault drawn from the waveforms in high- and low-frequencies with the BP/HBP methods and the waveform inversion, respectively, it is desirable to have the variants of BP/HBP methods that directly image the potency-rate-density distribution. Here we propose new formulations of the BP/HBP methods, which image the distribution of the potency-rate density by introducing alternative normalizing factors in the conventional formulations. For the BP method, the observed waveform is normalized with the maximum amplitude of P-phase of the corresponding GF. For the HBP method, we normalize the cross-correlation function with the squared-sum of the GF. The normalized waveforms or the cross-correlation functions are then stacked for all the stations to enhance the signal to noise ratio. We will present performance-tests of the new formulations by using synthetic waveforms and the

Accelerating simultaneous algebraic reconstruction technique with motion compensation using CUDA-enabled GPU.

PubMed

Pang, Wai-Man; Qin, Jing; Lu, Yuqiang; Xie, Yongming; Chui, Chee-Kong; Heng, Pheng-Ann

2011-03-01

To accelerate the simultaneous algebraic reconstruction technique (SART) with motion compensation for speedy and quality computed tomography reconstruction by exploiting CUDA-enabled GPU. Two core techniques are proposed to fit SART into the CUDA architecture: (1) a ray-driven projection along with hardware trilinear interpolation, and (2) a voxel-driven back-projection that can avoid redundant computation by combining CUDA shared memory. We utilize the independence of each ray and voxel on both techniques to design CUDA kernel to represent a ray in the projection and a voxel in the back-projection respectively. Thus, significant parallelization and performance boost can be achieved. For motion compensation, we rectify each ray's direction during the projection and back-projection stages based on a known motion vector field. Extensive experiments demonstrate the proposed techniques can provide faster reconstruction without compromising image quality. The process rate is nearly 100 projections s (-1), and it is about 150 times faster than a CPU-based SART. The reconstructed image is compared against ground truth visually and quantitatively by peak signal-to-noise ratio (PSNR) and line profiles. We further evaluate the reconstruction quality using quantitative metrics such as signal-to-noise ratio (SNR) and mean-square-error (MSE). All these reveal that satisfactory results are achieved. The effects of major parameters such as ray sampling interval and relaxation parameter are also investigated by a series of experiments. A simulated dataset is used for testing the effectiveness of our motion compensation technique. The results demonstrate our reconstructed volume can eliminate undesirable artifacts like blurring. Our proposed method has potential to realize instantaneous presentation of 3D CT volume to physicians once the projection data are acquired.

Angle Statistics Reconstruction: a robust reconstruction algorithm for Muon Scattering Tomography

NASA Astrophysics Data System (ADS)

Stapleton, M.; Burns, J.; Quillin, S.; Steer, C.

2014-11-01

Muon Scattering Tomography (MST) is a technique for using the scattering of cosmic ray muons to probe the contents of enclosed volumes. As a muon passes through material it undergoes multiple Coulomb scattering, where the amount of scattering is dependent on the density and atomic number of the material as well as the path length. Hence, MST has been proposed as a means of imaging dense materials, for instance to detect special nuclear material in cargo containers. Algorithms are required to generate an accurate reconstruction of the material density inside the volume from the muon scattering information and some have already been proposed, most notably the Point of Closest Approach (PoCA) and Maximum Likelihood/Expectation Maximisation (MLEM) algorithms. However, whilst PoCA-based algorithms are easy to implement, they perform rather poorly in practice. Conversely, MLEM is a complicated algorithm to implement and computationally intensive and there is currently no published, fast and easily-implementable algorithm that performs well in practice. In this paper, we first provide a detailed analysis of the source of inaccuracy in PoCA-based algorithms. We then motivate an alternative method, based on ideas first laid out by Morris et al, presenting and fully specifying an algorithm that performs well against simulations of realistic scenarios. We argue this new algorithm should be adopted by developers of Muon Scattering Tomography as an alternative to PoCA.

[High resolution reconstruction of PET images using the iterative OSEM algorithm].

PubMed

Doll, J; Henze, M; Bublitz, O; Werling, A; Adam, L E; Haberkorn, U; Semmler, W; Brix, G

2004-06-01

Improvement of the spatial resolution in positron emission tomography (PET) by incorporation of the image-forming characteristics of the scanner into the process of iterative image reconstruction. All measurements were performed at the whole-body PET system ECAT EXACT HR(+) in 3D mode. The acquired 3D sinograms were sorted into 2D sinograms by means of the Fourier rebinning (FORE) algorithm, which allows the usage of 2D algorithms for image reconstruction. The scanner characteristics were described by a spatially variant line-spread function (LSF), which was determined from activated copper-64 line sources. This information was used to model the physical degradation processes in PET measurements during the course of 2D image reconstruction with the iterative OSEM algorithm. To assess the performance of the high-resolution OSEM algorithm, phantom measurements performed at a cylinder phantom, the hotspot Jaszczack phantom, and the 3D Hoffmann brain phantom as well as different patient examinations were analyzed. Scanner characteristics could be described by a Gaussian-shaped LSF with a full-width at half-maximum increasing from 4.8 mm at the center to 5.5 mm at a radial distance of 10.5 cm. Incorporation of the LSF into the iteration formula resulted in a markedly improved resolution of 3.0 and 3.5 mm, respectively. The evaluation of phantom and patient studies showed that the high-resolution OSEM algorithm not only lead to a better contrast resolution in the reconstructed activity distributions but also to an improved accuracy in the quantification of activity concentrations in small structures without leading to an amplification of image noise or even the occurrence of image artifacts. The spatial and contrast resolution of PET scans can markedly be improved by the presented image restauration algorithm, which is of special interest for the examination of both patients with brain disorders and small animals.

Application of a dual-resolution voxelization scheme to compressed-sensing (CS)-based iterative reconstruction in digital tomosynthesis (DTS)

NASA Astrophysics Data System (ADS)

Park, S. Y.; Kim, G. A.; Cho, H. S.; Park, C. K.; Lee, D. Y.; Lim, H. W.; Lee, H. W.; Kim, K. S.; Kang, S. Y.; Park, J. E.; Kim, W. S.; Jeon, D. H.; Je, U. K.; Woo, T. H.; Oh, J. E.

2018-02-01

In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).

Alpha image reconstruction (AIR): A new iterative CT image reconstruction approach using voxel-wise alpha blending

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

Hofmann, Christian; Sawall, Stefan; Knaup, Michael

2014-06-15

Purpose: Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise (and thereby patient dose), to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporatea priori knowledge into iterative image reconstruction, for example, by adding additional constraints to the cost function, which penalize variations between neighboring voxels. However, this approach to regularization in general poses a resolution noise trade-off because the stronger the regularization, and thus the noise reduction, the stronger themore » loss of spatial resolution and thus loss of anatomical detail. The authors propose a method which tries to improve this trade-off. The proposed reconstruction algorithm is called alpha image reconstruction (AIR). One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The AIR algorithm reconstructs voxel-specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image, one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade-off. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and low contrast disks is simulated. A filtered backprojection (FBP) reconstruction with a Ram-Lak kernel is used as a reference reconstruction. The results of AIR are compared against the FBP results and against a penalized weighted least squares reconstruction which uses total variation as regularization. The simulations are based on the geometry of the Siemens Somatom Definition Flash scanner. To quantitatively assess image quality, the authors analyze line profiles through resolution patterns to define a

NEMA NU 4-Optimized Reconstructions for Therapy Assessment in Cancer Research with the Inveon Small Animal PET/CT System.

PubMed

Lasnon, Charline; Dugue, Audrey Emmanuelle; Briand, Mélanie; Blanc-Fournier, Cécile; Dutoit, Soizic; Louis, Marie-Hélène; Aide, Nicolas

2015-06-01

We compared conventional filtered back-projection (FBP), two-dimensional-ordered subsets expectation maximization (OSEM) and maximum a posteriori (MAP) NEMA NU 4-optimized reconstructions for therapy assessment. Varying reconstruction settings were used to determine the parameters for optimal image quality with two NEMA NU 4 phantom acquisitions. Subsequently, data from two experiments in which nude rats bearing subcutaneous tumors had received a dual PI3K/mTOR inhibitor were reconstructed with the NEMA NU 4-optimized parameters. Mann-Whitney tests were used to compare mean standardized uptake value (SUV(mean)) variations among groups. All NEMA NU 4-optimized reconstructions showed the same 2-deoxy-2-[(18)F]fluoro-D-glucose ([(18)F]FDG) kinetic patterns and detected a significant difference in SUV(mean) relative to day 0 between controls and treated groups for all time points with comparable p values. In the framework of therapy assessment in rats bearing subcutaneous tumors, all algorithms available on the Inveon system performed equally.

Polarization reconstruction algorithm for a Compton polarimeter

NASA Astrophysics Data System (ADS)

Vockert, M.; Weber, G.; Spillmann, U.; Krings, T.; Stöhlker, Th

2018-05-01

We present the technique of Compton polarimetry using X-ray detectors based on double-sided segmented semiconductor crystals that were developed within the SPARC collaboration. In addition, we discuss the polarization reconstruction algorithm with particular emphasis on systematic deviations between the observed detector response and our model function for the Compton scattering distribution inside the detector.

Development of a high-performance noise-reduction filter for tomographic reconstruction

NASA Astrophysics Data System (ADS)

Kao, Chien-Min; Pan, Xiaochuan

2001-07-01

We propose a new noise-reduction method for tomographic reconstruction. The method incorporates a priori information on the source image for allowing the derivation of the energy spectrum of its ideal sinogram. In combination with the energy spectrum of the Poisson noise in the measured sinogram, we are able to derive a Wiener-like filter for effective suppression of the sinogram noise. The filtered backprojection (FBP) algorithm, with a ramp filter, is then applied to the filtered sinogram to produce tomographic images. The resulting filter has a closed-form expression in the frequency space and contains a single user-adjustable regularization parameter. The proposed method is hence simple to implement and easy to use. In contrast to the ad hoc apodizing windows, such as Hanning and Butterworth filters, that are commonly used in the conventional FBP reconstruction, the proposed filter is theoretically more rigorous as it is derived by basing upon an optimization criterion, subject to a known class of source image intensity distributions.

Iterative reconstruction methods in atmospheric tomography: FEWHA, Kaczmarz and Gradient-based algorithm

NASA Astrophysics Data System (ADS)

Ramlau, R.; Saxenhuber, D.; Yudytskiy, M.

2014-07-01

The problem of atmospheric tomography arises in ground-based telescope imaging with adaptive optics (AO), where one aims to compensate in real-time for the rapidly changing optical distortions in the atmosphere. Many of these systems depend on a sufficient reconstruction of the turbulence profiles in order to obtain a good correction. Due to steadily growing telescope sizes, there is a strong increase in the computational load for atmospheric reconstruction with current methods, first and foremost the MVM. In this paper we present and compare three novel iterative reconstruction methods. The first iterative approach is the Finite Element- Wavelet Hybrid Algorithm (FEWHA), which combines wavelet-based techniques and conjugate gradient schemes to efficiently and accurately tackle the problem of atmospheric reconstruction. The method is extremely fast, highly flexible and yields superior quality. Another novel iterative reconstruction algorithm is the three step approach which decouples the problem in the reconstruction of the incoming wavefronts, the reconstruction of the turbulent layers (atmospheric tomography) and the computation of the best mirror correction (fitting step). For the atmospheric tomography problem within the three step approach, the Kaczmarz algorithm and the Gradient-based method have been developed. We present a detailed comparison of our reconstructors both in terms of quality and speed performance in the context of a Multi-Object Adaptive Optics (MOAO) system for the E-ELT setting on OCTOPUS, the ESO end-to-end simulation tool.

A framework for directional and higher-order reconstruction in photoacoustic tomography

NASA Astrophysics Data System (ADS)

Boink, Yoeri E.; Lagerwerf, Marinus J.; Steenbergen, Wiendelt; van Gils, Stephan A.; Manohar, Srirang; Brune, Christoph

2018-02-01

Photoacoustic tomography is a hybrid imaging technique that combines high optical tissue contrast with high ultrasound resolution. Direct reconstruction methods such as filtered back-projection, time reversal and least squares suffer from curved line artefacts and blurring, especially in the case of limited angles or strong noise. In recent years, there has been great interest in regularised iterative methods. These methods employ prior knowledge of the image to provide higher quality reconstructions. However, easy comparisons between regularisers and their properties are limited, since many tomography implementations heavily rely on the specific regulariser chosen. To overcome this bottleneck, we present a modular reconstruction framework for photoacoustic tomography, which enables easy comparisons between regularisers with different properties, e.g. nonlinear, higher-order or directional. We solve the underlying minimisation problem with an efficient first-order primal-dual algorithm. Convergence rates are optimised by choosing an operator-dependent preconditioning strategy. A variety of reconstruction methods are tested on challenging 2D synthetic and experimental data sets. They outperform direct reconstruction approaches for strong noise levels and limited angle measurements, offering immediate benefits in terms of acquisition time and quality. This work provides a basic platform for the investigation of future advanced regularisation methods in photoacoustic tomography.

Fast direct fourier reconstruction of radial and PROPELLER MRI data using the chirp transform algorithm on graphics hardware.

PubMed

Feng, Yanqiu; Song, Yanli; Wang, Cong; Xin, Xuegang; Feng, Qianjin; Chen, Wufan

2013-10-01

To develop and test a new algorithm for fast direct Fourier transform (DrFT) reconstruction of MR data on non-Cartesian trajectories composed of lines with equally spaced points. The DrFT, which is normally used as a reference in evaluating the accuracy of other reconstruction methods, can reconstruct images directly from non-Cartesian MR data without interpolation. However, DrFT reconstruction involves substantially intensive computation, which makes the DrFT impractical for clinical routine applications. In this article, the Chirp transform algorithm was introduced to accelerate the DrFT reconstruction of radial and Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) MRI data located on the trajectories that are composed of lines with equally spaced points. The performance of the proposed Chirp transform algorithm-DrFT algorithm was evaluated by using simulation and in vivo MRI data. After implementing the algorithm on a graphics processing unit, the proposed Chirp transform algorithm-DrFT algorithm achieved an acceleration of approximately one order of magnitude, and the speed-up factor was further increased to approximately three orders of magnitude compared with the traditional single-thread DrFT reconstruction. Implementation the Chirp transform algorithm-DrFT algorithm on the graphics processing unit can efficiently calculate the DrFT reconstruction of the radial and PROPELLER MRI data. Copyright © 2012 Wiley Periodicals, Inc.

Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?

PubMed Central

Pan, Xiaochuan; Sidky, Emil Y; Vannier, Michael

2010-01-01

Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years—the reconstruction algorithm. Fundamental advances have been made in the solution of inverse problems, especially tomographic reconstruction, but these works have not been translated into clinical and related practice. The reasons are not obvious and seldom discussed. This review seeks to examine the reasons for this discrepancy and provides recommendations on how it can be resolved. We take the example of field of compressive sensing (CS), summarizing this new area of research from the eyes of practical medical physicists and explaining the disconnection between theoretical and application-oriented research. Using a few issues specific to CT, which engineers have addressed in very specific ways, we try to distill the mathematical problem underlying each of these issues with the hope of demonstrating that there are interesting mathematical problems of general importance that can result from in depth analysis of specific issues. We then sketch some unconventional CT-imaging designs that have the potential to impact on CT applications, if the link between applied mathematicians and engineers/physicists were stronger. Finally, we close with some observations on how the link could be strengthened. There is, we believe, an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing these issues. PMID:20376330

SU-E-J-02: 4D Digital Tomosynthesis Based On Algebraic Image Reconstruction and Total-Variation Minimization for the Improvement of Image Quality

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

Kim, D; Kang, S; Kim, T

2014-06-01

Purpose: In this paper, we implemented the four-dimensional (4D) digital tomosynthesis (DTS) imaging based on algebraic image reconstruction technique and total-variation minimization method in order to compensate the undersampled projection data and improve the image quality. Methods: The projection data were acquired as supposed the cone-beam computed tomography system in linear accelerator by the Monte Carlo simulation and the in-house 4D digital phantom generation program. We performed 4D DTS based upon simultaneous algebraic reconstruction technique (SART) among the iterative image reconstruction technique and total-variation minimization method (TVMM). To verify the effectiveness of this reconstruction algorithm, we performed systematic simulation studiesmore » to investigate the imaging performance. Results: The 4D DTS algorithm based upon the SART and TVMM seems to give better results than that based upon the existing method, or filtered-backprojection. Conclusion: The advanced image reconstruction algorithm for the 4D DTS would be useful to validate each intra-fraction motion during radiation therapy. In addition, it will be possible to give advantage to real-time imaging for the adaptive radiation therapy. This research was supported by Leading Foreign Research Institute Recruitment Program (Grant No.2009-00420) and Basic Atomic Energy Research Institute (BAERI); (Grant No. 2009-0078390) through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning (MSIP)« less

Comparison of analytic and iterative digital tomosynthesis reconstructions for thin slab objects

NASA Astrophysics Data System (ADS)

Yun, J.; Kim, D. W.; Ha, S.; Kim, H. K.

2017-11-01

For digital x-ray tomosynthesis of thin slab objects, we compare the tomographic imaging performances obtained from the filtered backprojection (FBP) and simultaneous algebraic reconstruction (SART) algorithms. The imaging performance includes the in-plane molulation-transfer function (MTF), the signal difference-to-noise ratio (SDNR), and the out-of-plane blur artifact or artifact-spread function (ASF). The MTF is measured using a thin tungsten-wire phantom, and the SDNR and the ASF are measured using a thin aluminum-disc phantom embedded in a plastic cylinder. The FBP shows a better MTF performance than the SART. On the contrary, the SART outperforms the FBP with regard to the SDNR and ASF performances. Detailed experimental results and their analysis results are described in this paper. For a more proper use of digital tomosynthesis technique, this study suggests to use a reconstuction algorithm suitable for application-specific purposes.

A general Bayesian image reconstruction algorithm with entropy prior: Preliminary application to HST data

NASA Astrophysics Data System (ADS)

Nunez, Jorge; Llacer, Jorge

1993-10-01

This paper describes a general Bayesian iterative algorithm with entropy prior for image reconstruction. It solves the cases of both pure Poisson data and Poisson data with Gaussian readout noise. The algorithm maintains positivity of the solution; it includes case-specific prior information (default map) and flatfield corrections; it removes background and can be accelerated to be faster than the Richardson-Lucy algorithm. In order to determine the hyperparameter that balances the entropy and liklihood terms in the Bayesian approach, we have used a liklihood cross-validation technique. Cross-validation is more robust than other methods because it is less demanding in terms of the knowledge of exact data characteristics and of the point-spread function. We have used the algorithm to reconstruct successfully images obtained in different space-and ground-based imaging situations. It has been possible to recover most of the original intended capabilities of the Hubble Space Telescope (HST) wide field and planetary camera (WFPC) and faint object camera (FOC) from images obtained in their present state. Semireal simulations for the future wide field planetary camera 2 show that even after the repair of the spherical abberration problem, image reconstruction can play a key role in improving the resolution of the cameras, well beyond the design of the Hubble instruments. We also show that ground-based images can be reconstructed successfully with the algorithm. A technique which consists of dividing the CCD observations into two frames, with one-half the exposure time each, emerges as a recommended procedure for the utilization of the described algorithms. We have compared our technique with two commonly used reconstruction algorithms: the Richardson-Lucy and the Cambridge maximum entropy algorithms.

Formulation and implementation of nonstationary adaptive estimation algorithm with applications to air-data reconstruction

NASA Technical Reports Server (NTRS)

Whitmore, S. A.

1985-01-01

The dynamics model and data sources used to perform air-data reconstruction are discussed, as well as the Kalman filter. The need for adaptive determination of the noise statistics of the process is indicated. The filter innovations are presented as a means of developing the adaptive criterion, which is based on the true mean and covariance of the filter innovations. A method for the numerical approximation of the mean and covariance of the filter innovations is presented. The algorithm as developed is applied to air-data reconstruction for the space shuttle, and data obtained from the third landing are presented. To verify the performance of the adaptive algorithm, the reconstruction is also performed using a constant covariance Kalman filter. The results of the reconstructions are compared, and the adaptive algorithm exhibits better performance.

SubspaceEM: A Fast Maximum-a-posteriori Algorithm for Cryo-EM Single Particle Reconstruction

PubMed Central

Dvornek, Nicha C.; Sigworth, Fred J.; Tagare, Hemant D.

2015-01-01

Single particle reconstruction methods based on the maximum-likelihood principle and the expectation-maximization (E–M) algorithm are popular because of their ability to produce high resolution structures. However, these algorithms are computationally very expensive, requiring a network of computational servers. To overcome this computational bottleneck, we propose a new mathematical framework for accelerating maximum-likelihood reconstructions. The speedup is by orders of magnitude and the proposed algorithm produces similar quality reconstructions compared to the standard maximum-likelihood formulation. Our approach uses subspace approximations of the cryo-electron microscopy (cryo-EM) data and projection images, greatly reducing the number of image transformations and comparisons that are computed. Experiments using simulated and actual cryo-EM data show that speedup in overall execution time compared to traditional maximum-likelihood reconstruction reaches factors of over 300. PMID:25839831

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

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

PubMed Central

Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A.

2016-01-01

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction.

PubMed

Xu, Qiaofeng; Yang, Deshan; Tan, Jun; Sawatzky, Alex; Anastasio, Mark A

2016-04-01

The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two

Improving Cancer Detection and Dose Efficiency in Dedicated Breast Cancer CT

DTIC Science & Technology

2010-02-01

source trajectory and data truncation, which can however be solved with the back-projection filtration ( BPF ) algorithm [6,7]. I have used the BPF ...high to low radiation dose levels. I have investigated noise properties in images reconstructed by use of FDK and BPF algorithms at different noise...analytic algorithms such as the FDK and BPF algorithms are applied to sparse-view data, the reconstruction images will contain artifacts such as streak

Fundamental limits of reconstruction-based superresolution algorithms under local translation.

PubMed

Lin, Zhouchen; Shum, Heung-Yeung

2004-01-01

Superresolution is a technique that can produce images of a higher resolution than that of the originally captured ones. Nevertheless, improvement in resolution using such a technique is very limited in practice. This makes it significant to study the problem: "Do fundamental limits exist for superresolution?" In this paper, we focus on a major class of superresolution algorithms, called the reconstruction-based algorithms, which compute high-resolution images by simulating the image formation process. Assuming local translation among low-resolution images, this paper is the first attempt to determine the explicit limits of reconstruction-based algorithms, under both real and synthetic conditions. Based on the perturbation theory of linear systems, we obtain the superresolution limits from the conditioning analysis of the coefficient matrix. Moreover, we determine the number of low-resolution images that are sufficient to achieve the limit. Both real and synthetic experiments are carried out to verify our analysis.

GPU-accelerated compressed-sensing (CS) image reconstruction in chest digital tomosynthesis (CDT) using CUDA programming

NASA Astrophysics Data System (ADS)

Choi, Sunghoon; Lee, Haenghwa; Lee, Donghoon; Choi, Seungyeon; Shin, Jungwook; Jang, Woojin; Seo, Chang-Woo; Kim, Hee-Joung

2017-03-01

A compressed-sensing (CS) technique has been rapidly applied in medical imaging field for retrieving volumetric data from highly under-sampled projections. Among many variant forms, CS technique based on a total-variation (TV) regularization strategy shows fairly reasonable results in cone-beam geometry. In this study, we implemented the TV-based CS image reconstruction strategy in our prototype chest digital tomosynthesis (CDT) R/F system. Due to the iterative nature of time consuming processes in solving a cost function, we took advantage of parallel computing using graphics processing units (GPU) by the compute unified device architecture (CUDA) programming to accelerate our algorithm. In order to compare the algorithmic performance of our proposed CS algorithm, conventional filtered back-projection (FBP) and simultaneous algebraic reconstruction technique (SART) reconstruction schemes were also studied. The results indicated that the CS produced better contrast-to-noise ratios (CNRs) in the physical phantom images (Teflon region-of-interest) by factors of 3.91 and 1.93 than FBP and SART images, respectively. The resulted human chest phantom images including lung nodules with different diameters also showed better visual appearance in the CS images. Our proposed GPU-accelerated CS reconstruction scheme could produce volumetric data up to 80 times than CPU programming. Total elapsed time for producing 50 coronal planes with 1024×1024 image matrix using 41 projection views were 216.74 seconds for proposed CS algorithms on our GPU programming, which could match the clinically feasible time ( 3 min). Consequently, our results demonstrated that the proposed CS method showed a potential of additional dose reduction in digital tomosynthesis with reasonable image quality in a fast time.

ADART: an adaptive algebraic reconstruction algorithm for discrete tomography.

PubMed

Maestre-Deusto, F Javier; Scavello, Giovanni; Pizarro, Joaquín; Galindo, Pedro L

2011-08-01

In this paper we suggest an algorithm based on the Discrete Algebraic Reconstruction Technique (DART) which is capable of computing high quality reconstructions from substantially fewer projections than required for conventional continuous tomography. Adaptive DART (ADART) goes a step further than DART on the reduction of the number of unknowns of the associated linear system achieving a significant reduction in the pixel error rate of reconstructed objects. The proposed methodology automatically adapts the border definition criterion at each iteration, resulting in a reduction of the number of pixels belonging to the border, and consequently of the number of unknowns in the general algebraic reconstruction linear system to be solved, being this reduction specially important at the final stage of the iterative process. Experimental results show that reconstruction errors are considerably reduced using ADART when compared to original DART, both in clean and noisy environments.

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

Reconstruction of sparse-view X-ray computed tomography using adaptive iterative algorithms.

PubMed

Liu, Li; Lin, Weikai; Jin, Mingwu

2015-01-01

In this paper, we propose two reconstruction algorithms for sparse-view X-ray computed tomography (CT). Treating the reconstruction problems as data fidelity constrained total variation (TV) minimization, both algorithms adapt the alternate two-stage strategy: projection onto convex sets (POCS) for data fidelity and non-negativity constraints and steepest descent for TV minimization. The novelty of this work is to determine iterative parameters automatically from data, thus avoiding tedious manual parameter tuning. In TV minimization, the step sizes of steepest descent are adaptively adjusted according to the difference from POCS update in either the projection domain or the image domain, while the step size of algebraic reconstruction technique (ART) in POCS is determined based on the data noise level. In addition, projection errors are used to compare with the error bound to decide whether to perform ART so as to reduce computational costs. The performance of the proposed methods is studied and evaluated using both simulated and physical phantom data. Our methods with automatic parameter tuning achieve similar, if not better, reconstruction performance compared to a representative two-stage algorithm. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mars Entry Atmospheric Data System Trajectory Reconstruction Algorithms and Flight Results

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; Kutty, Prasad; Schoenenberger, Mark; Shidner, Jeremy; Munk, Michelle

2013-01-01

The Mars Entry Atmospheric Data System is a part of the Mars Science Laboratory, Entry, Descent, and Landing Instrumentation project. These sensors are a system of seven pressure transducers linked to ports on the entry vehicle forebody to record the pressure distribution during atmospheric entry. These measured surface pressures are used to generate estimates of atmospheric quantities based on modeled surface pressure distributions. Specifically, angle of attack, angle of sideslip, dynamic pressure, Mach number, and freestream atmospheric properties are reconstructed from the measured pressures. Such data allows for the aerodynamics to become decoupled from the assumed atmospheric properties, allowing for enhanced trajectory reconstruction and performance analysis as well as an aerodynamic reconstruction, which has not been possible in past Mars entry reconstructions. This paper provides details of the data processing algorithms that are utilized for this purpose. The data processing algorithms include two approaches that have commonly been utilized in past planetary entry trajectory reconstruction, and a new approach for this application that makes use of the pressure measurements. The paper describes assessments of data quality and preprocessing, and results of the flight data reduction from atmospheric entry, which occurred on August 5th, 2012.

Benchmarking Procedures for High-Throughput Context Specific Reconstruction Algorithms

PubMed Central

Pacheco, Maria P.; Pfau, Thomas; Sauter, Thomas

2016-01-01

Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX or HMR has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last 10 years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding. This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished: consistency testing and comparison based testing. The first is concerned with robustness against noise, e.g., missing data due to the impossibility to distinguish between the signal and the background of non-specific binding of probes in a microarray experiment, and whether distinct sets of input expressed genes corresponding to i.e., different tissues yield distinct models. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms. PMID:26834640

Reconstruction algorithm for polychromatic CT imaging: application to beam hardening correction

NASA Technical Reports Server (NTRS)

Yan, C. H.; Whalen, R. T.; Beaupre, G. S.; Yen, S. Y.; Napel, S.

2000-01-01

This paper presents a new reconstruction algorithm for both single- and dual-energy computed tomography (CT) imaging. By incorporating the polychromatic characteristics of the X-ray beam into the reconstruction process, the algorithm is capable of eliminating beam hardening artifacts. The single energy version of the algorithm assumes that each voxel in the scan field can be expressed as a mixture of two known substances, for example, a mixture of trabecular bone and marrow, or a mixture of fat and flesh. These assumptions are easily satisfied in a quantitative computed tomography (QCT) setting. We have compared our algorithm to three commonly used single-energy correction techniques. Experimental results show that our algorithm is much more robust and accurate. We have also shown that QCT measurements obtained using our algorithm are five times more accurate than that from current QCT systems (using calibration). The dual-energy mode does not require any prior knowledge of the object in the scan field, and can be used to estimate the attenuation coefficient function of unknown materials. We have tested the dual-energy setup to obtain an accurate estimate for the attenuation coefficient function of K2 HPO4 solution.

Superiorized algorithm for reconstruction of CT images from sparse-view and limited-angle polyenergetic data

NASA Astrophysics Data System (ADS)

Humphries, T.; Winn, J.; Faridani, A.

2017-08-01

Recent work in CT image reconstruction has seen increasing interest in the use of total variation (TV) and related penalties to regularize problems involving reconstruction from undersampled or incomplete data. Superiorization is a recently proposed heuristic which provides an automatic procedure to ‘superiorize’ an iterative image reconstruction algorithm with respect to a chosen objective function, such as TV. Under certain conditions, the superiorized algorithm is guaranteed to find a solution that is as satisfactory as any found by the original algorithm with respect to satisfying the constraints of the problem; this solution is also expected to be superior with respect to the chosen objective. Most work on superiorization has used reconstruction algorithms which assume a linear measurement model, which in the case of CT corresponds to data generated from a monoenergetic x-ray beam. Many CT systems generate x-rays from a polyenergetic spectrum, however, in which the measured data represent an integral of object attenuation over all energies in the spectrum. This inconsistency with the linear model produces the well-known beam hardening artifacts, which impair analysis of CT images. In this work we superiorize an iterative algorithm for reconstruction from polyenergetic data, using both TV and an anisotropic TV (ATV) penalty. We apply the superiorized algorithm in numerical phantom experiments modeling both sparse-view and limited-angle scenarios. In our experiments, the superiorized algorithm successfully finds solutions which are as constraints-compatible as those found by the original algorithm, with significantly reduced TV and ATV values. The superiorized algorithm thus produces images with greatly reduced sparse-view and limited angle artifacts, which are also largely free of the beam hardening artifacts that would be present if a superiorized version of a monoenergetic algorithm were used.

Greedy algorithms for diffuse optical tomography reconstruction

NASA Astrophysics Data System (ADS)

Dileep, B. P. V.; Das, Tapan; Dutta, Pranab K.

2018-03-01

Diffuse optical tomography (DOT) is a noninvasive imaging modality that reconstructs the optical parameters of a highly scattering medium. However, the inverse problem of DOT is ill-posed and highly nonlinear due to the zig-zag propagation of photons that diffuses through the cross section of tissue. The conventional DOT imaging methods iteratively compute the solution of forward diffusion equation solver which makes the problem computationally expensive. Also, these methods fail when the geometry is complex. Recently, the theory of compressive sensing (CS) has received considerable attention because of its efficient use in biomedical imaging applications. The objective of this paper is to solve a given DOT inverse problem by using compressive sensing framework and various Greedy algorithms such as orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP), and stagewise orthogonal matching pursuit (StOMP), regularized orthogonal matching pursuit (ROMP) and simultaneous orthogonal matching pursuit (S-OMP) have been studied to reconstruct the change in the absorption parameter i.e, Δα from the boundary data. Also, the Greedy algorithms have been validated experimentally on a paraffin wax rectangular phantom through a well designed experimental set up. We also have studied the conventional DOT methods like least square method and truncated singular value decomposition (TSVD) for comparison. One of the main features of this work is the usage of less number of source-detector pairs, which can facilitate the use of DOT in routine applications of screening. The performance metrics such as mean square error (MSE), normalized mean square error (NMSE), structural similarity index (SSIM), and peak signal to noise ratio (PSNR) have been used to evaluate the performance of the algorithms mentioned in this paper. Extensive simulation results confirm that CS based DOT reconstruction outperforms the conventional DOT imaging methods in terms of

Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm

PubMed Central

Sidky, Emil Y.; Jørgensen, Jakob H.; Pan, Xiaochuan

2012-01-01

The primal-dual optimization algorithm developed in Chambolle and Pock (CP), 2011 is applied to various convex optimization problems of interest in computed tomography (CT) image reconstruction. This algorithm allows for rapid prototyping of optimization problems for the purpose of designing iterative image reconstruction algorithms for CT. The primal-dual algorithm is briefly summarized in the article, and its potential for prototyping is demonstrated by explicitly deriving CP algorithm instances for many optimization problems relevant to CT. An example application modeling breast CT with low-intensity X-ray illumination is presented. PMID:22538474

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

A comparison of earthquake backprojection imaging methods for dense local arrays

NASA Astrophysics Data System (ADS)

Beskardes, G. D.; Hole, J. A.; Wang, K.; Michaelides, M.; Wu, Q.; Chapman, M. C.; Davenport, K. K.; Brown, L. D.; Quiros, D. A.

2018-03-01

Backprojection imaging has recently become a practical method for local earthquake detection and location due to the deployment of densely sampled, continuously recorded, local seismograph arrays. While backprojection sometimes utilizes the full seismic waveform, the waveforms are often pre-processed and simplified to overcome imaging challenges. Real data issues include aliased station spacing, inadequate array aperture, inaccurate velocity model, low signal-to-noise ratio, large noise bursts and varying waveform polarity. We compare the performance of backprojection with four previously used data pre-processing methods: raw waveform, envelope, short-term averaging/long-term averaging and kurtosis. Our primary goal is to detect and locate events smaller than noise by stacking prior to detection to improve the signal-to-noise ratio. The objective is to identify an optimized strategy for automated imaging that is robust in the presence of real-data issues, has the lowest signal-to-noise thresholds for detection and for location, has the best spatial resolution of the source images, preserves magnitude, and considers computational cost. Imaging method performance is assessed using a real aftershock data set recorded by the dense AIDA array following the 2011 Virginia earthquake. Our comparisons show that raw-waveform backprojection provides the best spatial resolution, preserves magnitude and boosts signal to detect events smaller than noise, but is most sensitive to velocity error, polarity error and noise bursts. On the other hand, the other methods avoid polarity error and reduce sensitivity to velocity error, but sacrifice spatial resolution and cannot effectively reduce noise by stacking. Of these, only kurtosis is insensitive to large noise bursts while being as efficient as the raw-waveform method to lower the detection threshold; however, it does not preserve the magnitude information. For automatic detection and location of events in a large data set, we

A fast rebinning algorithm for 3D positron emission tomography using John's equation

NASA Astrophysics Data System (ADS)

Defrise, Michel; Liu, Xuan

1999-08-01

Volume imaging in positron emission tomography (PET) requires the inversion of the three-dimensional (3D) x-ray transform. The usual solution to this problem is based on 3D filtered-backprojection (FBP), but is slow. Alternative methods have been proposed which factor the 3D data into independent 2D data sets corresponding to the 2D Radon transforms of a stack of parallel slices. Each slice is then reconstructed using 2D FBP. These so-called rebinning methods are numerically efficient but are approximate. In this paper a new exact rebinning method is derived by exploiting the fact that the 3D x-ray transform of a function is the solution to the second-order partial differential equation first studied by John. The method is proposed for two sampling schemes, one corresponding to a pair of infinite plane detectors and another one corresponding to a cylindrical multi-ring PET scanner. The new FORE-J algorithm has been implemented for this latter geometry and was compared with the approximate Fourier rebinning algorithm FORE and with another exact rebinning algorithm, FOREX. Results with simulated data demonstrate a significant improvement in accuracy compared to FORE, while the reconstruction time is doubled. Compared to FOREX, the FORE-J algorithm is slightly less accurate but more than three times faster.

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.

Region-of-interest image reconstruction with intensity weighting in circular cone-beam CT for image-guided radiation therapy

PubMed Central

Cho, Seungryong; Pearson, Erik; Pelizzari, Charles A.; Pan, Xiaochuan

2009-01-01

Imaging plays a vital role in radiation therapy and with recent advances in technology considerable emphasis has been placed on cone-beam CT (CBCT). Attaching a kV x-ray source and a flat panel detector directly to the linear accelerator gantry has enabled progress in target localization techniques, which can include daily CBCT setup scans for some treatments. However, with an increasing number of CT scans there is also an increasing concern for patient exposure. An intensity-weighted region-of-interest (IWROI) technique, which has the potential to greatly reduce CBCT dose, in conjunction with the chord-based backprojection-filtration (BPF) reconstruction algorithm, has been developed and its feasibility in clinical use is demonstrated in this article. A nonuniform filter is placed in the x-ray beam to create regions of two different beam intensities. In this manner, regions outside the target area can be given a reduced dose but still visualized with a lower contrast to noise ratio. Image artifacts due to transverse data truncation, which would have occurred in conventional reconstruction algorithms, are avoided and image noise levels of the low- and high-intensity regions are well controlled by use of the chord-based BPF reconstruction algorithm. The proposed IWROI technique can play an important role in image-guided radiation therapy. PMID:19472624

Accelerated fast iterative shrinkage thresholding algorithms for sparsity-regularized cone-beam CT image reconstruction

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

Xu, Qiaofeng; Sawatzky, Alex; Anastasio, Mark A., E-mail: anastasio@wustl.edu

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that ismore » solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and

Practical algorithms for simulation and reconstruction of digital in-line holograms.

PubMed

Latychevskaia, Tatiana; Fink, Hans-Werner

2015-03-20

Here we present practical methods for simulation and reconstruction of in-line digital holograms recorded with plane and spherical waves. The algorithms described here are applicable to holographic imaging of an object exhibiting absorption as well as phase-shifting properties. Optimal parameters, related to distances, sampling rate, and other factors for successful simulation and reconstruction of holograms are evaluated and criteria for the achievable resolution are worked out. Moreover, we show that the numerical procedures for the reconstruction of holograms recorded with plane and spherical waves are identical under certain conditions. Experimental examples of holograms and their reconstructions are also discussed.

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

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.

Generalized Fourier slice theorem for cone-beam image reconstruction.

PubMed

Zhao, Shuang-Ren; Jiang, Dazong; Yang, Kevin; Yang, Kang

2015-01-01

The cone-beam reconstruction theory has been proposed by Kirillov in 1961, Tuy in 1983, Feldkamp in 1984, Smith in 1985, Pierre Grangeat in 1990. The Fourier slice theorem is proposed by Bracewell 1956, which leads to the Fourier image reconstruction method for parallel-beam geometry. The Fourier slice theorem is extended to fan-beam geometry by Zhao in 1993 and 1995. By combining the above mentioned cone-beam image reconstruction theory and the above mentioned Fourier slice theory of fan-beam geometry, the Fourier slice theorem in cone-beam geometry is proposed by Zhao 1995 in short conference publication. This article offers the details of the derivation and implementation of this Fourier slice theorem for cone-beam geometry. Especially the problem of the reconstruction from Fourier domain has been overcome, which is that the value of in the origin of Fourier space is 0/0. The 0/0 type of limit is proper handled. As examples, the implementation results for the single circle and two perpendicular circle source orbits are shown. In the cone-beam reconstruction if a interpolation process is considered, the number of the calculations for the generalized Fourier slice theorem algorithm is O(N^4), which is close to the filtered back-projection method, here N is the image size of 1-dimension. However the interpolation process can be avoid, in that case the number of the calculations is O(N5).

Zooming in on vibronic structure by lowest-value projection reconstructed 4D coherent spectroscopy

NASA Astrophysics Data System (ADS)

Harel, Elad

2018-05-01

A fundamental goal of chemical physics is an understanding of microscopic interactions in liquids at and away from equilibrium. In principle, this microscopic information is accessible by high-order and high-dimensionality nonlinear optical measurements. Unfortunately, the time required to execute such experiments increases exponentially with the dimensionality, while the signal decreases exponentially with the order of the nonlinearity. Recently, we demonstrated a non-uniform acquisition method based on radial sampling of the time-domain signal [W. O. Hutson et al., J. Phys. Chem. Lett. 9, 1034 (2018)]. The four-dimensional spectrum was then reconstructed by filtered back-projection using an inverse Radon transform. Here, we demonstrate an alternative reconstruction method based on the statistical analysis of different back-projected spectra which results in a dramatic increase in sensitivity and at least a 100-fold increase in dynamic range compared to conventional uniform sampling and Fourier reconstruction. These results demonstrate that alternative sampling and reconstruction methods enable applications of increasingly high-order and high-dimensionality methods toward deeper insights into the vibronic structure of liquids.

3D noise power spectrum applied on clinical MDCT scanners: effects of reconstruction algorithms and reconstruction filters

NASA Astrophysics Data System (ADS)

Miéville, Frédéric A.; Bolard, Gregory; Benkreira, Mohamed; Ayestaran, Paul; Gudinchet, François; Bochud, François; Verdun, Francis R.

2011-03-01

The noise power spectrum (NPS) is the reference metric for understanding the noise content in computed tomography (CT) images. To evaluate the noise properties of clinical multidetector (MDCT) scanners, local 2D and 3D NPSs were computed for different acquisition reconstruction parameters. A 64- and a 128-MDCT scanners were employed. Measurements were performed on a water phantom in axial and helical acquisition modes. CT dose index was identical for both installations. Influence of parameters such as the pitch, the reconstruction filter (soft, standard and bone) and the reconstruction algorithm (filtered-back projection (FBP), adaptive statistical iterative reconstruction (ASIR)) were investigated. Images were also reconstructed in the coronal plane using a reformat process. Then 2D and 3D NPS methods were computed. In axial acquisition mode, the 2D axial NPS showed an important magnitude variation as a function of the z-direction when measured at the phantom center. In helical mode, a directional dependency with lobular shape was observed while the magnitude of the NPS was kept constant. Important effects of the reconstruction filter, pitch and reconstruction algorithm were observed on 3D NPS results for both MDCTs. With ASIR, a reduction of the NPS magnitude and a shift of the NPS peak to the low frequency range were visible. 2D coronal NPS obtained from the reformat images was impacted by the interpolation when compared to 2D coronal NPS obtained from 3D measurements. The noise properties of volume measured in last generation MDCTs was studied using local 3D NPS metric. However, impact of the non-stationarity noise effect may need further investigations.

Interferometric tomography of fuel cells for monitoring membrane water content.

PubMed

Waller, Laura; Kim, Jungik; Shao-Horn, Yang; Barbastathis, George

2009-08-17

We have developed a system that uses two 1D interferometric phase projections for reconstruction of 2D water content changes over time in situ in a proton exchange membrane (PEM) fuel cell system. By modifying the filtered backprojection tomographic algorithm, we are able to incorporate a priori information about the object distribution into a fast reconstruction algorithm which is suitable for real-time monitoring.

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

Accurate low-dose iterative CT reconstruction from few projections by Generalized Anisotropic Total Variation minimization for industrial CT.

PubMed

Debatin, Maurice; Hesser, Jürgen

2015-01-01

Reducing the amount of time for data acquisition and reconstruction in industrial CT decreases the operation time of the X-ray machine and therefore increases the sales. This can be achieved by reducing both, the dose and the pulse length of the CT system and the number of projections for the reconstruction, respectively. In this paper, a novel generalized Anisotropic Total Variation regularization for under-sampled, low-dose iterative CT reconstruction is discussed and compared to the standard methods, Total Variation, Adaptive weighted Total Variation and Filtered Backprojection. The novel regularization function uses a priori information about the Gradient Magnitude Distribution of the scanned object for the reconstruction. We provide a general parameterization scheme and evaluate the efficiency of our new algorithm for different noise levels and different number of projection views. When noise is not present, error-free reconstructions are achievable for AwTV and GATV from 40 projections. In cases where noise is simulated, our strategy achieves a Relative Root Mean Square Error that is up to 11 times lower than Total Variation-based and up to 4 times lower than AwTV-based iterative statistical reconstruction (e.g. for a SNR of 223 and 40 projections). To obtain the same reconstruction quality as achieved by Total Variation, the projection number and the pulse length, and the acquisition time and the dose respectively can be reduced by a factor of approximately 3.5, when AwTV is used and a factor of approximately 6.7, when our proposed algorithm is used.

Simultaneous reconstruction of 3D refractive index, temperature, and intensity distribution of combustion flame by double computed tomography technologies based on spatial phase-shifting method

NASA Astrophysics Data System (ADS)

Guo, Zhenyan; Song, Yang; Yuan, Qun; Wulan, Tuya; Chen, Lei

2017-06-01

In this paper, a transient multi-parameter three-dimensional (3D) reconstruction method is proposed to diagnose and visualize a combustion flow field. Emission and transmission tomography based on spatial phase-shifted technology are combined to reconstruct, simultaneously, the various physical parameter distributions of a propane flame. Two cameras triggered by the internal trigger mode capture the projection information of the emission and moiré tomography, respectively. A two-step spatial phase-shifting method is applied to extract the phase distribution in the moiré fringes. By using the filtered back-projection algorithm, we reconstruct the 3D refractive-index distribution of the combustion flow field. Finally, the 3D temperature distribution of the flame is obtained from the refractive index distribution using the Gladstone-Dale equation. Meanwhile, the 3D intensity distribution is reconstructed based on the radiation projections from the emission tomography. Therefore, the structure and edge information of the propane flame are well visualized.

Comparison of Reconstruction and Control algorithms on the ESO end-to-end simulator OCTOPUS

NASA Astrophysics Data System (ADS)

Montilla, I.; Béchet, C.; Lelouarn, M.; Correia, C.; Tallon, M.; Reyes, M.; Thiébaut, É.

Extremely Large Telescopes are very challenging concerning their Adaptive Optics requirements. Their diameters, the specifications demanded by the science for which they are being designed for, and the planned use of Extreme Adaptive Optics systems, imply a huge increment in the number of degrees of freedom in the deformable mirrors. It is necessary to study new reconstruction algorithms to implement the real time control in Adaptive Optics at the required speed. We have studied the performance, applied to the case of the European ELT, of three different algorithms: the matrix-vector multiplication (MVM) algorithm, considered as a reference; the Fractal Iterative Method (FrIM); and the Fourier Transform Reconstructor (FTR). The algorithms have been tested on ESO's OCTOPUS software, which simulates the atmosphere, the deformable mirror, the sensor and the closed-loop control. The MVM is the default reconstruction and control method implemented in OCTOPUS, but it scales in O(N2) operations per loop so it is not considered as a fast algorithm for wave-front reconstruction and control on an Extremely Large Telescope. The two other methods are the fast algorithms studied in the E-ELT Design Study. The performance, as well as their response in the presence of noise and with various atmospheric conditions, has been compared using a Single Conjugate Adaptive Optics configuration for a 42 m diameter ELT, with a total amount of 5402 actuators. Those comparisons made on a common simulator allow to enhance the pros and cons of the various methods, and give us a better understanding of the type of reconstruction algorithm that an ELT demands.

Imaging of voids by means of a physical-optics-based shape-reconstruction algorithm.

PubMed

Liseno, Angelo; Pierri, Rocco

2004-06-01

We analyze the performance of a shape-reconstruction algorithm for the retrieval of voids starting from the electromagnetic scattered field. Such an algorithm exploits the physical optics (PO) approximation to obtain a linear unknown-data relationship and performs inversions by means of the singular-value-decomposition approach. In the case of voids, in addition to a geometrical optics reflection, the presence of the lateral wave phenomenon must be considered. We analyze the effect of the presence of lateral waves on the reconstructions. For the sake of shape reconstruction, we can regard the PO algorithm as one of assuming the electric and magnetic field on the illuminated side as constant in amplitude and linear in phase, as far as the dependence on the frequency is concerned. Therefore we analyze how much the lateral wave phenomenon impairs such an assumption, and we show inversions for both one single and two circular voids, for different values of the background permittivity.

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.

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.

Optimization of CT image reconstruction algorithms for the lung tissue research consortium (LTRC)

NASA Astrophysics Data System (ADS)

McCollough, Cynthia; Zhang, Jie; Bruesewitz, Michael; Bartholmai, Brian

2006-03-01

To create a repository of clinical data, CT images and tissue samples and to more clearly understand the pathogenetic features of pulmonary fibrosis and emphysema, the National Heart, Lung, and Blood Institute (NHLBI) launched a cooperative effort known as the Lung Tissue Resource Consortium (LTRC). The CT images for the LTRC effort must contain accurate CT numbers in order to characterize tissues, and must have high-spatial resolution to show fine anatomic structures. This study was performed to optimize the CT image reconstruction algorithms to achieve these criteria. Quantitative analyses of phantom and clinical images were conducted. The ACR CT accreditation phantom containing five regions of distinct CT attenuations (CT numbers of approximately -1000 HU, -80 HU, 0 HU, 130 HU and 900 HU), and a high-contrast spatial resolution test pattern, was scanned using CT systems from two manufacturers (General Electric (GE) Healthcare and Siemens Medical Solutions). Phantom images were reconstructed using all relevant reconstruction algorithms. Mean CT numbers and image noise (standard deviation) were measured and compared for the five materials. Clinical high-resolution chest CT images acquired on a GE CT system for a patient with diffuse lung disease were reconstructed using BONE and STANDARD algorithms and evaluated by a thoracic radiologist in terms of image quality and disease extent. The clinical BONE images were processed with a 3 x 3 x 3 median filter to simulate a thicker slice reconstructed in smoother algorithms, which have traditionally been proven to provide an accurate estimation of emphysema extent in the lungs. Using a threshold technique, the volume of emphysema (defined as the percentage of lung voxels having a CT number lower than -950 HU) was computed for the STANDARD, BONE, and BONE filtered. The CT numbers measured in the ACR CT Phantom images were accurate for all reconstruction kernels for both manufacturers. As expected, visual evaluation of the

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.

Linearized image reconstruction method for ultrasound modulated electrical impedance tomography based on power density distribution

NASA Astrophysics Data System (ADS)

Song, Xizi; Xu, Yanbin; Dong, Feng

2017-04-01

Electrical resistance tomography (ERT) is a promising measurement technique with important industrial and clinical applications. However, with limited effective measurements, it suffers from poor spatial resolution due to the ill-posedness of the inverse problem. Recently, there has been an increasing research interest in hybrid imaging techniques, utilizing couplings of physical modalities, because these techniques obtain much more effective measurement information and promise high resolution. Ultrasound modulated electrical impedance tomography (UMEIT) is one of the newly developed hybrid imaging techniques, which combines electric and acoustic modalities. A linearized image reconstruction method based on power density is proposed for UMEIT. The interior data, power density distribution, is adopted to reconstruct the conductivity distribution with the proposed image reconstruction method. At the same time, relating the power density change to the change in conductivity, the Jacobian matrix is employed to make the nonlinear problem into a linear one. The analytic formulation of this Jacobian matrix is derived and its effectiveness is also verified. In addition, different excitation patterns are tested and analyzed, and opposite excitation provides the best performance with the proposed method. Also, multiple power density distributions are combined to implement image reconstruction. Finally, image reconstruction is implemented with the linear back-projection (LBP) algorithm. Compared with ERT, with the proposed image reconstruction method, UMEIT can produce reconstructed images with higher quality and better quantitative evaluation results.

Effect of Low-Dose MDCT and Iterative Reconstruction on Trabecular Bone Microstructure Assessment.

PubMed

Kopp, Felix K; Holzapfel, Konstantin; Baum, Thomas; Nasirudin, Radin A; Mei, Kai; Garcia, Eduardo G; Burgkart, Rainer; Rummeny, Ernst J; Kirschke, Jan S; Noël, Peter B

2016-01-01

We investigated the effects of low-dose multi detector computed tomography (MDCT) in combination with statistical iterative reconstruction algorithms on trabecular bone microstructure parameters. Twelve donated vertebrae were scanned with the routine radiation exposure used in our department (standard-dose) and a low-dose protocol. Reconstructions were performed with filtered backprojection (FBP) and maximum-likelihood based statistical iterative reconstruction (SIR). Trabecular bone microstructure parameters were assessed and statistically compared for each reconstruction. Moreover, fracture loads of the vertebrae were biomechanically determined and correlated to the assessed microstructure parameters. Trabecular bone microstructure parameters based on low-dose MDCT and SIR significantly correlated with vertebral bone strength. There was no significant difference between microstructure parameters calculated on low-dose SIR and standard-dose FBP images. However, the results revealed a strong dependency on the regularization strength applied during SIR. It was observed that stronger regularization might corrupt the microstructure analysis, because the trabecular structure is a very small detail that might get lost during the regularization process. As a consequence, the introduction of SIR for trabecular bone microstructure analysis requires a specific optimization of the regularization parameters. Moreover, in comparison to other approaches, superior noise-resolution trade-offs can be found with the proposed methods.

Layer-oriented multigrid wavefront reconstruction algorithms for multi-conjugate adaptive optics

NASA Astrophysics Data System (ADS)

Gilles, Luc; Ellerbroek, Brent L.; Vogel, Curtis R.

2003-02-01

Multi-conjugate adaptive optics (MCAO) systems with 104-105 degrees of freedom have been proposed for future giant telescopes. Using standard matrix methods to compute, optimize, and implement wavefront control algorithms for these systems is impractical, since the number of calculations required to compute and apply the reconstruction matrix scales respectively with the cube and the square of the number of AO degrees of freedom. In this paper, we develop an iterative sparse matrix implementation of minimum variance wavefront reconstruction for telescope diameters up to 32m with more than 104 actuators. The basic approach is the preconditioned conjugate gradient method, using a multigrid preconditioner incorporating a layer-oriented (block) symmetric Gauss-Seidel iterative smoothing operator. We present open-loop numerical simulation results to illustrate algorithm convergence.

Functional Validation and Comparison Framework for EIT Lung Imaging

PubMed Central

Meybohm, Patrick; Weiler, Norbert; Frerichs, Inéz; Adler, Andy

2014-01-01

Introduction Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited. Methods We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data. Results and Conclusions Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT. PMID:25110887

Functional validation and comparison framework for EIT lung imaging.

PubMed

Grychtol, Bartłomiej; Elke, Gunnar; Meybohm, Patrick; Weiler, Norbert; Frerichs, Inéz; Adler, Andy

2014-01-01

Electrical impedance tomography (EIT) is an emerging clinical tool for monitoring ventilation distribution in mechanically ventilated patients, for which many image reconstruction algorithms have been suggested. We propose an experimental framework to assess such algorithms with respect to their ability to correctly represent well-defined physiological changes. We defined a set of clinically relevant ventilation conditions and induced them experimentally in 8 pigs by controlling three ventilator settings (tidal volume, positive end-expiratory pressure and the fraction of inspired oxygen). In this way, large and discrete shifts in global and regional lung air content were elicited. We use the framework to compare twelve 2D EIT reconstruction algorithms, including backprojection (the original and still most frequently used algorithm), GREIT (a more recent consensus algorithm for lung imaging), truncated singular value decomposition (TSVD), several variants of the one-step Gauss-Newton approach and two iterative algorithms. We consider the effects of using a 3D finite element model, assuming non-uniform background conductivity, noise modeling, reconstructing for electrode movement, total variation (TV) reconstruction, robust error norms, smoothing priors, and using difference vs. normalized difference data. Our results indicate that, while variation in appearance of images reconstructed from the same data is not negligible, clinically relevant parameters do not vary considerably among the advanced algorithms. Among the analysed algorithms, several advanced algorithms perform well, while some others are significantly worse. Given its vintage and ad-hoc formulation backprojection works surprisingly well, supporting the validity of previous studies in lung EIT.

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

Timing Analysis with INTEGRAL: Comparing Different Reconstruction Algorithms

NASA Technical Reports Server (NTRS)

Grinberg, V.; Kreykenboehm, I.; Fuerst, F.; Wilms, J.; Pottschmidt, K.; Bel, M. Cadolle; Rodriquez, J.; Marcu, D. M.; Suchy, S.; Markowitz, A.;

Adaptive optics image restoration algorithm based on wavefront reconstruction and adaptive total variation method

NASA Astrophysics Data System (ADS)

Li, Dongming; Zhang, Lijuan; Wang, Ting; Liu, Huan; Yang, Jinhua; Chen, Guifen

2016-11-01

To improve the adaptive optics (AO) image's quality, we study the AO image restoration algorithm based on wavefront reconstruction technology and adaptive total variation (TV) method in this paper. Firstly, the wavefront reconstruction using Zernike polynomial is used for initial estimated for the point spread function (PSF). Then, we develop our proposed iterative solutions for AO images restoration, addressing the joint deconvolution issue. The image restoration experiments are performed to verify the image restoration effect of our proposed algorithm. The experimental results show that, compared with the RL-IBD algorithm and Wiener-IBD algorithm, we can see that GMG measures (for real AO image) from our algorithm are increased by 36.92%, and 27.44% respectively, and the computation time are decreased by 7.2%, and 3.4% respectively, and its estimation accuracy is significantly improved.

Investigation of undersampling and reconstruction algorithm dependence on respiratory correlated 4D-MRI for online MR-guided radiation therapy

NASA Astrophysics Data System (ADS)

Mickevicius, Nikolai J.; Paulson, Eric S.

2017-04-01

The purpose of this work is to investigate the effects of undersampling and reconstruction algorithm on the total processing time and image quality of respiratory phase-resolved 4D MRI data. Specifically, the goal is to obtain quality 4D-MRI data with a combined acquisition and reconstruction time of five minutes or less, which we reasoned would be satisfactory for pre-treatment 4D-MRI in online MRI-gRT. A 3D stack-of-stars, self-navigated, 4D-MRI acquisition was used to scan three healthy volunteers at three image resolutions and two scan durations. The NUFFT, CG-SENSE, SPIRiT, and XD-GRASP reconstruction algorithms were used to reconstruct each dataset on a high performance reconstruction computer. The overall image quality, reconstruction time, artifact prevalence, and motion estimates were compared. The CG-SENSE and XD-GRASP reconstructions provided superior image quality over the other algorithms. The combination of a 3D SoS sequence and parallelized reconstruction algorithms using computing hardware more advanced than those typically seen on product MRI scanners, can result in acquisition and reconstruction of high quality respiratory correlated 4D-MRI images in less than five minutes.

Performance of 3DOSEM and MAP algorithms for reconstructing low count SPECT acquisitions.

PubMed

Grootjans, Willem; Meeuwis, Antoi P W; Slump, Cornelis H; de Geus-Oei, Lioe-Fee; Gotthardt, Martin; Visser, Eric P

2016-12-01

Low count single photon emission computed tomography (SPECT) is becoming more important in view of whole body SPECT and reduction of radiation dose. In this study, we investigated the performance of several 3D ordered subset expectation maximization (3DOSEM) and maximum a posteriori (MAP) algorithms for reconstructing low count SPECT images. Phantom experiments were conducted using the National Electrical Manufacturers Association (NEMA) NU2 image quality (IQ) phantom. The background compartment of the phantom was filled with varying concentrations of pertechnetate and indiumchloride, simulating various clinical imaging conditions. Images were acquired using a hybrid SPECT/CT scanner and reconstructed with 3DOSEM and MAP reconstruction algorithms implemented in Siemens Syngo MI.SPECT (Flash3D) and Hermes Hybrid Recon Oncology (Hyrid Recon 3DOSEM and MAP). Image analysis was performed by calculating the contrast recovery coefficient (CRC),percentage background variability (N%), and contrast-to-noise ratio (CNR), defined as the ratio between CRC and N%. Furthermore, image distortion is characterized by calculating the aspect ratio (AR) of ellipses fitted to the hot spheres. Additionally, the performance of these algorithms to reconstruct clinical images was investigated. Images reconstructed with 3DOSEM algorithms demonstrated superior image quality in terms of contrast and resolution recovery when compared to images reconstructed with filtered-back-projection (FBP), OSEM and 2DOSEM. However, occurrence of correlated noise patterns and image distortions significantly deteriorated the quality of 3DOSEM reconstructed images. The mean AR for the 37, 28, 22, and 17mm spheres was 1.3, 1.3, 1.6, and 1.7 respectively. The mean N% increase in high and low count Flash3D and Hybrid Recon 3DOSEM from 5.9% and 4.0% to 11.1% and 9.0%, respectively. Similarly, the mean CNR decreased in high and low count Flash3D and Hybrid Recon 3DOSEM from 8.7 and 8.8 to 3.6 and 4.2, respectively

Identifying Septal Support Reconstructions for Saddle Nose Deformity: The Cakmak Algorithm.

PubMed

Cakmak, Ozcan; Emre, Ismet Emrah; Ozkurt, Fazil Emre

2015-01-01

The saddle nose deformity is one of the most challenging problems in nasal surgery with a less predictable and reproducible result than other nasal procedures. The main feature of this deformity is loss of septal support with both functional and aesthetic implications. Most reports on saddle nose have focused on aesthetic improvement and neglected the reestablishment of septal support to improve airway. To explain how the Cakmak algorithm, an algorithm that describes various fixation techniques and grafts in different types of saddle nose deformities, aids in identifying saddle nose reconstructions that restore supportive nasal framework and provide the aesthetic improvements typically associated with procedures to correct saddle nose deformities. This algorithm presents septal support reconstruction of patients with saddle nose deformity based on the experience of the senior author in 206 patients with saddle nose deformity. Preoperative examination, intraoperative assessment, reconstruction techniques, graft materials, and patient evaluation of aesthetic success were documented, and 4 different types of saddle nose deformities were defined. The Cakmak algorithm classifies varying degrees of saddle nose deformity from type 0 to type 4 and helps identify the most appropriate surgical procedure to restore the supportive nasal framework and aesthetic dorsum. Among the 206 patients, 110 women and 96 men, mean (range) age was 39.7 years (15-68 years), and mean (range) of follow-up was 32 months (6-148 months). All but 12 patients had a history of previous nasal surgeries. Application of the Cakmak algorithm resulted in 36 patients categorized with type 0 saddle nose deformities; 79, type 1; 50, type 2; 20, type 3a; 7, type 3b; and 14, type 4. Postoperative photographs showed improvement of deformities, and patient surveys revealed aesthetic improvement in 201 patients and improvement in nasal breathing in 195 patients. Three patients developed postoperative infection

Algorithms for bioluminescence tomography incorporating anatomical information and reconstruction of tissue optical properties

PubMed Central

Naser, Mohamed A.; Patterson, Michael S.

2010-01-01

Reconstruction algorithms are presented for a two-step solution of the bioluminescence tomography (BLT) problem. In the first step, a priori anatomical information provided by x-ray computed tomography or by other methods is used to solve the continuous wave (cw) diffuse optical tomography (DOT) problem. A Taylor series expansion approximates the light fluence rate dependence on the optical properties of each region where first and second order direct derivatives of the light fluence rate with respect to scattering and absorption coefficients are obtained and used for the reconstruction. In the second step, the reconstructed optical properties at different wavelengths are used to calculate the Green’s function of the system. Then an iterative minimization solution based on the L1 norm shrinks the permissible regions where the sources are allowed by selecting points with higher probability to contribute to the source distribution. This provides an efficient BLT reconstruction algorithm with the ability to determine relative source magnitudes and positions in the presence of noise. PMID:21258486

Algorithms to eliminate the influence of non-uniform intensity distributions on wavefront reconstruction by quadri-wave lateral shearing interferometers

NASA Astrophysics Data System (ADS)

Chen, Xiao-jun; Dong, Li-zhi; Wang, Shuai; Yang, Ping; Xu, Bing

2017-11-01

In quadri-wave lateral shearing interferometry (QWLSI), when the intensity distribution of the incident light wave is non-uniform, part of the information of the intensity distribution will couple with the wavefront derivatives to cause wavefront reconstruction errors. In this paper, we propose two algorithms to reduce the influence of a non-uniform intensity distribution on wavefront reconstruction. Our simulation results demonstrate that the reconstructed amplitude distribution (RAD) algorithm can effectively reduce the influence of the intensity distribution on the wavefront reconstruction and that the collected amplitude distribution (CAD) algorithm can almost eliminate it.

Rapid execution of fan beam image reconstruction algorithms using efficient computational techniques and special-purpose processors

NASA Astrophysics Data System (ADS)

Gilbert, B. K.; Robb, R. A.; Chu, A.; Kenue, S. K.; Lent, A. H.; Swartzlander, E. E., Jr.

1981-02-01

Rapid advances during the past ten years of several forms of computer-assisted tomography (CT) have resulted in the development of numerous algorithms to convert raw projection data into cross-sectional images. These reconstruction algorithms are either 'iterative,' in which a large matrix algebraic equation is solved by successive approximation techniques; or 'closed form'. Continuing evolution of the closed form algorithms has allowed the newest versions to produce excellent reconstructed images in most applications. This paper will review several computer software and special-purpose digital hardware implementations of closed form algorithms, either proposed during the past several years by a number of workers or actually implemented in commercial or research CT scanners. The discussion will also cover a number of recently investigated algorithmic modifications which reduce the amount of computation required to execute the reconstruction process, as well as several new special-purpose digital hardware implementations under development in laboratories at the Mayo Clinic.

Improved Wallis Dodging Algorithm for Large-Scale Super-Resolution Reconstruction Remote Sensing Images.

PubMed

Fan, Chong; Chen, Xushuai; Zhong, Lei; Zhou, Min; Shi, Yun; Duan, Yulin

2017-03-18

A sub-block algorithm is usually applied in the super-resolution (SR) reconstruction of images because of limitations in computer memory. However, the sub-block SR images can hardly achieve a seamless image mosaicking because of the uneven distribution of brightness and contrast among these sub-blocks. An effectively improved weighted Wallis dodging algorithm is proposed, aiming at the characteristic that SR reconstructed images are gray images with the same size and overlapping region. This algorithm can achieve consistency of image brightness and contrast. Meanwhile, a weighted adjustment sequence is presented to avoid the spatial propagation and accumulation of errors and the loss of image information caused by excessive computation. A seam line elimination method can share the partial dislocation in the seam line to the entire overlapping region with a smooth transition effect. Subsequently, the improved method is employed to remove the uneven illumination for 900 SR reconstructed images of ZY-3. Then, the overlapping image mosaic method is adopted to accomplish a seamless image mosaic based on the optimal seam line.

A reconstruction algorithm for three-dimensional object-space data using spatial-spectral multiplexing

NASA Astrophysics Data System (ADS)

Wu, Zhejun; Kudenov, Michael W.

2017-05-01

This paper presents a reconstruction algorithm for the Spatial-Spectral Multiplexing (SSM) optical system. The goal of this algorithm is to recover the three-dimensional spatial and spectral information of a scene, given that a one-dimensional spectrometer array is used to sample the pupil of the spatial-spectral modulator. The challenge of the reconstruction is that the non-parametric representation of the three-dimensional spatial and spectral object requires a large number of variables, thus leading to an underdetermined linear system that is hard to uniquely recover. We propose to reparameterize the spectrum using B-spline functions to reduce the number of unknown variables. Our reconstruction algorithm then solves the improved linear system via a least- square optimization of such B-spline coefficients with additional spatial smoothness regularization. The ground truth object and the optical model for the measurement matrix are simulated with both spatial and spectral assumptions according to a realistic field of view. In order to test the robustness of the algorithm, we add Poisson noise to the measurement and test on both two-dimensional and three-dimensional spatial and spectral scenes. Our analysis shows that the root mean square error of the recovered results can be achieved within 5.15%.

Postinjection single photon transmission tomography with ordered-subset algorithms for whole-body PET imaging

NASA Astrophysics Data System (ADS)

Bai, Chuanyong; Kinahan, P. E.; Brasse, D.; Comtat, C.; Townsend, D. W.

2002-02-01

We have evaluated the penalized ordered-subset transmission reconstruction (OSTR) algorithm for postinjection single photon transmission scanning. The OSTR algorithm of Erdogan and Fessler (1999) uses a more accurate model for transmission tomography than ordered-subsets expectation-maximization (OSEM) when OSEM is applied to the logarithm of the transmission data. The OSTR algorithm is directly applicable to postinjection transmission scanning with a single photon source, as emission contamination from the patient mimics the effect, in the original derivation of OSTR, of random coincidence contamination in a positron source transmission scan. Multiple noise realizations of simulated postinjection transmission data were reconstructed using OSTR, filtered backprojection (FBP), and OSEM algorithms. Due to the nonspecific task performance, or multiple uses, of the transmission image, multiple figures of merit were evaluated, including image noise, contrast, uniformity, and root mean square (rms) error. We show that: 1) the use of a three-dimensional (3-D) regularizing image roughness penalty with OSTR improves the tradeoffs in noise, contrast, and rms error relative to the use of a two-dimensional penalty; 2) OSTR with a 3-D penalty has improved tradeoffs in noise, contrast, and rms error relative to FBP or OSEM; and 3) the use of image standard deviation from a single realization to estimate the true noise can be misleading in the case of OSEM. We conclude that using OSTR with a 3-D penalty potentially allows for shorter postinjection transmission scans in single photon transmission tomography in positron emission tomography (PET) relative to FBP or OSEM reconstructed images with the same noise properties. This combination of singles+OSTR is particularly suitable for whole-body PET oncology imaging.

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning.

PubMed

Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-02-22

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.

TOPICAL REVIEW: Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?

NASA Astrophysics Data System (ADS)

Pan, Xiaochuan; Sidky, Emil Y.; Vannier, Michael

2009-12-01

Despite major advances in x-ray sources, detector arrays, gantry mechanical design and especially computer performance, one component of computed tomography (CT) scanners has remained virtually constant for the past 25 years—the reconstruction algorithm. Fundamental advances have been made in the solution of inverse problems, especially tomographic reconstruction, but these works have not been translated into clinical and related practice. The reasons are not obvious and seldom discussed. This review seeks to examine the reasons for this discrepancy and provides recommendations on how it can be resolved. We take the example of field of compressive sensing (CS), summarizing this new area of research from the eyes of practical medical physicists and explaining the disconnection between theoretical and application-oriented research. Using a few issues specific to CT, which engineers have addressed in very specific ways, we try to distill the mathematical problem underlying each of these issues with the hope of demonstrating that there are interesting mathematical problems of general importance that can result from in depth analysis of specific issues. We then sketch some unconventional CT-imaging designs that have the potential to impact on CT applications, if the link between applied mathematicians and engineers/physicists were stronger. Finally, we close with some observations on how the link could be strengthened. There is, we believe, an important opportunity to rapidly improve the performance of CT and related tomographic imaging techniques by addressing these issues.

SU-E-J-218: Evaluation of CT Images Created Using a New Metal Artifact Reduction Reconstruction Algorithm for Radiation Therapy Treatment Planning

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

Niemkiewicz, J; Palmiotti, A; Miner, M

2014-06-01

Purpose: Metal in patients creates streak artifacts in CT images. When used for radiation treatment planning, these artifacts make it difficult to identify internal structures and affects radiation dose calculations, which depend on HU numbers for inhomogeneity correction. This work quantitatively evaluates a new metal artifact reduction (MAR) CT image reconstruction algorithm (GE Healthcare CT-0521-04.13-EN-US DOC1381483) when metal is present. Methods: A Gammex Model 467 Tissue Characterization phantom was used. CT images were taken of this phantom on a GE Optima580RT CT scanner with and without steel and titanium plugs using both the standard and MAR reconstruction algorithms. HU valuesmore » were compared pixel by pixel to determine if the MAR algorithm altered the HUs of normal tissues when no metal is present, and to evaluate the effect of using the MAR algorithm when metal is present. Also, CT images of patients with internal metal objects using standard and MAR reconstruction algorithms were compared. Results: Comparing the standard and MAR reconstructed images of the phantom without metal, 95.0% of pixels were within ±35 HU and 98.0% of pixels were within ±85 HU. Also, the MAR reconstruction algorithm showed significant improvement in maintaining HUs of non-metallic regions in the images taken of the phantom with metal. HU Gamma analysis (2%, 2mm) of metal vs. non-metal phantom imaging using standard reconstruction resulted in an 84.8% pass rate compared to 96.6% for the MAR reconstructed images. CT images of patients with metal show significant artifact reduction when reconstructed with the MAR algorithm. Conclusion: CT imaging using the MAR reconstruction algorithm provides improved visualization of internal anatomy and more accurate HUs when metal is present compared to the standard reconstruction algorithm. MAR reconstructed CT images provide qualitative and quantitative improvements over current reconstruction algorithms, thus improving radiation

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.

3D Compton scattering imaging and contour reconstruction for a class of Radon transforms

NASA Astrophysics Data System (ADS)

Rigaud, Gaël; Hahn, Bernadette N.

2018-07-01

Compton scattering imaging is a nascent concept arising from the current development of high-sensitive energy detectors and is devoted to exploit the scattering radiation to image the electron density of the studied medium. Such detectors are able to collect incoming photons in terms of energy. This paper introduces potential 3D modalities in Compton scattering imaging (CSI). The associated measured data are modeled using a class of generalized Radon transforms. The study of this class of operators leads to build a filtered back-projection kind algorithm preserving the contours of the sought-for function and offering a fast approach to partially solve the associated inverse problems. Simulation results including Poisson noise demonstrate the potential of this new imaging concept as well as the proposed image reconstruction approach.

A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

PubMed Central

Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

2018-01-01

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406

Myocardial perfusion magnetic resonance imaging using sliding-window conjugate-gradient highly constrained back-projection reconstruction for detection of coronary artery disease.

PubMed

Ma, Heng; Yang, Jun; Liu, Jing; Ge, Lan; An, Jing; Tang, Qing; Li, Han; Zhang, Yu; Chen, David; Wang, Yong; Liu, Jiabin; Liang, Zhigang; Lin, Kai; Jin, Lixin; Bi, Xiaoming; Li, Kuncheng; Li, Debiao

2012-04-15

Myocardial perfusion magnetic resonance imaging (MRI) with sliding-window conjugate-gradient highly constrained back-projection reconstruction (SW-CG-HYPR) allows whole left ventricular coverage, improved temporal and spatial resolution and signal/noise ratio, and reduced cardiac motion-related image artifacts. The accuracy of this technique for detecting coronary artery disease (CAD) has not been determined in a large number of patients. We prospectively evaluated the diagnostic performance of myocardial perfusion MRI with SW-CG-HYPR in patients with suspected CAD. A total of 50 consecutive patients who were scheduled for coronary angiography with suspected CAD underwent myocardial perfusion MRI with SW-CG-HYPR at 3.0 T. The perfusion defects were interpreted qualitatively by 2 blinded observers and were correlated with x-ray angiographic stenoses ≥50%. The prevalence of CAD was 56%. In the per-patient analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of SW-CG-HYPR was 96% (95% confidence interval 82% to 100%), 82% (95% confidence interval 60% to 95%), 87% (95% confidence interval 70% to 96%), 95% (95% confidence interval 74% to100%), and 90% (95% confidence interval 82% to 98%), respectively. In the per-vessel analysis, the corresponding values were 98% (95% confidence interval 91% to 100%), 89% (95% confidence interval 80% to 94%), 86% (95% confidence interval 76% to 93%), 99% (95% confidence interval 93% to 100%), and 93% (95% confidence interval 89% to 97%), respectively. In conclusion, myocardial perfusion MRI using SW-CG-HYPR allows whole left ventricular coverage and high resolution and has high diagnostic accuracy in patients with suspected CAD. Copyright © 2012 Elsevier Inc. All rights reserved.

Metal-induced streak artifact reduction using iterative reconstruction algorithms in x-ray computed tomography image of the dentoalveolar region.

PubMed

Dong, Jian; Hayakawa, Yoshihiko; Kannenberg, Sven; Kober, Cornelia

2013-02-01

The objective of this study was to reduce metal-induced streak artifact on oral and maxillofacial x-ray computed tomography (CT) images by developing the fast statistical image reconstruction system using iterative reconstruction algorithms. Adjacent CT images often depict similar anatomical structures in thin slices. So, first, images were reconstructed using the same projection data of an artifact-free image. Second, images were processed by the successive iterative restoration method where projection data were generated from reconstructed image in sequence. Besides the maximum likelihood-expectation maximization algorithm, the ordered subset-expectation maximization algorithm (OS-EM) was examined. Also, small region of interest (ROI) setting and reverse processing were applied for improving performance. Both algorithms reduced artifacts instead of slightly decreasing gray levels. The OS-EM and small ROI reduced the processing duration without apparent detriments. Sequential and reverse processing did not show apparent effects. Two alternatives in iterative reconstruction methods were effective for artifact reduction. The OS-EM algorithm and small ROI setting improved the performance. Copyright © 2012 Elsevier Inc. All rights reserved.

Postburn Head and Neck Reconstruction: An Algorithmic Approach.

PubMed

Heidekrueger, Paul Immanuel; Broer, Peter Niclas; Tanna, Neil; Ninkovic, Milomir

2016-01-01

Optimizing functional and aesthetic outcomes in postburn head and neck reconstruction remains a surgical challenge. Recurrent contractures, impaired range of motion, and disfigurement because of disruption of the aesthetic subunits of the face, can result in poor patient satisfaction and ultimately, contribute to social isolation of the patient. In an effort to improve the quality of life of these patients, this study evaluates different surgical approaches with an emphasis on tissue expansion of free and regional flaps. Regional and free-flap reconstruction was performed in 20 patients (26 flaps) with severe postburn head and neck contractures. To minimize donor site morbidity and obtain large amounts of thin and pliable tissue, pre-expansion was performed in all patients treated with locoregional flap reconstructions (12/12), and 62% (8/14) of patients with free-flap reconstructions. Algorithms regarding pre- and intraoperative decision-making are discussed, and complications between the techniques as well as long-term (mean follow-up 3 years) results are analyzed. Complications, including tissue expander infection with need for removal or exchange, partial or full flap loss, were evaluated and occurred in 25% (3/12) of patients with locoregional and 36% (5/14) of patients receiving free-flap reconstructions. Secondary revision surgery was performed in 33% (4/12) of locoregional flaps and 93% (13/14) of free flaps. Both locoregional as well as distant tissue transfers have their role in postburn head and neck reconstruction, whereas pre-expansion remains an invaluable tool. Paying attention to the presented principles and keeping the importance of aesthetic facial subunits in mind, range of motion, aesthetics, and patient satisfaction were improved long term in all our patients, while minimizing donor site morbidity.

A framelet-based iterative maximum-likelihood reconstruction algorithm for spectral CT

NASA Astrophysics Data System (ADS)

Wang, Yingmei; Wang, Ge; Mao, Shuwei; Cong, Wenxiang; Ji, Zhilong; Cai, Jian-Feng; Ye, Yangbo

2016-11-01

Standard computed tomography (CT) cannot reproduce spectral information of an object. Hardware solutions include dual-energy CT which scans the object twice in different x-ray energy levels, and energy-discriminative detectors which can separate lower and higher energy levels from a single x-ray scan. In this paper, we propose a software solution and give an iterative algorithm that reconstructs an image with spectral information from just one scan with a standard energy-integrating detector. The spectral information obtained can be used to produce color CT images, spectral curves of the attenuation coefficient μ (r,E) at points inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. Our software solution requires no change on hardware of a CT machine. With the Shepp-Logan phantom, we have found that although the photoelectric and Compton components were not perfectly reconstructed, their composite effect was very accurately reconstructed as compared to the ground truth and the dual-energy CT counterpart. This means that our proposed method has an intrinsic benefit in beam hardening correction and metal artifact reduction. The algorithm is based on a nonlinear polychromatic acquisition model for x-ray CT. The key technique is a sparse representation of iterations in a framelet system. Convergence of the algorithm is studied. This is believed to be the first application of framelet imaging tools to a nonlinear inverse problem.

Dual energy CT with one full scan and a second sparse-view scan using structure preserving iterative reconstruction (SPIR)

NASA Astrophysics Data System (ADS)

Wang, Tonghe; Zhu, Lei

2016-09-01

Conventional dual-energy CT (DECT) reconstruction requires two full-size projection datasets with two different energy spectra. In this study, we propose an iterative algorithm to enable a new data acquisition scheme which requires one full scan and a second sparse-view scan for potential reduction in imaging dose and engineering cost of DECT. A bilateral filter is calculated as a similarity matrix from the first full-scan CT image to quantify the similarity between any two pixels, which is assumed unchanged on a second CT image since DECT scans are performed on the same object. The second CT image from reduced projections is reconstructed by an iterative algorithm which updates the image by minimizing the total variation of the difference between the image and its filtered image by the similarity matrix under data fidelity constraint. As the redundant structural information of the two CT images is contained in the similarity matrix for CT reconstruction, we refer to the algorithm as structure preserving iterative reconstruction (SPIR). The proposed method is evaluated on both digital and physical phantoms, and is compared with the filtered-backprojection (FBP) method, the conventional total-variation-regularization-based algorithm (TVR) and prior-image-constrained-compressed-sensing (PICCS). SPIR with a second 10-view scan reduces the image noise STD by a factor of one order of magnitude with same spatial resolution as full-view FBP image. SPIR substantially improves over TVR on the reconstruction accuracy of a 10-view scan by decreasing the reconstruction error from 6.18% to 1.33%, and outperforms TVR at 50 and 20-view scans on spatial resolution with a higher frequency at the modulation transfer function value of 10% by an average factor of 4. Compared with the 20-view scan PICCS result, the SPIR image has 7 times lower noise STD with similar spatial resolution. The electron density map obtained from the SPIR-based DECT images with a second 10-view scan has an

WE-EF-207-07: Dual Energy CT with One Full Scan and a Second Sparse-View Scan Using Structure Preserving Iterative Reconstruction (SPIR)

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

Wang, T; Zhu, L

Purpose: Conventional dual energy CT (DECT) reconstructs CT and basis material images from two full-size projection datasets with different energy spectra. To relax the data requirement, we propose an iterative DECT reconstruction algorithm using one full scan and a second sparse-view scan by utilizing redundant structural information of the same object acquired at two different energies. Methods: We first reconstruct a full-scan CT image using filtered-backprojection (FBP) algorithm. The material similarities of each pixel with other pixels are calculated by an exponential function about pixel value differences. We assume that the material similarities of pixels remains in the second CTmore » scan, although pixel values may vary. An iterative method is designed to reconstruct the second CT image from reduced projections. Under the data fidelity constraint, the algorithm minimizes the L2 norm of the difference between pixel value and its estimation, which is the average of other pixel values weighted by their similarities. The proposed algorithm, referred to as structure preserving iterative reconstruction (SPIR), is evaluated on physical phantoms. Results: On the Catphan600 phantom, SPIR-based DECT method with a second 10-view scan reduces the noise standard deviation of a full-scan FBP CT reconstruction by a factor of 4 with well-maintained spatial resolution, while iterative reconstruction using total-variation regularization (TVR) degrades the spatial resolution at the same noise level. The proposed method achieves less than 1% measurement difference on electron density map compared with the conventional two-full-scan DECT. On an anthropomorphic pediatric phantom, our method successfully reconstructs the complicated vertebra structures and decomposes bone and soft tissue. Conclusion: We develop an effective method to reduce the number of views and therefore data acquisition in DECT. We show that SPIR-based DECT using one full scan and a second 10-view scan

Direct aperture optimization using an inverse form of back-projection.

PubMed

Zhu, Xiaofeng; Cullip, Timothy; Tracton, Gregg; Tang, Xiaoli; Lian, Jun; Dooley, John; Chang, Sha X

2014-03-06

Direct aperture optimization (DAO) has been used to produce high dosimetric quality intensity-modulated radiotherapy (IMRT) treatment plans with fast treatment delivery by directly modeling the multileaf collimator segment shapes and weights. To improve plan quality and reduce treatment time for our in-house treatment planning system, we implemented a new DAO approach without using a global objective function (GFO). An index concept is introduced as an inverse form of back-projection used in the CT multiplicative algebraic reconstruction technique (MART). The index, introduced for IMRT optimization in this work, is analogous to the multiplicand in MART. The index is defined as the ratio of the optima over the current. It is assigned to each voxel and beamlet to optimize the fluence map. The indices for beamlets and segments are used to optimize multileaf collimator (MLC) segment shapes and segment weights, respectively. Preliminary data show that without sacrificing dosimetric quality, the implementation of the DAO reduced average IMRT treatment time from 13 min to 8 min for the prostate, and from 15 min to 9 min for the head and neck using our in-house treatment planning system PlanUNC. The DAO approach has also shown promise in optimizing rotational IMRT with burst mode in a head and neck test case.

Image reconstruction algorithms for electrical capacitance tomography based on ROF model using new numerical techniques

NASA Astrophysics Data System (ADS)

Chen, Jiaoxuan; Zhang, Maomao; Liu, Yinyan; Chen, Jiaoliao; Li, Yi

2017-03-01

Electrical capacitance tomography (ECT) is a promising technique applied in many fields. However, the solutions for ECT are not unique and highly sensitive to the measurement noise. To remain a good shape of reconstructed object and endure a noisy data, a Rudin-Osher-Fatemi (ROF) model with total variation regularization is applied to image reconstruction in ECT. Two numerical methods, which are simplified augmented Lagrangian (SAL) and accelerated alternating direction method of multipliers (AADMM), are innovatively introduced to try to solve the above mentioned problems in ECT. The effect of the parameters and the number of iterations for different algorithms, and the noise level in capacitance data are discussed. Both simulation and experimental tests were carried out to validate the feasibility of the proposed algorithms, compared to the Landweber iteration (LI) algorithm. The results show that the SAL and AADMM algorithms can handle a high level of noise and the AADMM algorithm outperforms other algorithms in identifying the object from its background.

Total variation iterative constraint algorithm for limited-angle tomographic reconstruction of non-piecewise-constant structures

NASA Astrophysics Data System (ADS)

Krauze, W.; Makowski, P.; Kujawińska, M.

2015-06-01

Standard tomographic algorithms applied to optical limited-angle tomography result in the reconstructions that have highly anisotropic resolution and thus special algorithms are developed. State of the art approaches utilize the Total Variation (TV) minimization technique. These methods give very good results but are applicable to piecewise constant structures only. In this paper, we propose a novel algorithm for 3D limited-angle tomography - Total Variation Iterative Constraint method (TVIC) which enhances the applicability of the TV regularization to non-piecewise constant samples, like biological cells. This approach consists of two parts. First, the TV minimization is used as a strong regularizer to create a sharp-edged image converted to a 3D binary mask which is then iteratively applied in the tomographic reconstruction as a constraint in the object domain. In the present work we test the method on a synthetic object designed to mimic basic structures of a living cell. For simplicity, the test reconstructions were performed within the straight-line propagation model (SIRT3D solver from the ASTRA Tomography Toolbox), but the strategy is general enough to supplement any algorithm for tomographic reconstruction that supports arbitrary geometries of plane-wave projection acquisition. This includes optical diffraction tomography solvers. The obtained reconstructions present resolution uniformity and general shape accuracy expected from the TV regularization based solvers, but keeping the smooth internal structures of the object at the same time. Comparison between three different patterns of object illumination arrangement show very small impact of the projection acquisition geometry on the image quality.

Dense Matching Comparison Between Census and a Convolutional Neural Network Algorithm for Plant Reconstruction

NASA Astrophysics Data System (ADS)

Xia, Y.; Tian, J.; d'Angelo, P.; Reinartz, P.

2018-05-01

3D reconstruction of plants is hard to implement, as the complex leaf distribution highly increases the difficulty level in dense matching. Semi-Global Matching has been successfully applied to recover the depth information of a scene, but may perform variably when different matching cost algorithms are used. In this paper two matching cost computation algorithms, Census transform and an algorithm using a convolutional neural network, are tested for plant reconstruction based on Semi-Global Matching. High resolution close-range photogrammetric images from a handheld camera are used for the experiment. The disparity maps generated based on the two selected matching cost methods are comparable with acceptable quality, which shows the good performance of Census and the potential of neural networks to improve the dense matching.

GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging

DOE PAGES

Pryor, Alan; Yang, Yongsoo; Rana, Arjun; ...

2017-09-05

Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE), for high-resolution 3D reconstruction from a limited number of 2D projections. GENFIRE first assembles a 3D Fourier grid with oversampling and then iteratesmore » between real and reciprocal space to search for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques through numerical simulations and by experimentally reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. As a result, equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.« less

GENFIRE: A generalized Fourier iterative reconstruction algorithm for high-resolution 3D imaging

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

Pryor, Alan; Yang, Yongsoo; Rana, Arjun

Tomography has made a radical impact on diverse fields ranging from the study of 3D atomic arrangements in matter to the study of human health in medicine. Despite its very diverse applications, the core of tomography remains the same, that is, a mathematical method must be implemented to reconstruct the 3D structure of an object from a number of 2D projections. Here, we present the mathematical implementation of a tomographic algorithm, termed GENeralized Fourier Iterative REconstruction (GENFIRE), for high-resolution 3D reconstruction from a limited number of 2D projections. GENFIRE first assembles a 3D Fourier grid with oversampling and then iteratesmore » between real and reciprocal space to search for a global solution that is concurrently consistent with the measured data and general physical constraints. The algorithm requires minimal human intervention and also incorporates angular refinement to reduce the tilt angle error. We demonstrate that GENFIRE can produce superior results relative to several other popular tomographic reconstruction techniques through numerical simulations and by experimentally reconstructing the 3D structure of a porous material and a frozen-hydrated marine cyanobacterium. As a result, equipped with a graphical user interface, GENFIRE is freely available from our website and is expected to find broad applications across different disciplines.« less

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

Best-next-view algorithm for three-dimensional scene reconstruction using range images

NASA Astrophysics Data System (ADS)

Banta, J. E.; Zhien, Yu; Wang, X. Z.; Zhang, G.; Smith, M. T.; Abidi, Mongi A.

1995-10-01

The primary focus of the research detailed in this paper is to develop an intelligent sensing module capable of automatically determining the optimal next sensor position and orientation during scene reconstruction. To facilitate a solution to this problem, we have assembled a system for reconstructing a 3D model of an object or scene from a sequence of range images. Candidates for the best-next-view position are determined by detecting and measuring occlusions to the range camera's view in an image. Ultimately, the candidate which will reveal the greatest amount of unknown scene information is selected as the best-next-view position. Our algorithm uses ray tracing to determine how much new information a given sensor perspective will reveal. We have tested our algorithm successfully on several synthetic range data streams, and found the system's results to be consistent with an intuitive human search. The models recovered by our system from range data compared well with the ideal models. Essentially, we have proven that range information of physical objects can be employed to automatically reconstruct a satisfactory dynamic 3D computer model at a minimal computational expense. This has obvious implications in the contexts of robot navigation, manufacturing, and hazardous materials handling. The algorithm we developed takes advantage of no a priori information in finding the best-next-view position.

ODTbrain: a Python library for full-view, dense diffraction tomography.

PubMed

Müller, Paul; Schürmann, Mirjam; Guck, Jochen

2015-11-04

Analyzing the three-dimensional (3D) refractive index distribution of a single cell makes it possible to describe and characterize its inner structure in a marker-free manner. A dense, full-view tomographic data set is a set of images of a cell acquired for multiple rotational positions, densely distributed from 0 to 360 degrees. The reconstruction is commonly realized by projection tomography, which is based on the inversion of the Radon transform. The reconstruction quality of projection tomography is greatly improved when first order scattering, which becomes relevant when the imaging wavelength is comparable to the characteristic object size, is taken into account. This advanced reconstruction technique is called diffraction tomography. While many implementations of projection tomography are available today, there is no publicly available implementation of diffraction tomography so far. We present a Python library that implements the backpropagation algorithm for diffraction tomography in 3D. By establishing benchmarks based on finite-difference time-domain (FDTD) simulations, we showcase the superiority of the backpropagation algorithm over the backprojection algorithm. Furthermore, we discuss how measurment parameters influence the reconstructed refractive index distribution and we also give insights into the applicability of diffraction tomography to biological cells. The present software library contains a robust implementation of the backpropagation algorithm. The algorithm is ideally suited for the application to biological cells. Furthermore, the implementation is a drop-in replacement for the classical backprojection algorithm and is made available to the large user community of the Python programming language.

Micro-CT images reconstruction and 3D visualization for small animal studying

NASA Astrophysics Data System (ADS)

Gong, Hui; Liu, Qian; Zhong, Aijun; Ju, Shan; Fang, Quan; Fang, Zheng

2005-01-01

A small-animal x-ray micro computed tomography (micro-CT) system has been constructed to screen laboratory small animals and organs. The micro-CT system consists of dual fiber-optic taper-coupled CCD detectors with a field-of-view of 25x50 mm2, a microfocus x-ray source, a rotational subject holder. For accurate localization of rotation center, coincidence between the axis of rotation and centre of image was studied by calibration with a polymethylmethacrylate cylinder. Feldkamp"s filtered back-projection cone-beam algorithm is adopted for three-dimensional reconstruction on account of the effective corn-beam angle is 5.67° of the micro-CT system. 200x1024x1024 matrix data of micro-CT is obtained with the magnification of 1.77 and pixel size of 31x31μm2. In our reconstruction software, output image size of micro-CT slices data, magnification factor and rotation sample degree can be modified in the condition of different computational efficiency and reconstruction region. The reconstructed image matrix data is processed and visualization by Visualization Toolkit (VTK). Data parallelism of VTK is performed in surface rendering of reconstructed data in order to improve computing speed. Computing time of processing a 512x512x512 matrix datasets is about 1/20 compared with serial program when 30 CPU is used. The voxel size is 54x54x108 μm3. The reconstruction and 3-D visualization images of laboratory rat ear are presented.

Missing texture reconstruction method based on error reduction algorithm using Fourier transform magnitude estimation scheme.

PubMed

Ogawa, Takahiro; Haseyama, Miki

2013-03-01

A missing texture reconstruction method based on an error reduction (ER) algorithm, including a novel estimation scheme of Fourier transform magnitudes is presented in this brief. In our method, Fourier transform magnitude is estimated for a target patch including missing areas, and the missing intensities are estimated by retrieving its phase based on the ER algorithm. Specifically, by monitoring errors converged in the ER algorithm, known patches whose Fourier transform magnitudes are similar to that of the target patch are selected from the target image. In the second approach, the Fourier transform magnitude of the target patch is estimated from those of the selected known patches and their corresponding errors. Consequently, by using the ER algorithm, we can estimate both the Fourier transform magnitudes and phases to reconstruct the missing areas.

Experimental validation of an OSEM-type iterative reconstruction algorithm for inverse geometry computed tomography

NASA Astrophysics Data System (ADS)

David, Sabrina; Burion, Steve; Tepe, Alan; Wilfley, Brian; Menig, Daniel; Funk, Tobias

2012-03-01

Iterative reconstruction methods have emerged as a promising avenue to reduce dose in CT imaging. Another, perhaps less well-known, advance has been the development of inverse geometry CT (IGCT) imaging systems, which can significantly reduce the radiation dose delivered to a patient during a CT scan compared to conventional CT systems. Here we show that IGCT data can be reconstructed using iterative methods, thereby combining two novel methods for CT dose reduction. A prototype IGCT scanner was developed using a scanning beam digital X-ray system - an inverse geometry fluoroscopy system with a 9,000 focal spot x-ray source and small photon counting detector. 90 fluoroscopic projections or "superviews" spanning an angle of 360 degrees were acquired of an anthropomorphic phantom mimicking a 1 year-old boy. The superviews were reconstructed with a custom iterative reconstruction algorithm, based on the maximum-likelihood algorithm for transmission tomography (ML-TR). The normalization term was calculated based on flat-field data acquired without a phantom. 15 subsets were used, and a total of 10 complete iterations were performed. Initial reconstructed images showed faithful reconstruction of anatomical details. Good edge resolution and good contrast-to-noise properties were observed. Overall, ML-TR reconstruction of IGCT data collected by a bench-top prototype was shown to be viable, which may be an important milestone in the further development of inverse geometry CT.

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM☆

PubMed Central

López, J.D.; Litvak, V.; Espinosa, J.J.; Friston, K.; Barnes, G.R.

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy—an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. PMID:24041874

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J

2017-12-01

To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine

High-definition computed tomography for coronary artery stents imaging: Initial evaluation of the optimal reconstruction algorithm.

PubMed

Cui, Xiaoming; Li, Tao; Li, Xin; Zhou, Weihua

2015-05-01

The aim of this study was to evaluate the in vivo performance of four image reconstruction algorithms in a high-definition CT (HDCT) scanner with improved spatial resolution for the evaluation of coronary artery stents and intrastent lumina. Thirty-nine consecutive patients with a total of 71 implanted coronary stents underwent coronary CT angiography (CCTA) on a HDCT (Discovery CT 750 HD; GE Healthcare) with the high-resolution scanning mode. Four different reconstruction algorithms (HD-stand, HD-detail; HD-stand-plus; HD-detail-plus) were applied to reconstruct the stented coronary arteries. Image quality for stent characterization was assessed. Image noise and intrastent luminal diameter were measured. The relationship between the measurement of inner stent diameter (ISD) and the true stent diameter (TSD) and stent type were analysed. The stent-dedicated kernel (HD-detail) offered the highest percentage (53.5%) of good image quality for stent characterization and the highest ratio (68.0±8.4%) of visible stent lumen/true stent lumen for luminal diameter measurement at the expense of an increased overall image noise. The Pearson correlation coefficient between the ISD and TSD measurement and spearman correlation coefficient between the ISD measurement and stent type were 0.83 and 0.48, respectively. Compared with standard reconstruction algorithms, high-definition CT imaging technique with dedicated high-resolution reconstruction algorithm provides more accurate stent characterization and intrastent luminal diameter measurement. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

80-kVp CT Using Iterative Reconstruction in Image Space Algorithm for the Detection of Hypervascular Hepatocellular Carcinoma: Phantom and Initial Clinical Experience

PubMed Central

Hur, Saebeom; Kim, Soo Jin; Park, Ji Hoon; Han, Joon Koo; Choi, Byung Ihn

2012-01-01

Objective To investigate whether the low-tube-voltage (80-kVp), intermediate-tube-current (340-mAs) MDCT using the Iterative Reconstruction in Image Space (IRIS) algorithm improves lesion-to-liver contrast at reduced radiation dosage while maintaining acceptable image noise in the detection of hepatocellular carcinomas (HCC) in thin (mean body mass index, 24 ± 0.4 kg/m2) adults. Subjects and Methods A phantom simulating the liver with HCC was scanned at 50-400 mAs for 80, 100, 120 and 140-kVp. In addition, fifty patients with HCC who underwent multiphasic liver CT using dual-energy (80-kVp and 140-kVp) arterial scans were enrolled. Virtual 120-kVP scans (protocol A) and 80-kVp scans (protocol B) of the late arterial phase were reconstructed with filtered back-projection (FBP), while corresponding 80-kVp scans were reconstructed with IRIS (protocol C). Contrast-to-noise ratio (CNR) of HCCs and abdominal organs were assessed quantitatively, whereas lesion conspicuity, image noise, and overall image quality were assessed qualitatively. Results IRIS effectively reduced image noise, and yielded 29% higher CNR than the FBP at equivalent tube voltage and current in the phantom study. In the quantitative patient study, protocol C helped improve CNR by 51% and 172% than protocols A and B (p < 0.001), respectively, at equivalent radiation dosage. In the qualitative study, protocol C acquired the highest score for lesion conspicuity albeit with an inferior score to protocol A for overall image quality (p < 0.001). Mean effective dose was 2.63-mSv with protocol A and 1.12-mSv with protocols B and C. Conclusion CT using the low-tube-voltage, intermediate-tube-current and IRIS help improve lesion-to-liver CNR of HCC in thin adults during the arterial phase at a lower radiation dose when compared with the standard technique using 120-kVp and FBP. PMID:22438682

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.

The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson's disease.

PubMed

Tomše, Petra; Jensterle, Luka; Rep, Sebastijan; Grmek, Marko; Zaletel, Katja; Eidelberg, David; Dhawan, Vijay; Ma, Yilong; Trošt, Maja

2017-09-01

To evaluate the reproducibility of the expression of Parkinson's Disease Related Pattern (PDRP) across multiple sets of 18F-FDG-PET brain images reconstructed with different reconstruction algorithms. 18F-FDG-PET brain imaging was performed in two independent cohorts of Parkinson's disease (PD) patients and normal controls (NC). Slovenian cohort (20 PD patients, 20 NC) was scanned with Siemens Biograph mCT camera and reconstructed using FBP, FBP+TOF, OSEM, OSEM+TOF, OSEM+PSF and OSEM+PSF+TOF. American Cohort (20 PD patients, 7 NC) was scanned with GE Advance camera and reconstructed using 3DRP, FORE-FBP and FORE-Iterative. Expressions of two previously-validated PDRP patterns (PDRP-Slovenia and PDRP-USA) were calculated. We compared the ability of PDRP to discriminate PD patients from NC, differences and correlation between the corresponding subject scores and ROC analysis results across the different reconstruction algorithms. The expression of PDRP-Slovenia and PDRP-USA networks was significantly elevated in PD patients compared to NC (p<0.0001), regardless of reconstruction algorithms. PDRP expression strongly correlated between all studied algorithms and the reference algorithm (r⩾0.993, p<0.0001). Average differences in the PDRP expression among different algorithms varied within 0.73 and 0.08 of the reference value for PDRP-Slovenia and PDRP-USA, respectively. ROC analysis confirmed high similarity in sensitivity, specificity and AUC among all studied reconstruction algorithms. These results show that the expression of PDRP is reproducible across a variety of reconstruction algorithms of 18F-FDG-PET brain images. PDRP is capable of providing a robust metabolic biomarker of PD for multicenter 18F-FDG-PET images acquired in the context of differential diagnosis or clinical trials. Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Improved adaptive genetic algorithm with sparsity constraint applied to thermal neutron CT reconstruction of two-phase flow

NASA Astrophysics Data System (ADS)

Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang

2018-05-01

Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.

Evaluation of the spline reconstruction technique for PET

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

Kastis, George A., E-mail: gkastis@academyofathens.gr; Kyriakopoulou, Dimitra; Gaitanis, Anastasios

2014-04-15

Purpose: The spline reconstruction technique (SRT), based on the analytic formula for the inverse Radon transform, has been presented earlier in the literature. In this study, the authors present an improved formulation and numerical implementation of this algorithm and evaluate it in comparison to filtered backprojection (FBP). Methods: The SRT is based on the numerical evaluation of the Hilbert transform of the sinogram via an approximation in terms of “custom made” cubic splines. By restricting reconstruction only within object pixels and by utilizing certain mathematical symmetries, the authors achieve a reconstruction time comparable to that of FBP. The authors havemore » implemented SRT in STIR and have evaluated this technique using simulated data from a clinical positron emission tomography (PET) system, as well as real data obtained from clinical and preclinical PET scanners. For the simulation studies, the authors have simulated sinograms of a point-source and three digital phantoms. Using these sinograms, the authors have created realizations of Poisson noise at five noise levels. In addition to visual comparisons of the reconstructed images, the authors have determined contrast and bias for different regions of the phantoms as a function of noise level. For the real-data studies, sinograms of an{sup 18}F-FDG injected mouse, a NEMA NU 4-2008 image quality phantom, and a Derenzo phantom have been acquired from a commercial PET system. The authors have determined: (a) coefficient of variations (COV) and contrast from the NEMA phantom, (b) contrast for the various sections of the Derenzo phantom, and (c) line profiles for the Derenzo phantom. Furthermore, the authors have acquired sinograms from a whole-body PET scan of an {sup 18}F-FDG injected cancer patient, using the GE Discovery ST PET/CT system. SRT and FBP reconstructions of the thorax have been visually evaluated. Results: The results indicate an improvement in FWHM and FWTM in both simulated

Real-time quasi-3D tomographic reconstruction

NASA Astrophysics Data System (ADS)

Buurlage, Jan-Willem; Kohr, Holger; Palenstijn, Willem Jan; Joost Batenburg, K.

2018-06-01

Developments in acquisition technology and a growing need for time-resolved experiments pose great computational challenges in tomography. In addition, access to reconstructions in real time is a highly demanded feature but has so far been out of reach. We show that by exploiting the mathematical properties of filtered backprojection-type methods, having access to real-time reconstructions of arbitrarily oriented slices becomes feasible. Furthermore, we present , software for visualization and on-demand reconstruction of slices. A user of can interactively shift and rotate slices in a GUI, while the software updates the slice in real time. For certain use cases, the possibility to study arbitrarily oriented slices in real time directly from the measured data provides sufficient visual and quantitative insight. Two such applications are discussed in this article.

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging.

PubMed

Yan, Hao; Zhen, Xin; Folkerts, Michael; Li, Yongbao; Pan, Tinsu; Cervino, Laura; Jiang, Steve B; Jia, Xun

2014-07-01

4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is invented to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3-0.5 mm for patients 1-3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1-1.5 min per phase. High-quality 4D-CBCT imaging based

A hybrid reconstruction algorithm for fast and accurate 4D cone-beam CT imaging

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

Yan, Hao; Folkerts, Michael; Jiang, Steve B., E-mail: xun.jia@utsouthwestern.edu, E-mail: steve.jiang@UTSouthwestern.edu

2014-07-15

Purpose: 4D cone beam CT (4D-CBCT) has been utilized in radiation therapy to provide 4D image guidance in lung and upper abdomen area. However, clinical application of 4D-CBCT is currently limited due to the long scan time and low image quality. The purpose of this paper is to develop a new 4D-CBCT reconstruction method that restores volumetric images based on the 1-min scan data acquired with a standard 3D-CBCT protocol. Methods: The model optimizes a deformation vector field that deforms a patient-specific planning CT (p-CT), so that the calculated 4D-CBCT projections match measurements. A forward-backward splitting (FBS) method is inventedmore » to solve the optimization problem. It splits the original problem into two well-studied subproblems, i.e., image reconstruction and deformable image registration. By iteratively solving the two subproblems, FBS gradually yields correct deformation information, while maintaining high image quality. The whole workflow is implemented on a graphic-processing-unit to improve efficiency. Comprehensive evaluations have been conducted on a moving phantom and three real patient cases regarding the accuracy and quality of the reconstructed images, as well as the algorithm robustness and efficiency. Results: The proposed algorithm reconstructs 4D-CBCT images from highly under-sampled projection data acquired with 1-min scans. Regarding the anatomical structure location accuracy, 0.204 mm average differences and 0.484 mm maximum difference are found for the phantom case, and the maximum differences of 0.3–0.5 mm for patients 1–3 are observed. As for the image quality, intensity errors below 5 and 20 HU compared to the planning CT are achieved for the phantom and the patient cases, respectively. Signal-noise-ratio values are improved by 12.74 and 5.12 times compared to results from FDK algorithm using the 1-min data and 4-min data, respectively. The computation time of the algorithm on a NVIDIA GTX590 card is 1–1.5 min per

Performance evaluation of algebraic reconstruction technique (ART) for prototype chest digital tomosynthesis (CDT) system

NASA Astrophysics Data System (ADS)

Lee, Haenghwa; Choi, Sunghoon; Jo, Byungdu; Kim, Hyemi; Lee, Donghoon; Kim, Dohyeon; Choi, Seungyeon; Lee, Youngjin; Kim, Hee-Joung

2017-03-01

Chest digital tomosynthesis (CDT) is a new 3D imaging technique that can be expected to improve the detection of subtle lung disease over conventional chest radiography. Algorithm development for CDT system is challenging in that a limited number of low-dose projections are acquired over a limited angular range. To confirm the feasibility of algebraic reconstruction technique (ART) method under variations in key imaging parameters, quality metrics were conducted using LUNGMAN phantom included grand-glass opacity (GGO) tumor. Reconstructed images were acquired from the total 41 projection images over a total angular range of +/-20°. We evaluated contrast-to-noise ratio (CNR) and artifacts spread function (ASF) to investigate the effect of reconstruction parameters such as number of iterations, relaxation parameter and initial guess on image quality. We found that proper value of ART relaxation parameter could improve image quality from the same projection. In this study, proper value of relaxation parameters for zero-image (ZI) and back-projection (BP) initial guesses were 0.4 and 0.6, respectively. Also, the maximum CNR values and the minimum full width at half maximum (FWHM) of ASF were acquired in the reconstructed images after 20 iterations and 3 iterations, respectively. According to the results, BP initial guess for ART method could provide better image quality than ZI initial guess. In conclusion, ART method with proper reconstruction parameters could improve image quality due to the limited angular range in CDT system.

Mitigating artifacts in back-projection source imaging with implications for frequency-dependent properties of the Tohoku-Oki earthquake

NASA Astrophysics Data System (ADS)

Meng, Lingsen; Ampuero, Jean-Paul; Luo, Yingdi; Wu, Wenbo; Ni, Sidao

2012-12-01

Comparing teleseismic array back-projection source images of the 2011 Tohoku-Oki earthquake with results from static and kinematic finite source inversions has revealed little overlap between the regions of high- and low-frequency slip. Motivated by this interesting observation, back-projection studies extended to intermediate frequencies, down to about 0.1 Hz, have suggested that a progressive transition of rupture properties as a function of frequency is observable. Here, by adapting the concept of array response function to non-stationary signals, we demonstrate that the "swimming artifact", a systematic drift resulting from signal non-stationarity, induces significant bias on beamforming back-projection at low frequencies. We introduce a "reference window strategy" into the multitaper-MUSIC back-projection technique and significantly mitigate the "swimming artifact" at high frequencies (1 s to 4 s). At lower frequencies, this modification yields notable, but significantly smaller, artifacts than time-domain stacking. We perform extensive synthetic tests that include a 3D regional velocity model for Japan. We analyze the recordings of the Tohoku-Oki earthquake at the USArray and at the European array at periods from 1 s to 16 s. The migration of the source location as a function of period, regardless of the back-projection methods, has characteristics that are consistent with the expected effect of the "swimming artifact". In particular, the apparent up-dip migration as a function of frequency obtained with the USArray can be explained by the "swimming artifact". This indicates that the most substantial frequency-dependence of the Tohoku-Oki earthquake source occurs at periods longer than 16 s. Thus, low-frequency back-projection needs to be further tested and validated in order to contribute to the characterization of frequency-dependent rupture properties.

Automated wavelet denoising of photoacoustic signals for circulating melanoma cell detection and burn image reconstruction.

PubMed

Holan, Scott H; Viator, John A

2008-06-21

Photoacoustic image reconstruction may involve hundreds of point measurements, each of which contributes unique information about the subsurface absorbing structures under study. For backprojection imaging, two or more point measurements of photoacoustic waves induced by irradiating a biological sample with laser light are used to produce an image of the acoustic source. Each of these measurements must undergo some signal processing, such as denoising or system deconvolution. In order to process the numerous signals, we have developed an automated wavelet algorithm for denoising signals. We appeal to the discrete wavelet transform for denoising photoacoustic signals generated in a dilute melanoma cell suspension and in thermally coagulated blood. We used 5, 9, 45 and 270 melanoma cells in the laser beam path as test concentrations. For the burn phantom, we used coagulated blood in 1.6 mm silicon tube submerged in Intralipid. Although these two targets were chosen as typical applications for photoacoustic detection and imaging, they are of independent interest. The denoising employs level-independent universal thresholding. In order to accommodate nonradix-2 signals, we considered a maximal overlap discrete wavelet transform (MODWT). For the lower melanoma cell concentrations, as the signal-to-noise ratio approached 1, denoising allowed better peak finding. For coagulated blood, the signals were denoised to yield a clean photoacoustic resulting in an improvement of 22% in the reconstructed image. The entire signal processing technique was automated so that minimal user intervention was needed to reconstruct the images. Such an algorithm may be used for image reconstruction and signal extraction for applications such as burn depth imaging, depth profiling of vascular lesions in skin and the detection of single cancer cells in blood samples.

Autofocus algorithm for synthetic aperture radar imaging with large curvilinear apertures

NASA Astrophysics Data System (ADS)

Bleszynski, E.; Bleszynski, M.; Jaroszewicz, T.

2013-05-01

An approach to autofocusing for large curved synthetic aperture radar (SAR) apertures is presented. Its essential feature is that phase corrections are being extracted not directly from SAR images, but rather from reconstructed SAR phase-history data representing windowed patches of the scene, of sizes sufficiently small to allow the linearization of the forward- and back-projection formulae. The algorithm processes data associated with each patch independently and in two steps. The first step employs a phase-gradient-type method in which phase correction compensating (possibly rapid) trajectory perturbations are estimated from the reconstructed phase history for the dominant scattering point on the patch. The second step uses phase-gradient-corrected data and extracts the absolute phase value, removing in this way phase ambiguities and reducing possible imperfections of the first stage, and providing the distances between the sensor and the scattering point with accuracy comparable to the wavelength. The features of the proposed autofocusing method are illustrated in its applications to intentionally corrupted small-scene 2006 Gotcha data. The examples include the extraction of absolute phases (ranges) for selected prominent point targets. They are then used to focus the scene and determine relative target-target distances.

Reconstructing cone-beam CT with spatially varying qualities for adaptive radiotherapy: a proof-of-principle study.

PubMed

Lu, Wenting; Yan, Hao; Gu, Xuejun; Tian, Zhen; Luo, Ouyang; Yang, Liu; Zhou, Linghong; Cervino, Laura; Wang, Jing; Jiang, Steve; Jia, Xun

2014-10-21

With the aim of maximally reducing imaging dose while meeting requirements for adaptive radiation therapy (ART), we propose in this paper a new cone beam CT (CBCT) acquisition and reconstruction method that delivers images with a low noise level inside a region of interest (ROI) and a relatively high noise level outside the ROI. The acquired projection images include two groups: densely sampled projections at a low exposure with a large field of view (FOV) and sparsely sampled projections at a high exposure with a small FOV corresponding to the ROI. A new algorithm combining the conventional filtered back-projection algorithm and the tight-frame iterative reconstruction algorithm is also designed to reconstruct the CBCT based on these projection data. We have validated our method on a simulated head-and-neck (HN) patient case, a semi-real experiment conducted on a HN cancer patient under a full-fan scan mode, as well as a Catphan phantom under a half-fan scan mode. Relative root-mean-square errors (RRMSEs) of less than 3% for the entire image and ~1% within the ROI compared to the ground truth have been observed. These numbers demonstrate the ability of our proposed method to reconstruct high-quality images inside the ROI. As for the part outside ROI, although the images are relatively noisy, it can still provide sufficient information for radiation dose calculations in ART. Dose distributions calculated on our CBCT image and on a standard CBCT image are in agreement, with a mean relative difference of 0.082% inside the ROI and 0.038% outside the ROI. Compared with the standard clinical CBCT scheme, an imaging dose reduction of approximately 3-6 times inside the ROI was achieved, as well as an 8 times outside the ROI. Regarding computational efficiency, it takes 1-3 min to reconstruct a CBCT image depending on the number of projections used. These results indicate that the proposed method has the potential for application in ART.

The reconstruction algorithm used for [68Ga]PSMA-HBED-CC PET/CT reconstruction significantly influences the number of detected lymph node metastases and coeliac ganglia.

PubMed

Krohn, Thomas; Birmes, Anita; Winz, Oliver H; Drude, Natascha I; Mottaghy, Felix M; Behrendt, Florian F; Verburg, Frederik A

2017-04-01

To investigate whether the numbers of lymph node metastases and coeliac ganglia delineated on [ 68 Ga]PSMA-HBED-CC PET/CT scans differ among datasets generated using different reconstruction algorithms. Data were constructed using the BLOB-OS-TF, BLOB-OS and 3D-RAMLA algorithms. All reconstructions were assessed by two nuclear medicine physicians for the number of pelvic/paraaortal lymph node metastases as well the number of coeliac ganglia. Standardized uptake values (SUV) were also calculated in different regions. At least one [ 68 Ga]PSMA-HBED-CC PET/CT-positive pelvic or paraaortal lymph node metastasis was found in 49 and 35 patients using the BLOB-OS-TF algorithm, in 42 and 33 patients using the BLOB-OS algorithm, and in 41 and 31 patients using the 3D-RAMLA algorithm, respectively, and a positive ganglion was found in 92, 59 and 24 of 100 patients using the three algorithms, respectively. Quantitatively, the SUVmean and SUVmax were significantly higher with the BLOB-OS algorithm than with either the BLOB-OS-TF or the 3D-RAMLA algorithm in all measured regions (p < 0.001 for all comparisons). The differences between the SUVs with the BLOB-OS-TF- and 3D-RAMLA algorithms were not significant in the aorta (SUVmean, p = 0.93; SUVmax, p = 0.97) but were significant in all other regions (p < 0.001 in all cases). The SUVmean ganglion/gluteus ratio was significantly higher with the BLOB-OS-TF algorithm than with either the BLOB-OS or the 3D-RAMLA algorithm and was significantly higher with the BLOB-OS than with the 3D-RAMLA algorithm (p < 0.001 in all cases). The results of [ 68 Ga]PSMA-HBED-CC PET/CT are affected by the reconstruction algorithm used. The highest number of lesions and physiological structures will be visualized using a modern algorithm employing time-of-flight information.

Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction.

PubMed

Liu, Hesheng; Schimpf, Paul H; Dong, Guoya; Gao, Xiaorong; Yang, Fusheng; Gao, Shangkai

2005-10-01

This paper presents a new algorithm called Standardized Shrinking LORETA-FOCUSS (SSLOFO) for solving the electroencephalogram (EEG) inverse problem. Multiple techniques are combined in a single procedure to robustly reconstruct the underlying source distribution with high spatial resolution. This algorithm uses a recursive process which takes the smooth estimate of sLORETA as initialization and then employs the re-weighted minimum norm introduced by FOCUSS. An important technique called standardization is involved in the recursive process to enhance the localization ability. The algorithm is further improved by automatically adjusting the source space according to the estimate of the previous step, and by the inclusion of temporal information. Simulation studies are carried out on both spherical and realistic head models. The algorithm achieves very good localization ability on noise-free data. It is capable of recovering complex source configurations with arbitrary shapes and can produce high quality images of extended source distributions. We also characterized the performance with noisy data in a realistic head model. An important feature of this algorithm is that the temporal waveforms are clearly reconstructed, even for closely spaced sources. This provides a convenient way to estimate neural dynamics directly from the cortical sources.

Evaluation of noise and blur effects with SIRT-FISTA-TV reconstruction algorithm: Application to fast environmental transmission electron tomography.

PubMed

Banjak, Hussein; Grenier, Thomas; Epicier, Thierry; Koneti, Siddardha; Roiban, Lucian; Gay, Anne-Sophie; Magnin, Isabelle; Peyrin, Françoise; Maxim, Voichita

2018-06-01

Fast tomography in Environmental Transmission Electron Microscopy (ETEM) is of a great interest for in situ experiments where it allows to observe 3D real-time evolution of nanomaterials under operating conditions. In this context, we are working on speeding up the acquisition step to a few seconds mainly with applications on nanocatalysts. In order to accomplish such rapid acquisitions of the required tilt series of projections, a modern 4K high-speed camera is used, that can capture up to 100 images per second in a 2K binning mode. However, due to the fast rotation of the sample during the tilt procedure, noise and blur effects may occur in many projections which in turn would lead to poor quality reconstructions. Blurred projections make classical reconstruction algorithms inappropriate and require the use of prior information. In this work, a regularized algebraic reconstruction algorithm named SIRT-FISTA-TV is proposed. The performance of this algorithm using blurred data is studied by means of a numerical blur introduced into simulated images series to mimic possible mechanical instabilities/drifts during fast acquisitions. We also present reconstruction results from noisy data to show the robustness of the algorithm to noise. Finally, we show reconstructions with experimental datasets and we demonstrate the interest of fast tomography with an ultra-fast acquisition performed under environmental conditions, i.e. gas and temperature, in the ETEM. Compared to classically used SIRT and SART approaches, our proposed SIRT-FISTA-TV reconstruction algorithm provides higher quality tomograms allowing easier segmentation of the reconstructed volume for a better final processing and analysis. Copyright © 2018 Elsevier B.V. All rights reserved.

GPU implementation of prior image constrained compressed sensing (PICCS)

NASA Astrophysics Data System (ADS)

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

2010-04-01

The Prior Image Constrained Compressed Sensing (PICCS) algorithm (Med. Phys. 35, pg. 660, 2008) has been applied to several computed tomography applications with both standard CT systems and flat-panel based systems designed for guiding interventional procedures and radiation therapy treatment delivery. The PICCS algorithm typically utilizes a prior image which is reconstructed via the standard Filtered Backprojection (FBP) reconstruction algorithm. The algorithm then iteratively solves for the image volume that matches the measured data, while simultaneously assuring the image is similar to the prior image. The PICCS algorithm has demonstrated utility in several applications including: improved temporal resolution reconstruction, 4D respiratory phase specific reconstructions for radiation therapy, and cardiac reconstruction from data acquired on an interventional C-arm. One disadvantage of the PICCS algorithm, just as other iterative algorithms, is the long computation times typically associated with reconstruction. In order for an algorithm to gain clinical acceptance reconstruction must be achievable in minutes rather than hours. In this work the PICCS algorithm has been implemented on the GPU in order to significantly reduce the reconstruction time of the PICCS algorithm. The Compute Unified Device Architecture (CUDA) was used in this implementation.

Dose reduction potential of iterative reconstruction algorithms in neck CTA-a simulation study.

PubMed

Ellmann, Stephan; Kammerer, Ferdinand; Allmendinger, Thomas; Brand, Michael; Janka, Rolf; Hammon, Matthias; Lell, Michael M; Uder, Michael; Kramer, Manuel

2016-10-01

This study aimed to determine the degree of radiation dose reduction in neck CT angiography (CTA) achievable with Sinogram-affirmed iterative reconstruction (SAFIRE) algorithms. 10 consecutive patients scheduled for neck CTA were included in this study. CTA images of the external carotid arteries either were reconstructed with filtered back projection (FBP) at full radiation dose level or underwent simulated dose reduction by proprietary reconstruction software. The dose-reduced images were reconstructed using either SAFIRE 3 or SAFIRE 5 and compared with full-dose FBP images in terms of vessel definition. 5 observers performed a total of 3000 pairwise comparisons. SAFIRE allowed substantial radiation dose reductions in neck CTA while maintaining vessel definition. The possible levels of radiation dose reduction ranged from approximately 34 to approximately 90% and depended on the SAFIRE algorithm strength and the size of the vessel of interest. In general, larger vessels permitted higher degrees of radiation dose reduction, especially with higher SAFIRE strength levels. With small vessels, the superiority of SAFIRE 5 over SAFIRE 3 was lost. Neck CTA can be performed with substantially less radiation dose when SAFIRE is applied. The exact degree of radiation dose reduction should be adapted to the clinical question, in particular to the smallest vessel needing excellent definition.

Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM.

PubMed

López, J D; Litvak, V; Espinosa, J J; Friston, K; Barnes, G R

2014-01-01

The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy-an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithm. © 2013. Published by Elsevier Inc. All rights reserved.

SU-C-BRD-07: Three-Dimensional Dose Reconstruction in the Presence of Inhomogeneities Using Fast EPID-Based Back-Projection Method

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

Ren, Q; Cao, R; Pei, X

2015-06-15

Purpose: Three-dimensional dose verification can detect errors introduced by the treatment planning system (TPS) or differences between planned and delivered dose distribution during the treatment. The aim of the study is to extend a previous in-house developed three-dimensional dose reconstructed model in homogeneous phantom to situtions in which tissue inhomogeneities are present. Methods: The method was based on the portal grey images from an electronic portal imaging device (EPID) and the relationship between beamlets and grey-scoring voxels at the position of the EPID. The relationship was expressed in the form of grey response matrix that was quantified using thickness-dependence scattermore » kernels determined by series of experiments. From the portal grey-value distribution information measured by the EPID the two-dimensional incident fluence distribution was reconstructed based on the grey response matrix using a fast iterative algorithm. The accuracy of this approach was verified using a four-field intensity-modulated radiotherapy (IMRT) plan for the treatment of lung cancer in anthopomorphic phantom. Each field had between twenty and twenty-eight segments and was evaluated by comparing the reconstructed dose distribution with the measured dose. Results: The gamma-evaluation method was used with various evaluation criteria of dose difference and distance-to-agreement: 3%/3mm and 2%/2 mm. The dose comparison for all irradiated fields showed a pass rate of 100% with the criterion of 3%/3mm, and a pass rate of higher than 92% with the criterion of 2%/2mm. Conclusion: Our experimental results demonstrate that our method is capable of accurately reconstructing three-dimensional dose distribution in the presence of inhomogeneities. Using the method, the combined planning and treatment delivery process is verified, offing an easy-to-use tool for the verification of complex treatments.« less

Thermal depth profiling of vascular lesions: automated regularization of reconstruction algorithms

NASA Astrophysics Data System (ADS)

Verkruysse, Wim; Choi, Bernard; Zhang, Jenny R.; Kim, Jeehyun; Nelson, J. Stuart

2008-03-01

Pulsed photo-thermal radiometry (PPTR) is a non-invasive, non-contact diagnostic technique used to locate cutaneous chromophores such as melanin (epidermis) and hemoglobin (vascular structures). Clinical utility of PPTR is limited because it typically requires trained user intervention to regularize the inversion solution. Herein, the feasibility of automated regularization was studied. A second objective of this study was to depart from modeling port wine stain PWS, a vascular skin lesion frequently studied with PPTR, as strictly layered structures since this may influence conclusions regarding PPTR reconstruction quality. Average blood vessel depths, diameters and densities derived from histology of 30 PWS patients were used to generate 15 randomized lesion geometries for which we simulated PPTR signals. Reconstruction accuracy for subjective regularization was compared with that for automated regularization methods. The objective regularization approach performed better. However, the average difference was much smaller than the variation between the 15 simulated profiles. Reconstruction quality depended more on the actual profile to be reconstructed than on the reconstruction algorithm or regularization method. Similar, or better, accuracy reconstructions can be achieved with an automated regularization procedure which enhances prospects for user friendly implementation of PPTR to optimize laser therapy on an individual patient basis.

Trigger and Reconstruction Algorithms for the Japanese Experiment Module- Extreme Universe Space Observatory (JEM-EUSO)

NASA Technical Reports Server (NTRS)

Adams, J. H., Jr.; Andreev, Valeri; Christl, M. J.; Cline, David B.; Crawford, Hank; Judd, E. G.; Pennypacker, Carl; Watts, J. W.

2007-01-01

The JEM-EUSO collaboration intends to study high energy cosmic ray showers using a large downward looking telescope mounted on the Japanese Experiment Module of the International Space Station. The telescope focal plane is instrumented with approx.300k pixels operating as a digital camera, taking snapshots at approx. 1MHz rate. We report an investigation of the trigger and reconstruction efficiency of various algorithms based on time and spatial analysis of the pixel images. Our goal is to develop trigger and reconstruction algorithms that will allow the instrument to detect energies low enough to connect smoothly to ground-based observations.

Photoacoustic tomography from weak and noisy signals by using a pulse decomposition algorithm in the time-domain.

PubMed

Liu, Liangbing; Tao, Chao; Liu, XiaoJun; Deng, Mingxi; Wang, Senhua; Liu, Jun

2015-10-19

Photoacoustic tomography is a promising and rapidly developed methodology of biomedical imaging. It confronts an increasing urgent problem to reconstruct the image from weak and noisy photoacoustic signals, owing to its high benefit in extending the imaging depth and decreasing the dose of laser exposure. Based on the time-domain characteristics of photoacoustic signals, a pulse decomposition algorithm is proposed to reconstruct a photoacoustic image from signals with low signal-to-noise ratio. In this method, a photoacoustic signal is decomposed as the weighted summation of a set of pulses in the time-domain. Images are reconstructed from the weight factors, which are directly related to the optical absorption coefficient. Both simulation and experiment are conducted to test the performance of the method. Numerical simulations show that when the signal-to-noise ratio is -4 dB, the proposed method decreases the reconstruction error to about 17%, in comparison with the conventional back-projection method. Moreover, it can produce acceptable images even when the signal-to-noise ratio is decreased to -10 dB. Experiments show that, when the laser influence level is low, the proposed method achieves a relatively clean image of a hair phantom with some well preserved pattern details. The proposed method demonstrates imaging potential of photoacoustic tomography in expanding applications.

Fat-constrained 18F-FDG PET reconstruction using Dixon MR imaging and the origin ensemble algorithm

NASA Astrophysics Data System (ADS)

Wülker, Christian; Heinzer, Susanne; Börnert, Peter; Renisch, Steffen; Prevrhal, Sven

2015-03-01

Combined PET/MR imaging allows to incorporate the high-resolution anatomical information delivered by MRI into the PET reconstruction algorithm for improvement of PET accuracy beyond standard corrections. We used the working hypothesis that glucose uptake in adipose tissue is low. Thus, our aim was to shift 18F-FDG PET signal into image regions with a low fat content. Dixon MR imaging can be used to generate fat-only images via the water/fat chemical shift difference. On the other hand, the Origin Ensemble (OE) algorithm, a novel Markov chain Monte Carlo method, allows to reconstruct PET data without the use of forward- and back projection operations. By adequate modifications to the Markov chain transition kernel, it is possible to include anatomical a priori knowledge into the OE algorithm. In this work, we used the OE algorithm to reconstruct PET data of a modified IEC/NEMA Body Phantom simulating body water/fat composition. Reconstruction was performed 1) natively, 2) informed with the Dixon MR fat image to down-weight 18F-FDG signal in fatty tissue compartments in favor of adjacent regions, and 3) informed with the fat image to up-weight 18F-FDG signal in fatty tissue compartments, for control purposes. Image intensity profiles confirmed the visibly improved contrast and reduced partial volume effect at water/fat interfaces. We observed a 17+/-2% increased SNR of hot lesions surrounded by fat, while image quality was almost completely retained in fat-free image regions. An additional in vivo experiment proved the applicability of the presented technique in practice, and again verified the beneficial impact of fat-constrained OE reconstruction on PET image quality.

Influence of radiation dose and reconstruction algorithm in MDCT assessment of airway wall thickness: A phantom study

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

Gomez-Cardona, Daniel; Nagle, Scott K.; Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, 600 Highland Avenue, Madison, Wisconsin 53792

Purpose: Wall thickness (WT) is an airway feature of great interest for the assessment of morphological changes in the lung parenchyma. Multidetector computed tomography (MDCT) has recently been used to evaluate airway WT, but the potential risk of radiation-induced carcinogenesis—particularly in younger patients—might limit a wider use of this imaging method in clinical practice. The recent commercial implementation of the statistical model-based iterative reconstruction (MBIR) algorithm, instead of the conventional filtered back projection (FBP) algorithm, has enabled considerable radiation dose reduction in many other clinical applications of MDCT. The purpose of this work was to study the impact of radiationmore » dose and MBIR in the MDCT assessment of airway WT. Methods: An airway phantom was scanned using a clinical MDCT system (Discovery CT750 HD, GE Healthcare) at 4 kV levels and 5 mAs levels. Both FBP and a commercial implementation of MBIR (Veo{sup TM}, GE Healthcare) were used to reconstruct CT images of the airways. For each kV–mAs combination and each reconstruction algorithm, the contrast-to-noise ratio (CNR) of the airways was measured, and the WT of each airway was measured and compared with the nominal value; the relative bias and the angular standard deviation in the measured WT were calculated. For each airway and reconstruction algorithm, the overall performance of WT quantification across all of the 20 kV–mAs combinations was quantified by the sum of squares (SSQs) of the difference between the measured and nominal WT values. Finally, the particular kV–mAs combination and reconstruction algorithm that minimized radiation dose while still achieving a reference WT quantification accuracy level was chosen as the optimal acquisition and reconstruction settings. Results: The wall thicknesses of seven airways of different sizes were analyzed in the study. Compared with FBP, MBIR improved the CNR of the airways, particularly at low

Experimental study on the 3D image reconstruction in a truncated Archimedean-like spiral geometry with a long-rectangular detector and its image characteristics

NASA Astrophysics Data System (ADS)

Hong, Daeki; Cho, Heemoon; Cho, Hyosung; Choi, Sungil; Je, Uikyu; Park, Yeonok; Park, Chulkyu; Lim, Hyunwoo; Park, Soyoung; Woo, Taeho

2015-11-01

In this work, we performed a feasibility study on the three-dimensional (3D) image reconstruction in a truncated Archimedean-like spiral geometry with a long-rectangular detector for application to high-accurate, cost-effective dental x-ray imaging. Here an x-ray tube and a detector rotate together around the rotational axis several times and, concurrently, the detector moves horizontally in the detector coordinate at a constant speed to cover the whole imaging volume during the projection data acquisition. We established a table-top setup which mainly consists of an x-ray tube (60 kVp, 5 mA), a narrow CMOS-type detector (198-μm pixel resolution, 184 (W)×1176 (H) pixel dimension), and a rotational stage for sample mounting and performed a systematic experiment to demonstrate the viability of the proposed approach to volumetric dental imaging. For the image reconstruction, we employed a compressed-sensing (CS)-based algorithm, rather than a common filtered-backprojection (FBP) one, for more accurate reconstruction. We successfully reconstructed 3D images of considerably high quality and investigated the image characteristics in terms of the image value profile, the contrast-to-noise ratio (CNR), and the spatial resolution.

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.

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

Design of 4D x-ray tomography experiments for reconstruction using regularized iterative algorithms

NASA Astrophysics Data System (ADS)

Mohan, K. Aditya

2017-10-01

4D X-ray computed tomography (4D-XCT) is widely used to perform non-destructive characterization of time varying physical processes in various materials. The conventional approach to improving temporal resolution in 4D-XCT involves the development of expensive and complex instrumentation that acquire data faster with reduced noise. It is customary to acquire data with many tomographic views at a high signal to noise ratio. Instead, temporal resolution can be improved using regularized iterative algorithms that are less sensitive to noise and limited views. These algorithms benefit from optimization of other parameters such as the view sampling strategy while improving temporal resolution by reducing the total number of views or the detector exposure time. This paper presents the design principles of 4D-XCT experiments when using regularized iterative algorithms derived using the framework of model-based reconstruction. A strategy for performing 4D-XCT experiments is presented that allows for improving the temporal resolution by progressively reducing the number of views or the detector exposure time. Theoretical analysis of the effect of the data acquisition parameters on the detector signal to noise ratio, spatial reconstruction resolution, and temporal reconstruction resolution is also presented in this paper.

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

Measurement of vascular wall attenuation: comparison of CT angiography using model-based iterative reconstruction with standard filtered back-projection algorithm CT in vitro.

PubMed

Suzuki, Shigeru; Machida, Haruhiko; Tanaka, Isao; Ueno, Eiko

2012-11-01

To compare the performance of model-based iterative reconstruction (MBIR) with that of standard filtered back projection (FBP) for measuring vascular wall attenuation. After subjecting 9 vascular models (actual attenuation value of wall, 89 HU) with wall thickness of 0.5, 1.0, or 1.5 mm that we filled with contrast material of 275, 396, or 542 HU to scanning using 64-detector computed tomography (CT), we reconstructed images using MBIR and FBP (Bone, Detail kernels) and measured wall attenuation at the center of the wall for each model. We performed attenuation measurements for each model and additional supportive measurements by a differentiation curve. We analyzed statistics using analyzes of variance with repeated measures. Using the Bone kernel, standard deviation of the measurement exceeded 30 HU in most conditions. In measurements at the wall center, the attenuation values obtained using MBIR were comparable to or significantly closer to the actual wall attenuation than those acquired using Detail kernel. Using differentiation curves, we could measure attenuation for models with walls of 1.0- or 1.5-mm thickness using MBIR but only those of 1.5-mm thickness using Detail kernel. We detected no significant differences among the attenuation values of the vascular walls of either thickness (MBIR, P=0.1606) or among the 3 densities of intravascular contrast material (MBIR, P=0.8185; Detail kernel, P=0.0802). Compared with FBP, MBIR reduces both reconstruction blur and image noise simultaneously, facilitates recognition of vascular wall boundaries, and can improve accuracy in measuring wall attenuation. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Novel automated inversion algorithm for temperature reconstruction using gas isotopes from ice cores

NASA Astrophysics Data System (ADS)

Döring, Michael; Leuenberger, Markus C.

2018-06-01

Greenland past temperature history can be reconstructed by forcing the output of a firn-densification and heat-diffusion model to fit multiple gas-isotope data (δ15N or δ40Ar or δ15Nexcess) extracted from ancient air in Greenland ice cores using published accumulation-rate (Acc) datasets. We present here a novel methodology to solve this inverse problem, by designing a fully automated algorithm. To demonstrate the performance of this novel approach, we begin by intentionally constructing synthetic temperature histories and associated δ15N datasets, mimicking real Holocene data that we use as true values (targets) to be compared to the output of the algorithm. This allows us to quantify uncertainties originating from the algorithm itself. The presented approach is completely automated and therefore minimizes the subjective impact of manual parameter tuning, leading to reproducible temperature estimates. In contrast to many other ice-core-based temperature reconstruction methods, the presented approach is completely independent from ice-core stable-water isotopes, providing the opportunity to validate water-isotope-based reconstructions or reconstructions where water isotopes are used together with δ15N or δ40Ar. We solve the inverse problem T(δ15N, Acc) by using a combination of a Monte Carlo based iterative approach and the analysis of remaining mismatches between modelled and target data, based on cubic-spline filtering of random numbers and the laboratory-determined temperature sensitivity for nitrogen isotopes. Additionally, the presented reconstruction approach was tested by fitting measured δ40Ar and δ15Nexcess data, which led as well to a robust agreement between modelled and measured data. The obtained final mismatches follow a symmetric standard-distribution function. For the study on synthetic data, 95 % of the mismatches compared to the synthetic target data are in an envelope between 3.0 to 6.3 permeg for δ15N and 0.23 to 0.51 K

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

NASA Astrophysics Data System (ADS)

Felix, Simon; Bolzern, Roman; Battaglia, Marina

2017-11-01

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS_CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS_CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

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

Comparison of interpolation functions to improve a rebinning-free CT-reconstruction algorithm.

PubMed

de las Heras, Hugo; Tischenko, Oleg; Xu, Yuan; Hoeschen, Christoph

2008-01-01

The robust algorithm OPED for the reconstruction of images from Radon data has been recently developed. This reconstructs an image from parallel data within a special scanning geometry that does not need rebinning but only a simple re-ordering, so that the acquired fan data can be used directly for the reconstruction. However, if the number of rays per fan view is increased, there appear empty cells in the sinogram. These cells need to be filled by interpolation before the reconstruction can be carried out. The present paper analyzes linear interpolation, cubic splines and parametric (or "damped") splines for the interpolation task. The reconstruction accuracy in the resulting images was measured by the Normalized Mean Square Error (NMSE), the Hilbert Angle, and the Mean Relative Error. The spatial resolution was measured by the Modulation Transfer Function (MTF). Cubic splines were confirmed to be the most recommendable method. The reconstructed images resulting from cubic spline interpolation show a significantly lower NMSE than the ones from linear interpolation and have the largest MTF for all frequencies. Parametric splines proved to be advantageous only for small sinograms (below 50 fan views).

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.

Effect of filters and reconstruction algorithms on I-124 PET in Siemens Inveon PET scanner

NASA Astrophysics Data System (ADS)

Ram Yu, A.; Kim, Jin Su

2015-10-01

Purpose: To assess the effects of filtering and reconstruction on Siemens I-124 PET data. Methods: A Siemens Inveon PET was used. Spatial resolution of I-124 was measured to a transverse offset of 50 mm from the center FBP, 2D ordered subset expectation maximization (OSEM2D), 3D re-projection algorithm (3DRP), and maximum a posteriori (MAP) methods were tested. Non-uniformity (NU), recovery coefficient (RC), and spillover ratio (SOR) parameterized image quality. Mini deluxe phantom data of I-124 was also assessed. Results: Volumetric resolution was 7.3 mm3 from the transverse FOV center when FBP reconstruction algorithms with ramp filter was used. MAP yielded minimal NU with β =1.5. OSEM2D yielded maximal RC. SOR was below 4% for FBP with ramp, Hamming, Hanning, or Shepp-Logan filters. Based on the mini deluxe phantom results, an FBP with Hanning or Parzen filters, or a 3DRP with Hanning filter yielded feasible I-124 PET data.Conclusions: Reconstruction algorithms and filters were compared. FBP with Hanning or Parzen filters, or 3DRP with Hanning filter yielded feasible data for quantifying I-124 PET.

Algorithmic aspects for the reconstruction of spatio-spectral data cubes in the perspective of the SKA

NASA Astrophysics Data System (ADS)

Mary, D.; Ferrari, A.; Ferrari, C.; Deguignet, J.; Vannier, M.

2016-12-01

With millions of receivers leading to TerraByte data cubes, the story of the giant SKA telescope is also that of collaborative efforts from radioastronomy, signal processing, optimization and computer sciences. Reconstructing SKA cubes poses two challenges. First, the majority of existing algorithms work in 2D and cannot be directly translated into 3D. Second, the reconstruction implies solving an inverse problem and it is not clear what ultimate limit we can expect on the error of this solution. This study addresses (of course partially) both challenges. We consider an extremely simple data acquisition model, and we focus on strategies making it possible to implement 3D reconstruction algorithms that use state-of-the-art image/spectral regularization. The proposed approach has two main features: (i) reduced memory storage with respect to a previous approach; (ii) efficient parallelization and ventilation of the computational load over the spectral bands. This work will allow to implement and compare various 3D reconstruction approaches in a large scale framework.

Principle and Reconstruction Algorithm for Atomic-Resolution Holography

NASA Astrophysics Data System (ADS)

Matsushita, Tomohiro; Muro, Takayuki; Matsui, Fumihiko; Happo, Naohisa; Hosokawa, Shinya; Ohoyama, Kenji; Sato-Tomita, Ayana; Sasaki, Yuji C.; Hayashi, Kouichi

2018-06-01

Atomic-resolution holography makes it possible to obtain the three-dimensional (3D) structure around a target atomic site. Translational symmetry of the atomic arrangement of the sample is not necessary, and the 3D atomic image can be measured when the local structure of the target atomic site is oriented. Therefore, 3D local atomic structures such as dopants and adsorbates are observable. Here, the atomic-resolution holography comprising photoelectron holography, X-ray fluorescence holography, neutron holography, and their inverse modes are treated. Although the measurement methods are different, they can be handled with a unified theory. The algorithm for reconstructing 3D atomic images from holograms plays an important role. Although Fourier transform-based methods have been proposed, they require the multiple-energy holograms. In addition, they cannot be directly applied to photoelectron holography because of the phase shift problem. We have developed methods based on the fitting method for reconstructing from single-energy and photoelectron holograms. The developed methods are applicable to all types of atomic-resolution holography.

Evaluation of algorithms for point cloud surface reconstruction through the analysis of shape parameters

NASA Astrophysics Data System (ADS)

Cao, Lu; Verbeek, Fons J.

2012-03-01

In computer graphics and visualization, reconstruction of a 3D surface from a point cloud is an important research area. As the surface contains information that can be measured, i.e. expressed in features, the application of surface reconstruction can be potentially important for application in bio-imaging. Opportunities in this application area are the motivation for this study. In the past decade, a number of algorithms for surface reconstruction have been proposed. Generally speaking, these methods can be separated into two categories: i.e., explicit representation and implicit approximation. Most of the aforementioned methods are firmly based in theory; however, so far, no analytical evaluation between these methods has been presented. The straightforward way of evaluation has been by convincing through visual inspection. Through evaluation we search for a method that can precisely preserve the surface characteristics and that is robust in the presence of noise. The outcome will be used to improve reliability in surface reconstruction of biological models. We, therefore, use an analytical approach by selecting features as surface descriptors and measure these features in varying conditions. We selected surface distance, surface area and surface curvature as three major features to compare quality of the surface created by the different algorithms. Our starting point has been ground truth values obtained from analytical shapes such as the sphere and the ellipsoid. In this paper we present four classical surface reconstruction methods from the two categories mentioned above, i.e. the Power Crust, the Robust Cocone, the Fourier-based method and the Poisson reconstruction method. The results obtained from our experiments indicate that Poisson reconstruction method performs the best in the presence of noise.

Optimized image acquisition for breast tomosynthesis in projection and reconstruction space.

PubMed

Chawla, Amarpreet S; Lo, Joseph Y; Baker, Jay A; Samei, Ehsan

2009-11-01

Breast tomosynthesis has been an exciting new development in the field of breast imaging. While the diagnostic improvement via tomosynthesis is notable, the full potential of tomosynthesis has not yet been realized. This may be attributed to the dependency of the diagnostic quality of tomosynthesis on multiple variables, each of which needs to be optimized. Those include dose, number of angular projections, and the total angular span of those projections. In this study, the authors investigated the effects of these acquisition parameters on the overall diagnostic image quality of breast tomosynthesis in both the projection and reconstruction space. Five mastectomy specimens were imaged using a prototype tomosynthesis system. 25 angular projections of each specimen were acquired at 6.2 times typical single-view clinical dose level. Images at lower dose levels were then simulated using a noise modification routine. Each projection image was supplemented with 84 simulated 3 mm 3D lesions embedded at the center of 84 nonoverlapping ROIs. The projection images were then reconstructed using a filtered backprojection algorithm at different combinations of acquisition parameters to investigate which of the many possible combinations maximizes the performance. Performance was evaluated in terms of a Laguerre-Gauss channelized Hotelling observer model-based measure of lesion detectability. The analysis was also performed without reconstruction by combining the model results from projection images using Bayesian decision fusion algorithm. The effect of acquisition parameters on projection images and reconstructed slices were then compared to derive an optimization rule for tomosynthesis. The results indicated that projection images yield comparable but higher performance than reconstructed images. Both modes, however, offered similar trends: Performance improved with an increase in the total acquisition dose level and the angular span. Using a constant dose level and angular

Technical Note: FreeCT_wFBP: A robust, efficient, open-source implementation of weighted filtered backprojection for helical, fan-beam CT.

PubMed

Hoffman, John; Young, Stefano; Noo, Frédéric; McNitt-Gray, Michael

2016-03-01

With growing interest in quantitative imaging, radiomics, and CAD using CT imaging, the need to explore the impacts of acquisition and reconstruction parameters has grown. This usually requires extensive access to the scanner on which the data were acquired and its workflow is not designed for large-scale reconstruction projects. Therefore, the authors have developed a freely available, open-source software package implementing a common reconstruction method, weighted filtered backprojection (wFBP), for helical fan-beam CT applications. FreeCT_wFBP is a low-dependency, GPU-based reconstruction program utilizing c for the host code and Nvidia CUDA C for GPU code. The software is capable of reconstructing helical scans acquired with arbitrary pitch-values, and sampling techniques such as flying focal spots and a quarter-detector offset. In this work, the software has been described and evaluated for reconstruction speed, image quality, and accuracy. Speed was evaluated based on acquisitions of the ACR CT accreditation phantom under four different flying focal spot configurations. Image quality was assessed using the same phantom by evaluating CT number accuracy, uniformity, and contrast to noise ratio (CNR). Finally, reconstructed mass-attenuation coefficient accuracy was evaluated using a simulated scan of a FORBILD thorax phantom and comparing reconstructed values to the known phantom values. The average reconstruction time evaluated under all flying focal spot configurations was found to be 17.4 ± 1.0 s for a 512 row × 512 column × 32 slice volume. Reconstructions of the ACR phantom were found to meet all CT Accreditation Program criteria including CT number, CNR, and uniformity tests. Finally, reconstructed mass-attenuation coefficient values of water within the FORBILD thorax phantom agreed with original phantom values to within 0.0001 mm(2)/g (0.01%). FreeCT_wFBP is a fast, highly configurable reconstruction package for third-generation CT available under

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.

A method for velocity signal reconstruction of AFDISAR/PDV based on crazy-climber algorithm

NASA Astrophysics Data System (ADS)

Peng, Ying-cheng; Guo, Xian; Xing, Yuan-ding; Chen, Rong; Li, Yan-jie; Bai, Ting

2017-10-01

The resolution of Continuous wavelet transformation (CWT) is different when the frequency is different. For this property, the time-frequency signal of coherent signal obtained by All Fiber Displacement Interferometer System for Any Reflector (AFDISAR) is extracted. Crazy-climber Algorithm is adopted to extract wavelet ridge while Velocity history curve of the measuring object is obtained. Numerical simulation is carried out. The reconstruction signal is completely consistent with the original signal, which verifies the accuracy of the algorithm. Vibration of loudspeaker and free end of Hopkinson incident bar under impact loading are measured by AFDISAR, and the measured coherent signals are processed. Velocity signals of loudspeaker and free end of Hopkinson incident bar are reconstructed respectively. Comparing with the theoretical calculation, the particle vibration arrival time difference error of the free end of Hopkinson incident bar is 2μs. It is indicated from the results that the algorithm is of high accuracy, and is of high adaptability to signals of different time-frequency feature. The algorithm overcomes the limitation of modulating the time window artificially according to the signal variation when adopting STFT, and is suitable for extracting signal measured by AFDISAR.

A neighboring structure reconstructed matching algorithm based on LARK features

NASA Astrophysics Data System (ADS)

Xue, Taobei; Han, Jing; Zhang, Yi; Bai, Lianfa

2015-11-01

Aimed at the low contrast ratio and high noise of infrared images, and the randomness and ambient occlusion of its objects, this paper presents a neighboring structure reconstructed matching (NSRM) algorithm based on LARK features. The neighboring structure relationships of local window are considered based on a non-negative linear reconstruction method to build a neighboring structure relationship matrix. Then the LARK feature matrix and the NSRM matrix are processed separately to get two different similarity images. By fusing and analyzing the two similarity images, those infrared objects are detected and marked by the non-maximum suppression. The NSRM approach is extended to detect infrared objects with incompact structure. High performance is demonstrated on infrared body set, indicating a lower false detecting rate than conventional methods in complex natural scenes.

Regularized iterative integration combined with non-linear diffusion filtering for phase-contrast x-ray computed tomography.

PubMed

Burger, Karin; Koehler, Thomas; Chabior, Michael; Allner, Sebastian; Marschner, Mathias; Fehringer, Andreas; Willner, Marian; Pfeiffer, Franz; Noël, Peter

2014-12-29

Phase-contrast x-ray computed tomography has a high potential to become clinically implemented because of its complementarity to conventional absorption-contrast.In this study, we investigate noise-reducing but resolution-preserving analytical reconstruction methods to improve differential phase-contrast imaging. We apply the non-linear Perona-Malik filter on phase-contrast data prior or post filtered backprojected reconstruction. Secondly, the Hilbert kernel is replaced by regularized iterative integration followed by ramp filtered backprojection as used for absorption-contrast imaging. Combining the Perona-Malik filter with this integration algorithm allows to successfully reveal relevant sample features, quantitatively confirmed by significantly increased structural similarity indices and contrast-to-noise ratios. With this concept, phase-contrast imaging can be performed at considerably lower dose.

Comparison of forward- and back-projection in vivo EPID dosimetry for VMAT treatment of the prostate

NASA Astrophysics Data System (ADS)

Bedford, James L.; Hanson, Ian M.; Hansen, Vibeke N.

2018-01-01

In the forward-projection method of portal dosimetry for volumetric modulated arc therapy (VMAT), the integrated signal at the electronic portal imaging device (EPID) is predicted at the time of treatment planning, against which the measured integrated image is compared. In the back-projection method, the measured signal at each gantry angle is back-projected through the patient CT scan to give a measure of total dose to the patient. This study aims to investigate the practical agreement between the two types of EPID dosimetry for prostate radiotherapy. The AutoBeam treatment planning system produced VMAT plans together with corresponding predicted portal images, and a total of 46 sets of gantry-resolved portal images were acquired in 13 patients using an iViewGT portal imager. For the forward-projection method, each acquisition of gantry-resolved images was combined into a single integrated image and compared with the predicted image. For the back-projection method, iViewDose was used to calculate the dose distribution in the patient for comparison with the planned dose. A gamma index for 3% and 3 mm was used for both methods. The results were investigated by delivering the same plans to a phantom and repeating some of the deliveries with deliberately introduced errors. The strongest agreement between forward- and back-projection methods is seen in the isocentric intensity/dose difference, with moderate agreement in the mean gamma. The strongest correlation is observed within a given patient, with less correlation between patients, the latter representing the accuracy of prediction of the two methods. The error study shows that each of the two methods has its own distinct sensitivity to errors, but that overall the response is similar. The forward- and back-projection EPID dosimetry methods show moderate agreement in this series of prostate VMAT patients, indicating that both methods can contribute to the verification of dose delivered to the patient.

Oblique reconstructions in tomosynthesis. II. Super-resolution

PubMed Central

Acciavatti, Raymond J.; Maidment, Andrew D. A.

2013-01-01

Purpose: In tomosynthesis, super-resolution has been demonstrated using reconstruction planes parallel to the detector. Super-resolution allows for subpixel resolution relative to the detector. The purpose of this work is to develop an analytical model that generalizes super-resolution to oblique reconstruction planes. Methods: In a digital tomosynthesis system, a sinusoidal test object is modeled along oblique angles (i.e., “pitches”) relative to the plane of the detector in a 3D divergent-beam acquisition geometry. To investigate the potential for super-resolution, the input frequency is specified to be greater than the alias frequency of the detector. Reconstructions are evaluated in an oblique plane along the extent of the object using simple backprojection (SBP) and filtered backprojection (FBP). By comparing the amplitude of the reconstruction against the attenuation coefficient of the object at various frequencies, the modulation transfer function (MTF) is calculated to determine whether modulation is within detectable limits for super-resolution. For experimental validation of super-resolution, a goniometry stand was used to orient a bar pattern phantom along various pitches relative to the breast support in a commercial digital breast tomosynthesis system. Results: Using theoretical modeling, it is shown that a single projection image cannot resolve a sine input whose frequency exceeds the detector alias frequency. The high frequency input is correctly visualized in SBP or FBP reconstruction using a slice along the pitch of the object. The Fourier transform of this reconstructed slice is maximized at the input frequency as proof that the object is resolved. Consistent with the theoretical results, experimental images of a bar pattern phantom showed super-resolution in oblique reconstructions. At various pitches, the highest frequency with detectable modulation was determined by visual inspection of the bar patterns. The dependency of the highest

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

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

Felix, Simon; Bolzern, Roman; Battaglia, Marina, E-mail: simon.felix@fhnw.ch, E-mail: roman.bolzern@fhnw.ch, E-mail: marina.battaglia@fhnw.ch

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS-CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS-CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation ofmore » quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.« less

Some Nonlinear Reconstruction Algorithms for Electrical Impedance Tomography

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

Berryman, J G

2001-03-09

An impedance camera [Henderson and Webster, 1978; Dines and Lytle, 1981]--or what is now more commonly called electrical impedance tomography--attempts to image the electrical impedance (or just the conductivity) distribution inside a body using electrical measurements on its boundary. The method has been used successfully in both biomedical [Brown, 1983; Barber and Brown, 1986; J. C. Newell, D. G. Gisser, and D. Isaacson, 1988; Webster, 1990] and geophysical applications [Wexler, Fry, and Neurnan, 1985; Daily, Lin, and Buscheck, 1987], but the analysis of optimal reconstruction algorithms is still progressing [Murai and Kagawa, 1985; Wexler, Fry, and Neurnan, 1985; Kohn andmore » Vogelius, 1987; Yorkey and Webster, 1987; Yorkey, Webster, and Tompkins, 1987; Berryman and Kohn, 1990; Kohn and McKenney, 1990; Santosa and Vogelius, 1990; Yorkey, 1990]. The most common application is monitoring the influx or efflux of a highly conducting fluid (such as brine in a porous rock or blood in the human body) through the volume being imaged. For biomedical applications, this met hod does not have the resolution of radiological methods, but it is comparatively safe and inexpensive and therefore provides a valuable alternative when continuous monitoring of a patient or process is desired. The following discussion is intended first t o summarize the physics of electrical impedance tomography, then to provide a few details of the data analysis and forward modeling requirements, and finally to outline some of the reconstruction algorithms that have proven to be most useful in practice. Pointers to the literature are provided throughout this brief narrative and the reader is encouraged to explore the references for more complete discussions of the various issues raised here.« less

A spectral image processing algorithm for evaluating the influence of the illuminants on the reconstructed reflectance

NASA Astrophysics Data System (ADS)

Toadere, Florin

2017-12-01

A spectral image processing algorithm that allows the illumination of the scene with different illuminants together with the reconstruction of the scene's reflectance is presented. Color checker spectral image and CIE A (warm light 2700 K), D65 (cold light 6500 K) and Cree TW Series LED T8 (4000 K) are employed for scene illumination. Illuminants used in the simulations have different spectra and, as a result of their illumination, the colors of the scene change. The influence of the illuminants on the reconstruction of the scene's reflectance is estimated. Demonstrative images and reflectance showing the operation of the algorithm are illustrated.

Multi-thread parallel algorithm for reconstructing 3D large-scale porous structures

NASA Astrophysics Data System (ADS)

Ju, Yang; Huang, Yaohui; Zheng, Jiangtao; Qian, Xu; Xie, Heping; Zhao, Xi

2017-04-01

Geomaterials inherently contain many discontinuous, multi-scale, geometrically irregular pores, forming a complex porous structure that governs their mechanical and transport properties. The development of an efficient reconstruction method for representing porous structures can significantly contribute toward providing a better understanding of the governing effects of porous structures on the properties of porous materials. In order to improve the efficiency of reconstructing large-scale porous structures, a multi-thread parallel scheme was incorporated into the simulated annealing reconstruction method. In the method, four correlation functions, which include the two-point probability function, the linear-path functions for the pore phase and the solid phase, and the fractal system function for the solid phase, were employed for better reproduction of the complex well-connected porous structures. In addition, a random sphere packing method and a self-developed pre-conditioning method were incorporated to cast the initial reconstructed model and select independent interchanging pairs for parallel multi-thread calculation, respectively. The accuracy of the proposed algorithm was evaluated by examining the similarity between the reconstructed structure and a prototype in terms of their geometrical, topological, and mechanical properties. Comparisons of the reconstruction efficiency of porous models with various scales indicated that the parallel multi-thread scheme significantly shortened the execution time for reconstruction of a large-scale well-connected porous model compared to a sequential single-thread procedure.

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.

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

Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE

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

Solomon, Justin, E-mail: justin.solomon@duke.edu; Samei, Ehsan

2014-09-15

Purpose: Quantum noise properties of CT images are generally assessed using simple geometric phantoms with uniform backgrounds. Such phantoms may be inadequate when assessing nonlinear reconstruction or postprocessing algorithms. The purpose of this study was to design anatomically informed textured phantoms and use the phantoms to assess quantum noise properties across two clinically available reconstruction algorithms, filtered back projection (FBP) and sinogram affirmed iterative reconstruction (SAFIRE). Methods: Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom included intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based onmore » a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and, along with a uniform phantom of similar size, were imaged on a Siemens SOMATOM Definition Flash CT scanner and reconstructed with FBP and SAFIRE. Fifty repeated acquisitions were acquired for each background type and noise was assessed by estimating pixel-value statistics, such as standard deviation (i.e., noise magnitude), autocorrelation, and noise power spectrum. Noise stationarity was also assessed by examining the spatial distribution of noise magnitude. The noise properties were compared across background types and between the two reconstruction algorithms. Results: In FBP and SAFIRE images, noise was globally nonstationary for all phantoms. In FBP images of all phantoms, and in SAFIRE images of the uniform phantom, noise appeared to be locally stationary (within a reasonably small region of interest). Noise was locally nonstationary in SAFIRE images of the textured phantoms with edge pixels showing higher noise magnitude compared to pixels in more homogenous regions. For pixels in uniform regions, noise

Compressed-sensing-based content-driven hierarchical reconstruction: Theory and application to C-arm cone-beam tomography

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

Langet, Hélène; Laboratoire des Signaux et Systèmes, CentraleSupélec, Gif-sur-Yvette F-91192; Center for Visual Computing, CentraleSupélec, Châtenay-Malabry F-92295

2015-09-15

Purpose: This paper addresses the reconstruction of x-ray cone-beam computed tomography (CBCT) for interventional C-arm systems. Subsampling of CBCT is a significant issue with C-arms due to their slow rotation and to the low frame rate of their flat panel x-ray detectors. The aim of this work is to propose a novel method able to handle the subsampling artifacts generally observed with analytical reconstruction, through a content-driven hierarchical reconstruction based on compressed sensing. Methods: The central idea is to proceed with a hierarchical method where the most salient features (high intensities or gradients) are reconstructed first to reduce the artifactsmore » these features induce. These artifacts are addressed first because their presence contaminates less salient features. Several hierarchical schemes aiming at streak artifacts reduction are introduced for C-arm CBCT: the empirical orthogonal matching pursuit approach with the ℓ{sub 0} pseudonorm for reconstructing sparse vessels; a convex variant using homotopy with the ℓ{sub 1}-norm constraint of compressed sensing, for reconstructing sparse vessels over a nonsparse background; homotopy with total variation (TV); and a novel empirical extension to nonlinear diffusion (NLD). Such principles are implemented with penalized iterative filtered backprojection algorithms. For soft-tissue imaging, the authors compare the use of TV and NLD filters as sparsity constraints, both optimized with the alternating direction method of multipliers, using a threshold for TV and a nonlinear weighting for NLD. Results: The authors show on simulated data that their approach provides fast convergence to good approximations of the solution of the TV-constrained minimization problem introduced by the compressed sensing theory. Using C-arm CBCT clinical data, the authors show that both TV and NLD can deliver improved image quality by reducing streaks. Conclusions: A flexible compressed-sensing-based algorithmic

A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans.

PubMed

Jia, Yuanyuan; Gholipour, Ali; He, Zhongshi; Warfield, Simon K

2017-05-01

In magnetic resonance (MR), hardware limitations, scan time constraints, and patient movement often result in the acquisition of anisotropic 3-D MR images with limited spatial resolution in the out-of-plane views. Our goal is to construct an isotropic high-resolution (HR) 3-D MR image through upsampling and fusion of orthogonal anisotropic input scans. We propose a multiframe super-resolution (SR) reconstruction technique based on sparse representation of MR images. Our proposed algorithm exploits the correspondence between the HR slices and the low-resolution (LR) sections of the orthogonal input scans as well as the self-similarity of each input scan to train pairs of overcomplete dictionaries that are used in a sparse-land local model to upsample the input scans. The upsampled images are then combined using wavelet fusion and error backprojection to reconstruct an image. Features are learned from the data and no extra training set is needed. Qualitative and quantitative analyses were conducted to evaluate the proposed algorithm using simulated and clinical MR scans. Experimental results show that the proposed algorithm achieves promising results in terms of peak signal-to-noise ratio, structural similarity image index, intensity profiles, and visualization of small structures obscured in the LR imaging process due to partial volume effects. Our novel SR algorithm outperforms the nonlocal means (NLM) method using self-similarity, NLM method using self-similarity and image prior, self-training dictionary learning-based SR method, averaging of upsampled scans, and the wavelet fusion method. Our SR algorithm can reduce through-plane partial volume artifact by combining multiple orthogonal MR scans, and thus can potentially improve medical image analysis, research, and clinical diagnosis.

An optimal algorithm for reconstructing images from binary measurements

NASA Astrophysics Data System (ADS)

Yang, Feng; Lu, Yue M.; Sbaiz, Luciano; Vetterli, Martin

2010-01-01

We have studied a camera with a very large number of binary pixels referred to as the gigavision camera [1] or the gigapixel digital film camera [2, 3]. Potential advantages of this new camera design include improved dynamic range, thanks to its logarithmic sensor response curve, and reduced exposure time in low light conditions, due to its highly sensitive photon detection mechanism. We use maximum likelihood estimator (MLE) to reconstruct a high quality conventional image from the binary sensor measurements of the gigavision camera. We prove that when the threshold T is "1", the negative loglikelihood function is a convex function. Therefore, optimal solution can be achieved using convex optimization. Base on filter bank techniques, fast algorithms are given for computing the gradient and the multiplication of a vector and Hessian matrix of the negative log-likelihood function. We show that with a minor change, our algorithm also works for estimating conventional images from multiple binary images. Numerical experiments with synthetic 1-D signals and images verify the effectiveness and quality of the proposed algorithm. Experimental results also show that estimation performance can be improved by increasing the oversampling factor or the number of binary images.

Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: A phantom study.

PubMed

Kim, Hyungjin; Park, Chang Min; Song, Yong Sub; Lee, Sang Min; Goo, Jin Mo

2014-05-01

To evaluate the influence of radiation dose settings and reconstruction algorithms on the measurement accuracy and reproducibility of semi-automated pulmonary nodule volumetry. CT scans were performed on a chest phantom containing various nodules (10 and 12mm; +100, -630 and -800HU) at 120kVp with tube current-time settings of 10, 20, 50, and 100mAs. Each CT was reconstructed using filtered back projection (FBP), iDose(4) and iterative model reconstruction (IMR). Semi-automated volumetry was performed by two radiologists using commercial volumetry software for nodules at each CT dataset. Noise, contrast-to-noise ratio and signal-to-noise ratio of CT images were also obtained. The absolute percentage measurement errors and differences were then calculated for volume and mass. The influence of radiation dose and reconstruction algorithm on measurement accuracy, reproducibility and objective image quality metrics was analyzed using generalized estimating equations. Measurement accuracy and reproducibility of nodule volume and mass were not significantly associated with CT radiation dose settings or reconstruction algorithms (p>0.05). Objective image quality metrics of CT images were superior in IMR than in FBP or iDose(4) at all radiation dose settings (p<0.05). Semi-automated nodule volumetry can be applied to low- or ultralow-dose chest CT with usage of a novel iterative reconstruction algorithm without losing measurement accuracy and reproducibility. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Evaluation of reconstruction techniques in regional cerebral blood flow SPECT using trade-off plots: a Monte Carlo study.

PubMed

Olsson, Anna; Arlig, Asa; Carlsson, Gudrun Alm; Gustafsson, Agnetha

2007-09-01

The image quality of single photon emission computed tomography (SPECT) depends on the reconstruction algorithm used. The purpose of the present study was to evaluate parameters in ordered subset expectation maximization (OSEM) and to compare systematically with filtered back-projection (FBP) for reconstruction of regional cerebral blood flow (rCBF) SPECT, incorporating attenuation and scatter correction. The evaluation was based on the trade-off between contrast recovery and statistical noise using different sizes of subsets, number of iterations and filter parameters. Monte Carlo simulated SPECT studies of a digital human brain phantom were used. The contrast recovery was calculated as measured contrast divided by true contrast. Statistical noise in the reconstructed images was calculated as the coefficient of variation in pixel values. A constant contrast level was reached above 195 equivalent maximum likelihood expectation maximization iterations. The choice of subset size was not crucial as long as there were > or = 2 projections per subset. The OSEM reconstruction was found to give 5-14% higher contrast recovery than FBP for all clinically relevant noise levels in rCBF SPECT. The Butterworth filter, power 6, achieved the highest stable contrast recovery level at all clinically relevant noise levels. The cut-off frequency should be chosen according to the noise level accepted in the image. Trade-off plots are shown to be a practical way of deciding the number of iterations and subset size for the OSEM reconstruction and can be used for other examination types in nuclear medicine.

An adaptive displacement estimation algorithm for improved reconstruction of thermal strain.

PubMed

Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M; Tillman, Bryan; Leers, Steven A; Kim, Kang

2015-01-01

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas' estimator and time-shift estimators such as normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas' estimator is limited by phase-wrapping and NXcorr performs poorly when the SNR is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas' estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas' estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas' estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI showed that the adaptive displacement estimator was less biased than either Loupas' estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7 to 350% and the spatial accuracy by 1.2 to 23.0% (P < 0.001). An ex vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and resulted in improved strain reconstruction.

An Adaptive Displacement Estimation Algorithm for Improved Reconstruction of Thermal Strain

PubMed Central

Ding, Xuan; Dutta, Debaditya; Mahmoud, Ahmed M.; Tillman, Bryan; Leers, Steven A.; Kim, Kang

2014-01-01

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction. PMID:25585398

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

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.

Quantitative analysis of a reconstruction method for fully three-dimensional PET.

PubMed

Suckling, J; Ott, R J; Deehan, B J

1992-03-01

The major advantage of positron emission tomography (PET) using large area planar detectors over scintillator-based commercial ring systems is the potentially larger (by a factor of two or three) axial field-of-view (FOV). However, to achieve the space invariance of the point spread function necessary for Fourier filtering a polar angle rejection criterion is applied to the data during backprojection resulting in a trade-off between FOV size and sensitivity. A new algorithm due to Defrise and co-workers developed for list-mode data overcomes this problem with a solution involving the division of the image into several subregions. A comparison between the existing backprojection-then-filter algorithm and the new method (with three subregions) has been made using both simulated and real data collected from the MUP-PET positron camera. Signal-to-noise analysis reveals that improvements of up to a factor of 1.4 are possible resulting from an increased data usage of up to a factor of 2.5 depending on the axial extent of the imaged object. Quantitation is also improved.

An Algorithm for the Reconstruction of Neutrino-induced Showers in the ANTARES Neutrino Telescope

NASA Astrophysics Data System (ADS)

Albert, A.; André, M.; Anghinolfi, M.; Anton, G.; Ardid, M.; Aubert, J.-J.; Avgitas, T.; Baret, B.; Barrios-Martí, J.; Basa, S.; Belhorma, B.; Bertin, V.; Biagi, S.; Bormuth, R.; Bourret, S.; Bouwhuis, M. C.; Brânzaş, H.; Bruijn, R.; Brunner, J.; Busto, J.; Capone, A.; Caramete, L.; Carr, J.; Celli, S.; Cherkaoui El Moursli, R.; Chiarusi, T.; Circella, M.; Coelho, J. A. B.; Coleiro, A.; Coniglione, R.; Costantini, H.; Coyle, P.; Creusot, A.; Díaz, A. F.; Deschamps, A.; De Bonis, G.; Distefano, C.; Di Palma, I.; Domi, A.; Donzaud, C.; Dornic, D.; Drouhin, D.; Eberl, T.; El Bojaddaini, I.; El Khayati, N.; Elsässer, D.; Enzenhöfer, A.; Ettahiri, A.; Fassi, F.; Felis, I.; Fusco, L. A.; Gay, P.; Giordano, V.; Glotin, H.; Grégoire, T.; Ruiz, R. Gracia; Graf, K.; Hallmann, S.; van Haren, H.; Heijboer, A. J.; Hello, Y.; Hernández-Rey, J. J.; Hößl, J.; Hofestädt, J.; Hugon, C.; Illuminati, G.; James, C. W.; de Jong, M.; Jongen, M.; Kadler, M.; Kalekin, O.; Katz, U.; Kießling, D.; Kouchner, A.; Kreter, M.; Kreykenbohm, I.; Kulikovskiy, V.; Lachaud, C.; Lahmann, R.; Lefèvre, D.; Leonora, E.; Lotze, M.; Loucatos, S.; Marcelin, M.; Margiotta, A.; Marinelli, A.; Martínez-Mora, J. A.; Mele, R.; Melis, K.; Michael, T.; Migliozzi, P.; Moussa, A.; Navas, S.; Nezri, E.; Organokov, M.; Păvălaş, G. E.; Pellegrino, C.; Perrina, C.; Piattelli, P.; Popa, V.; Pradier, T.; Quinn, L.; Racca, C.; Riccobene, G.; Sánchez-Losa, A.; Saldaña, M.; Salvadori, I.; Samtleben, D. F. E.; Sanguineti, M.; Sapienza, P.; Schüssler, F.; Sieger, C.; Spurio, M.; Stolarczyk, Th.; Taiuti, M.; Tayalati, Y.; Trovato, A.; Turpin, D.; Tönnis, C.; Vallage, B.; Van Elewyck, V.; Versari, F.; Vivolo, D.; Vizzoca, A.; Wilms, J.; Zornoza, J. D.; Zúñiga, J.

2017-12-01

Muons created by {ν }μ charged current (CC) interactions in the water surrounding the ANTARES neutrino telescope have been almost exclusively used so far in searches for cosmic neutrino sources. Due to their long range, highly energetic muons inducing Cherenkov radiation in the water are reconstructed with dedicated algorithms that allow for the determination of the parent neutrino direction with a median angular resolution of about 0.°4 for an {E}-2 neutrino spectrum. In this paper, an algorithm optimized for accurate reconstruction of energy and direction of shower events in the ANTARES detector is presented. Hadronic showers of electrically charged particles are produced by the disintegration of the nucleus both in CC and neutral current interactions of neutrinos in water. In addition, electromagnetic showers result from the CC interactions of electron neutrinos while the decay of a tau lepton produced in {ν }τ CC interactions will, in most cases, lead to either a hadronic or an electromagnetic shower. A shower can be approximated as a point source of photons. With the presented method, the shower position is reconstructed with a precision of about 1 m; the neutrino direction is reconstructed with a median angular resolution between 2° and 3° in the energy range of 1-1000 TeV. In this energy interval, the uncertainty on the reconstructed neutrino energy is about 5%-10%. The increase in the detector sensitivity due to the use of additional information from shower events in the searches for a cosmic neutrino flux is also presented.

Experimental verification of a 4D MLEM reconstruction algorithm used for in-beam PET measurements in particle therapy

NASA Astrophysics Data System (ADS)

Stützer, K.; Bert, C.; Enghardt, W.; Helmbrecht, S.; Parodi, K.; Priegnitz, M.; Saito, N.; Fiedler, F.

2013-08-01

In-beam positron emission tomography (PET) has been proven to be a reliable technique in ion beam radiotherapy for the in situ and non-invasive evaluation of the correct dose deposition in static tumour entities. In the presence of intra-fractional target motion an appropriate time-resolved (four-dimensional, 4D) reconstruction algorithm has to be used to avoid reconstructed activity distributions suffering from motion-related blurring artefacts and to allow for a dedicated dose monitoring. Four-dimensional reconstruction algorithms from diagnostic PET imaging that can properly handle the typically low counting statistics of in-beam PET data have been adapted and optimized for the characteristics of the double-head PET scanner BASTEI installed at GSI Helmholtzzentrum Darmstadt, Germany (GSI). Systematic investigations with moving radioactive sources demonstrate the more effective reduction of motion artefacts by applying a 4D maximum likelihood expectation maximization (MLEM) algorithm instead of the retrospective co-registration of phasewise reconstructed quasi-static activity distributions. Further 4D MLEM results are presented from in-beam PET measurements of irradiated moving phantoms which verify the accessibility of relevant parameters for the dose monitoring of intra-fractionally moving targets. From in-beam PET listmode data sets acquired together with a motion surrogate signal, valuable images can be generated by the 4D MLEM reconstruction for different motion patterns and motion-compensated beam delivery techniques.

Mean-variance analysis of block-iterative reconstruction algorithms modeling 3D detector response in SPECT

NASA Astrophysics Data System (ADS)

Lalush, D. S.; Tsui, B. M. W.

1998-06-01

We study the statistical convergence properties of two fast iterative reconstruction algorithms, the rescaled block-iterative (RBI) and ordered subset (OS) EM algorithms, in the context of cardiac SPECT with 3D detector response modeling. The Monte Carlo method was used to generate nearly noise-free projection data modeling the effects of attenuation, detector response, and scatter from the MCAT phantom. One thousand noise realizations were generated with an average count level approximating a typical T1-201 cardiac study. Each noise realization was reconstructed using the RBI and OS algorithms for cases with and without detector response modeling. For each iteration up to twenty, we generated mean and variance images, as well as covariance images for six specific locations. Both OS and RBI converged in the mean to results that were close to the noise-free ML-EM result using the same projection model. When detector response was not modeled in the reconstruction, RBI exhibited considerably lower noise variance than OS for the same resolution. When 3D detector response was modeled, the RBI-EM provided a small improvement in the tradeoff between noise level and resolution recovery, primarily in the axial direction, while OS required about half the number of iterations of RBI to reach the same resolution. We conclude that OS is faster than RBI, but may be sensitive to errors in the projection model. Both OS-EM and RBI-EM are effective alternatives to the EVIL-EM algorithm, but noise level and speed of convergence depend on the projection model used.

Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA).

PubMed

Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A

2015-02-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms.

Fast Parallel MR Image Reconstruction via B1-based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA)

PubMed Central

Noll, Douglas C.; Fessler, Jeffrey A.

2014-01-01

Sparsity-promoting regularization is useful for combining compressed sensing assumptions with parallel MRI for reducing scan time while preserving image quality. Variable splitting algorithms are the current state-of-the-art algorithms for SENSE-type MR image reconstruction with sparsity-promoting regularization. These methods are very general and have been observed to work with almost any regularizer; however, the tuning of associated convergence parameters is a commonly-cited hindrance in their adoption. Conversely, majorize-minimize algorithms based on a single Lipschitz constant have been observed to be slow in shift-variant applications such as SENSE-type MR image reconstruction since the associated Lipschitz constants are loose bounds for the shift-variant behavior. This paper bridges the gap between the Lipschitz constant and the shift-variant aspects of SENSE-type MR imaging by introducing majorizing matrices in the range of the regularizer matrix. The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting. Furthermore, the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting algorithms. PMID:25330484

Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.

PubMed

Chen, Chi-Kan

2017-07-26

The identification of genetic regulatory networks (GRNs) provides insights into complex cellular processes. A class of recurrent neural networks (RNNs) captures the dynamics of GRN. Algorithms combining the RNN and machine learning schemes were proposed to reconstruct small-scale GRNs using gene expression time series. We present new GRN reconstruction methods with neural networks. The RNN is extended to a class of recurrent multilayer perceptrons (RMLPs) with latent nodes. Our methods contain two steps: the edge rank assignment step and the network construction step. The former assigns ranks to all possible edges by a recursive procedure based on the estimated weights of wires of RNN/RMLP (RE RNN /RE RMLP ), and the latter constructs a network consisting of top-ranked edges under which the optimized RNN simulates the gene expression time series. The particle swarm optimization (PSO) is applied to optimize the parameters of RNNs and RMLPs in a two-step algorithm. The proposed RE RNN -RNN and RE RMLP -RNN algorithms are tested on synthetic and experimental gene expression time series of small GRNs of about 10 genes. The experimental time series are from the studies of yeast cell cycle regulated genes and E. coli DNA repair genes. The unstable estimation of RNN using experimental time series having limited data points can lead to fairly arbitrary predicted GRNs. Our methods incorporate RNN and RMLP into a two-step structure learning procedure. Results show that the RE RMLP using the RMLP with a suitable number of latent nodes to reduce the parameter dimension often result in more accurate edge ranks than the RE RNN using the regularized RNN on short simulated time series. Combining by a weighted majority voting rule the networks derived by the RE RMLP -RNN using different numbers of latent nodes in step one to infer the GRN, the method performs consistently and outperforms published algorithms for GRN reconstruction on most benchmark time series. The framework of two

Investigation of statistical iterative reconstruction for dedicated breast CT

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

Makeev, Andrey; Glick, Stephen J.

2013-08-15

Purpose: Dedicated breast CT has great potential for improving the detection and diagnosis of breast cancer. Statistical iterative reconstruction (SIR) in dedicated breast CT is a promising alternative to traditional filtered backprojection (FBP). One of the difficulties in using SIR is the presence of free parameters in the algorithm that control the appearance of the resulting image. These parameters require tuning in order to achieve high quality reconstructions. In this study, the authors investigated the penalized maximum likelihood (PML) method with two commonly used types of roughness penalty functions: hyperbolic potential and anisotropic total variation (TV) norm. Reconstructed images weremore » compared with images obtained using standard FBP. Optimal parameters for PML with the hyperbolic prior are reported for the task of detecting microcalcifications embedded in breast tissue.Methods: Computer simulations were used to acquire projections in a half-cone beam geometry. The modeled setup describes a realistic breast CT benchtop system, with an x-ray spectra produced by a point source and an a-Si, CsI:Tl flat-panel detector. A voxelized anthropomorphic breast phantom with 280 μm microcalcification spheres embedded in it was used to model attenuation properties of the uncompressed woman's breast in a pendant position. The reconstruction of 3D images was performed using the separable paraboloidal surrogates algorithm with ordered subsets. Task performance was assessed with the ideal observer detectability index to determine optimal PML parameters.Results: The authors' findings suggest that there is a preferred range of values of the roughness penalty weight and the edge preservation threshold in the penalized objective function with the hyperbolic potential, which resulted in low noise images with high contrast microcalcifications preserved. In terms of numerical observer detectability index, the PML method with optimal parameters yielded substantially

Investigation of statistical iterative reconstruction for dedicated breast CT

PubMed Central

Makeev, Andrey; Glick, Stephen J.

2013-01-01

Purpose: Dedicated breast CT has great potential for improving the detection and diagnosis of breast cancer. Statistical iterative reconstruction (SIR) in dedicated breast CT is a promising alternative to traditional filtered backprojection (FBP). One of the difficulties in using SIR is the presence of free parameters in the algorithm that control the appearance of the resulting image. These parameters require tuning in order to achieve high quality reconstructions. In this study, the authors investigated the penalized maximum likelihood (PML) method with two commonly used types of roughness penalty functions: hyperbolic potential and anisotropic total variation (TV) norm. Reconstructed images were compared with images obtained using standard FBP. Optimal parameters for PML with the hyperbolic prior are reported for the task of detecting microcalcifications embedded in breast tissue. Methods: Computer simulations were used to acquire projections in a half-cone beam geometry. The modeled setup describes a realistic breast CT benchtop system, with an x-ray spectra produced by a point source and an a-Si, CsI:Tl flat-panel detector. A voxelized anthropomorphic breast phantom with 280 μm microcalcification spheres embedded in it was used to model attenuation properties of the uncompressed woman's breast in a pendant position. The reconstruction of 3D images was performed using the separable paraboloidal surrogates algorithm with ordered subsets. Task performance was assessed with the ideal observer detectability index to determine optimal PML parameters. Results: The authors' findings suggest that there is a preferred range of values of the roughness penalty weight and the edge preservation threshold in the penalized objective function with the hyperbolic potential, which resulted in low noise images with high contrast microcalcifications preserved. In terms of numerical observer detectability index, the PML method with optimal parameters yielded substantially improved

3-Dimensional stereo implementation of photoacoustic imaging based on a new image reconstruction algorithm without using discrete Fourier transform

NASA Astrophysics Data System (ADS)

Ham, Woonchul; Song, Chulgyu

2017-05-01

In this paper, we propose a new three-dimensional stereo image reconstruction algorithm for a photoacoustic medical imaging system. We also introduce and discuss a new theoretical algorithm by using the physical concept of Radon transform. The main key concept of proposed theoretical algorithm is to evaluate the existence possibility of the acoustic source within a searching region by using the geometric distance between each sensor element of acoustic detector and the corresponding searching region denoted by grid. We derive the mathematical equation for the magnitude of the existence possibility which can be used for implementing a new proposed algorithm. We handle and derive mathematical equations of proposed algorithm for the one-dimensional sensing array case as well as two dimensional sensing array case too. A mathematical k-wave simulation data are used for comparing the image quality of the proposed algorithm with that of general conventional algorithm in which the FFT should be necessarily used. From the k-wave Matlab simulation results, we can prove the effectiveness of the proposed reconstruction algorithm.

Localized water reverberation phases and its impact on back-projection images

NASA Astrophysics Data System (ADS)

Yue, H.; Castillo, J.; Yu, C.; Meng, L.; Zhan, Z.

2017-12-01

Coherent radiators imaged by back-projections (BP) are commonly interpreted as part of the rupture process. Nevertheless, artifacts introduced by structure related phases are rarely discriminated from the rupture process. In this study, we adopt the logic of empirical Greens' function analysis (EGF) to discriminate between rupture and structure effect. We re-examine the waveforms and BP images of the 2012 Mw 7.2 Indian Ocean earthquake and an EGF event (Mw 6.2). The P wave codas of both events present similar shape with characteristic period of approximately 10 s, which are back-projected as coherent radiators near the trench. S wave BP doesn't image energy radiation near the trench. We interpret those coda waves as localized water reverberation phases excited near the trench. We perform a 2D waveform modeling using realistic bathymetry model, and find that the sharp near-trench bathymetry traps the acoustic water waves forming localized reverberation phases. These waves can be imaged as coherent near-trench radiators with similar features as that in the observations. We present a set of methodology to discriminate between the rupture and propagation effects in BP images, which can serve as a criterion of subevent identification.

Experimental study of stochastic noise propagation in SPECT images reconstructed using the conjugate gradient algorithm.

PubMed

Mariano-Goulart, D; Fourcade, M; Bernon, J L; Rossi, M; Zanca, M

2003-01-01

Thanks to an experimental study based on simulated and physical phantoms, the propagation of the stochastic noise in slices reconstructed using the conjugate gradient algorithm has been analysed versus iterations. After a first increase corresponding to the reconstruction of the signal, the noise stabilises before increasing linearly with iterations. The level of the plateau as well as the slope of the subsequent linear increase depends on the noise in the projection data.

Promote quantitative ischemia imaging via myocardial perfusion CT iterative reconstruction with tensor total generalized variation regularization

NASA Astrophysics Data System (ADS)

Gu, Chengwei; Zeng, Dong; Lin, Jiahui; Li, Sui; He, Ji; Zhang, Hao; Bian, Zhaoying; Niu, Shanzhou; Zhang, Zhang; Huang, Jing; Chen, Bo; Zhao, Dazhe; Chen, Wufan; Ma, Jianhua

2018-06-01

Myocardial perfusion computed tomography (MPCT) imaging is commonly used to detect myocardial ischemia quantitatively. A limitation in MPCT is that an additional radiation dose is required compared to unenhanced CT due to its repeated dynamic data acquisition. Meanwhile, noise and streak artifacts in low-dose cases are the main factors that degrade the accuracy of quantifying myocardial ischemia and hamper the diagnostic utility of the filtered backprojection reconstructed MPCT images. Moreover, it is noted that the MPCT images are composed of a series of 2/3D images, which can be naturally regarded as a 3/4-order tensor, and the MPCT images are globally correlated along time and are sparse across space. To obtain higher fidelity ischemia from low-dose MPCT acquisitions quantitatively, we propose a robust statistical iterative MPCT image reconstruction algorithm by incorporating tensor total generalized variation (TTGV) regularization into a penalized weighted least-squares framework. Specifically, the TTGV regularization fuses the spatial correlation of the myocardial structure and the temporal continuation of the contrast agent intake during the perfusion. Then, an efficient iterative strategy is developed for the objective function optimization. Comprehensive evaluations have been conducted on a digital XCAT phantom and a preclinical porcine dataset regarding the accuracy of the reconstructed MPCT images, the quantitative differentiation of ischemia and the algorithm’s robustness and efficiency.