Electron Trajectory Reconstruction for Advanced Compton Imaging of Gamma Rays
NASA Astrophysics Data System (ADS)
Plimley, Brian Christopher
Gamma-ray imaging is useful for detecting, characterizing, and localizing sources in a variety of fields, including nuclear physics, security, nuclear accident response, nuclear medicine, and astronomy. Compton imaging in particular provides sensitivity to weak sources and good angular resolution in a large field of view. However, the photon origin in a single event sequence is normally only limited to the surface of a cone. If the initial direction of the Compton-scattered electron can be measured, the cone can be reduced to a cone segment with width depending on the uncertainty in the direction measurement, providing a corresponding increase in imaging sensitivity. Measurement of the electron's initial direction in an efficient detection material requires very fine position resolution due to the electron's short range and tortuous path. A thick (650 mum), fully-depleted charge-coupled device (CCD) developed for infrared astronomy has 10.5-mum position resolution in two dimensions, enabling the initial trajectory measurement of electrons of energy as low as 100 keV. This is the first time the initial trajectories of electrons of such low energies have been measured in a solid material. In this work, the CCD's efficacy as a gamma-ray detector is demonstrated experimentally, using a reconstruction algorithm to measure the initial electron direction from the CCD track image. In addition, models of fast electron interaction physics, charge transport and readout were used to generate modeled tracks with known initial direction. These modeled tracks allowed the development and refinement of the reconstruction algorithm. The angular sensitivity of the reconstruction algorithm is evaluated extensively with models for tracks below 480 keV, showing a FWHM as low as 20° in the pixel plane, and 30° RMS sensitivity to the magnitude of the out-of-plane angle. The measurement of the trajectories of electrons with energies as low as 100 keV have the potential to make electron
Microwave imaging for breast cancer detection: advances in three--dimensional image reconstruction.
Golnabi, Amir H; Meaney, Paul M; Epstein, Neil R; Paulsen, Keith D
2011-01-01
Microwave imaging is based on the electrical property (permittivity and conductivity) differences in materials. Microwave imaging for biomedical applications is particularly interesting, mainly due to the fact that available range of dielectric properties for different tissues can provide important functional information about their health. Under the assumption that a 3D scattering problem can be reasonably represented as a simplified 2D model, one can take advantage of the simplicity and lower computational cost of 2D models to characterize such 3D phenomenon. Nonetheless, by eliminating excessive model simplifications, 3D microwave imaging provides potentially more valuable information over 2D techniques, and as a result, more accurate dielectric property maps may be obtained. In this paper, we present some advances we have made in three-dimensional image reconstruction, and show the results from a 3D breast phantom experiment using our clinical microwave imaging system at Dartmouth Hitchcock Medical Center (DHMC), NH.
MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction
Chen, G; Pan, X; Stayman, J; Samei, E
2014-06-15
Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within the reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical
MO-DE-202-02: Advances in Image Registration and Reconstruction for Image-Guided Neurosurgery.
Siewerdsen, J
2016-06-01
At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: (1) Keyvan Farahani, "Image-guided focused ultrasound surgery and therapy" (2) Jeffrey H. Siewerdsen, "Advances in image registration and reconstruction for image-guided neurosurgery" (3) Tina Kapur, "Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite" (4) Raj Shekhar, "Multimodality image-guided interventions: Multimodality for the rest of us" Learning Objectives: 1. Understand the principles and applications of HIFU in surgical ablation. 2. Learn about recent advances in 3D-2D and 3D deformable image registration in support of surgical safety and precision. 3. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. 4. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. 5. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and
Advances in image reconstruction software in nuclear cardiology: Is all that glitters gold?
Marcassa, Claudio; Zoccarato, Orazio
2017-02-01
The cornerstone results of nuclear cardiology in the last 25 years were obtained with the Filtered Back Projection as the preferred reconstruction method for tomographic studies. Recently, evolution of the OSEM iterative reconstruction algorithms was implemented by different vendors. The value and limitations of the new methods are briefly addressed.
Efficient holoscopy image reconstruction.
Hillmann, Dierck; Franke, Gesa; Lührs, Christian; Koch, Peter; Hüttmann, Gereon
2012-09-10
Holoscopy is a tomographic imaging technique that combines digital holography and Fourier-domain optical coherence tomography (OCT) to gain tomograms with diffraction limited resolution and uniform sensitivity over several Rayleigh lengths. The lateral image information is calculated from the spatial interference pattern formed by light scattered from the sample and a reference beam. The depth information is obtained from the spectral dependence of the recorded digital holograms. Numerous digital holograms are acquired at different wavelengths and then reconstructed for a common plane in the sample. Afterwards standard Fourier-domain OCT signal processing achieves depth discrimination. Here we describe and demonstrate an optimized data reconstruction algorithm for holoscopy which is related to the inverse scattering reconstruction of wavelength-scanned full-field optical coherence tomography data. Instead of calculating a regularized pseudoinverse of the forward operator, the recorded optical fields are propagated back into the sample volume. In one processing step the high frequency components of the scattering potential are reconstructed on a non-equidistant grid in three-dimensional spatial frequency space. A Fourier transform yields an OCT equivalent image of the object structure. In contrast to the original holoscopy reconstruction with backpropagation and Fourier transform with respect to the wavenumber, the required processing time does neither depend on the confocal parameter nor on the depth of the volume. For an imaging NA of 0.14, the processing time was decreased by a factor of 15, at higher NA the gain in reconstruction speed may reach two orders of magnitude.
Augmented Likelihood Image Reconstruction.
Stille, Maik; Kleine, Matthias; Hägele, Julian; Barkhausen, Jörg; Buzug, Thorsten M
2016-01-01
The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as dark and bright streaks. A new reconstruction algorithm is proposed with the aim to reduce these artifacts by incorporating information about shape and known attenuation coefficients of a metal implant. Image reconstruction is considered as a variational optimization problem. The afore-mentioned prior knowledge is introduced in terms of equality constraints. An augmented Lagrangian approach is adapted in order to minimize the associated log-likelihood function for transmission CT. During iterations, temporally appearing artifacts are reduced with a bilateral filter and new projection values are calculated, which are used later on for the reconstruction. A detailed evaluation in cooperation with radiologists is performed on software and hardware phantoms, as well as on clinically relevant patient data of subjects with various metal implants. Results show that the proposed reconstruction algorithm is able to outperform contemporary metal artifact reduction methods such as normalized metal artifact reduction.
Computational methods for image reconstruction.
Chung, Julianne; Ruthotto, Lars
2017-04-01
Reconstructing images from indirect measurements is a central problem in many applications, including the subject of this special issue, quantitative susceptibility mapping (QSM). The process of image reconstruction typically requires solving an inverse problem that is ill-posed and large-scale and thus challenging to solve. Although the research field of inverse problems is thriving and very active with diverse applications, in this part of the special issue we will focus on recent advances in inverse problems that are specific to deconvolution problems, the class of problems to which QSM belongs. We will describe analytic tools that can be used to investigate underlying ill-posedness and apply them to the QSM reconstruction problem and the related extensively studied image deblurring problem. We will discuss state-of-the-art computational tools and methods for image reconstruction, including regularization approaches and regularization parameter selection methods. We finish by outlining some of the current trends and future challenges. Copyright © 2016 John Wiley & Sons, Ltd.
Advances in thermographic signal reconstruction
NASA Astrophysics Data System (ADS)
Shepard, Steven M.; Frendberg Beemer, Maria
2015-05-01
Since its introduction in 2001, the Thermographic Signal Reconstruction (TSR) method has emerged as one of the most widely used methods for enhancement and analysis of thermographic sequences, with applications extending beyond industrial NDT into biomedical research, art restoration and botany. The basic TSR process, in which a noise reduced replica of each pixel time history is created, yields improvement over unprocessed image data that is sufficient for many applications. However, examination of the resulting logarithmic time derivatives of each TSR pixel replica provides significant insight into the physical mechanisms underlying the active thermography process. The deterministic and invariant properties of the derivatives have enabled the successful implementation of automated defect recognition and measurement systems. Unlike most approaches to analysis of thermography data, TSR does not depend on flawbackground contrast, so that it can also be applied to characterization and measurement of thermal properties of flaw-free samples. We present a summary of recent advances in TSR, a review of the underlying theory and examples of its implementation.
Exercises in PET Image Reconstruction
NASA Astrophysics Data System (ADS)
Nix, Oliver
These exercises are complementary to the theoretical lectures about positron emission tomography (PET) image reconstruction. They aim at providing some hands on experience in PET image reconstruction and focus on demonstrating the different data preprocessing steps and reconstruction algorithms needed to obtain high quality PET images. Normalisation, geometric-, attenuation- and scatter correction are introduced. To explain the necessity of those some basics about PET scanner hardware, data acquisition and organisation are reviewed. During the course the students use a software application based on the STIR (software for tomographic image reconstruction) library 1,2 which allows them to dynamically select or deselect corrections and reconstruction methods as well as to modify their most important parameters. Following the guided tutorial, the students get an impression on the effect the individual data precorrections have on image quality and what happens if they are forgotten. Several data sets in sinogram format are provided, such as line source data, Jaszczak phantom data sets with high and low statistics and NEMA whole body phantom data. The two most frequently used reconstruction algorithms in PET image reconstruction, filtered back projection (FBP) and the iterative OSEM (ordered subset expectation maximation) approach are used to reconstruct images. The exercise should help the students gaining an understanding what the reasons for inferior image quality and artefacts are and how to improve quality by a clever choice of reconstruction parameters.
Craniofacial reconstruction - series (image)
Patients requiring craniofacial reconstruction have: birth defects (such as hypertelorism, Crouzon's disease, Apert's syndrome) injuries to the head, face, or jaws (maxillofacial) tumors deformities caused by treatments of tumors
Reconstruction algorithm for improved ultrasound image quality.
Madore, Bruno; Meral, F Can
2012-02-01
A new algorithm is proposed for reconstructing raw RF data into ultrasound images. Previous delay-and-sum beamforming reconstruction algorithms are essentially one-dimensional, because a sum is performed across all receiving elements. In contrast, the present approach is two-dimensional, potentially allowing any time point from any receiving element to contribute to any pixel location. Computer-intensive matrix inversions are performed once, in advance, to create a reconstruction matrix that can be reused indefinitely for a given probe and imaging geometry. Individual images are generated through a single matrix multiplication with the raw RF data, without any need for separate envelope detection or gridding steps. Raw RF data sets were acquired using a commercially available digital ultrasound engine for three imaging geometries: a 64-element array with a rectangular field-of- view (FOV), the same probe with a sector-shaped FOV, and a 128-element array with rectangular FOV. The acquired data were reconstructed using our proposed method and a delay- and-sum beamforming algorithm for comparison purposes. Point spread function (PSF) measurements from metal wires in a water bath showed that the proposed method was able to reduce the size of the PSF and its spatial integral by about 20 to 38%. Images from a commercially available quality-assurance phantom had greater spatial resolution and contrast when reconstructed with the proposed approach.
Trigonometric Transforms for Image Reconstruction
1998-06-01
applying trigo - nometric transforms to image reconstruction problems. Many existing linear image reconstruc- tion techniques rely on knowledge of...ancestors. The research performed for this dissertation represents the first time the symmetric convolution-multiplication property of trigo - nometric...Fourier domain. The traditional representation of these filters will be similar to new trigo - nometric transform versions derived in later chapters
Modern methods of image reconstruction.
NASA Astrophysics Data System (ADS)
Puetter, R. C.
The author reviews the image restoration or reconstruction problem in its general setting. He first discusses linear methods for solving the problem of image deconvolution, i.e. the case in which the data are a convolution of a point-spread function and an underlying unblurred image. Next, non-linear methods are introduced in the context of Bayesian estimation, including maximum likelihood and maximum entropy methods. Then, the author discusses the role of language and information theory concepts for data compression and solving the inverse problem. The concept of algorithmic information content (AIC) is introduced and is shown to be crucial to achieving optimal data compression and optimized Bayesian priors for image reconstruction. The dependence of the AIC on the selection of language then suggests how efficient coordinate systems for the inverse problem may be selected. The author also introduced pixon-based image restoration and reconstruction methods. The relation between image AIC and the Bayesian incarnation of Occam's Razor is discussed, as well as the relation of multiresolution pixon languages and image fractal dimension. Also discussed is the relation of pixons to the role played by the Heisenberg uncertainty principle in statistical physics and how pixon-based image reconstruction provides a natural extension to the Akaike information criterion for maximum likelihood. The author presents practical applications of pixon-based Bayesian estimation to the restoration of astronomical images. He discusses the effects of noise, effects of finite sampling on resolution, and special problems associated with spatially correlated noise introduced by mosaicing. Comparisons to other methods demonstrate the significant improvements afforded by pixon-based methods and illustrate the science that such performance improvements allow.
Image processing and reconstruction
Chartrand, Rick
2012-06-15
This talk will examine some mathematical methods for image processing and the solution of underdetermined, linear inverse problems. The talk will have a tutorial flavor, mostly accessible to undergraduates, while still presenting research results. The primary approach is the use of optimization problems. We will find that relaxing the usual assumption of convexity will give us much better results.
2014-09-26
across the ellipsoid as functions of the dimensions and pole of the body and the asterocenteric position of the Earth and • .Sun are derived...the dimensions and pole of the Earth - approaching asteroid 433 Eros, confirming the results obtained by other indirect, long-term, methods. Similarly...1 1.1 "High Resolution Imaging Potential of MMT . .. .. . . . . . 1 ŕ.2 Earth Satellite Observations . 2 1.3 -)-tero-i/Planetary
Computational Imaging for VLBI Image Reconstruction
NASA Astrophysics Data System (ADS)
Bouman, Katherine L.; Johnson, Michael D.; Zoran, Daniel; Fish, Vincent L.; Doeleman, Sheperd S.; Freeman, William T.
2016-03-01
Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods often require careful tuning and parameter selection for different types of data, our method (CHIRP) produces good results under different settings such as low SNR or extended emission. The success of our method is demonstrated on realistic synthetic experiments as well as publicly available real data. We present this problem in a way that is accessible to members of the community, and provide a dataset website (vlbiimaging.csail.mit.edu) that facilitates controlled comparisons! across algorithms.
Image reconstruction in optical tomography.
Arridge, S R; Schweiger, M
1997-01-01
Optical tomography is a new medical imaging modality that is at the threshold of realization. A large amount of clinical work has shown the very real benefits that such a method could provide. At the same time a considerable effort has been put into theoretical studies of its probable success. At present there exist gaps between these two realms. In this paper we review some general approaches to inverse problems to set the context for optical tomography, defining both the terms forward problem and inverse problem. An essential requirement is to treat the problem in a nonlinear fashion, by using an iterative method. This in turn requires a convenient method of evaluating the forward problem, and its derivatives and variance. Photon transport models are described for obtaining analytical and numerical solutions for the most commonly used ones are reviewed. The inverse problem is approached by classical gradient-based solution methods. In order to develop practical implementations of these methods, we discuss the important topic of photon measurement density functions, which represent the derivative of the forward problem. We show some results that represent the most complex and realistic simulations of optical tomography yet developed. We suggest, in particular, that both time-resolved, and intensity-modulated systems can reconstruct variations in both optical absorption and scattering, but that unmodulated, non-time-resolved systems are prone to severe artefact. We believe that optical tomography reconstruction methods can now be reliably applied to a wide variety of real clinical data. The expected resolution of the method is poor, meaning that it is unlikely that the type of high-resolution images seen in computed tomography or medical resonance imaging can ever be obtained. Nevertheless we strongly expect the functional nature of these images to have a high degree of clinical significance. PMID:9232860
Method for position emission mammography image reconstruction
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.
Reconstructing HST Images of Asteroids
NASA Astrophysics Data System (ADS)
Storrs, A. D.; Bank, S.; Gerhardt, H.; Makhoul, K.
2003-12-01
We present reconstructions of images of 22 large main belt asteroids that were observed by Hubble Space Telescope with the Wide-Field/Planetary cameras. All images were restored with the MISTRAL program (Mugnier, Fusco, and Conan 2003) at enhanced spatial resolution. This is possible thanks to the well-studied and stable point spread function (PSF) on HST. We present some modeling of this process and determine that the Strehl ratio for WF/PC (aberrated) images can be improved to 130 ratio of 80 We will report sizes, shapes, and albedos for these objects, as well as any surface features. Images taken with the WFPC-2 instrument were made in a variety of filters so that it should be possible to investigate changes in mineralogy across the surface of the larger asteroids in a manner similar to that done on 4 Vesta by Binzel et al. (1997). Of particular interest are a possible water of hydration feature on 1 Ceres, and the non-observation of a constriction or gap between the components of 216 Kleopatra. Reduction of this data was aided by grant HST-GO-08583.08A from the Space Telescope Science Institute. References: Mugnier, L.M., T. Fusco, and J.-M. Conan, 2003. JOSA A (submitted) Binzel, R.P., Gaffey, M.J., Thomas, P.C., Zellner, B.H., Storrs, A.D., and Wells, E.N. 1997. Icarus 128 pp. 95-103
Heuristic reconstructions of neutron penumbral images
Nozaki, Shinya; Chen Yenwei
2004-10-01
Penumbral imaging is a technique of coded aperture imaging proposed for imaging of highly penetrating radiations. To date, the penumbral imaging technique has been successfully applied to neutron imaging in laser fusion experiments. Since the reconstruction of penumbral images is based on linear deconvolution methods, such as inverse filter and Wiener filer, the point spread function of apertures should be space invariant; it is also sensitive to the noise contained in penumbral images. In this article, we propose a new heuristic reconstruction method for neutron penumbral imaging, which can be used for a space-variant imaging system and is also very tolerant to the noise.
Image reconstruction: an overview for clinicians.
Hansen, Michael S; Kellman, Peter
2015-03-01
Image reconstruction plays a critical role in the clinical use of magnetic resonance imaging (MRI). The MRI raw data is not acquired in image space and the role of the image reconstruction process is to transform the acquired raw data into images that can be interpreted clinically. This process involves multiple signal processing steps that each have an impact on the image quality. This review explains the basic terminology used for describing and quantifying image quality in terms of signal-to-noise ratio and point spread function. In this context, several commonly used image reconstruction components are discussed. The image reconstruction components covered include noise prewhitening for phased array data acquisition, interpolation needed to reconstruct square pixels, raw data filtering for reducing Gibbs ringing artifacts, Fourier transforms connecting the raw data with image space, and phased array coil combination. The treatment of phased array coils includes a general explanation of parallel imaging as a coil combination technique. The review is aimed at readers with no signal processing experience and should enable them to understand what role basic image reconstruction steps play in the formation of clinical images and how the resulting image quality is described.
Prior image constrained image reconstruction in emerging computed tomography applications
NASA Astrophysics Data System (ADS)
Brunner, Stephen T.
Advances have been made in computed tomography (CT), especially in the past five years, by incorporating prior images into the image reconstruction process. In this dissertation, we investigate prior image constrained image reconstruction in three emerging CT applications: dual-energy CT, multi-energy photon-counting CT, and cone-beam CT in image-guided radiation therapy. First, we investigate the application of Prior Image Constrained Compressed Sensing (PICCS) in dual-energy CT, which has been called "one of the hottest research areas in CT." Phantom and animal studies are conducted using a state-of-the-art 64-slice GE Discovery 750 HD CT scanner to investigate the extent to which PICCS can enable radiation dose reduction in material density and virtual monochromatic imaging. Second, we extend the application of PICCS from dual-energy CT to multi-energy photon-counting CT, which has been called "one of the 12 topics in CT to be critical in the next decade." Numerical simulations are conducted to generate multiple energy bin images for a photon-counting CT acquisition and to investigate the extent to which PICCS can enable radiation dose efficiency improvement. Third, we investigate the performance of a newly proposed prior image constrained scatter correction technique to correct scatter-induced shading artifacts in cone-beam CT, which, when used in image-guided radiation therapy procedures, can assist in patient localization, and potentially, dose verification and adaptive radiation therapy. Phantom studies are conducted using a Varian 2100 EX system with an on-board imager to investigate the extent to which the prior image constrained scatter correction technique can mitigate scatter-induced shading artifacts in cone-beam CT. Results show that these prior image constrained image reconstruction techniques can reduce radiation dose in dual-energy CT by 50% in phantom and animal studies in material density and virtual monochromatic imaging, can lead to radiation
Studies on image compression and image reconstruction
NASA Technical Reports Server (NTRS)
Sayood, Khalid; Nori, Sekhar; Araj, A.
1994-01-01
During this six month period our works concentrated on three, somewhat different areas. We looked at and developed a number of error concealment schemes for use in a variety of video coding environments. This work is described in an accompanying (draft) Masters thesis. In the thesis we describe application of this techniques to the MPEG video coding scheme. We felt that the unique frame ordering approach used in the MPEG scheme would be a challenge to any error concealment/error recovery technique. We continued with our work in the vector quantization area. We have also developed a new type of vector quantizer, which we call a scan predictive vector quantization. The scan predictive VQ was tested on data processed at Goddard to approximate Landsat 7 HRMSI resolution and compared favorably with existing VQ techniques. A paper describing this work is included. The third area is concerned more with reconstruction than compression. While there is a variety of efficient lossless image compression schemes, they all have a common property that they use past data to encode future data. This is done either via taking differences, context modeling, or by building dictionaries. When encoding large images, this common property becomes a common flaw. When the user wishes to decode just a portion of the image, the requirement that the past history be available forces the decoding of a significantly larger portion of the image than desired by the user. Even with intelligent partitioning of the image dataset, the number of pixels decoded may be four times the number of pixels requested. We have developed an adaptive scanning strategy which can be used with any lossless compression scheme and which lowers the additional number of pixels to be decoded to about 7 percent of the number of pixels requested! A paper describing these results is included.
1982-06-01
document requires that it 1e returncd: ADVANCED IMACINGC TRACKER Dr . L. E. Schmutz Contractor: Adaptive Optics Associates, Inc. Contt-ict Number: F30602-80...Code Number: IE20 Period of Worl: Covered: jun 80 - D’:c 81 Principal Investigator: Dr . Larry Schmut~z Phone: 617 547-2786 Project Engineer: Captaia...yaJPODCVR~ ADVANCED IMAGING TRACKER 10Jun 80 - ’,’ Dec 81 PiRFORMiNO7 01G. REPORT NUMBER 7 ATII~(. ONTPA OR GRANTY NUMDERf.) Dr . 1L. E. Schiiut
NASA Technical Reports Server (NTRS)
1992-01-01
This document describes the Advanced Imaging System CCD based camera. The AIS1 camera system was developed at Photometric Ltd. in Tucson, Arizona as part of a Phase 2 SBIR contract No. NAS5-30171 from the NASA/Goddard Space Flight Center in Greenbelt, Maryland. The camera project was undertaken as a part of the Space Telescope Imaging Spectrograph (STIS) project. This document is intended to serve as a complete manual for the use and maintenance of the camera system. All the different parts of the camera hardware and software are discussed and complete schematics and source code listings are provided.
Image Reconstruction Using Analysis Model Prior
Han, Yu; Du, Huiqian; Lam, Fan; Mei, Wenbo; Fang, Liping
2016-01-01
The analysis model has been previously exploited as an alternative to the classical sparse synthesis model for designing image reconstruction methods. Applying a suitable analysis operator on the image of interest yields a cosparse outcome which enables us to reconstruct the image from undersampled data. In this work, we introduce additional prior in the analysis context and theoretically study the uniqueness issues in terms of analysis operators in general position and the specific 2D finite difference operator. We establish bounds on the minimum measurement numbers which are lower than those in cases without using analysis model prior. Based on the idea of iterative cosupport detection (ICD), we develop a novel image reconstruction model and an effective algorithm, achieving significantly better reconstruction performance. Simulation results on synthetic and practical magnetic resonance (MR) images are also shown to illustrate our theoretical claims. PMID:27379171
Geoaccurate three-dimensional reconstruction via image-based geometry
NASA Astrophysics Data System (ADS)
Walvoord, Derek J.; Rossi, Adam J.; Paul, Bradley D.; Brower, Bernie; Pellechia, Matthew F.
2013-05-01
Recent technological advances in computing capabilities and persistent surveillance systems have led to increased focus on new methods of exploiting geospatial data, bridging traditional photogrammetric techniques and state-of-the-art multiple view geometry methodology. The structure from motion (SfM) problem in Computer Vision addresses scene reconstruction from uncalibrated cameras, and several methods exist to remove the inherent projective ambiguity. However, the reconstruction remains in an arbitrary world coordinate frame without knowledge of its relationship to a xed earth-based coordinate system. This work presents a novel approach for obtaining geoaccurate image-based 3-dimensional reconstructions in the absence of ground control points by using a SfM framework and the full physical sensor model of the collection system. Absolute position and orientation information provided by the imaging platform can be used to reconstruct the scene in a xed world coordinate system. Rather than triangulating pixels from multiple image-to-ground functions, each with its own random error, the relative reconstruction is computed via image-based geometry, i.e., geometry derived from image feature correspondences. In other words, the geolocation accuracy is improved using the relative distances provided by the SfM reconstruction. Results from the Exelis Wide-Area Motion Imagery (WAMI) system are provided to discuss conclusions and areas for future work.
Iterative image reconstruction and its role in cardiothoracic computed tomography.
Singh, Sarabjeet; Khawaja, Ranish Deedar Ali; Pourjabbar, Sarvenaz; Padole, Atul; Lira, Diego; Kalra, Mannudeep K
2013-11-01
Revolutionary developments in multidetector-row computed tomography (CT) scanner technology offer several advantages for imaging of cardiothoracic disorders. As a result, expanding applications of CT now account for >85 million CT examinations annually in the United States alone. Given the large number of CT examinations performed, concerns over increase in population-based risk for radiation-induced carcinogenesis have made CT radiation dose a top safety concern in health care. In response to this concern, several technologies have been developed to reduce the dose with more efficient use of scan parameters and the use of "newer" image reconstruction techniques. Although iterative image reconstruction algorithms were first introduced in the 1970s, filtered back projection was chosen as the conventional image reconstruction technique because of its simplicity and faster reconstruction times. With subsequent advances in computational speed and power, iterative reconstruction techniques have reemerged and have shown the potential of radiation dose optimization without adversely influencing diagnostic image quality. In this article, we review the basic principles of different iterative reconstruction algorithms and their implementation for various clinical applications in cardiothoracic CT examinations for reducing radiation dose.
Image reconstruction for robot assisted ultrasound tomography
NASA Astrophysics Data System (ADS)
Aalamifar, Fereshteh; Zhang, Haichong K.; Rahmim, Arman; Boctor, Emad M.
2016-04-01
An investigation of several image reconstruction methods for robot-assisted ultrasound (US) tomography setup is presented. In the robot-assisted setup, an expert moves the US probe to the location of interest, and a robotic arm automatically aligns another US probe with it. The two aligned probes can then transmit and receive US signals which are subsequently used for tomographic reconstruction. This study focuses on reconstruction of the speed of sound. In various simulation evaluations as well as in an experiment with a millimeter-range inaccuracy, we demonstrate that the limited data provided by two probes can be used to reconstruct pixel-wise images differentiating between media with different speeds of sound. Combining the results of this investigation with the developed robot-assisted US tomography setup, we envision feasibility of this setup for tomographic imaging in applications beyond breast imaging, with potentially significant efficacy in cancer diagnosis.
Fast parallel algorithm for CT image reconstruction.
Flores, Liubov A; Vidal, Vicent; Mayo, Patricia; Rodenas, Francisco; Verdú, Gumersindo
2012-01-01
In X-ray computed tomography (CT) the X rays are used to obtain the projection data needed to generate an image of the inside of an object. The image can be generated with different techniques. Iterative methods are more suitable for the reconstruction of images with high contrast and precision in noisy conditions and from a small number of projections. Their use may be important in portable scanners for their functionality in emergency situations. However, in practice, these methods are not widely used due to the high computational cost of their implementation. In this work we analyze iterative parallel image reconstruction with the Portable Extensive Toolkit for Scientific computation (PETSc).
Bayesian image reconstruction - The pixon and optimal image modeling
NASA Technical Reports Server (NTRS)
Pina, R. K.; Puetter, R. C.
1993-01-01
In this paper we describe the optimal image model, maximum residual likelihood method (OptMRL) for image reconstruction. OptMRL is a Bayesian image reconstruction technique for removing point-spread function blurring. OptMRL uses both a goodness-of-fit criterion (GOF) and an 'image prior', i.e., a function which quantifies the a priori probability of the image. Unlike standard maximum entropy methods, which typically reconstruct the image on the data pixel grid, OptMRL varies the image model in order to find the optimal functional basis with which to represent the image. We show how an optimal basis for image representation can be selected and in doing so, develop the concept of the 'pixon' which is a generalized image cell from which this basis is constructed. By allowing both the image and the image representation to be variable, the OptMRL method greatly increases the volume of solution space over which the image is optimized. Hence the likelihood of the final reconstructed image is greatly increased. For the goodness-of-fit criterion, OptMRL uses the maximum residual likelihood probability distribution introduced previously by Pina and Puetter (1992). This GOF probability distribution, which is based on the spatial autocorrelation of the residuals, has the advantage that it ensures spatially uncorrelated image reconstruction residuals.
Bayesian image reconstruction - The pixon and optimal image modeling
NASA Astrophysics Data System (ADS)
Pina, R. K.; Puetter, R. C.
1993-06-01
In this paper we describe the optimal image model, maximum residual likelihood method (OptMRL) for image reconstruction. OptMRL is a Bayesian image reconstruction technique for removing point-spread function blurring. OptMRL uses both a goodness-of-fit criterion (GOF) and an 'image prior', i.e., a function which quantifies the a priori probability of the image. Unlike standard maximum entropy methods, which typically reconstruct the image on the data pixel grid, OptMRL varies the image model in order to find the optimal functional basis with which to represent the image. We show how an optimal basis for image representation can be selected and in doing so, develop the concept of the 'pixon' which is a generalized image cell from which this basis is constructed. By allowing both the image and the image representation to be variable, the OptMRL method greatly increases the volume of solution space over which the image is optimized. Hence the likelihood of the final reconstructed image is greatly increased. For the goodness-of-fit criterion, OptMRL uses the maximum residual likelihood probability distribution introduced previously by Pina and Puetter (1992). This GOF probability distribution, which is based on the spatial autocorrelation of the residuals, has the advantage that it ensures spatially uncorrelated image reconstruction residuals.
Heuristic optimization in penumbral image for high resolution reconstructed image
Azuma, R.; Nozaki, S.; Fujioka, S.; Chen, Y. W.; Namihira, Y.
2010-10-15
Penumbral imaging is a technique which uses the fact that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The size of the penumbral image on the detector can be mathematically determined as its aperture size, object size, and magnification. Conventional reconstruction methods are very sensitive to noise. On the other hand, the heuristic reconstruction method is very tolerant of noise. However, the aperture size influences the accuracy and resolution of the reconstructed image. In this article, we propose the optimization of the aperture size for the neutron penumbral imaging.
Image reconstruction algorithms with wavelet filtering for optoacoustic imaging
NASA Astrophysics Data System (ADS)
Gawali, S.; Leggio, L.; Broadway, C.; González, P.; Sánchez, M.; Rodríguez, S.; Lamela, H.
2016-03-01
Optoacoustic imaging (OAI) is a hybrid biomedical imaging modality based on the generation and detection of ultrasound by illuminating the target tissue by laser light. Typically, laser light in visible or near infrared spectrum is used as an excitation source. OAI is based on the implementation of image reconstruction algorithms using the spatial distribution of optical absorption in tissues. In this work, we apply a time-domain back-projection (BP) reconstruction algorithm and a wavelet filtering for point and line detection, respectively. A comparative study between point detection and integrated line detection has been carried out by evaluating their effects on the image reconstructed. Our results demonstrate that the back-projection algorithm proposed is efficient for reconstructing high-resolution images of absorbing spheres embedded in a non-absorbing medium when it is combined with the wavelet filtering.
Superresolution images reconstructed from aliased images
NASA Astrophysics Data System (ADS)
Vandewalle, Patrick; Susstrunk, Sabine E.; Vetterli, Martin
2003-06-01
In this paper, we present a simple method to almost quadruple the spatial resolution of aliased images. From a set of four low resolution, undersampled and shifted images, a new image is constructed with almost twice the resolution in each dimension. The resulting image is aliasing-free. A small aliasing-free part of the frequency domain of the images is used to compute the exact subpixel shifts. When the relative image positions are known, a higher resolution image can be constructed using the Papoulis-Gerchberg algorithm. The proposed method is tested in a simulation where all simulation parameters are well controlled, and where the resulting image can be compared with its original. The algorithm is also applied to real, noisy images from a digital camera. Both experiments show very good results.
Personnel screening with advanced multistatic imaging technology
NASA Astrophysics Data System (ADS)
Ahmed, Sherif S.
2013-05-01
Personnel screening is demanded nowadays for securing air traffic as well as critical infrastructures. The millimeter-waves are able to penetrate clothes and detect concealed objects, making them an attractive choice for security screening. Imaging methods based on multistatic architecture can ensure high quality imagery in terms of resolution and dynamic range. Following the advances in semiconductor technology, fully electronic solutions delivering real-time imaging are becoming feasible. Furthermore, the continuously increasing capabilities of digital signal processing units allow for the utilization of digital-beamforming techniques for image reconstruction, thus offering new opportunities for imaging systems to use sophisticated operation modes. Based on these modern technologies, an advanced realization addressing personnel screening in E-band with planar multistatic sparse array design is demonstrated.
Low dose dynamic myocardial CT perfusion using advanced iterative reconstruction
NASA Astrophysics Data System (ADS)
Eck, Brendan L.; Fahmi, Rachid; Fuqua, Christopher; Vembar, Mani; Dhanantwari, Amar; Bezerra, Hiram G.; Wilson, David L.
2015-03-01
Dynamic myocardial CT perfusion (CTP) can provide quantitative functional information for the assessment of coronary artery disease. However, x-ray dose in dynamic CTP is high, typically from 10mSv to >20mSv. We compared the dose reduction potential of advanced iterative reconstruction, Iterative Model Reconstruction (IMR, Philips Healthcare, Cleveland, Ohio) to hybrid iterative reconstruction (iDose4) and filtered back projection (FBP). Dynamic CTP scans were obtained using a porcine model with balloon-induced ischemia in the left anterior descending coronary artery to prescribed fractional flow reserve values. High dose dynamic CTP scans were acquired at 100kVp/100mAs with effective dose of 23mSv. Low dose scans at 75mAs, 50mAs, and 25mAs were simulated by adding x-ray quantum noise and detector electronic noise to the projection space data. Images were reconstructed with FBP, iDose4, and IMR at each dose level. Image quality in static CTP images was assessed by SNR and CNR. Blood flow was obtained using a dynamic CTP analysis pipeline and blood flow image quality was assessed using flow-SNR and flow-CNR. IMR showed highest static image quality according to SNR and CNR. Blood flow in FBP was increasingly over-estimated at reduced dose. Flow was more consistent for iDose4 from 100mAs to 50mAs, but was over-estimated at 25mAs. IMR was most consistent from 100mAs to 25mAs. Static images and flow maps for 100mAs FBP, 50mAs iDose4, and 25mAs IMR showed comparable, clear ischemia, CNR, and flow-CNR values. These results suggest that IMR can enable dynamic CTP at significantly reduced dose, at 5.8mSv or 25% of the comparable 23mSv FBP protocol.
Proton computed tomography images with algebraic reconstruction
NASA Astrophysics Data System (ADS)
Bruzzi, M.; Civinini, C.; Scaringella, M.; Bonanno, D.; Brianzi, M.; Carpinelli, M.; Cirrone, G. A. P.; Cuttone, G.; Presti, D. Lo; Maccioni, G.; Pallotta, S.; Randazzo, N.; Romano, F.; Sipala, V.; Talamonti, C.; Vanzi, E.
2017-02-01
A prototype of proton Computed Tomography (pCT) system for hadron-therapy has been manufactured and tested in a 175 MeV proton beam with a non-homogeneous phantom designed to simulate high-contrast material. BI-SART reconstruction algorithms have been implemented with GPU parallelism, taking into account of most likely paths of protons in matter. Reconstructed tomography images with density resolutions r.m.s. down to 1% and spatial resolutions <1 mm, achieved within processing times of 15‧ for a 512×512 pixels image prove that this technique will be beneficial if used instead of X-CT in hadron-therapy.
Reconstruction of 3d Digital Image of Weepingforsythia Pollen
NASA Astrophysics Data System (ADS)
Liu, Dongwu; Chen, Zhiwei; Xu, Hongzhi; Liu, Wenqi; Wang, Lina
Confocal microscopy, which is a major advance upon normal light microscopy, has been used in a number of scientific fields. By confocal microscopy techniques, cells and tissues can be visualized deeply, and three-dimensional images created. Compared with conventional microscopes, confocal microscope improves the resolution of images by eliminating out-of-focus light. Moreover, confocal microscope has a higher level of sensitivity due to highly sensitive light detectors and the ability to accumulate images captured over time. In present studies, a series of Weeping Forsythia pollen digital images (35 images in total) were acquired with confocal microscope, and the three-dimensional digital image of the pollen reconstructed with confocal microscope. Our results indicate that it's a very easy job to analysis threedimensional digital image of the pollen with confocal microscope and the probe Acridine orange (AO).
Imaging appearances of lateral ankle ligament reconstruction.
Chien, Alexander J; Jacobson, Jon A; Jamadar, David A; Brigido, Monica Kalume; Femino, John E; Hayes, Curtis W
2004-01-01
Six patients were retrospectively identified as having undergone lateral ligament reconstruction surgery. The surgical procedures were categorized into four groups: direct lateral ligament repair, peroneus brevis tendon rerouting, peroneus brevis tendon loop, and peroneus brevis tendon split and rerouting. At radiography and magnetic resonance (MR) imaging, the presence of one or more suture anchors in the region of the anterior talofibular ligament indicates direct ligament repair, whereas a fibular tunnel indicates peroneus brevis tendon rerouting or loop. Both ultrasonography (US) and MR imaging demonstrate rerouted tendons as part of lateral ankle reconstruction; however, MR imaging can also depict the rerouted tendon within an osseous tunnel if present, especially if T1-weighted sequences are used. Artifact from suture material may obscure the tendon at MR imaging but not at US. With both modalities, the integrity of the rerouted peroneus brevis tendon is best evaluated by following the tendon proximally from its distal attachment site, which typically remains unchanged. The rerouted tendon or portion of the tendon can then be traced proximally to its reattachment site. Familiarity with the surgical procedures most commonly used for lateral ankle ligament reconstruction, and with the imaging features of these procedures, is essential for avoiding diagnostic pitfalls and ensuring accurate assessment of the ligament reconstruction.
Image superresolution reconstruction via granular computing clustering.
Liu, Hongbing; Zhang, Fan; Wu, Chang-an; Huang, Jun
2014-01-01
The problem of generating a superresolution (SR) image from a single low-resolution (LR) input image is addressed via granular computing clustering in the paper. Firstly, and the training images are regarded as SR image and partitioned into some SR patches, which are resized into LS patches, the training set is composed of the SR patches and the corresponding LR patches. Secondly, the granular computing (GrC) clustering is proposed by the hypersphere representation of granule and the fuzzy inclusion measure compounded by the operation between two granules. Thirdly, the granule set (GS) including hypersphere granules with different granularities is induced by GrC and used to form the relation between the LR image and the SR image by lasso. Experimental results showed that GrC achieved the least root mean square errors between the reconstructed SR image and the original image compared with bicubic interpolation, sparse representation, and NNLasso.
Iterative image reconstruction in spectral CT
NASA Astrophysics Data System (ADS)
Hernandez, Daniel; Michel, Eric; Kim, Hye S.; Kim, Jae G.; Han, Byung H.; Cho, Min H.; Lee, Soo Y.
2012-03-01
Scan time of spectral-CTs is much longer than conventional CTs due to limited number of x-ray photons detectable by photon-counting detectors. However, the spectral pixel information in spectral-CT has much richer information on physiological and pathological status of the tissues than the CT-number in conventional CT, which makes the spectral- CT one of the promising future imaging modalities. One simple way to reduce the scan time in spectral-CT imaging is to reduce the number of views in the acquisition of projection data. But, this may result in poorer SNR and strong streak artifacts which can severely compromise the image quality. In this work, spectral-CT projection data were obtained from a lab-built spectral-CT consisting of a single CdTe photon counting detector, a micro-focus x-ray tube and scan mechanics. For the image reconstruction, we used two iterative image reconstruction methods, the simultaneous iterative reconstruction technique (SIRT) and the total variation minimization based on conjugate gradient method (CG-TV), along with the filtered back-projection (FBP) to compare the image quality. From the imaging of the iodine containing phantoms, we have observed that SIRT and CG-TV are superior to the FBP method in terms of SNR and streak artifacts.
Stochastic image reconstruction for a dual-particle imaging system
NASA Astrophysics Data System (ADS)
Hamel, M. C.; Polack, J. K.; Poitrasson-Rivière, A.; Flaska, M.; Clarke, S. D.; Pozzi, S. A.; Tomanin, A.; Peerani, P.
2016-02-01
Stochastic image reconstruction has been applied to a dual-particle imaging system being designed for nuclear safeguards applications. The dual-particle imager (DPI) is a combined Compton-scatter and neutron-scatter camera capable of producing separate neutron and photon images. The stochastic origin ensembles (SOE) method was investigated as an imaging method for the DPI because only a minimal estimation of system response is required to produce images with quality that is comparable to common maximum-likelihood methods. This work contains neutron and photon SOE image reconstructions for a 252Cf point source, two mixed-oxide (MOX) fuel canisters representing point sources, and the MOX fuel canisters representing a distributed source. Simulation of the DPI using MCNPX-PoliMi is validated by comparison of simulated and measured results. Because image quality is dependent on the number of counts and iterations used, the relationship between these quantities is investigated.
Fast Image Reconstruction with L2-Regularization
Bilgic, Berkin; Chatnuntawech, Itthi; Fan, Audrey P.; Setsompop, Kawin; Cauley, Stephen F.; Wald, Lawrence L.; Adalsteinsson, Elfar
2014-01-01
Purpose We introduce L2-regularized reconstruction algorithms with closed-form solutions that achieve dramatic computational speed-up relative to state of the art L1- and L2-based iterative algorithms while maintaining similar image quality for various applications in MRI reconstruction. Materials and Methods We compare fast L2-based methods to state of the art algorithms employing iterative L1- and L2-regularization in numerical phantom and in vivo data in three applications; 1) Fast Quantitative Susceptibility Mapping (QSD), 2) Lipid artifact suppression in Magnetic Resonance Spectroscopic Imaging (MRSI), and 3) Diffusion Spectrum Imaging (DSI). In all cases, proposed L2-based methods are compared with the state of the art algorithms, and two to three orders of magnitude speed up is demonstrated with similar reconstruction quality. Results The closed-form solution developed for regularized QSM allows processing of a 3D volume under 5 seconds, the proposed lipid suppression algorithm takes under 1 second to reconstruct single-slice MRSI data, while the PCA based DSI algorithm estimates diffusion propagators from undersampled q-space for a single slice under 30 seconds, all running in Matlab using a standard workstation. Conclusion For the applications considered herein, closed-form L2-regularization can be a faster alternative to its iterative counterpart or L1-based iterative algorithms, without compromising image quality. PMID:24395184
Image reconstruction from photon sparse data
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J.
2017-01-01
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected. PMID:28169363
Image reconstruction from photon sparse data
NASA Astrophysics Data System (ADS)
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J.
2017-02-01
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected.
Image reconstruction from photon sparse data.
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J
2017-02-07
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected.
Advances in alimentary tract imaging.
Maglinte, Dean-Dt; Sandrasegaran, Kumaresan; Tann, Mark
2006-05-28
Advances in imaging techniques are changing the way radiologists undertake imaging of the gastrointestinal tract and their ability to answer questions posed by surgeons. In this paper we discuss the technological improvements of imaging studies that have occurred in the last few years and how these help to better diagnosing alimentary tract disease.
Image reconstruction for PET/CT scanners: past achievements and future challenges
Tong, Shan; Alessio, Adam M; Kinahan, Paul E
2011-01-01
PET is a medical imaging modality with proven clinical value for disease diagnosis and treatment monitoring. The integration of PET and CT on modern scanners provides a synergy of the two imaging modalities. Through different mathematical algorithms, PET data can be reconstructed into the spatial distribution of the injected radiotracer. With dynamic imaging, kinetic parameters of specific biological processes can also be determined. Numerous efforts have been devoted to the development of PET image reconstruction methods over the last four decades, encompassing analytic and iterative reconstruction methods. This article provides an overview of the commonly used methods. Current challenges in PET image reconstruction include more accurate quantitation, TOF imaging, system modeling, motion correction and dynamic reconstruction. Advances in these aspects could enhance the use of PET/CT imaging in patient care and in clinical research studies of pathophysiology and therapeutic interventions. PMID:21339831
Optimal Discretization Resolution in Algebraic Image Reconstruction
NASA Astrophysics Data System (ADS)
Sharif, Behzad; Kamalabadi, Farzad
2005-11-01
In this paper, we focus on data-limited tomographic imaging problems where the underlying linear inverse problem is ill-posed. A typical regularized reconstruction algorithm uses algebraic formulation with a predetermined discretization resolution. If the selected resolution is too low, we may loose useful details of the underlying image and if it is too high, the reconstruction will be unstable and the representation will fit irrelevant features. In this work, two approaches are introduced to address this issue. The first approach is using Mallow's CL method or generalized cross-validation. For each of the two methods, a joint estimator of regularization parameter and discretization resolution is proposed and their asymptotic optimality is investigated. The second approach is a Bayesian estimator of the model order using a complexity-penalizing prior. Numerical experiments focus on a space imaging application from a set of limited-angle tomographic observations.
Hu, Y.
2015-06-15
MRI-guided treatment is a growing area of medicine, particularly in radiotherapy and surgery. The exquisite soft tissue anatomic contrast offered by MRI, along with functional imaging, makes the use of MRI during therapeutic procedures very attractive. Challenging the utility of MRI in the therapy room are many issues including the physics of MRI and the impact on the environment and therapeutic instruments, the impact of the room and instruments on the MRI; safety, space, design and cost. In this session, the applications and challenges of MRI-guided treatment will be described. The session format is: Past, present and future: MRI-guided radiotherapy from 2005 to 2025: Jan Lagendijk Battling Maxwell’s equations: Physics challenges and solutions for hybrid MRI systems: Paul Keall I want it now!: Advances in MRI acquisition, reconstruction and the use of priors to enable fast anatomic and physiologic imaging to inform guidance and adaptation decisions: Yanle Hu MR in the OR: The growth and applications of MRI for interventional radiology and surgery: Rebecca Fahrig Learning Objectives: To understand the history and trajectory of MRI-guided radiotherapy To understand the challenges of integrating MR imaging systems with linear accelerators To understand the latest in fast MRI methods to enable the visualisation of anatomy and physiology on radiotherapy treatment timescales To understand the growing role and challenges of MRI for image-guided surgical procedures My disclosures are publicly available and updated at: http://sydney.edu.au/medicine/radiation-physics/about-us/disclosures.php.
Improved Reconstruction for MR Spectroscopic Imaging
Maudsley, Andrew A.
2009-01-01
Sensitivity limitations of in vivo magnetic resonance spectroscopic imaging (MRSI) require that the extent of spatial-frequency (k-space) sampling be limited, thereby reducing spatial resolution and increasing the effects of Gibbs ringing that is associated with the use of Fourier transform reconstruction. Additional problems occur in the spectral dimension, where quantitation of individual spectral components is made more difficult by the typically low signal-to-noise ratios, variable lineshapes, and baseline distortions, particularly in areas of significant magnetic field inhomogeneity. Given the potential of in vivo MRSI measurements for a number of clinical and biomedical research applications, there is considerable interest in improving the quality of the metabolite image reconstructions. In this report, a reconstruction method is described that makes use of parametric modeling and MRI-derived tissue distribution functions to enhance the MRSI spatial reconstruction. Additional preprocessing steps are also proposed to avoid difficulties associated with image regions containing spectra of inadequate quality, which are commonly present in the in vivo MRSI data. PMID:17518063
Speckle image reconstruction of the adaptive optics solar images.
Zhong, Libo; Tian, Yu; Rao, Changhui
2014-11-17
Speckle image reconstruction, in which the speckle transfer function (STF) is modeled as annular distribution according to the angular dependence of adaptive optics (AO) compensation and the individual STF in each annulus is obtained by the corresponding Fried parameter calculated from the traditional spectral ratio method, is used to restore the solar images corrected by AO system in this paper. The reconstructions of the solar images acquired by a 37-element AO system validate this method and the image quality is improved evidently. Moreover, we found the photometric accuracy of the reconstruction is field dependent due to the influence of AO correction. With the increase of angular separation of the object from the AO lockpoint, the relative improvement becomes approximately more and more effective and tends to identical in the regions far away the central field of view. The simulation results show this phenomenon is mainly due to the disparity of the calculated STF from the real AO STF with the angular dependence.
Propagation phasor approach for holographic image reconstruction
Luo, Wei; Zhang, Yibo; Göröcs, Zoltán; Feizi, Alborz; Ozcan, Aydogan
2016-01-01
To achieve high-resolution and wide field-of-view, digital holographic imaging techniques need to tackle two major challenges: phase recovery and spatial undersampling. Previously, these challenges were separately addressed using phase retrieval and pixel super-resolution algorithms, which utilize the diversity of different imaging parameters. Although existing holographic imaging methods can achieve large space-bandwidth-products by performing pixel super-resolution and phase retrieval sequentially, they require large amounts of data, which might be a limitation in high-speed or cost-effective imaging applications. Here we report a propagation phasor approach, which for the first time combines phase retrieval and pixel super-resolution into a unified mathematical framework and enables the synthesis of new holographic image reconstruction methods with significantly improved data efficiency. In this approach, twin image and spatial aliasing signals, along with other digital artifacts, are interpreted as noise terms that are modulated by phasors that analytically depend on the lateral displacement between hologram and sensor planes, sample-to-sensor distance, wavelength, and the illumination angle. Compared to previous holographic reconstruction techniques, this new framework results in five- to seven-fold reduced number of raw measurements, while still achieving a competitive resolution and space-bandwidth-product. We also demonstrated the success of this approach by imaging biological specimens including Papanicolaou and blood smears. PMID:26964671
Proton Computed Tomography: iterative image reconstruction and dose evaluation
NASA Astrophysics Data System (ADS)
Civinini, C.; Bonanno, D.; Brianzi, M.; Carpinelli, M.; Cirrone, G. A. P.; Cuttone, G.; Lo Presti, D.; Maccioni, G.; Pallotta, S.; Randazzo, N.; Scaringella, M.; Romano, F.; Sipala, V.; Talamonti, C.; Vanzi, E.; Bruzzi, M.
2017-01-01
Proton Computed Tomography (pCT) is a medical imaging method with a potential for increasing accuracy of treatment planning and patient positioning in hadron therapy. A pCT system based on a Silicon microstrip tracker and a YAG:Ce crystal calorimeter has been developed within the INFN Prima-RDH collaboration. The prototype has been tested with a 175 MeV proton beam at The Svedberg Laboratory (Uppsala, Sweden) with the aim to reconstruct and characterize a tomographic image. Algebraic iterative reconstruction methods (ART), together with the most likely path formalism, have been used to obtain tomographies of an inhomogeneous phantom to eventually extract density and spatial resolutions. These results will be presented and discussed together with an estimation of the average dose delivered to the phantom and the dependence of the image quality on the dose. Due to the heavy computation load required by the algebraic algorithms the reconstruction programs have been implemented to fully exploit the high calculation parallelism of Graphics Processing Units. An extended field of view pCT system is in an advanced construction stage. This apparatus will be able to reconstruct objects of the size of a human head making possible to characterize this pCT approach in a pre-clinical environment.
Weissleder, Ralph; Nahrendorf, Matthias
2015-11-24
Imaging reveals complex structures and dynamic interactive processes, located deep inside the body, that are otherwise difficult to decipher. Numerous imaging modalities harness every last inch of the energy spectrum. Clinical modalities include magnetic resonance imaging (MRI), X-ray computed tomography (CT), ultrasound, and light-based methods [endoscopy and optical coherence tomography (OCT)]. Research modalities include various light microscopy techniques (confocal, multiphoton, total internal reflection, superresolution fluorescence microscopy), electron microscopy, mass spectrometry imaging, fluorescence tomography, bioluminescence, variations of OCT, and optoacoustic imaging, among a few others. Although clinical imaging and research microscopy are often isolated from one another, we argue that their combination and integration is not only informative but also essential to discovering new biology and interpreting clinical datasets in which signals invariably originate from hundreds to thousands of cells per voxel.
Weissleder, Ralph; Nahrendorf, Matthias
2015-01-01
Imaging reveals complex structures and dynamic interactive processes, located deep inside the body, that are otherwise difficult to decipher. Numerous imaging modalities harness every last inch of the energy spectrum. Clinical modalities include magnetic resonance imaging (MRI), X-ray computed tomography (CT), ultrasound, and light-based methods [endoscopy and optical coherence tomography (OCT)]. Research modalities include various light microscopy techniques (confocal, multiphoton, total internal reflection, superresolution fluorescence microscopy), electron microscopy, mass spectrometry imaging, fluorescence tomography, bioluminescence, variations of OCT, and optoacoustic imaging, among a few others. Although clinical imaging and research microscopy are often isolated from one another, we argue that their combination and integration is not only informative but also essential to discovering new biology and interpreting clinical datasets in which signals invariably originate from hundreds to thousands of cells per voxel. PMID:26598657
Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography
NASA Astrophysics Data System (ADS)
Wang, Kun; Su, Richard; Oraevsky, Alexander A.; Anastasio, Mark A.
2012-09-01
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 the 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.
Performance-based assessment of reconstructed images
Hanson, Kenneth
2009-01-01
During the early 90s, I engaged in a productive and enjoyable collaboration with Robert Wagner and his colleague, Kyle Myers. We explored the ramifications of the principle that tbe quality of an image should be assessed on the basis of how well it facilitates the performance of appropriate visual tasks. We applied this principle to algorithms used to reconstruct scenes from incomplete and/or noisy projection data. For binary visual tasks, we used both the conventional disk detection and a new challenging task, inspired by the Rayleigh resolution criterion, of deciding whether an object was a blurred version of two dots or a bar. The results of human and machine observer tests were summarized with the detectability index based on the area under the ROC curve. We investigated a variety of reconstruction algorithms, including ART, with and without a nonnegativity constraint, and the MEMSYS3 algorithm. We concluded that the performance of the Raleigh task was optimized when the strength of the prior was near MEMSYS's default 'classic' value for both human and machine observers. A notable result was that the most-often-used metric of rms error in the reconstruction was not necessarily indicative of the value of a reconstructed image for the purpose of performing visual tasks.
Hyperspectral image reconstruction for diffuse optical tomography
Larusson, Fridrik; Fantini, Sergio; Miller, Eric L.
2011-01-01
We explore the development and performance of algorithms for hyperspectral diffuse optical tomography (DOT) for which data from hundreds of wavelengths are collected and used to determine the concentration distribution of chromophores in the medium under investigation. An efficient method is detailed for forming the images using iterative algorithms applied to a linearized Born approximation model assuming the scattering coefficient is spatially constant and known. The L-surface framework is employed to select optimal regularization parameters for the inverse problem. We report image reconstructions using 126 wavelengths with estimation error in simulations as low as 0.05 and mean square error of experimental data of 0.18 and 0.29 for ink and dye concentrations, respectively, an improvement over reconstructions using fewer specifically chosen wavelengths. PMID:21483616
Modern Imaging Technology: Recent Advances
Welch, Michael J.; Eckelman, William C.
2004-06-18
This 2-day conference is designed to bring scientist working in nuclear medicine, as well as nuclear medicine practitioners together to discuss the advances in four selected areas of imaging: Biochemical Parameters using Small Animal Imaging, Developments in Small Animal PET Imaging, Cell Labeling, and Imaging Angiogenesis Using Multiple Modality. The presentations will be on molecular imaging applications at the forefront of research, up to date on the status of molecular imaging in nuclear medicine as well as in related imaging areas. Experts will discuss the basic science of imaging techniques, and scheduled participants will engage in an exciting program that emphasizes the current status of molecular imaging as well as the role of DOE funded research in this area.
1981-12-01
Applications to Computer Technology (McGraw-Hill, New York, 1967). . 3. B. Kruse, "System Architecture for Image Analysis," Chapter 7 of Structured ... Computer Vision, edited by S. Tanimoto and A. Klinger (Academic Press, 1980). 107
Advanced reconstruction of attenuation maps using SPECT emission data only
NASA Astrophysics Data System (ADS)
Salomon, André; Goedicke, Andreas; Aach, Til
2009-02-01
Today, attenuation corrected SPECT, typically performed using CT or Gadolinium line source based transmission scans, is more and more becoming standard in many medical applications. Moreover, the information about the material density distribution provided by these scans is key for other artifact compensation approaches in advanced SPECT reconstruction. Major drawbacks of these approaches are the additional patient radiation and hardware/maintenance costs as well as the additional workflow effort, e.g. if the CT scans are not performed on a hybrid scanner. It has been investigated in the past, whether it is possible to recover this structural information solely from the SPECT scan data. However, the investigated methods often result in noticeable image artifacts due to cross-dependences between attenuation and activity distribution estimation. With the simultaneous reconstruction method presented in this paper, we aim to effectively prevent these typical cross-talk artifacts using a-priori known atlas information of a human body. At first, an initial 3D shape model is coarsely registered to the SPECT data using anatomical landmarks and each organ structure within the model is identified with its typical attenuation coefficient. During the iterative reconstruction based on a modified ML-EM scheme, the algorithm simultaneously adapts both, the local activity estimation and the 3D shape model in order to improve the overall consistency between measured and estimated sinogram data. By explicitly avoiding topology modifications resulting in a non-anatomical state, we ensure that the estimated attenuation map remains realistic. Several tests with simulated as well as real patient SPECT data were performed to test the proposed algorithm, which demonstrated reliable convergence behaviour in both cases. Comparing the achieved results with available reference data, an overall good agreement for both cold as well as hot activity regions could be observed (mean deviation: -5.98%).
Deep Reconstruction Models for Image Set Classification.
Hayat, Munawar; Bennamoun, Mohammed; An, Senjian
2015-04-01
Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it offers more promises and has therefore attracted significant research attention in recent years. Unlike many existing methods which assume images of a set to lie on a certain geometric surface, this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure. Specifically, a Template Deep Reconstruction Model (TDRM) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The initialized TDRM is then separately trained for images of each class and class-specific DRMs are learnt. Based on the minimum reconstruction errors from the learnt class-specific models, three different voting strategies are devised for classification. Extensive experiments are performed to demonstrate the efficacy of the proposed framework for the tasks of face and object recognition from image sets. Experimental results show that the proposed method consistently outperforms the existing state of the art methods.
Reconstructing MR images from under- or unevenly-sampled k-space
NASA Astrophysics Data System (ADS)
Vu, Linda; Hajian, Arsen R.; Calamai, Paul H.; Cenko, Andrew T.; Rasheed, Sarbast; Kim, Jae K.; Piron, Cameron; So, Simon S.; Meade, Jeff T.; Knuth, Kevin H.
2010-08-01
In MRI, non-rectilinear sampling trajectories are applied in k-space to enable faster imaging. Traditional image reconstruction methods such as a fast Fourier transform (FFT) can not process datasets sampled in non-rectilinear forms (e.g., radial, spiral, random, etc.) and more advanced algorithms are required. The Fourier reduction of optical interferometer data (FROID) algorithm is a novel image reconstruction method1-3 proven to be successful in reconstructing spectra from sparsely and unevenly sampled astronomical interferometer data. The framework presented allows a priori information, such as the locations of the sampled points, to be incorporated into the reconstruction of images. In this paper, the FROID algorithm has been adapted and implemented to reconstruct magnetic resonance (MR) images from data acquired in k-space where the sampling positions are known. Also, simulated data, including randomly sampled data, are tested and analyzed.
A Sparse Reconstruction Algorithm for Ultrasonic Images in Nondestructive Testing
Guarneri, Giovanni Alfredo; Pipa, Daniel Rodrigues; Junior, Flávio Neves; de Arruda, Lúcia Valéria Ramos; Zibetti, Marcelo Victor Wüst
2015-01-01
Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, ω-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan—about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR). PMID:25905700
Advanced imaging communication system
NASA Technical Reports Server (NTRS)
Hilbert, E. E.; Rice, R. F.
1977-01-01
Key elements of system are imaging and nonimaging sensors, data compressor/decompressor, interleaved Reed-Solomon block coder, convolutional-encoded/Viterbi-decoded telemetry channel, and Reed-Solomon decoding. Data compression provides efficient representation of sensor data, and channel coding improves reliability of data transmission.
Image reconstruction with analytical point spread functions
NASA Astrophysics Data System (ADS)
Asensio Ramos, A.; López Ariste, A.
2010-07-01
Context. The image degradation produced by atmospheric turbulence and optical aberrations is usually alleviated using post-facto image reconstruction techniques, even when observing with adaptive optics systems. Aims: These techniques rely on the development of the wavefront using Zernike functions and the non-linear optimization of a certain metric. The resulting optimization procedure is computationally heavy. Our aim is to alleviate this computational burden. Methods: We generalize the extended Zernike-Nijboer theory to carry out the analytical integration of the Fresnel integral and present a natural basis set for the development of the point spread function when the wavefront is described using Zernike functions. Results: We present a linear expansion of the point spread function in terms of analytic functions, which, in addition, takes defocusing into account in a natural way. This expansion is used to develop a very fast phase-diversity reconstruction technique, which is demonstrated in terms of some applications. Conclusions: We propose that the linear expansion of the point spread function can be applied to accelerate other reconstruction techniques in use that are based on blind deconvolution.
Intraoperative imaging in orbital and midface reconstruction.
Wilde, Frank; Schramm, Alexander
2014-10-01
The orbit is very often affected by injuries which can leave patients not only with esthetic deficits, but also with functional impairments if reconstruction is inadequate. Computer-assisted surgery helps to achieve predictable outcomes in reconstruction. Today, intraoperative three-dimensional (3D) imaging is an important element in the workflow of computer-assisted orbital surgery. Clinical and radiological diagnosis by means of computed tomography is followed by preoperative computer-assisted planning to define and simulate the desired outcome of reconstruction. In difficult cases, intraoperative navigation helps in the implementation of procedure plans at the site of surgery. Intraoperative 3D imaging then allows an intraoperative final control to be made and the outcome of the surgery to be validated. Today, this is preferably done using 3D C-arm devices based on cone beam computed tomography. They help to avoid malpositioning of bone fragments and/or inserted implants assuring the quality of complex operations and reducing the number of secondary interventions necessary.
Variational Reconstruction of Left Cardiac Structure from CMR Images
Wan, Min; Huang, Wei; Zhang, Jun-Mei; Zhao, Xiaodan; Tan, Ru San; Wan, Xiaofeng; Zhong, Liang
2015-01-01
Cardiovascular Disease (CVD), accounting for 17% of overall deaths in the USA, is the leading cause of death over the world. Advances in medical imaging techniques make the quantitative assessment of both the anatomy and function of heart possible. The cardiac modeling is an invariable prerequisite for quantitative analysis. In this study, a novel method is proposed to reconstruct the left cardiac structure from multi-planed cardiac magnetic resonance (CMR) images and contours. Routine CMR examination was performed to acquire both long axis and short axis images. Trained technologists delineated the endocardial contours. Multiple sets of two dimensional contours were projected into the three dimensional patient-based coordinate system and registered to each other. The union of the registered point sets was applied a variational surface reconstruction algorithm based on Delaunay triangulation and graph-cuts. The resulting triangulated surfaces were further post-processed. Quantitative evaluation on our method was performed via computing the overlapping ratio between the reconstructed model and the manually delineated long axis contours, which validates our method. We envisage that this method could be used by radiographers and cardiologists to diagnose and assess cardiac function in patients with diverse heart diseases. PMID:26689551
Spatially adaptive regularized iterative high-resolution image reconstruction algorithm
NASA Astrophysics Data System (ADS)
Lim, Won Bae; Park, Min K.; Kang, Moon Gi
2000-12-01
High resolution images are often required in applications such as remote sensing, frame freeze in video, military and medical imaging. Digital image sensor arrays, which are used for image acquisition in many imaging systems, are not dense enough to prevent aliasing, so the acquired images will be degraded by aliasing effects. To prevent aliasing without loss of resolution, a dense detector array is required. But it may be very costly or unavailable, thus, many imaging systems are designed to allow some level of aliasing during image acquisition. The purpose of our work is to reconstruct an unaliased high resolution image from the acquired aliased image sequence. In this paper, we propose a spatially adaptive regularized iterative high resolution image reconstruction algorithm for blurred, noisy and down-sampled image sequences. The proposed approach is based on a Constrained Least Squares (CLS) high resolution reconstruction algorithm, with spatially adaptive regularization operators and parameters. These regularization terms are shown to improve the reconstructed image quality by forcing smoothness, while preserving edges in the reconstructed high resolution image. Accurate sub-pixel motion registration is the key of the success of the high resolution image reconstruction algorithm. However, sub-pixel motion registration may have some level of registration error. Therefore, a reconstruction algorithm which is robust against the registration error is required. The registration algorithm uses a gradient based sub-pixel motion estimator which provides shift information for each of the recorded frames. The proposed algorithm is based on a technique of high resolution image reconstruction, and it solves spatially adaptive regularized constrained least square minimization functionals. In this paper, we show that the reconstruction algorithm gives dramatic improvements in the resolution of the reconstructed image and is effective in handling the aliased information. The
Advanced Land Imager Assessment System
NASA Technical Reports Server (NTRS)
Chander, Gyanesh; Choate, Mike; Christopherson, Jon; Hollaren, Doug; Morfitt, Ron; Nelson, Jim; Nelson, Shar; Storey, James; Helder, Dennis; Ruggles, Tim; Kaita, Ed; Levy, Raviv; Ong, Lawrence; Markham, Brian; Schweiss, Robert
2008-01-01
The Advanced Land Imager Assessment System (ALIAS) supports radiometric and geometric image processing for the Advanced Land Imager (ALI) instrument onboard NASA s Earth Observing-1 (EO-1) satellite. ALIAS consists of two processing subsystems for radiometric and geometric processing of the ALI s multispectral imagery. The radiometric processing subsystem characterizes and corrects, where possible, radiometric qualities including: coherent, impulse; and random noise; signal-to-noise ratios (SNRs); detector operability; gain; bias; saturation levels; striping and banding; and the stability of detector performance. The geometric processing subsystem and analysis capabilities support sensor alignment calibrations, sensor chip assembly (SCA)-to-SCA alignments and band-to-band alignment; and perform geodetic accuracy assessments, modulation transfer function (MTF) characterizations, and image-to-image characterizations. ALIAS also characterizes and corrects band-toband registration, and performs systematic precision and terrain correction of ALI images. This system can geometrically correct, and automatically mosaic, the SCA image strips into a seamless, map-projected image. This system provides a large database, which enables bulk trending for all ALI image data and significant instrument telemetry. Bulk trending consists of two functions: Housekeeping Processing and Bulk Radiometric Processing. The Housekeeping function pulls telemetry and temperature information from the instrument housekeeping files and writes this information to a database for trending. The Bulk Radiometric Processing function writes statistical information from the dark data acquired before and after the Earth imagery and the lamp data to the database for trending. This allows for multi-scene statistical analyses.
An improved image reconstruction method for optical intensity correlation Imaging
NASA Astrophysics Data System (ADS)
Gao, Xin; Feng, Lingjie; Li, Xiyu
2016-12-01
The intensity correlation imaging method is a novel kind of interference imaging and it has favorable prospects in deep space recognition. However, restricted by the low detecting signal-to-noise ratio (SNR), it's usually very difficult to obtain high-quality image of deep space object like high-Earth-orbit (HEO) satellite with existing phase retrieval methods. In this paper, based on the priori intensity statistical distribution model of the object and characteristics of measurement noise distribution, an improved method of Prior Information Optimization (PIO) is proposed to reduce the ambiguous images and accelerate the phase retrieval procedure thus realizing fine image reconstruction. As the simulations and experiments show, compared to previous methods, our method could acquire higher-resolution images with less error in low SNR condition.
Advances in Optical Spectroscopy and Imaging of Breast Lesions
Demos, S; Vogel, A J; Gandjbakhche, A H
2006-01-03
A review is presented of recent advances in optical imaging and spectroscopy and the use of light for addressing breast cancer issues. Spectroscopic techniques offer the means to characterize tissue components and obtain functional information in real time. Three-dimensional optical imaging of the breast using various illumination and signal collection schemes in combination with image reconstruction algorithms may provide a new tool for cancer detection and monitoring of treatment.
SU-E-I-73: Clinical Evaluation of CT Image Reconstructed Using Interior Tomography
Zhang, J; Ge, G; Winkler, M; Cong, W; Wang, G
2014-06-01
Purpose: Radiation dose reduction has been a long standing challenge in CT imaging of obese patients. Recent advances in interior tomography (reconstruction of an interior region of interest (ROI) from line integrals associated with only paths through the ROI) promise to achieve significant radiation dose reduction without compromising image quality. This study is to investigate the application of this technique in CT imaging through evaluating imaging quality reconstructed from patient data. Methods: Projection data were directly obtained from patients who had CT examinations in a Dual Source CT scanner (DSCT). Two detectors in a DSCT acquired projection data simultaneously. One detector provided projection data for full field of view (FOV, 50 cm) while another detectors provided truncated projection data for a FOV of 26 cm. Full FOV CT images were reconstructed using both filtered back projection and iterative algorithm; while interior tomography algorithm was implemented to reconstruct ROI images. For comparison reason, FBP was also used to reconstruct ROI images. Reconstructed CT images were evaluated by radiologists and compared with images from CT scanner. Results: The results show that the reconstructed ROI image was in excellent agreement with the truth inside the ROI, obtained from images from CT scanner, and the detailed features in the ROI were quantitatively accurate. Radiologists evaluation shows that CT images reconstructed with interior tomography met diagnosis requirements. Radiation dose may be reduced up to 50% using interior tomography, depending on patient size. Conclusion: This study shows that interior tomography can be readily employed in CT imaging for radiation dose reduction. It may be especially useful in imaging obese patients, whose subcutaneous tissue is less clinically relevant but may significantly increase radiation dose.
Multiple-wavelength Color Digital Holography for Monochromatic Image Reconstruction
NASA Astrophysics Data System (ADS)
Cheremkhin, P. A.; Shevkunov, I. A.; Petrov, N. V.
In this paper, we consider the opposite problem, namely, using of color digital holograms simultaneously recorded on several wavelengths for the reconstruction of monochromatic images. Special feature of the procedure of monochromatic image reconstruction from the color hologram is the necessity of extracting information from separate spectral channels with a corresponding overlaying of obtained images to avoid mismatching of their spatial position caused by dependence of methods of numerical reconstruction from the laser wavelength.
Images from Bits: Non-Iterative Image Reconstruction for Quanta Image Sensors
Chan, Stanley H.; Elgendy, Omar A.; Wang, Xiran
2016-01-01
A quanta image sensor (QIS) is a class of single-photon imaging devices that measure light intensity using oversampled binary observations. Because of the stochastic nature of the photon arrivals, data acquired by QIS is a massive stream of random binary bits. The goal of image reconstruction is to recover the underlying image from these bits. In this paper, we present a non-iterative image reconstruction algorithm for QIS. Unlike existing reconstruction methods that formulate the problem from an optimization perspective, the new algorithm directly recovers the images through a pair of nonlinear transformations and an off-the-shelf image denoising algorithm. By skipping the usual optimization procedure, we achieve orders of magnitude improvement in speed and even better image reconstruction quality. We validate the new algorithm on synthetic datasets, as well as real videos collected by one-bit single-photon avalanche diode (SPAD) cameras. PMID:27879687
Images from Bits: Non-Iterative Image Reconstruction for Quanta Image Sensors.
Chan, Stanley H; Elgendy, Omar A; Wang, Xiran
2016-11-22
A quanta image sensor (QIS) is a class of single-photon imaging devices that measure light intensity using oversampled binary observations. Because of the stochastic nature of the photon arrivals, data acquired by QIS is a massive stream of random binary bits. The goal of image reconstruction is to recover the underlying image from these bits. In this paper, we present a non-iterative image reconstruction algorithm for QIS. Unlike existing reconstruction methods that formulate the problem from an optimization perspective, the new algorithm directly recovers the images through a pair of nonlinear transformations and an off-the-shelf image denoising algorithm. By skipping the usual optimization procedure, we achieve orders of magnitude improvement in speed and even better image reconstruction quality. We validate the new algorithm on synthetic datasets, as well as real videos collected by one-bit single-photon avalanche diode (SPAD) cameras.
Advances in abdominal MR imaging.
Ferrucci, J T
1998-01-01
Major technical advances in MR imaging have led to its wider use in the evaluation of abdominal disease. The principle new pulse sequence is the RARE sequence for T2-weighted imaging. Multishot and breath-hold single-shot RARE techniques are now widely used, and both have performed as well as conventional spin-echo imaging with far shorter acquisition times. The most notable improvements have been in the detection and characterization of hepatic lesions. Two liver-specific contrast agents received FDA approval during 1997: SPIO particles or ferumoxide and mangafodipir trisodium, a hepatocyte-specific agent. Both of these agents provide considerable benefit in the detection and characterization of hepatic lesions. Manganese enhancement has also proved useful in MR imaging of the pancreas, although fat-suppressed T1-weighted imaging with dynamic gadolinium enhancement has also yielded results comparable with those of contrast-enhanced CT. MR hydrography, a generic term for static fluid imaging, is another derivative of RARE fast T2-weighted imaging. MRCP, the best known example of MR hydrography, has been rapidly and widely employed as a primary method for imaging the biliary and pancreatic ducts and has become competitive with ERCP. MR vascular imaging, especially portal venography, has been used for noninvasive imaging of portal venous disease in Budd Chiari disease, before placement of transjugular intrahepatic portosystemic shunts, and for pancreatic cancer staging. Finally, the development of conventional phased-array body coils and endorectal coils has enabled high-quality MR imaging of perirectal disease (including Crohn disease, fistula in ano, and postpartum sphincter dysfunction). Future abdominal applications of MR imaging will involve second-generation MR interventional techniques, including use of open systems, functional or diffusion-weighted imaging exploiting the molecular activity of tissues, and virtual MR endoscopy. Although CT continues to evolve
Neural net classification and LMS reconstruction to halftone images
NASA Astrophysics Data System (ADS)
Chang, Pao-Chi; Yu, Che-Sheng
1998-01-01
The objective of this work is to reconstruct high quality gray-level images from halftone images, or the inverse halftoning process. We develop high performance halftone reconstruction methods for several commonly used halftone techniques. For better reconstruction quality, image classification based on halftone techniques is placed before the reconstruction process so that the halftone reconstruction process can be fine tuned for each halftone technique. The classification is based on enhanced 1D correlation of halftone images and processed with a three- layer back propagation neural network. This classification method reached 100 percent accuracy with a limited set of images processed by dispersed-dot ordered dithering, clustered-dot ordered dithering, constrained average, and error diffusion methods in our experiments. For image reconstruction, we apply the least-mean-square adaptive filtering algorithm which intends to discover the optimal filter weights and the mask shapes. As a result, it yields very good reconstruction image quality. The error diffusion yields the best reconstructed quality among the halftone methods. In addition, the LMS method generates optimal image masks which are significantly different for each halftone method. These optimal masks can also be applied to more sophisticated reconstruction methods as the default filter masks.
An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging.
Van Eyndhoven, Geert; Batenburg, K Joost; Kazantsev, Daniil; Van Nieuwenhove, Vincent; Lee, Peter D; Dobson, Katherine J; Sijbers, Jan
2015-11-01
The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed.
TOPICAL REVIEW: Recent advances in diffuse optical imaging
NASA Astrophysics Data System (ADS)
Gibson, A. P.; Hebden, J. C.; Arridge, S. R.
2005-02-01
We review the current state-of-the-art of diffuse optical imaging, which is an emerging technique for functional imaging of biological tissue. It involves generating images using measurements of visible or near-infrared light scattered across large (greater than several centimetres) thicknesses of tissue. We discuss recent advances in experimental methods and instrumentation, and examine new theoretical techniques applied to modelling and image reconstruction. We review recent work on in vivo applications including imaging the breast and brain, and examine future challenges.
Recent imaging advances in neurology.
Rocchi, Lorenzo; Niccolini, Flavia; Politis, Marios
2015-09-01
Over the recent years, the application of neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) has considerably advanced the understanding of complex neurological disorders. PET is a powerful molecular imaging tool, which investigates the distribution and binding of radiochemicals attached to biologically relevant molecules; as such, this technique is able to give information on biochemistry and metabolism of the brain in health and disease. MRI uses high intensity magnetic fields and radiofrequency pulses to provide structural and functional information on tissues and organs in intact or diseased individuals, including the evaluation of white matter integrity, grey matter thickness and brain perfusion. The aim of this article is to review the most recent advances in neuroimaging research in common neurological disorders such as movement disorders, dementia, epilepsy, traumatic brain injury and multiple sclerosis, and to evaluate their contribution in the diagnosis and management of patients.
Imaging, Reconstruction, And Display Of Corneal Topography
NASA Astrophysics Data System (ADS)
Klyce, Stephen D.; Wilson, Steven E.
1989-12-01
The cornea is the major refractive element in the eye; even minor surface distortions can produce a significant reduction in visual acuity. Standard clinical methods used to evaluate corneal shape include keratometry, which assumes the cornea is ellipsoidal in shape, and photokeratoscopy, which images a series of concentric light rings on the corneal surface. These methods fail to document many of the corneal distortions that can degrade visual acuity. Algorithms have been developed to reconstruct the three dimensional shape of the cornea from keratoscope images, and to present these data in the clinically useful display of color-coded contour maps of corneal surface power. This approach has been implemented on a new generation video keratoscope system (Computed Anatomy, Inc.) with rapid automatic digitization of the image rings by a rule-based approach. The system has found clinical use in the early diagnosis of corneal shape anomalies such as keratoconus and contact lens-induced corneal warpage, in the evaluation of cataract and corneal transplant procedures, and in the assessment of corneal refractive surgical procedures. Currently, ray tracing techniques are being used to correlate corneal surface topography with potential visual acuity in an effort to more fully understand the tolerances of corneal shape consistent with good vision and to help determine the site of dysfunction in the visually impaired.
Photogrammetric 3D reconstruction using mobile imaging
NASA Astrophysics Data System (ADS)
Fritsch, Dieter; Syll, Miguel
2015-03-01
In our paper we demonstrate the development of an Android Application (AndroidSfM) for photogrammetric 3D reconstruction that works on smartphones and tablets likewise. The photos are taken with mobile devices, and can thereafter directly be calibrated using standard calibration algorithms of photogrammetry and computer vision, on that device. Due to still limited computing resources on mobile devices, a client-server handshake using Dropbox transfers the photos to the sever to run AndroidSfM for the pose estimation of all photos by Structure-from-Motion and, thereafter, uses the oriented bunch of photos for dense point cloud estimation by dense image matching algorithms. The result is transferred back to the mobile device for visualization and ad-hoc on-screen measurements.
Advances in multimodality molecular imaging
Zaidi, Habib; Prasad, Rameshwar
2009-01-01
Multimodality molecular imaging using high resolution positron emission tomography (PET) combined with other modalities is now playing a pivotal role in basic and clinical research. The introduction of combined PET/CT systems in clinical setting has revolutionized the practice of diagnostic imaging. The complementarity between the intrinsically aligned anatomic (CT) and functional or metabolic (PET) information provided in a “one-stop shop” and the possibility to use CT images for attenuation correction of the PET data has been the driving force behind the success of this technology. On the other hand, combining PET with Magnetic Resonance Imaging (MRI) in a single gantry is technically more challenging owing to the strong magnetic fields. Nevertheless, significant progress has been made resulting in the design of few preclinical PET systems and one human prototype dedicated for simultaneous PET/MR brain imaging. This paper discusses recent advances in PET instrumentation and the advantages and challenges of multimodality imaging systems. Future opportunities and the challenges facing the adoption of multimodality imaging instrumentation will also be addressed. PMID:20098557
Reconstruction-based 3D/2D image registration.
Tomazevic, Dejan; Likar, Bostjan; Pernus, Franjo
2005-01-01
In this paper we present a novel 3D/2D registration method, where first, a 3D image is reconstructed from a few 2D X-ray images and next, the preoperative 3D image is brought into the best possible spatial correspondence with the reconstructed image by optimizing a similarity measure. Because the quality of the reconstructed image is generally low, we introduce a novel asymmetric mutual information similarity measure, which is able to cope with low image quality as well as with different imaging modalities. The novel 3D/2D registration method has been evaluated using standardized evaluation methodology and publicly available 3D CT, 3DRX, and MR and 2D X-ray images of two spine phantoms, for which gold standard registrations were known. In terms of robustness, reliability and capture range the proposed method outperformed the gradient-based method and the method based on digitally reconstructed radiographs (DRRs).
Wang, Depeng; Wang, Yuehang; Zhou, Yang; Lovell, Jonathan F; Xia, Jun
2016-05-01
While the majority of photoacoustic imaging systems used custom-made transducer arrays, commercially-available linear transducer arrays hold the benefits of affordable price, handheld convenience and wide clinical recognition. They are not widely used in photoacoustic imaging primarily because of the poor elevation resolution. Here, without modifying the imaging geometry and system, we propose addressing this limitation purely through image reconstruction. Our approach is based on the integration of two advanced image reconstruction techniques: focal-line-based three-dimensional image reconstruction and coherent weighting. We first numerically validated our approach through simulation and then experimentally tested it in phantom and in vivo. Both simulation and experimental results proved that the method can significantly improve the elevation resolution (up to 4 times in our experiment) and enhance object contrast.
Wang, Depeng; Wang, Yuehang; Zhou, Yang; Lovell, Jonathan F.; Xia, Jun
2016-01-01
While the majority of photoacoustic imaging systems used custom-made transducer arrays, commercially-available linear transducer arrays hold the benefits of affordable price, handheld convenience and wide clinical recognition. They are not widely used in photoacoustic imaging primarily because of the poor elevation resolution. Here, without modifying the imaging geometry and system, we propose addressing this limitation purely through image reconstruction. Our approach is based on the integration of two advanced image reconstruction techniques: focal-line-based three-dimensional image reconstruction and coherent weighting. We first numerically validated our approach through simulation and then experimentally tested it in phantom and in vivo. Both simulation and experimental results proved that the method can significantly improve the elevation resolution (up to 4 times in our experiment) and enhance object contrast. PMID:27231634
Advances in multimodal molecular imaging.
Auletta, Luigi; Gramanzini, Matteo; Gargiulo, Sara; Albanese, Sandra; Salvatore, Marco; Greco, Adelaide
2017-03-01
Preclinical molecular imaging is an emerging field. Improving the ability of scientists to study the molecular basis of human pathology in animals is of the utmost importance for future advances in all fields of human medicine. Moreover, the possibility of developing new imaging techniques or of implementing old ones adapted to the clinic is a significant area. Cardiology, neurology, immunology and oncology have all been studied with preclinical molecular imaging. The functional techniques of photoacoustic imaging (PAI), fluorescence molecular tomography (FMT), positron emission tomography (PET), and single photon emission computed tomography (SPECT) in association with each other or with the anatomic reference provided by computed tomography (CT) as well as with anatomic and functional information provided by magnetic resonance (MR) have all been proficiently applied to animal models of human disease. All the above-mentioned imaging techniques have shown their ability to explore the molecular mechanisms involved in animal models of disease. The clinical translatability of most of the techniques motivates the ongoing study of their possible fields of application. The ability to combine two or more techniques allows obtaining as much information as possible on the molecular processes involved in pathologies, reducing the number of animals necessary in each experiment. Merging molecular probes compatible with various imaging technique will further expand the capability to achieve the best results.
Accounting for hardware imperfections in EIT image reconstruction algorithms.
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.
Recent Advances in Morphological Cell Image Analysis
Chen, Shengyong; Zhao, Mingzhu; Wu, Guang; Yao, Chunyan; Zhang, Jianwei
2012-01-01
This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed. PMID:22272215
Advanced imaging of the scapholunate ligamentous complex.
Shahabpour, Maryam; Staelens, Barbara; Van Overstraeten, Luc; De Maeseneer, Michel; Boulet, Cedric; De Mey, Johan; Scheerlinck, Thierry
2015-12-01
The scapholunate joint is one of the most involved in wrist injuries. Its stability depends on primary and secondary stabilisers forming together the scapholunate complex. This ligamentous complex is often evaluated by wrist arthroscopy. To avoid surgery as diagnostic procedure, optimization of MR imaging parameters as use of three-dimensional (3D) sequences with very thin slices and high spatial resolution, is needed to detect lesions of the intrinsic and extrinsic ligaments of the scapholunate complex. The paper reviews the literature on imaging of radial-sided carpal ligaments with advanced computed tomographic arthrography (CTA) and magnetic resonance arthrography (MRA) to evaluate the scapholunate complex. Anatomy and pathology of the ligamentous complex are described and illustrated with CTA, MRA and corresponding arthroscopy. Sprains, mid-substance tears, avulsions and fibrous infiltrations of carpal ligaments could be identified on CTA and MRA images using 3D fat-saturated PD and 3D DESS (dual echo with steady-state precession) sequences with 0.5-mm-thick slices. Imaging signs of scapholunate complex pathology include: discontinuity, nonvisualization, changes in signal intensity, contrast extravasation (MRA), contour irregularity and waviness and periligamentous infiltration by edema, granulation tissue or fibrosis. Based on this preliminary experience, we believe that 3 T MRA using 3D sequences with 0.5-mm-thick slices and multiplanar reconstructions is capable to evaluate the scapholunate complex and could help to reduce the number of diagnostic arthroscopies.
Iterative feature refinement for accurate undersampled MR image reconstruction
NASA Astrophysics Data System (ADS)
Wang, Shanshan; Liu, Jianbo; Liu, Qiegen; Ying, Leslie; Liu, Xin; Zheng, Hairong; Liang, Dong
2016-05-01
Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate MR image reconstruction from undersampled K-space data. Integrating IFR with CSMRI which is equipped with fixed transforms, we develop an IFR-CS method to restore meaningful structures and details that are originally discarded without introducing too much additional complexity. Specifically, the proposed IFR-CS is realized with three iterative steps, namely sparsity-promoting denoising, feature refinement and Tikhonov regularization. Experimental results on both simulated and in vivo MR datasets have shown that the proposed module has a strong capability to capture image details, and that IFR-CS is comparable and even superior to other state-of-the-art reconstruction approaches.
Synergistic image reconstruction for hybrid ultrasound and photoacoustic computed tomography
NASA Astrophysics Data System (ADS)
Matthews, Thomas P.; Wang, Kun; Wang, Lihong V.; Anastasio, Mark A.
2015-03-01
Conventional photoacoustic computed tomography (PACT) image reconstruction methods assume that the object and surrounding medium are described by a constant speed-of-sound (SOS) value. In order to accurately recover fine structures, SOS heterogeneities should be quantified and compensated for during PACT reconstruction. To address this problem, several groups have proposed hybrid systems that combine PACT with ultrasound computed tomography (USCT). In such systems, a SOS map is reconstructed first via USCT. Consequently, this SOS map is employed to inform the PACT reconstruction method. Additionally, the SOS map can provide structural information regarding tissue, which is complementary to the functional information from the PACT image. We propose a paradigm shift in the way that images are reconstructed in hybrid PACT-USCT imaging. Inspired by our observation that information about the SOS distribution is encoded in PACT measurements, we propose to jointly reconstruct the absorbed optical energy density and SOS distributions from a combined set of USCT and PACT measurements, thereby reducing the two reconstruction problems into one. This innovative approach has several advantages over conventional approaches in which PACT and USCT images are reconstructed independently: (1) Variations in the SOS will automatically be accounted for, optimizing PACT image quality; (2) The reconstructed PACT and USCT images will possess minimal systematic artifacts because errors in the imaging models will be optimally balanced during the joint reconstruction; (3) Due to the exploitation of information regarding the SOS distribution in the full-view PACT data, our approach will permit high-resolution reconstruction of the SOS distribution from sparse array data.
Calibration and Image Reconstruction for the Hurricane Imaging Radiometer (HIRAD)
NASA Technical Reports Server (NTRS)
Ruf, Christopher; Roberts, J. Brent; Biswas, Sayak; James, Mark W.; Miller, Timothy
2012-01-01
The Hurricane Imaging Radiometer (HIRAD) is a new airborne passive microwave synthetic aperture radiometer designed to provide wide swath images of ocean surface wind speed under heavy precipitation and, in particular, in tropical cyclones. It operates at 4, 5, 6 and 6.6 GHz and uses interferometric signal processing to synthesize a pushbroom imager in software from a low profile planar antenna with no mechanical scanning. HIRAD participated in NASA s Genesis and Rapid Intensification Processes (GRIP) mission during Fall 2010 as its first science field campaign. HIRAD produced images of upwelling brightness temperature over a aprox 70 km swath width with approx 3 km spatial resolution. From this, ocean surface wind speed and column averaged atmospheric liquid water content can be retrieved across the swath. The calibration and image reconstruction algorithms that were used to verify HIRAD functional performance during and immediately after GRIP were only preliminary and used a number of simplifying assumptions and approximations about the instrument design and performance. The development and performance of a more detailed and complete set of algorithms are reported here.
Numerical modelling and image reconstruction in diffuse optical tomography
Dehghani, Hamid; Srinivasan, Subhadra; Pogue, Brian W.; Gibson, Adam
2009-01-01
The development of diffuse optical tomography as a functional imaging modality has relied largely on the use of model-based image reconstruction. The recovery of optical parameters from boundary measurements of light propagation within tissue is inherently a difficult one, because the problem is nonlinear, ill-posed and ill-conditioned. Additionally, although the measured near-infrared signals of light transmission through tissue provide high imaging contrast, the reconstructed images suffer from poor spatial resolution due to the diffuse propagation of light in biological tissue. The application of model-based image reconstruction is reviewed in this paper, together with a numerical modelling approach to light propagation in tissue as well as generalized image reconstruction using boundary data. A comprehensive review and details of the basis for using spatial and structural prior information are also discussed, whereby the use of spectral and dual-modality systems can improve contrast and spatial resolution. PMID:19581256
Reconstruction of biofilm images: combining local and global structural parameters
Resat, Haluk; Renslow, Ryan S.; Beyenal, Haluk
2014-10-20
Digitized images can be used for quantitative comparison of biofilms grown under different conditions. Using biofilm image reconstruction, it was previously found that biofilms with a completely different look can have nearly identical structural parameters and that the most commonly utilized global structural parameters were not sufficient to uniquely define these biofilms. Here, additional local and global parameters are introduced to show that these parameters considerably increase the reliability of the image reconstruction process. Assessment using human evaluators indicated that the correct identification rate of the reconstructed images increased from 50% to 72% with the introduction of the new parameters into the reconstruction procedure. An expanded set of parameters especially improved the identification of biofilm structures with internal orientational features and of structures in which colony sizes and spatial locations varied. Hence, the newly introduced structural parameter sets helped to better classify the biofilms by incorporating finer local structural details into the reconstruction process.
[Image reconstruction in electrical impedance tomography based on genetic algorithm].
Hou, Weidong; Mo, Yulong
2003-03-01
Image reconstruction in electrical impedance tomography (EIT) is a highly ill-posed, non-linear inverse problem. The modified Newton-Raphson (MNR) iteration algorithm is deduced from the strictest theoretic analysis. It is an optimization algorithm based on minimizing the object function. The MNR algorithm with regularization technique is usually not stable, due to the serious image reconstruction model error and measurement noise. So the reconstruction precision is not high when used in static EIT. A new static image reconstruction method for EIT based on genetic algorithm (GA-EIT) is proposed in this paper. The experimental results indicate that the performance (including stability, the precision and space resolution in reconstructing the static EIT image) of the GA-EIT algorithm is better than that of the MNR algorithm.
Undersampled MR Image Reconstruction with Data-Driven Tight Frame
Liu, Jianbo; Wang, Shanshan; Peng, Xi; Liang, Dong
2015-01-01
Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image reconstruction accuracy without introducing too much computation, this paper proposes a data-driven tight frame magnetic image reconstruction (DDTF-MRI) method. By taking advantage of the efficiency and effectiveness of data-driven tight frame, DDTF-MRI trains an adaptive tight frame to sparsify the to-be-reconstructed MR image. Furthermore, a two-level Bregman iteration algorithm has been developed to solve the proposed model. The proposed method has been compared to two state-of-the-art methods on four datasets and encouraging performances have been achieved by DDTF-MRI. PMID:26199641
Four dimensional reconstruction and analysis of plume images
NASA Astrophysics Data System (ADS)
Dhawan, Atam P.; Disimile, Peter J.; Peck, Charles, III
Results of a time-history based three-dimensional reconstruction of cross-sectional images corresponding to a specific planar location of the jet structure are reported. The experimental set-up is described, and three-dimensional displays of time-history based reconstruction of the jet structure are presented. Future developments in image analysis, quantification and interpretation, and flow visualization of rocket engine plume images are expected to provide a tool for correlating engine diagnostic features with visible flow structures.
Quantitative image quality evaluation for cardiac CT reconstructions
NASA Astrophysics Data System (ADS)
Tseng, Hsin-Wu; Fan, Jiahua; Kupinski, Matthew A.; Balhorn, William; Okerlund, Darin R.
2016-03-01
Maintaining image quality in the presence of motion is always desirable and challenging in clinical Cardiac CT imaging. Different image-reconstruction algorithms are available on current commercial CT systems that attempt to achieve this goal. It is widely accepted that image-quality assessment should be task-based and involve specific tasks, observers, and associated figures of merits. In this work, we developed an observer model that performed the task of estimating the percentage of plaque in a vessel from CT images. We compared task performance of Cardiac CT image data reconstructed using a conventional FBP reconstruction algorithm and the SnapShot Freeze (SSF) algorithm, each at default and optimal reconstruction cardiac phases. The purpose of this work is to design an approach for quantitative image-quality evaluation of temporal resolution for Cardiac CT systems. To simulate heart motion, a moving coronary type phantom synchronized with an ECG signal was used. Three different percentage plaques embedded in a 3 mm vessel phantom were imaged multiple times under motion free, 60 bpm, and 80 bpm heart rates. Static (motion free) images of this phantom were taken as reference images for image template generation. Independent ROIs from the 60 bpm and 80 bpm images were generated by vessel tracking. The observer performed estimation tasks using these ROIs. Ensemble mean square error (EMSE) was used as the figure of merit. Results suggest that the quality of SSF images is superior to the quality of FBP images in higher heart-rate scans.
Basis Functions in Image Reconstruction From Projections: A Tutorial Introduction
NASA Astrophysics Data System (ADS)
Herman, Gabor T.
2015-11-01
The series expansion approaches to image reconstruction from projections assume that the object to be reconstructed can be represented as a linear combination of fixed basis functions and the task of the reconstruction algorithm is to estimate the coefficients in such a linear combination based on the measured projection data. It is demonstrated that using spherically symmetric basis functions (blobs), instead of ones based on the more traditional pixels, yields superior reconstructions of medically relevant objects. The demonstration uses simulated computerized tomography projection data of head cross-sections and the series expansion method ART for the reconstruction. In addition to showing the results of one anecdotal example, the relative efficacy of using pixel and blob basis functions in image reconstruction from projections is also evaluated using a statistical hypothesis testing based task oriented comparison methodology. The superiority of the efficacy of blob basis functions over that of pixel basis function is found to be statistically significant.
Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging
Zhang, Yichun; Shi, Tielin; Su, Lei; Wang, Xiao; Hong, Yuan; Chen, Kepeng; Liao, Guanglan
2016-01-01
Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l1-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging. PMID:27783040
Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging.
Zhang, Yichun; Shi, Tielin; Su, Lei; Wang, Xiao; Hong, Yuan; Chen, Kepeng; Liao, Guanglan
2016-10-24
Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l₁-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging.
Simbol-X Formation Flight and Image Reconstruction
NASA Astrophysics Data System (ADS)
Civitani, M.; Djalal, S.; Le Duigou, J. M.; La Marle, O.; Chipaux, R.
2009-05-01
Simbol-X is the first operational mission relying on two satellites flying in formation. The dynamics of the telescope, due to the formation flight concept, raises a variety of problematic, like image reconstruction, that can be better evaluated via a simulation tools. We present here the first results obtained with Simulos, simulation tool aimed to study the relative spacecrafts navigation and the weight of the different parameters in image reconstruction and telescope performance evaluation. The simulation relies on attitude and formation flight sensors models, formation flight dynamics and control, mirror model and focal plane model, while the image reconstruction is based on the Line of Sight (LOS) concept.
An image reconstruction method (IRBis) for optical/infrared interferometry
NASA Astrophysics Data System (ADS)
Hofmann, K.-H.; Weigelt, G.; Schertl, D.
2014-05-01
Aims: We present an image reconstruction method for optical/infrared long-baseline interferometry called IRBis (image reconstruction software using the bispectrum). We describe the theory and present applications to computer-simulated interferograms. Methods: The IRBis method can reconstruct an image from measured visibilities and closure phases. The applied optimization routine ASA_CG is based on conjugate gradients. The method allows the user to implement different regularizers, apply residual ratios as an additional metric for goodness-of-fit, and use previous iteration results as a prior to force convergence. Results: We present the theory of the IRBis method and several applications of the method to computer-simulated interferograms. The image reconstruction results show the dependence of the reconstructed image on the noise in the interferograms (e.g., for ten electron read-out noise and 139 to 1219 detected photons per interferogram), the regularization method, the angular resolution, and the reconstruction parameters applied. Furthermore, we present the IRBis reconstructions submitted to the interferometric imaging beauty contest 2012 initiated by the IAU Working Group on Optical/IR Interferometry and describe the performed data processing steps.
MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods
Schmidt, Johannes F. M.; Santelli, Claudio; Kozerke, Sebastian
2016-01-01
An approach to Magnetic Resonance (MR) image reconstruction from undersampled data is proposed. Undersampling artifacts are removed using an iterative thresholding algorithm applied to nonlinearly transformed image block arrays. Each block array is transformed using kernel principal component analysis where the contribution of each image block to the transform depends in a nonlinear fashion on the distance to other image blocks. Elimination of undersampling artifacts is achieved by conventional principal component analysis in the nonlinear transform domain, projection onto the main components and back-mapping into the image domain. Iterative image reconstruction is performed by interleaving the proposed undersampling artifact removal step and gradient updates enforcing consistency with acquired k-space data. The algorithm is evaluated using retrospectively undersampled MR cardiac cine data and compared to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT reconstruction. Evaluation of image quality and root-mean-squared-error (RMSE) reveal improved image reconstruction for up to 8-fold undersampled data with the proposed approach relative to k-t SPARSE-SENSE, block matching with spatial Fourier filtering and k-t ℓ1-SPIRiT. In conclusion, block matching and kernel methods can be used for effective removal of undersampling artifacts in MR image reconstruction and outperform methods using standard compressed sensing and ℓ1-regularized parallel imaging methods. PMID:27116675
Sparsity-constrained PET image reconstruction with learned dictionaries
NASA Astrophysics Data System (ADS)
Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie
2016-09-01
PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.
Sparsity-constrained PET image reconstruction with learned dictionaries.
Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie
2016-09-07
PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.
Fast reconstruction of digital tomosynthesis using on-board images
Yan Hui; Godfrey, Devon J.; Yin Fangfang
2008-05-15
Digital tomosynthesis (DTS) is a method to reconstruct pseudo three-dimensional (3D) volume images from two-dimensional x-ray projections acquired over limited scan angles. Compared with cone-beam computed tomography, which is frequently used for 3D image guided radiation therapy, DTS requires less imaging time and dose. Successful implementation of DTS for fast target localization requires the reconstruction process to be accomplished within tight clinical time constraints (usually within 2 min). To achieve this goal, substantial improvement of reconstruction efficiency is necessary. In this study, a reconstruction process based upon the algorithm proposed by Feldkamp, Davis, and Kress was implemented on graphics hardware for the purpose of acceleration. The performance of the novel reconstruction implementation was tested for phantom and real patient cases. The efficiency of DTS reconstruction was improved by a factor of 13 on average, without compromising image quality. With acceleration of the reconstruction algorithm, the whole DTS generation process including data preprocessing, reconstruction, and DICOM conversion is accomplished within 1.5 min, which ultimately meets clinical requirement for on-line target localization.
Fast reconstruction of digital tomosynthesis using on-board images.
Yan, Hui; Godfrey, Devon J; Yin, Fang-Fang
2008-05-01
Digital tomosynthesis (DTS) is a method to reconstruct pseudo three-dimensional (3D) volume images from two-dimensional x-ray projections acquired over limited scan angles. Compared with cone-beam computed tomography, which is frequently used for 3D image guided radiation therapy, DTS requires less imaging time and dose. Successful implementation of DTS for fast target localization requires the reconstruction process to be accomplished within tight clinical time constraints (usually within 2 min). To achieve this goal, substantial improvement of reconstruction efficiency is necessary. In this study, a reconstruction process based upon the algorithm proposed by Feldkamp, Davis, and Kress was implemented on graphics hardware for the purpose of acceleration. The performance of the novel reconstruction implementation was tested for phantom and real patient cases. The efficiency of DTS reconstruction was improved by a factor of 13 on average, without compromising image quality. With acceleration of the reconstruction algorithm, the whole DTS generation process including data preprocessing, reconstruction, and DICOM conversion is accomplished within 1.5 min, which ultimately meets clinical requirement for on-line target localization.
Infrared Astronomical Satellite (IRAS) image reconstruction and restoration
NASA Technical Reports Server (NTRS)
Gonsalves, R. A.; Lyons, T. D.; Price, S. D.; Levan, P. D.; Aumann, H. H.
1987-01-01
IRAS sky mapping data is being reconstructed as images, and an entropy-based restoration algorithm is being applied in an attempt to improve spatial resolution in extended sources. Reconstruction requires interpolation of non-uniformly sampled data. Restoration is accomplished with an iterative algorithm which begins with an inverse filter solution and iterates on it with a weighted entropy-based spectral subtraction.
Acoustic imaging for temperature distribution reconstruction
NASA Astrophysics Data System (ADS)
Jia, Ruixi; Xiong, Qingyu; Liang, Shan
2016-12-01
For several industrial processes, such as burning and drying, temperature distribution is important because it can reflect the internal running state of industrial equipment and assist to develop control strategy and ensure safety in operation of industrial equipment. The principle of this technique is mainly based on the relationship between acoustic velocity and temperature. In this paper, an algorithm for temperature distribution reconstruction is considered. Compared with reconstruction results of simulation experiments with the least square algorithm and the proposed one, the latter indicates a better information reflection of temperature distribution and relatively higher reconstruction accuracy.
Online reconstruction of 3D magnetic particle imaging data
NASA Astrophysics Data System (ADS)
Knopp, T.; Hofmann, M.
2016-06-01
Magnetic particle imaging is a quantitative functional imaging technique that allows imaging of the spatial distribution of super-paramagnetic iron oxide particles at high temporal resolution. The raw data acquisition can be performed at frame rates of more than 40 volumes s-1. However, to date image reconstruction is performed in an offline step and thus no direct feedback is available during the experiment. Considering potential interventional applications such direct feedback would be mandatory. In this work, an online reconstruction framework is implemented that allows direct visualization of the particle distribution on the screen of the acquisition computer with a latency of about 2 s. The reconstruction process is adaptive and performs block-averaging in order to optimize the signal quality for a given amount of reconstruction time.
Reconstruction algorithms for optoacoustic imaging based on fiber optic detectors
NASA Astrophysics Data System (ADS)
Lamela, Horacio; Díaz-Tendero, Gonzalo; Gutiérrez, Rebeca; Gallego, Daniel
2011-06-01
Optoacoustic Imaging (OAI), a novel hybrid imaging technology, offers high contrast, molecular specificity and excellent resolution to overcome limitations of the current clinical modalities for detection of solid tumors. The exact time-domain reconstruction formula produces images with excellent resolution but poor contrast. Some approximate time-domain filtered back-projection reconstruction algorithms have also been reported to solve this problem. A wavelet transform implementation filtering can be used to sharpen object boundaries while simultaneously preserving high contrast of the reconstructed objects. In this paper, several algorithms, based on Back Projection (BP) techniques, have been suggested to process OA images in conjunction with signal filtering for ultrasonic point detectors and integral detectors. We apply these techniques first directly to a numerical generated sample image and then to the laserdigitalized image of a tissue phantom, obtaining in both cases the best results in resolution and contrast for a waveletbased filter.
Three-dimensional surface reconstruction from multistatic SAR images.
Rigling, Brian D; Moses, Randolph L
2005-08-01
This paper discusses reconstruction of three-dimensional surfaces from multiple bistatic synthetic aperture radar (SAR) images. Techniques for surface reconstruction from multiple monostatic SAR images already exist, including interferometric processing and stereo SAR. We generalize these methods to obtain algorithms for bistatic interferometric SAR and bistatic stereo SAR. We also propose a framework for predicting the performance of our multistatic stereo SAR algorithm, and, from this framework, we suggest a metric for use in planning strategic deployment of multistatic assets.
Beyond maximum entropy: Fractal Pixon-based image reconstruction
NASA Technical Reports Server (NTRS)
Puetter, Richard C.; Pina, R. K.
1994-01-01
We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other competing methods, including Goodness-of-Fit methods such as Least-Squares fitting and Lucy-Richardson reconstruction, as well as Maximum Entropy (ME) methods such as those embodied in the MEMSYS algorithms. Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME. Our past work has shown how uniform information content pixons can be used to develop a 'Super-ME' method in which entropy is maximized exactly. Recently, however, we have developed a superior pixon basis for the image, the Fractal Pixon Basis (FPB). Unlike the Uniform Pixon Basis (UPB) of our 'Super-ME' method, the FPB basis is selected by employing fractal dimensional concepts to assess the inherent structure in the image. The Fractal Pixon Basis results in the best image reconstructions to date, superior to both UPB and the best ME reconstructions. In this paper, we review the theory of the UPB and FPB pixon and apply our methodology to the reconstruction of far-infrared imaging of the galaxy M51. The results of our reconstruction are compared to published reconstructions of the same data using the Lucy-Richardson algorithm, the Maximum Correlation Method developed at IPAC, and the MEMSYS ME algorithms. The results show that our reconstructed image has a spatial resolution a factor of two better than best previous methods (and a factor of 20 finer than the width of the point response function), and detects sources two orders of magnitude fainter than other methods.
[Three-dimension reconstruction of ocular fundus image].
Chen, Ji; Peng, Chenglin
2008-02-01
The mathematical model for 3D reconstruction of ocular fundus images is constructed according to both the reduced eye model and the simplified model of fundus camera optical system. The relationship between the images of emmetropic and ametropic eye and the true shape of ocular fundus retina is analyzed, and then the mapping relationship from 2D ocular fundus plan image to 3D surface image is obtained. As a result, the real example of 3D reconstruction for ocular fundus images is given. The max visual field of ocular fundus image for three-dimensional reconstruction is decided by the max visual field angle of fundus camera, which limits a size of the visual field of 3D reconstruction image and a range of z axis. According to the formulas of 3D mapping, the 2D data of ocular fundus image is mapped to 3D data and then veins mapping is carried out; thereafter, the 3D surface image of ocular fundus can be drawn immediately. This method makes use of the existing 2D imaging equipments to provide 3D surface image of patient's ocular fundus, and can provide ophthalmologist with beneficial reference and help to their clinical diagnosis and treatment.
NASA Astrophysics Data System (ADS)
Chvetsov, Alevei V.; Sandison, George A.; Schwartz, Jeffrey L.; Rengan, Ramesh
2015-11-01
The main objective of this article is to improve the stability of reconstruction algorithms for estimation of radiobiological parameters using serial tumor imaging data acquired during radiation therapy. Serial images of tumor response to radiation therapy represent a complex summation of several exponential processes as treatment induced cell inactivation, tumor growth rates, and the rate of cell loss. Accurate assessment of treatment response would require separation of these processes because they define radiobiological determinants of treatment response and, correspondingly, tumor control probability. However, the estimation of radiobiological parameters using imaging data can be considered an inverse ill-posed problem because a sum of several exponentials would produce the Fredholm integral equation of the first kind which is ill posed. Therefore, the stability of reconstruction of radiobiological parameters presents a problem even for the simplest models of tumor response. To study stability of the parameter reconstruction problem, we used a set of serial CT imaging data for head and neck cancer and a simplest case of a two-level cell population model of tumor response. Inverse reconstruction was performed using a simulated annealing algorithm to minimize a least squared objective function. Results show that the reconstructed values of cell surviving fractions and cell doubling time exhibit significant nonphysical fluctuations if no stabilization algorithms are applied. However, after applying a stabilization algorithm based on variational regularization, the reconstruction produces statistical distributions for survival fractions and doubling time that are comparable to published in vitro data. This algorithm is an advance over our previous work where only cell surviving fractions were reconstructed. We conclude that variational regularization allows for an increase in the number of free parameters in our model which enables development of more-advanced
Proposal of fault-tolerant tomographic image reconstruction
NASA Astrophysics Data System (ADS)
Kudo, Hiroyuki; Takaki, Keita; Yamazaki, Fukashi; Nemoto, Takuya
2016-10-01
This paper deals with tomographic image reconstruction under the situation where some of projection data bins are contaminated with abnormal data. Such situations occur in various instances of tomography. We propose a new reconstruction algorithm called the Fault-Tolerant reconstruction outlined as follows. The least-squares (L2- norm) error function || Ax- b||22 used in ordinary iterative reconstructions is sensitive to the existence of abnormal data. The proposed algorithm utilizes the L1-norm error function || Ax- b||11 instead of the L2-norm, and we develop a row-action-type iterative algorithm using the proximal splitting framework in convex optimization fields. We also propose an improved version of the L1-norm reconstruction called the L1-TV reconstruction, in which a weak Total Variation (TV) penalty is added to the cost function. Simulation results demonstrate that reconstructed images with the L2-norm were severely damaged by the effect of abnormal bins, whereas images with the L1-norm and L1-TV reconstructions were robust to the existence of abnormal bins.
Advanced analysis and event reconstruction for the CTA Observatory
NASA Astrophysics Data System (ADS)
Becherini, Y.; Khélifi, B.; Pita, S.; Punch, M.; CTA Consortium
2012-12-01
The planned Cherenkov Telescope Array (CTA) is a future observatory for very-high-energy (VHE) gamma-ray astronomy composed of one site per hemisphere [1]. It aims at 10 times better sensitivity, a better angular resolution and wider energy coverage than current installations such as H.E.S.S., MAGIC and VERITAS. In order to achieve this level of performance, both the design of the telescopes and the analysis algorithms are being studied and optimized within the CTA Monte-Carlo working group. Here, we present ongoing work on the data analysis for both the event reconstruction (energy, direction) and gamma/hadron separation, carried out within the HAP (H.E.S.S. Analysis Package) software framework of the H.E.S.S. collaboration, for this initial study. The event reconstruction uses both Hillas-parameter-based algorithms and an improved version of the 3D-Model algorithm [2]. For the gamma/hadron discrimination, original and robust discriminant variables are used and treated with Boosted Decision Trees (BDTs) in the TMVA [3] (Toolkit for Multivariate Data Analysis) framework. With this advanced analysis, known as Paris-MVA [4], the sensitivity is improved by a factor of ~ 2 in the core range of CTA relative to the standard analyses. Here we present the algorithms used for the reconstruction and discrimination, together with the resulting performance characteristics, with good confidence, since the method has been successfully applied for H.E.S.S.
NASA Astrophysics Data System (ADS)
Hardin, Perry J.; Long, David G.
1995-11-01
A scientific effort is currently underway to assess tropical forest degradation and its potential impact on Earth's climate. Because of the large continental regions involved, Advanced Very High Resolution Radiometer (AVHRR) imagery and its derivative vegetation index products with resolutions between 1 and 12 km are typically used to inventory the Earth's equatorial vegetation. Archival AVHRR imagery is also used to obtain a temporal baseline of historical forest extent. Recently however, 50-km Seasat-A Scatterometer (SASS) Ku-band imagery (acquired in 1978) has been reconstructed to approximately equals 4-km resolution, making it a supplement to AVHRR imagery for historical vegetation assessment. In order to test the utility of reconstructed Ku-band scatterometer imagery for this purpose, seasonal AVHRR vegetation index and SASS images of identical resolutions were constructed. Using the imagery, discrimination experiments involving 18 vegetation categories were conducted for a central South America study area. The results of these experiments indicate that AVHRR vegetation- index images are slightly superior to reconstructed SASS images for differentiating between equatorial vegetation classes when used alone. However, combining the scatterometer imagery with the vegetation-index images provides discrimination superior to any other combination of the data sets. Using the two data sets together, 90.3% of the test data could be correctly classified into broad classes of equatorial forest, degraded woodland/forest, woodland/savanna, and caatinga.
Method for image reconstruction of moving radionuclide source distribution
Stolin, Alexander V.; McKisson, John E.; Lee, Seung Joon; Smith, Mark Frederick
2012-12-18
A method for image reconstruction of moving radionuclide distributions. Its particular embodiment is for single photon emission computed tomography (SPECT) imaging of awake animals, though its techniques are general enough to be applied to other moving radionuclide distributions as well. The invention eliminates motion and blurring artifacts for image reconstructions of moving source distributions. This opens new avenues in the area of small animal brain imaging with radiotracers, which can now be performed without the perturbing influences of anesthesia or physical restraint on the biological system.
Reconstruction Techniques for Sparse Multistatic Linear Array Microwave Imaging
Sheen, David M.; Hall, Thomas E.
2014-06-09
Sequentially-switched linear arrays are an enabling technology for a number of near-field microwave imaging applications. Electronically sequencing along the array axis followed by mechanical scanning along an orthogonal axis allows dense sampling of a two-dimensional aperture in near real-time. In this paper, a sparse multi-static array technique will be described along with associated Fourier-Transform-based and back-projection-based image reconstruction algorithms. Simulated and measured imaging results are presented that show the effectiveness of the sparse array technique along with the merits and weaknesses of each image reconstruction approach.
Fuzzy-rule-based image reconstruction for positron emission tomography
NASA Astrophysics Data System (ADS)
Mondal, Partha P.; Rajan, K.
2005-09-01
Positron emission tomography (PET) and single-photon emission computed tomography have revolutionized the field of medicine and biology. Penalized iterative algorithms based on maximum a posteriori (MAP) estimation eliminate noisy artifacts by utilizing available prior information in the reconstruction process but often result in a blurring effect. MAP-based algorithms fail to determine the density class in the reconstructed image and hence penalize the pixels irrespective of the density class. Reconstruction with better edge information is often difficult because prior knowledge is not taken into account. The recently introduced median-root-prior (MRP)-based algorithm preserves the edges, but a steplike streaking effect is observed in the reconstructed image, which is undesirable. A fuzzy approach is proposed for modeling the nature of interpixel interaction in order to build an artifact-free edge-preserving reconstruction. The proposed algorithm consists of two elementary steps: (1) edge detection, in which fuzzy-rule-based derivatives are used for the detection of edges in the nearest neighborhood window (which is equivalent to recognizing nearby density classes), and (2) fuzzy smoothing, in which penalization is performed only for those pixels for which no edge is detected in the nearest neighborhood. Both of these operations are carried out iteratively until the image converges. Analysis shows that the proposed fuzzy-rule-based reconstruction algorithm is capable of producing qualitatively better reconstructed images than those reconstructed by MAP and MRP algorithms. The reconstructed images are sharper, with small features being better resolved owing to the nature of the fuzzy potential function.
Alignment Solution for CT Image Reconstruction using Fixed Point and Virtual Rotation Axis
Jun, Kyungtaek; Yoon, Seokhwan
2017-01-01
Since X-ray tomography is now widely adopted in many different areas, it becomes more crucial to find a robust routine of handling tomographic data to get better quality of reconstructions. Though there are several existing techniques, it seems helpful to have a more automated method to remove the possible errors that hinder clearer image reconstruction. Here, we proposed an alternative method and new algorithm using the sinogram and the fixed point. An advanced physical concept of Center of Attenuation (CA) was also introduced to figure out how this fixed point is applied to the reconstruction of image having errors we categorized in this article. Our technique showed a promising performance in restoring images having translation and vertical tilt errors. PMID:28120881
Alignment Solution for CT Image Reconstruction using Fixed Point and Virtual Rotation Axis
NASA Astrophysics Data System (ADS)
Jun, Kyungtaek; Yoon, Seokhwan
2017-01-01
Since X-ray tomography is now widely adopted in many different areas, it becomes more crucial to find a robust routine of handling tomographic data to get better quality of reconstructions. Though there are several existing techniques, it seems helpful to have a more automated method to remove the possible errors that hinder clearer image reconstruction. Here, we proposed an alternative method and new algorithm using the sinogram and the fixed point. An advanced physical concept of Center of Attenuation (CA) was also introduced to figure out how this fixed point is applied to the reconstruction of image having errors we categorized in this article. Our technique showed a promising performance in restoring images having translation and vertical tilt errors.
Bayesian 2D Current Reconstruction from Magnetic Images
NASA Astrophysics Data System (ADS)
Clement, Colin B.; Bierbaum, Matthew K.; Nowack, Katja; Sethna, James P.
We employ a Bayesian image reconstruction scheme to recover 2D currents from magnetic flux imaged with scanning SQUIDs (Superconducting Quantum Interferometric Devices). Magnetic flux imaging is a versatile tool to locally probe currents and magnetic moments, however present reconstruction methods sacrifice resolution due to numerical instability. Using state-of-the-art blind deconvolution techniques we recover the currents, point-spread function and height of the SQUID loop by optimizing the probability of measuring an image. We obtain uncertainties on these quantities by sampling reconstructions. This generative modeling technique could be used to develop calibration protocols for scanning SQUIDs, to diagnose systematic noise in the imaging process, and can be applied to many tools beyond scanning SQUIDs.
Matrix-based image reconstruction methods for tomography
Llacer, J.; Meng, J.D.
1984-10-01
Matrix methods of image reconstruction have not been used, in general, because of the large size of practical matrices, ill condition upon inversion and the success of Fourier-based techniques. An exception is the work that has been done at the Lawrence Berkeley Laboratory for imaging with accelerated radioactive ions. An extension of that work into more general imaging problems shows that, with a correct formulation of the problem, positron tomography with ring geometries results in well behaved matrices which can be used for image reconstruction with no distortion of the point response in the field of view and flexibility in the design of the instrument. Maximum Likelihood Estimator methods of reconstruction, which use the system matrices tailored to specific instruments and do not need matrix inversion, are shown to result in good preliminary images. A parallel processing computer structure based on multiple inexpensive microprocessors is proposed as a system to implement the matrix-MLE methods. 14 references, 7 figures.
Padhi, Shantanu K.; Howard, John
2013-01-01
Nonlinear microwave imaging heavily relies on an accurate numerical electromagnetic model of the antenna system. The model is used to simulate scattering data that is compared to its measured counterpart in order to reconstruct the image. In this paper an antenna system immersed in water is used to image different canonical objects in order to investigate the implication of modeling errors on the final reconstruction using a time domain-based iterative inverse reconstruction algorithm and three-dimensional FDTD modeling. With the test objects immersed in a background of air and tap water, respectively, we have studied the impact of antenna modeling errors, errors in the modeling of the background media, and made a comparison with a two-dimensional version of the algorithm. In conclusion even small modeling errors in the antennas can significantly alter the reconstructed image. Since the image reconstruction procedure is highly nonlinear general conclusions are very difficult to make. In our case it means that with the antenna system immersed in water and using our present FDTD-based electromagnetic model the imaging results are improved if refraining from modeling the water-wall-air interface and instead just use a homogeneous background of water in the model. PMID:23606825
Compensation for air voids in photoacoustic computed tomography image reconstruction
NASA Astrophysics Data System (ADS)
Matthews, Thomas P.; Li, Lei; Wang, Lihong V.; Anastasio, Mark A.
2016-03-01
Most image reconstruction methods in photoacoustic computed tomography (PACT) assume that the acoustic properties of the object and the surrounding medium are homogeneous. This can lead to strong artifacts in the reconstructed images when there are significant variations in sound speed or density. Air voids represent a particular challenge due to the severity of the differences between the acoustic properties of air and water. In whole-body small animal imaging, the presence of air voids in the lungs, stomach, and gastrointestinal system can limit image quality over large regions of the object. Iterative reconstruction methods based on the photoacoustic wave equation can account for these acoustic variations, leading to improved resolution, improved contrast, and a reduction in the number of imaging artifacts. However, the strong acoustic heterogeneities can lead to instability or errors in the numerical wave solver. Here, the impact of air voids on PACT image reconstruction is investigated, and procedures for their compensation are proposed. The contributions of sound speed and density variations to the numerical stability of the wave solver are considered, and a novel approach for mitigating the impact of air voids while reducing the computational burden of image reconstruction is identified. These results are verified by application to an experimental phantom.
Recent advances in imaging technologies in dentistry
Shah, Naseem; Bansal, Nikhil; Logani, Ajay
2014-01-01
Dentistry has witnessed tremendous advances in all its branches over the past three decades. With these advances, the need for more precise diagnostic tools, specially imaging methods, have become mandatory. From the simple intra-oral periapical X-rays, advanced imaging techniques like computed tomography, cone beam computed tomography, magnetic resonance imaging and ultrasound have also found place in modern dentistry. Changing from analogue to digital radiography has not only made the process simpler and faster but also made image storage, manipulation (brightness/contrast, image cropping, etc.) and retrieval easier. The three-dimensional imaging has made the complex cranio-facial structures more accessible for examination and early and accurate diagnosis of deep seated lesions. This paper is to review current advances in imaging technology and their uses in different disciplines of dentistry. PMID:25349663
Quantitative photoacoustic image reconstruction improves accuracy in deep tissue structures.
Mastanduno, Michael A; Gambhir, Sanjiv S
2016-10-01
Photoacoustic imaging (PAI) is emerging as a potentially powerful imaging tool with multiple applications. Image reconstruction for PAI has been relatively limited because of limited or no modeling of light delivery to deep tissues. This work demonstrates a numerical approach to quantitative photoacoustic image reconstruction that minimizes depth and spectrally derived artifacts. We present the first time-domain quantitative photoacoustic image reconstruction algorithm that models optical sources through acoustic data to create quantitative images of absorption coefficients. We demonstrate quantitative accuracy of less than 5% error in large 3 cm diameter 2D geometries with multiple targets and within 22% error in the largest size quantitative photoacoustic studies to date (6cm diameter). We extend the algorithm to spectral data, reconstructing 6 varying chromophores to within 17% of the true values. This quantitiative PA tomography method was able to improve considerably on filtered-back projection from the standpoint of image quality, absolute, and relative quantification in all our simulation geometries. We characterize the effects of time step size, initial guess, and source configuration on final accuracy. This work could help to generate accurate quantitative images from both endogenous absorbers and exogenous photoacoustic dyes in both preclinical and clinical work, thereby increasing the information content obtained especially from deep-tissue photoacoustic imaging studies.
Quantitative photoacoustic image reconstruction improves accuracy in deep tissue structures
Mastanduno, Michael A.; Gambhir, Sanjiv S.
2016-01-01
Photoacoustic imaging (PAI) is emerging as a potentially powerful imaging tool with multiple applications. Image reconstruction for PAI has been relatively limited because of limited or no modeling of light delivery to deep tissues. This work demonstrates a numerical approach to quantitative photoacoustic image reconstruction that minimizes depth and spectrally derived artifacts. We present the first time-domain quantitative photoacoustic image reconstruction algorithm that models optical sources through acoustic data to create quantitative images of absorption coefficients. We demonstrate quantitative accuracy of less than 5% error in large 3 cm diameter 2D geometries with multiple targets and within 22% error in the largest size quantitative photoacoustic studies to date (6cm diameter). We extend the algorithm to spectral data, reconstructing 6 varying chromophores to within 17% of the true values. This quantitiative PA tomography method was able to improve considerably on filtered-back projection from the standpoint of image quality, absolute, and relative quantification in all our simulation geometries. We characterize the effects of time step size, initial guess, and source configuration on final accuracy. This work could help to generate accurate quantitative images from both endogenous absorbers and exogenous photoacoustic dyes in both preclinical and clinical work, thereby increasing the information content obtained especially from deep-tissue photoacoustic imaging studies. PMID:27867695
Chen, Shuo; Ong, Yi Hong; Lin, Xiaoqian; Liu, Quan
2015-07-01
Raman spectroscopy has shown great potential in biomedical applications. However, intrinsically weak Raman signals cause slow data acquisition especially in Raman imaging. This problem can be overcome by narrow-band Raman imaging followed by spectral reconstruction. Our previous study has shown that Raman spectra free of fluorescence background can be reconstructed from narrow-band Raman measurements using traditional Wiener estimation. However, fluorescence-free Raman spectra are only available from those sophisticated Raman setups capable of fluorescence suppression. The reconstruction of Raman spectra with fluorescence background from narrow-band measurements is much more challenging due to the significant variation in fluorescence background. In this study, two advanced Wiener estimation methods, i.e. modified Wiener estimation and sequential weighted Wiener estimation, were optimized to achieve this goal. Both spontaneous Raman spectra and surface enhanced Raman spectra were evaluated. Compared with traditional Wiener estimation, two advanced methods showed significant improvement in the reconstruction of spontaneous Raman spectra. However, traditional Wiener estimation can work as effectively as the advanced methods for SERS spectra but much faster. The wise selection of these methods would enable accurate Raman reconstruction in a simple Raman setup without the function of fluorescence suppression for fast Raman imaging.
Compressed Sensing MR Image Reconstruction Exploiting TGV and Wavelet Sparsity
Du, Huiqian; Han, Yu; Mei, Wenbo
2014-01-01
Compressed sensing (CS) based methods make it possible to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. The reference-driven CS-MRI reconstruction schemes can further decrease the sampling ratio by exploiting the sparsity of the difference image between the target and the reference MR images in pixel domain. Unfortunately existing methods do not work well given that contrast changes are incorrectly estimated or motion compensation is inaccurate. In this paper, we propose to reconstruct MR images by utilizing the sparsity of the difference image between the target and the motion-compensated reference images in wavelet transform and gradient domains. The idea is attractive because it requires neither the estimation of the contrast changes nor multiple times motion compensations. In addition, we apply total generalized variation (TGV) regularization to eliminate the staircasing artifacts caused by conventional total variation (TV). Fast composite splitting algorithm (FCSA) is used to solve the proposed reconstruction problem in order to improve computational efficiency. Experimental results demonstrate that the proposed method can not only reduce the computational cost but also decrease sampling ratio or improve the reconstruction quality alternatively. PMID:25371704
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.
Digital Three-dimensional Reconstruction Based On Integral Imaging
Li, Chao; Chen, Qian; Hua, Hong; Mao, Chen; Shao, Ajun
2015-01-01
This paper presents a digital three dimensional reconstruction method based on a set of small-baseline elemental images captured with a micro-lens array and a CCD sensor. In this paper, we adopt the ASIFT (Affine Scale-invariant feature transform) operator as the image registration method. Among the set of captured elemental images, the elemental image located in the middle of the overall image field is used as the reference and corresponding matching points in each elemental image around the reference elemental are calculated, which enables to accurately compute the depth value of object points relatively to the reference image frame. Using optimization algorithm with redundant matching points can achieve 3D reconstruction finally. Our experimental results are presented to demonstrate excellent performance in accuracy and speed of the proposed algorithm. PMID:26236151
Advanced imaging in equine dental disease.
Selberg, Kurt; Easley, Jeremiah T
2013-08-01
Dental and sinus disorders are relatively common and of major clinical importance in equine medicine. Advanced diagnostic imaging has become an integral part of equine veterinary medicine. Advanced imaging has progressed the understanding, diagnosis, and treatment of dental- and sinus-related diseases. As a clinician, it is important to realize the value of advanced diagnostic imaging. Although computed tomography and magnetic resonance imaging are both significantly more expensive compared with other diagnostic tools, the financial cost of inaccurate diagnosis and treatment can often result in higher overall costs.
Image analysis in medical imaging: recent advances in selected examples.
Dougherty, G
2010-01-01
Medical imaging has developed into one of the most important fields within scientific imaging due to the rapid and continuing progress in computerised medical image visualisation and advances in analysis methods and computer-aided diagnosis. Several research applications are selected to illustrate the advances in image analysis algorithms and visualisation. Recent results, including previously unpublished data, are presented to illustrate the challenges and ongoing developments.
Probe and object function reconstruction in incoherent stem imaging
Nellist, P.D.; Pennycook, S.J.
1996-09-01
Using the phase-object approximation it is shown how an annular dark- field (ADF) detector in a scanning transmission electron microscope (STEM) leads to an image which can be described by an incoherent model. The point spread function is found to be simply the illuminating probe intensity. An important consequence of this is that there is no phase problem in the imaging process, which allows various image processing methods to be applied directly to the image intensity data. Using an image of a GaAs<110>, the probe intensity profile is reconstructed, confirming the existence of a 1.3 {Angstrom} probe in a 300kV STEM. It is shown that simply deconvolving this reconstructed probe from the image data does not improve its interpretability because the dominant effects of the imaging process arise simply from the restricted resolution of the microscope. However, use of the reconstructed probe in a maximum entropy reconstruction is demonstrated, which allows information beyond the resolution limit to be restored and does allow improved image interpretation.
3D Image Reconstruction: Determination of Pattern Orientation
Blankenbecler, Richard
2003-03-13
The problem of determining the euler angles of a randomly oriented 3-D object from its 2-D Fraunhofer diffraction patterns is discussed. This problem arises in the reconstruction of a positive semi-definite 3-D object using oversampling techniques. In such a problem, the data consists of a measured set of magnitudes from 2-D tomographic images of the object at several unknown orientations. After the orientation angles are determined, the object itself can then be reconstructed by a variety of methods using oversampling, the magnitude data from the 2-D images, physical constraints on the image and then iteration to determine the phases.
Bayesian image reconstruction for improving detection performance of muon tomography.
Wang, Guobao; Schultz, Larry J; Qi, Jinyi
2009-05-01
Muon tomography is a novel technology that is being developed for detecting high-Z materials in vehicles or cargo containers. Maximum likelihood methods have been developed for reconstructing the scattering density image from muon measurements. However, the instability of maximum likelihood estimation often results in noisy images and low detectability of high-Z targets. In this paper, we propose using regularization to improve the image quality of muon tomography. We formulate the muon reconstruction problem in a Bayesian framework by introducing a prior distribution on scattering density images. An iterative shrinkage algorithm is derived to maximize the log posterior distribution. At each iteration, the algorithm obtains the maximum a posteriori update by shrinking an unregularized maximum likelihood update. Inverse quadratic shrinkage functions are derived for generalized Laplacian priors and inverse cubic shrinkage functions are derived for generalized Gaussian priors. Receiver operating characteristic studies using simulated data demonstrate that the Bayesian reconstruction can greatly improve the detection performance of muon tomography.
Reconstruction of indoor scene from a single image
NASA Astrophysics Data System (ADS)
Wu, Di; Li, Hongyu; Zhang, Lin
2015-03-01
Given a single image of an indoor scene without any prior knowledge, is it possible for a computer to automatically reconstruct the structure of the scene? This letter proposes a reconstruction method, called RISSIM, to recover the 3D modelling of an indoor scene from a single image. The proposed method is composed of three steps: the estimation of vanishing points, the detection and classification of lines, and the plane mapping. To find vanishing points, a new feature descriptor, named "OCR", is defined to describe the texture orientation. With Phrase Congruency and Harris Detector, the line segments can be detected exactly, which is a prerequisite. Perspective transform is a defined as a reliable method whereby the points on the image can be represented on a 3D model. Experimental results show that the 3D structure of an indoor scene can be well reconstructed from a single image although the available depth information is limited.
Recent Advances in Computed Tomographic Technology: Cardiopulmonary Imaging Applications.
Tabari, Azadeh; Lo Gullo, Roberto; Murugan, Venkatesh; Otrakji, Alexi; Digumarthy, Subba; Kalra, Mannudeep
2017-03-01
Cardiothoracic diseases result in substantial morbidity and mortality. Chest computed tomography (CT) has been an imaging modality of choice for assessing a host of chest diseases, and technologic advances have enabled the emergence of coronary CT angiography as a robust noninvasive test for cardiac imaging. Technologic developments in CT have also enabled the application of dual-energy CT scanning for assessing pulmonary vascular and neoplastic processes. Concerns over increasing radiation dose from CT scanning are being addressed with introduction of more dose-efficient wide-area detector arrays and iterative reconstruction techniques. This review article discusses the technologic innovations in CT and their effect on cardiothoracic applications.
Image reconstruction by phase retrieval with transverse translation diversity
NASA Astrophysics Data System (ADS)
Guizar-Sicairos, Manuel; Fienup, James R.
2008-08-01
Measuring a series of far-field intensity patterns from an object, taken after a transverse translation of the object with respect to a known illumination pattern, has been shown to make the problem of image reconstruction by phase retrieval much more robust. However, previously reported reconstruction algorithms [Phys. Rev. Lett. 93, 023903 (2004)] rely on an accurate knowledge of the translations and illumination pattern for a successful reconstruction. We developed a nonlinear optimization algorithm that allows optimization over the translations and illumination pattern, dramatically improving the reconstructions if the system parameters are inaccurately known [Opt. Express 16, 7264 (2008)]. In this paper we compare reconstructions obtained with these algorithms under realistic experimental scenarios.
Prospective regularization design in prior-image-based reconstruction
Dang, Hao; Siewerdsen, Jeffrey H.; Stayman, J. Webster
2015-01-01
Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g., through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in
Prospective regularization design in prior-image-based reconstruction
NASA Astrophysics Data System (ADS)
Dang, Hao; Siewerdsen, Jeffrey H.; Webster Stayman, J.
2015-12-01
Prior-image-based reconstruction (PIBR) methods leveraging patient-specific anatomical information from previous imaging studies and/or sequences have demonstrated dramatic improvements in dose utilization and image quality for low-fidelity data. However, a proper balance of information from the prior images and information from the measurements is required (e.g. through careful tuning of regularization parameters). Inappropriate selection of reconstruction parameters can lead to detrimental effects including false structures and failure to improve image quality. Traditional methods based on heuristics are subject to error and sub-optimal solutions, while exhaustive searches require a large number of computationally intensive image reconstructions. In this work, we propose a novel method that prospectively estimates the optimal amount of prior image information for accurate admission of specific anatomical changes in PIBR without performing full image reconstructions. This method leverages an analytical approximation to the implicitly defined PIBR estimator, and introduces a predictive performance metric leveraging this analytical form and knowledge of a particular presumed anatomical change whose accurate reconstruction is sought. Additionally, since model-based PIBR approaches tend to be space-variant, a spatially varying prior image strength map is proposed to optimally admit changes everywhere in the image (eliminating the need to know change locations a priori). Studies were conducted in both an ellipse phantom and a realistic thorax phantom emulating a lung nodule surveillance scenario. The proposed method demonstrated accurate estimation of the optimal prior image strength while achieving a substantial computational speedup (about a factor of 20) compared to traditional exhaustive search. Moreover, the use of the proposed prior strength map in PIBR demonstrated accurate reconstruction of anatomical changes without foreknowledge of change locations in
DCT and DST Based Image Compression for 3D Reconstruction
NASA Astrophysics Data System (ADS)
Siddeq, Mohammed M.; Rodrigues, Marcos A.
2017-03-01
This paper introduces a new method for 2D image compression whose quality is demonstrated through accurate 3D reconstruction using structured light techniques and 3D reconstruction from multiple viewpoints. The method is based on two discrete transforms: (1) A one-dimensional Discrete Cosine Transform (DCT) is applied to each row of the image. (2) The output from the previous step is transformed again by a one-dimensional Discrete Sine Transform (DST), which is applied to each column of data generating new sets of high-frequency components followed by quantization of the higher frequencies. The output is then divided into two parts where the low-frequency components are compressed by arithmetic coding and the high frequency ones by an efficient minimization encoding algorithm. At decompression stage, a binary search algorithm is used to recover the original high frequency components. The technique is demonstrated by compressing 2D images up to 99% compression ratio. The decompressed images, which include images with structured light patterns for 3D reconstruction and from multiple viewpoints, are of high perceptual quality yielding accurate 3D reconstruction. Perceptual assessment and objective quality of compression are compared with JPEG and JPEG2000 through 2D and 3D RMSE. Results show that the proposed compression method is superior to both JPEG and JPEG2000 concerning 3D reconstruction, and with equivalent perceptual quality to JPEG2000.
A measurement system and image reconstruction in magnetic induction tomography.
Vauhkonen, M; Hamsch, M; Igney, C H
2008-06-01
Magnetic induction tomography (MIT) is a technique for imaging the internal conductivity distribution of an object. In MIT current-carrying coils are used to induce eddy currents in the object and the induced voltages are sensed with other coils. From these measurements, the internal conductivity distribution of the object can be reconstructed. In this paper, we introduce a 16-channel MIT measurement system that is capable of parallel readout of 16 receiver channels. The parallel measurements are carried out using high-quality audio sampling devices. Furthermore, approaches for reconstructing MIT images developed for the 16-channel MIT system are introduced. We consider low conductivity applications, conductivity less than 5 S m(-1), and we use a frequency of 10 MHz. In the image reconstruction, we use time-harmonic Maxwell's equation for the electric field. This equation is solved with the finite element method using edge elements and the images are reconstructed using a generalized Tikhonov regularization approach. Both difference and static image reconstruction approaches are considered. Results from simulations and real measurements collected with the Philips 16-channel MIT system are shown.
PET image reconstruction: a robust state space approach.
Liu, Huafeng; Tian, Yi; Shi, Pengcheng
2005-01-01
Statistical iterative reconstruction algorithms have shown improved image quality over conventional nonstatistical methods in PET by using accurate system response models and measurement noise models. Strictly speaking, however, PET measurements, pre-corrected for accidental coincidences, are neither Poisson nor Gaussian distributed and thus do not meet basic assumptions of these algorithms. In addition, the difficulty in determining the proper system response model also greatly affects the quality of the reconstructed images. In this paper, we explore the usage of state space principles for the estimation of activity map in tomographic PET imaging. The proposed strategy formulates the organ activity distribution through tracer kinetics models, and the photon-counting measurements through observation equations, thus makes it possible to unify the dynamic reconstruction problem and static reconstruction problem into a general framework. Further, it coherently treats the uncertainties of the statistical model of the imaging system and the noisy nature of measurement data. Since H(infinity) filter seeks minimummaximum-error estimates without any assumptions on the system and data noise statistics, it is particular suited for PET image reconstruction where the statistical properties of measurement data and the system model are very complicated. The performance of the proposed framework is evaluated using Shepp-Logan simulated phantom data and real phantom data with favorable results.
NASA Astrophysics Data System (ADS)
Archer, Glen E.; Bos, Jeremy P.; Roggemann, Michael C.
2012-05-01
Terrestrial imaging over very long horizontal paths is increasingly common in surveillance and defense systems. All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. This paper explores the Mean-Square-Error (MSE) performance of a multi-frame-blind-deconvolution-based reconstruction technique using a non-linear optimization strategy to recover a reconstructed object. Three sets of 70 images representing low, moderate and severe turbulence degraded images were simulated from a diffraction limited image taken with a professional digital camera. Reconstructed objects showed significant, 54, 22 and 14 percent improvement in mean squared error for low, moderate, and severe turbulence cases respectively.
Parallel hyperspectral image reconstruction using random projections
NASA Astrophysics Data System (ADS)
Sevilla, Jorge; Martín, Gabriel; Nascimento, José M. P.
2016-10-01
Spaceborne sensors systems are characterized by scarce onboard computing and storage resources and by communication links with reduced bandwidth. Random projections techniques have been demonstrated as an effective and very light way to reduce the number of measurements in hyperspectral data, thus, the data to be transmitted to the Earth station is reduced. However, the reconstruction of the original data from the random projections may be computationally expensive. SpeCA is a blind hyperspectral reconstruction technique that exploits the fact that hyperspectral vectors often belong to a low dimensional subspace. SpeCA has shown promising results in the task of recovering hyperspectral data from a reduced number of random measurements. In this manuscript we focus on the implementation of the SpeCA algorithm for graphics processing units (GPU) using the compute unified device architecture (CUDA). Experimental results conducted using synthetic and real hyperspectral datasets on the GPU architecture by NVIDIA: GeForce GTX 980, reveal that the use of GPUs can provide real-time reconstruction. The achieved speedup is up to 22 times when compared with the processing time of SpeCA running on one core of the Intel i7-4790K CPU (3.4GHz), with 32 Gbyte memory.
Shape-based image reconstruction using linearized deformations
NASA Astrophysics Data System (ADS)
Öktem, Ozan; Chen, Chong; Onur Domaniç, Nevzat; Ravikumar, Pradeep; Bajaj, Chandrajit
2017-03-01
We introduce a reconstruction framework that can account for shape related prior information in imaging-related inverse problems. It is a variational scheme that uses a shape functional, whose definition is based on deformable template machinery from computational anatomy. We prove existence and, as a proof of concept, we apply the proposed shape-based reconstruction to 2D tomography with very sparse and/or highly noisy measurements.
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-05-15
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
An adaptive filtered back-projection for photoacoustic image reconstruction
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong
2015-01-01
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
Beyond maximum entropy: Fractal pixon-based image reconstruction
NASA Technical Reports Server (NTRS)
Puetter, R. C.; Pina, R. K.
1994-01-01
We have developed a new Bayesian image reconstruction method that has been shown to be superior to the best implementations of other methods, including Goodness-of-Fit (e.g. Least-Squares and Lucy-Richardson) and Maximum Entropy (ME). Our new method is based on the concept of the pixon, the fundamental, indivisible unit of picture information. Use of the pixon concept provides an improved image model, resulting in an image prior which is superior to that of standard ME.
Exposing digital image forgeries by 3D reconstruction technology
NASA Astrophysics Data System (ADS)
Wang, Yongqiang; Xu, Xiaojing; Li, Zhihui; Liu, Haizhen; Li, Zhigang; Huang, Wei
2009-11-01
Digital images are easy to tamper and edit due to availability of powerful image processing and editing software. Especially, forged images by taking from a picture of scene, because of no manipulation was made after taking, usual methods, such as digital watermarks, statistical correlation technology, can hardly detect the traces of image tampering. According to image forgery characteristics, a method, based on 3D reconstruction technology, which detect the forgeries by discriminating the dimensional relationship of each object appeared on image, is presented in this paper. This detection method includes three steps. In the first step, all the parameters of images were calibrated and each crucial object on image was chosen and matched. In the second step, the 3D coordinates of each object were calculated by bundle adjustment. In final step, the dimensional relationship of each object was analyzed. Experiments were designed to test this detection method; the 3D reconstruction and the forged image 3D reconstruction were computed independently. Test results show that the fabricating character in digital forgeries can be identified intuitively by this method.
Respiratory motion correction in emission tomography image reconstruction.
Reyes, Mauricio; Malandain, Grégoire; Koulibaly, Pierre Malick; González Ballester, Miguel A; Darcourt, Jacques
2005-01-01
In Emission Tomography imaging, respiratory motion causes artifacts in lungs and cardiac reconstructed images, which lead to misinterpretations and imprecise diagnosis. Solutions like respiratory gating, correlated dynamic PET techniques, list-mode data based techniques and others have been tested with improvements over the spatial activity distribution in lungs lesions, but with the disadvantages of requiring additional instrumentation or discarding part of the projection data used for reconstruction. The objective of this study is to incorporate respiratory motion correction directly into the image reconstruction process, without any additional acquisition protocol consideration. To this end, we propose an extension to the Maximum Likelihood Expectation Maximization (MLEM) algorithm that includes a respiratory motion model, which takes into account the displacements and volume deformations produced by the respiratory motion during the data acquisition process. We present results from synthetic simulations incorporating real respiratory motion as well as from phantom and patient data.
X-ray imaging and 3D reconstruction of in-flight exploding foil initiator flyers
NASA Astrophysics Data System (ADS)
Willey, T. M.; Champley, K.; Hodgin, R.; Lauderbach, L.; Bagge-Hansen, M.; May, C.; Sanchez, N.; Jensen, B. J.; Iverson, A.; van Buuren, T.
2016-06-01
Exploding foil initiators (EFIs), also known as slapper initiators or detonators, offer clear safety and timing advantages over other means of initiating detonation in high explosives. This work outlines a new capability for imaging and reconstructing three-dimensional images of operating EFIs. Flyer size and intended velocity were chosen based on parameters of the imaging system. The EFI metal plasma and plastic flyer traveling at 2.5 km/s were imaged with short ˜80 ps pulses spaced 153.4 ns apart. A four-camera system acquired 4 images from successive x-ray pulses from each shot. The first frame was prior to bridge burst, the 2nd images the flyer about 0.16 mm above the surface but edges of the foil and/or flyer are still attached to the substrate. The 3rd frame captures the flyer in flight, while the 4th shows a completely detached flyer in a position that is typically beyond where slappers strike initiating explosives. Multiple acquisitions at different incident angles and advanced computed tomography reconstruction algorithms were used to produce a 3-dimensional image of the flyer at 0.16 and 0.53 mm above the surface. Both the x-ray images and the 3D reconstruction show a strong anisotropy in the shape of the flyer and underlying foil parallel vs. perpendicular to the initiating current and electrical contacts. These results provide detailed flyer morphology during the operation of the EFI.
Iterative image reconstruction for CBCT using edge-preserving prior
Wang, Jing; Li, Tianfang; Xing, Lei
2009-01-01
On-board cone-beam computed tomography (CBCT) is a new imaging technique for radiation therapy guidance, which provides volumetric information of a patient at treatment position. CBCT improves the setup accuracy and may be used for dose reconstruction. However, there is great concern that the repeated use of CBCT during a treatment course delivers too much of an extra dose to the patient. To reduce the CBCT dose, one needs to lower the total mAs of the x-ray tube current, which usually leads to reduced image quality. Our goal of this work is to develop an effective method that enables one to achieve a clinically acceptable CBCT image with as low as possible mAs without compromising quality. An iterative image reconstruction algorithm based on a penalized weighted least-squares (PWLS) principle was developed for this purpose. To preserve edges in the reconstructed images, we designed an anisotropic penalty term of a quadratic form. The algorithm was evaluated with a CT quality assurance phantom and an anthropomorphic head phantom. Compared with conventional isotropic penalty, the PWLS image reconstruction algorithm with anisotropic penalty shows better resolution preservation. PMID:19235393
Sparse representation for the ISAR image reconstruction
NASA Astrophysics Data System (ADS)
Hu, Mengqi; Montalbo, John; Li, Shuxia; Sun, Ligang; Qiao, Zhijun G.
2016-05-01
In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.
NASA Astrophysics Data System (ADS)
Yu, Zong-Han; Wu, Chun-Ming; Lin, Yo-Wei; Chuang, Ming-Lung; Tsai, Jui-che; Sun, Chia-Wei
2008-02-01
Diffuse optical tomography (DOT) is an emerging technique for biomedical imaging. The imaging quality of the DOT strongly depends on the reconstruction algorithm. In this paper, four inhomogeneities with various shapes of absorption distributions are simulated by a continues-wave DOT system. The DOT images are obtained based on the simultaneous iterative reconstruction technique (SIRT) method. To solve the trade-off problem between time consumption of reconstruction process and accuracy of reconstructed image, the iteration process needs a optimization criterion in algorithm. In this paper, the comparison between the root mean square error (RMSE) and the convergence rate (CR) in SIRT algorithm are demonstrated. From the simulation results, the CR reveals the information of global minimum in the iteration process. Based on the CR calculation, the SIRT can offer higher efficient image reconstructing in DOT system.
Tomographic image reconstruction via estimation of sparse unidirectional gradients.
Polak, Adam G; Mroczka, Janusz; Wysoczański, Dariusz
2017-02-01
Since computed tomography (CT) was developed over 35 years ago, new mathematical ideas and computational algorithms have been continuingly elaborated to improve the quality of reconstructed images. In recent years, a considerable effort can be noticed to apply the sparse solution of underdetermined system theory to the reconstruction of CT images from undersampled data. Its significance stems from the possibility of obtaining good quality CT images from low dose projections. Among diverse approaches, total variation (TV) minimizing 2D gradients of an image, seems to be the most popular method. In this paper, a new method for CT image reconstruction via sparse gradients estimation (SGE), is proposed. It consists in estimating 1D gradients specified in four directions using the iterative reweighting algorithm. To investigate its properties and to compare it with TV and other related methods, numerical simulations were performed according to the Monte Carlo scheme, using the Shepp-Logan and more realistic brain phantoms scanned at 9-60 directions in the range from 0 to 179°, with measurement data disturbed by additive Gaussians noise characterized by the relative level of 0.1%, 0.2%, 0.5%, 1%, 2% and 5%. The accuracy of image reconstruction was assessed in terms of the relative root-mean-square (RMS) error. The results show that the proposed SGE algorithm has returned more accurate images than TV for the cases fulfilling the sparsity conditions. Particularly, it preserves sharp edges of regions representing different tissues or organs and yields images of much better quality reconstructed from a small number of projections disturbed by relatively low measurement noise.
Hofmann, Christian; Sawall, Stefan; Knaup, Michael; Kachelrieß, Marc
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 the 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 contrast
Three dimensional reconstruction of conventional stereo optic disc image.
Kong, H J; Kim, S K; Seo, J M; Park, K H; Chung, H; Park, K S; Kim, H C
2004-01-01
Stereo disc photograph was analyzed and reconstructed as 3 dimensional contour image to evaluate the status of the optic nerve head for the early detection of glaucoma and the evaluation of the efficacy of treatment. Stepwise preprocessing was introduced to detect the edge of the optic nerve head and retinal vessels and reduce noises. Paired images were registered by power cepstrum method and zero-mean normalized cross-correlation. After Gaussian blurring, median filter application and disparity pair searching, depth information in the 3 dimensionally reconstructed image was calculated by the simple triangulation formula. Calculated depth maps were smoothed through cubic B-spline interpolation and retinal vessels were visualized more clearly by adding reference image. Resulted 3 dimensional contour image showed optic cups, retinal vessels and the notching of the neural rim of the optic disc clearly and intuitively, helping physicians in understanding and interpreting the stereo disc photograph.
Computationally attractive reconstruction of bandlimited images from irregular samples.
Strohmer, T
1997-01-01
An efficient method for the reconstruction of bandlimited images and the approximation of arbitrary images from nonuniform sampling values is developed. The novel method is based on the observation that the reconstruction problem can be formulated as linear system of equations using two-dimensional (2-D) trigonometric polynomials, where the matrix is of block-Toeplitz type with Toeplitz blocks. This system is solved iteratively by the conjugate gradient (CG) method. We show that the use of so-called adaptive weights in the establishment of the block Toeplitz matrix can be seen as efficient preconditioning. The superiority of the new method over conventional approaches is demonstrated by numerical experiments.
Image Reconstruction Using Large Optical Telescopes.
1982-02-15
imaged the Pluto/Charon system, resolved a multiple QSO (quasar) and we have mapped and imaged asymmetries in the envelope around the supergiant star ...fringes for point source. 38 11.7. Interference fringes for binary star . 40 1I.8. Power spectrum of C Tau. 42 III.1. PG 1115+080. 50 111.2. Tracking...Dawe’s limit given above. An example of short exposure star photos, at very large image scale, is given in Figure 1.1. The overall size of these
Very fast approximate reconstruction of MR images.
Angelidis, P A
1998-11-01
The ultra fast Fourier transform (UFFT) provides the means for a very fast computation of a magnetic resonance (MR) image, because it is implemented using only additions and no multiplications at all. It achieves this by approximating the complex exponential functions involved in the Fourier transform (FT) sum with computationally simpler periodic functions. This approximation introduces erroneous spectrum peaks of small magnitude. We examine the performance of this transform in some typical MRI signals. The results show that this transform can very quickly provide an MR image. It is proposed to be used as a replacement of the classically used FFT whenever a fast general overview of an image is required.
Dictionary Approaches to Image Compression and Reconstruction
NASA Technical Reports Server (NTRS)
Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.
1998-01-01
This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as phi(sub gamma), are discrete time signals, where gamma represents the dictionary index. A dictionary with a collection of these waveforms is typically complete or overcomplete. Given such a dictionary, the goal is to obtain a representation image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.
Dictionary Approaches to Image Compression and Reconstruction
NASA Technical Reports Server (NTRS)
Ziyad, Nigel A.; Gilmore, Erwin T.; Chouikha, Mohamed F.
1998-01-01
This paper proposes using a collection of parameterized waveforms, known as a dictionary, for the purpose of medical image compression. These waveforms, denoted as lambda, are discrete time signals, where y represents the dictionary index. A dictionary with a collection of these waveforms Is typically complete or over complete. Given such a dictionary, the goal is to obtain a representation Image based on the dictionary. We examine the effectiveness of applying Basis Pursuit (BP), Best Orthogonal Basis (BOB), Matching Pursuits (MP), and the Method of Frames (MOF) methods for the compression of digitized radiological images with a wavelet-packet dictionary. The performance of these algorithms is studied for medical images with and without additive noise.
Super-resolution reconstruction of terahertz images
NASA Astrophysics Data System (ADS)
Li, Yue; Li, Li; Hellicar, Andrew; Guo, Y. Jay
2008-04-01
A prototype of terahertz imaging system has been built in CSIRO. This imager uses a backward wave oscillator as the source and a Schottky diode as the detector. It has a bandwidth of 500-700 GHz and a source power 10 mW. The resolution at 610 GHz is about 0.85 mm. Even though this imaging system is a coherent system, only the signal power is measured at the detector and the phase information of the detected wave is lost. Some initial images of tree leaves, chocolate bars and pinholes have been acquired with this system. In this paper, we report experimental results of an attempt to improve the resolution of this imaging system beyond the limitation of diffraction (super-resolution). Due to the lack of phase information needed for applying any coherent super-resolution algorithms, the performance of the incoherent Richardson-Lucy super-resolution algorithm has been evaluated. Experimental results have demonstrated that the Richardson-Lucy algorithm can significantly improve the resolution of these images in some sample areas and produce some artifacts in other areas. These experimental results are analyzed and discussed.
Advanced Pointing Imaging Camera (APIC) Concept
NASA Astrophysics Data System (ADS)
Park, R. S.; Bills, B. G.; Jorgensen, J.; Jun, I.; Maki, J. N.; McEwen, A. S.; Riedel, E.; Walch, M.; Watkins, M. M.
2016-10-01
The Advanced Pointing Imaging Camera (APIC) concept is envisioned as an integrated system, with optical bench and flight-proven components, designed for deep-space planetary missions with 2-DOF control capability.
Advanced imaging techniques for the study of plant growth and development
Sozzani, Rosangela; Busch, Wolfgang; Spalding, Edgar P.; Benfey, Philip N.
2014-01-01
A variety of imaging methodologies are being used to collect data for quantitative studies of plant growth and development from living plants. Multi-level data, from macroscopic to molecular, and from weeks to seconds, can be acquired. Furthermore, advances in parallelized and automated image acquisition enable the throughput to capture images from large populations of plants under specific growth conditions. Image-processing capabilities allow for 3D or 4D reconstruction of image data and automated quantification of biological features. These advances facilitate the integration of imaging data with genome-wide molecular data to enable systems-level modeling. PMID:24434036
Advanced imaging techniques for the study of plant growth and development.
Sozzani, Rosangela; Busch, Wolfgang; Spalding, Edgar P; Benfey, Philip N
2014-05-01
A variety of imaging methodologies are being used to collect data for quantitative studies of plant growth and development from living plants. Multi-level data, from macroscopic to molecular, and from weeks to seconds, can be acquired. Furthermore, advances in parallelized and automated image acquisition enable the throughput to capture images from large populations of plants under specific growth conditions. Image-processing capabilities allow for 3D or 4D reconstruction of image data and automated quantification of biological features. These advances facilitate the integration of imaging data with genome-wide molecular data to enable systems-level modeling.
Gadgetron: an open source framework for medical image reconstruction.
Hansen, Michael Schacht; Sørensen, Thomas Sangild
2013-06-01
This work presents a new open source framework for medical image reconstruction called the "Gadgetron." The framework implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or "Gadgets" from raw data to reconstructed images. The data processing pipeline is configured dynamically at run-time based on an extensible markup language configuration description. The framework promotes reuse and sharing of reconstruction modules and new Gadgets can be added to the Gadgetron framework through a plugin-like architecture without recompiling the basic framework infrastructure. Gadgets are typically implemented in C/C++, but the framework includes wrapper Gadgets that allow the user to implement new modules in the Python scripting language for rapid prototyping. In addition to the streaming framework infrastructure, the Gadgetron comes with a set of dedicated toolboxes in shared libraries for medical image reconstruction. This includes generic toolboxes for data-parallel (e.g., GPU-based) execution of compute-intensive components. The basic framework architecture is independent of medical imaging modality, but this article focuses on its application to Cartesian and non-Cartesian parallel magnetic resonance imaging.
Penalized maximum-likelihood image reconstruction for lesion detection
NASA Astrophysics Data System (ADS)
Qi, Jinyi; Huesman, Ronald H.
2006-08-01
Detecting cancerous lesions is one major application in emission tomography. In this paper, we study penalized maximum-likelihood image reconstruction for this important clinical task. Compared to analytical reconstruction methods, statistical approaches can improve the image quality by accurately modelling the photon detection process and measurement noise in imaging systems. To explore the full potential of penalized maximum-likelihood image reconstruction for lesion detection, we derived simplified theoretical expressions that allow fast evaluation of the detectability of a random lesion. The theoretical results are used to design the regularization parameters to improve lesion detectability. We conducted computer-based Monte Carlo simulations to compare the proposed penalty function, conventional penalty function, and a penalty function for isotropic point spread function. The lesion detectability is measured by a channelized Hotelling observer. The results show that the proposed penalty function outperforms the other penalty functions for lesion detection. The relative improvement is dependent on the size of the lesion. However, we found that the penalty function optimized for a 5 mm lesion still outperforms the other two penalty functions for detecting a 14 mm lesion. Therefore, it is feasible to use the penalty function designed for small lesions in image reconstruction, because detection of large lesions is relatively easy.
PET image reconstruction: mean, variance, and optimal minimax criterion
NASA Astrophysics Data System (ADS)
Liu, Huafeng; Gao, Fei; Guo, Min; Xue, Liying; Nie, Jing; Shi, Pengcheng
2015-04-01
Given the noise nature of positron emission tomography (PET) measurements, it is critical to know the image quality and reliability as well as expected radioactivity map (mean image) for both qualitative interpretation and quantitative analysis. While existing efforts have often been devoted to providing only the reconstructed mean image, we present a unified framework for joint estimation of the mean and corresponding variance of the radioactivity map based on an efficient optimal min-max criterion. The proposed framework formulates the PET image reconstruction problem to be a transformation from system uncertainties to estimation errors, where the minimax criterion is adopted to minimize the estimation errors with possibly maximized system uncertainties. The estimation errors, in the form of a covariance matrix, express the measurement uncertainties in a complete way. The framework is then optimized by ∞-norm optimization and solved with the corresponding H∞ filter. Unlike conventional statistical reconstruction algorithms, that rely on the statistical modeling methods of the measurement data or noise, the proposed joint estimation stands from the point of view of signal energies and can handle from imperfect statistical assumptions to even no a priori statistical assumptions. The performance and accuracy of reconstructed mean and variance images are validated using Monte Carlo simulations. Experiments on phantom scans with a small animal PET scanner and real patient scans are also conducted for assessment of clinical potential.
Recent Advancements in Microwave Imaging Plasma Diagnostics
H. Park; C.C. Chang; B.H. Deng; C.W. Domier; A.J.H. Donni; K. Kawahata; C. Liang; X.P. Liang; H.J. Lu; N.C. Luhmann, Jr.; A. Mase; H. Matsuura; E. Mazzucato; A. Miura; K. Mizuno; T. Munsat; K. and Y. Nagayama; M.J. van de Pol; J. Wang; Z.G. Xia; W-K. Zhang
2002-03-26
Significant advances in microwave and millimeter wave technology over the past decade have enabled the development of a new generation of imaging diagnostics for current and envisioned magnetic fusion devices. Prominent among these are revolutionary microwave electron cyclotron emission imaging (ECEI), microwave phase imaging interferometers, imaging microwave scattering and microwave imaging reflectometer (MIR) systems for imaging electron temperature and electron density fluctuations (both turbulent and coherent) and profiles (including transport barriers) on toroidal devices such as tokamaks, spherical tori, and stellarators. The diagnostic technology is reviewed, and typical diagnostic systems are analyzed. Representative experimental results obtained with these novel diagnostic systems are also presented.
Coronary x-ray angiographic reconstruction and image orientation
Sprague, Kevin; Drangova, Maria; Lehmann, Glen
2006-03-15
We have developed an interactive geometric method for 3D reconstruction of the coronary arteries using multiple single-plane angiographic views with arbitrary orientations. Epipolar planes and epipolar lines are employed to trace corresponding vessel segments on these views. These points are utilized to reconstruct 3D vessel centerlines. The accuracy of the reconstruction is assessed using: (1) near-intersection distances of the rays that connect x-ray sources with projected points, (2) distances between traced and projected centerlines. These same two measures enter into a fitness function for a genetic search algorithm (GA) employed to orient the angiographic image planes automatically in 3D avoiding local minima in the search for optimized parameters. Furthermore, the GA utilizes traced vessel shapes (as opposed to isolated anchor points) to assist the optimization process. Differences between two-view and multiview reconstructions are evaluated. Vessel radii are measured and used to render the coronary tree in 3D as a surface. Reconstruction fidelity is demonstrated via (1) virtual phantom, (2) real phantom, and (3) patient data sets, the latter two of which utilize the GA. These simulated and measured angiograms illustrate that the vessel centerlines are reconstructed in 3D with accuracy below 1 mm. The reconstruction method is thus accurate compared to typical vessel dimensions of 1-3 mm. The methods presented should enable a combined interpretation of the severity of coronary artery stenoses and the hemodynamic impact on myocardial perfusion in patients with coronary artery disease.
Hepatocellular carcinoma: Advances in diagnostic imaging.
Sun, Haoran; Song, Tianqiang
2015-10-01
Thanks to the growing knowledge on biological behaviors of hepatocellular carcinomas (HCC), as well as continuous improvement in imaging techniques and experienced interpretation of imaging features of the nodules in cirrhotic liver, the detection and characterization of HCC has improved in the past decade. A number of practice guidelines for imaging diagnosis have been developed to reduce interpretation variability and standardize management of HCC, and they are constantly updated with advances in imaging techniques and evidence based data from clinical series. In this article, we strive to review the imaging techniques and the characteristic features of hepatocellular carcinoma associated with cirrhotic liver, with emphasis on the diagnostic value of advanced magnetic resonance imaging (MRI) techniques and utilization of hepatocyte-specific MRI contrast agents. We also briefly describe the concept of liver imaging reporting and data systems and discuss the consensus and controversy of major practice guidelines.
Image reconstruction for synchronous data acquisition in fluorescence molecular tomography.
Zhang, Xuanxuan; Liu, Fei; Zuo, Siming; Bai, Jing; Luo, Jianwen
2015-01-01
The present full-angle, free-space fluorescence molecular tomography (FMT) system uses a step-by-step strategy to acquire measurements, which consumes time for both the rotation of the object and the integration of the charge-coupled device (CCD) camera. Completing the integration during the rotation is a more time-efficient strategy called synchronous data acquisition. However, the positions of sources and detectors in this strategy are not stationary, which is not taken into account in the conventional reconstruction algorithm. In this paper we propose a reconstruction algorithm based on the finite element method (FEM) to overcome this problem. Phantom experiments were carried out to validate the performance of the algorithm. The results show that, compared with the conventional reconstruction algorithm used in the step-by-step data acquisition strategy, the proposed algorithm can reconstruct images with more accurate location data and lower relative errors when used with the synchronous data acquisition strategy.
Reconstruction techniques for sparse multistatic linear array microwave imaging
NASA Astrophysics Data System (ADS)
Sheen, David M.; Hall, Thomas E.
2014-06-01
Sequentially-switched linear arrays are an enabling technology for a number of near-field microwave imaging applications. Electronically sequencing along the array axis followed by mechanical scanning along an orthogonal axis allows dense sampling of a two-dimensional aperture in near real-time. The Pacific Northwest National Laboratory (PNNL) has developed this technology for several applications including concealed weapon detection, groundpenetrating radar, and non-destructive inspection and evaluation. These techniques form three-dimensional images by scanning a diverging beam swept frequency transceiver over a two-dimensional aperture and mathematically focusing or reconstructing the data into three-dimensional images. Recently, a sparse multi-static array technology has been developed that reduces the number of antennas required to densely sample the linear array axis of the spatial aperture. This allows a significant reduction in cost and complexity of the linear-array-based imaging system. The sparse array has been specifically designed to be compatible with Fourier-Transform-based image reconstruction techniques; however, there are limitations to the use of these techniques, especially for extreme near-field operation. In the extreme near-field of the array, back-projection techniques have been developed that account for the exact location of each transmitter and receiver in the linear array and the 3-D image location. In this paper, the sparse array technique will be described along with associated Fourier-Transform-based and back-projection-based image reconstruction algorithms. Simulated imaging results are presented that show the effectiveness of the sparse array technique along with the merits and weaknesses of each image reconstruction approach.
Generalized Fourier slice theorem for cone-beam image reconstruction.
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
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.
Improved satellite image compression and reconstruction via genetic algorithms
NASA Astrophysics Data System (ADS)
Babb, Brendan; Moore, Frank; Peterson, Michael; Lamont, Gary
2008-10-01
A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.
The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation.
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.
Microscopy imaging device with advanced imaging properties
Ghosh, Kunal; Burns, Laurie; El Gamal, Abbas; Schnitzer, Mark J.; Cocker, Eric; Ho, Tatt Wei
2016-10-25
Systems, methods and devices are implemented for microscope imaging solutions. One embodiment of the present disclosure is directed toward an epifluorescence microscope. The microscope includes an image capture circuit including an array of optical sensor. An optical arrangement is configured to direct excitation light of less than about 1 mW to a target object in a field of view of that is at least 0.5 mm.sup.2 and to direct epi-fluorescence emission caused by the excitation light to the array of optical sensors. The optical arrangement and array of optical sensors are each sufficiently close to the target object to provide at least 2.5 .mu.m resolution for an image of the field of view.
Microscopy imaging device with advanced imaging properties
Ghosh, Kunal; Burns, Laurie; El Gamal, Abbas; Schnitzer, Mark J.; Cocker, Eric; Ho, Tatt Wei
2016-11-22
Systems, methods and devices are implemented for microscope imaging solutions. One embodiment of the present disclosure is directed toward an epifluorescence microscope. The microscope includes an image capture circuit including an array of optical sensor. An optical arrangement is configured to direct excitation light of less than about 1 mW to a target object in a field of view of that is at least 0.5 mm.sup.2 and to direct epi-fluorescence emission caused by the excitation light to the array of optical sensors. The optical arrangement and array of optical sensors are each sufficiently close to the target object to provide at least 2.5 .mu.m resolution for an image of the field of view.
Microscopy imaging device with advanced imaging properties
Ghosh, Kunal; Burns, Laurie; El Gamal, Abbas; Schnitzer, Mark J.; Cocker, Eric; Ho, Tatt Wei
2015-11-24
Systems, methods and devices are implemented for microscope imaging solutions. One embodiment of the present disclosure is directed toward an epifluorescence microscope. The microscope includes an image capture circuit including an array of optical sensor. An optical arrangement is configured to direct excitation light of less than about 1 mW to a target object in a field of view of that is at least 0.5 mm.sup.2 and to direct epi-fluorescence emission caused by the excitation light to the array of optical sensors. The optical arrangement and array of optical sensors are each sufficiently close to the target object to provide at least 2.5 .mu.m resolution for an image of the field of view.
Whole Mouse Brain Image Reconstruction from Serial Coronal Sections Using FIJI (ImageJ).
Paletzki, Ronald; Gerfen, Charles R
2015-10-01
Whole-brain reconstruction of the mouse enables comprehensive analysis of the distribution of neurochemical markers, the distribution of anterogradely labeled axonal projections or retrogradely labeled neurons projecting to a specific brain site, or the distribution of neurons displaying activity-related markers in behavioral paradigms. This unit describes a method to produce whole-brain reconstruction image sets from coronal brain sections with up to four fluorescent markers using the freely available image-processing program FIJI (ImageJ).
Building facade reconstruction by fusing terrestrial laser points and images.
Pu, Shi; Vosselman, George
2009-01-01
Laser data and optical data have a complementary nature for three dimensional feature extraction. Efficient integration of the two data sources will lead to a more reliable and automated extraction of three dimensional features. This paper presents a semiautomatic building facade reconstruction approach, which efficiently combines information from terrestrial laser point clouds and close range images. A building facade's general structure is discovered and established using the planar features from laser data. Then strong lines in images are extracted using Canny extractor and Hough transformation, and compared with current model edges for necessary improvement. Finally, textures with optimal visibility are selected and applied according to accurate image orientations. Solutions to several challenge problems throughout the collaborated reconstruction, such as referencing between laser points and multiple images and automated texturing, are described. The limitations and remaining works of this approach are also discussed.
Progress Update on Iterative Reconstruction of Neutron Tomographic Images
Hausladen, Paul; Gregor, Jens
2016-09-15
This report satisfies the fiscal year 2016 technical deliverable to report on progress in development of fast iterative reconstruction algorithms for project OR16-3DTomography-PD2Jb, "3D Tomography and Image Processing Using Fast Neutrons." This project has two overall goals. The first of these goals is to extend associated-particle fast neutron transmission and, particularly, induced-reaction tomographic imaging algorithms to three dimensions. The second of these goals is to automatically segment the resultant tomographic images into constituent parts, and then extract information about the parts, such as the class of shape and potentially shape parameters. This report addresses of the component of the project concerned with three-dimensional (3D) image reconstruction.
Morphological reconstruction of semantic layers in map images
NASA Astrophysics Data System (ADS)
Podlasov, Alexey; Ageenko, Eugene J.; Franti, Pasi
2006-01-01
Map images are composed of semantic layers depicted in arbitrary color. Color separation is often needed to divide the image into layers for storage and processing. Separation can result in severe artifacts because of the overlapping of the layers. In this work, we introduce a technique to restore the original semantic layers after the color separation. The proposed restoration technique improves compression performance of the reconstructed layers in comparison to the corrupted ones when compressed by lossless algorithms such as International Communication Unit (ITU) Group 4 (TIFF G4), Portable Network Graphics (PNG), Joint Bi-level Image experts Group (JBIG), and context tree method. The resulting technique also provides good visual quality of the reconstructed image layers, and can therefore be applied for selective layer removal/extraction in other map processing applications, e.g., area measurement.
Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis
Xu, Shiyu; Lu, Jianping; Zhou, Otto; Chen, Ying
2015-01-01
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 based 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. PMID:26328987
Elasticity reconstructive imaging by means of stimulated echo MRI.
Chenevert, T L; Skovoroda, A R; O'Donnell, M; Emelianov, S Y
1998-03-01
A method is introduced to measure internal mechanical displacement and strain by means of MRI. Such measurements are needed to reconstruct an image of the elastic Young's modulus. A stimulated echo acquisition sequence with additional gradient pulses encodes internal displacements in response to an externally applied differential deformation. The sequence provides an accurate measure of static displacement by limiting the mechanical transitions to the mixing period of the simulated echo. Elasticity reconstruction involves definition of a region of interest having uniform Young's modulus along its boundary and subsequent solution of the discretized elasticity equilibrium equations. Data acquisition and reconstruction were performed on a urethane rubber phantom of known elastic properties and an ex vivo canine kidney phantom using <2% differential deformation. Regional elastic properties are well represented on Young's modulus images. The long-term objective of this work is to provide a means for remote palpation and elasticity quantitation in deep tissues otherwise inaccessible to manual palpation.
Advanced ground-penetrating, imaging radar for bridge inspection
Warhus, J.P.; Mast, J.E.; Johansson, E.M.; Nelson, S.E.; Lee, Hua
1993-08-01
Inspecting high-value structures, like bridges and buildings using Ground Penetrating Radar (GPR) is an application of the technology that is growing in importance. In a typical inspection application, inspectors use GPR to locate structural components, like reinforcing bars embedded in concrete, to avoid weakening the structure while collecting core samples for detailed inspection. Advanced GPR, integrated with imaging technologies for use as an NDE tool, can provide the capability to locate and characterize construction flaws and wear- or age-induced damage in these structures without the need for destructive techniques like coring. In the following sections, we discuss an important inspection application, namely, concrete bridge deck inspection. We describe an advanced bridge deck inspection system concept and provide an overview of a program aimed at developing such a system. Examples of modeling, image reconstruction, and experimental results are presented.
Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction
Wang, Bigong; Li, Liang
2015-01-01
As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training. PMID:26089956
Cortical Surface Reconstruction from High-Resolution MR Brain Images
Osechinskiy, Sergey; Kruggel, Frithjof
2012-01-01
Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the human brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction approaches are typically optimized for standard resolution (~1 mm) data and are not directly applicable to higher resolution images. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and scales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The method uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping, has nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow sulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained by topology-preserving level set approach. The method's performance is illustrated on exvivo images with 0.25–0.35 mm isotropic voxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data sets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects. PMID:22481909
RECONSTRUCTION OF HUMAN LUNG MORPHOLOGY MODELS FROM MAGNETIC RESONANCE IMAGES
Reconstruction of Human Lung Morphology Models from Magnetic Resonance Images
T. B. Martonen (Experimental Toxicology Division, U.S. EPA, Research Triangle Park, NC 27709) and K. K. Isaacs (School of Public Health, University of North Carolina, Chapel Hill, NC 27514)
Advanced Atmospheric Sounder and Imaging Radiometer (AASIR)
NASA Technical Reports Server (NTRS)
1977-01-01
Design information for the Advanced Atmospheric Sounder and Imaging Radiometer is reported, which was developed to determine the configuration of a sensor for IR and visible imaging. The areas of technology reported include: systems design, optics, mechanics, electronics, detectors, radiative cooler, and radiometric calibration.
Optimized satellite image compression and reconstruction via evolution strategies
NASA Astrophysics Data System (ADS)
Babb, Brendan; Moore, Frank; Peterson, Michael
2009-05-01
This paper describes the automatic discovery, via an Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), of vectors of real-valued coefficients representing matched forward and inverse transforms that outperform the 9/7 Cohen-Daubechies-Feauveau (CDF) discrete wavelet transform (DWT) for satellite image compression and reconstruction under conditions subject to quantization error. The best transform evolved during this study reduces the mean squared error (MSE) present in reconstructed satellite images by an average of 33.78% (1.79 dB), while maintaining the average information entropy (IE) of compressed images at 99.57% in comparison to the wavelet. In addition, this evolved transform achieves 49.88% (3.00 dB) average MSE reduction when tested on 80 images from the FBI fingerprint test set, and 42.35% (2.39 dB) average MSE reduction when tested on a set of 18 digital photographs, while achieving average IE of 104.36% and 100.08%, respectively. These results indicate that our evolved transform greatly improves the quality of reconstructed images without substantial loss of compression capability over a broad range of image classes.
An automated 3D reconstruction method of UAV images
NASA Astrophysics Data System (ADS)
Liu, Jun; Wang, He; Liu, Xiaoyang; Li, Feng; Sun, Guangtong; Song, Ping
2015-10-01
In this paper a novel fully automated 3D reconstruction approach based on low-altitude unmanned aerial vehicle system (UAVs) images will be presented, which does not require previous camera calibration or any other external prior knowledge. Dense 3D point clouds are generated by integrating orderly feature extraction, image matching, structure from motion (SfM) and multi-view stereo (MVS) algorithms, overcoming many of the cost, time limitations of rigorous photogrammetry techniques. An image topology analysis strategy is introduced to speed up large scene reconstruction by taking advantage of the flight-control data acquired by UAV. Image topology map can significantly reduce the running time of feature matching by limiting the combination of images. A high-resolution digital surface model of the study area is produced base on UAV point clouds by constructing the triangular irregular network. Experimental results show that the proposed approach is robust and feasible for automatic 3D reconstruction of low-altitude UAV images, and has great potential for the acquisition of spatial information at large scales mapping, especially suitable for rapid response and precise modelling in disaster emergency.
Advanced Imaging Algorithms for Radiation Imaging Systems
Marleau, Peter
2015-10-01
The intent of the proposed work, in collaboration with University of Michigan, is to develop the algorithms that will bring the analysis from qualitative images to quantitative attributes of objects containing SNM. The first step to achieving this is to develop an indepth understanding of the intrinsic errors associated with the deconvolution and MLEM algorithms. A significant new effort will be undertaken to relate the image data to a posited three-dimensional model of geometric primitives that can be adjusted to get the best fit. In this way, parameters of the model such as sizes, shapes, and masses can be extracted for both radioactive and non-radioactive materials. This model-based algorithm will need the integrated response of a hypothesized configuration of material to be calculated many times. As such, both the MLEM and the model-based algorithm require significant increases in calculation speed in order to converge to solutions in practical amounts of time.
Integrated imaging of neuromagnetic reconstructions and morphological magnetic resonance data.
Kullmann, W H; Fuchs, M
1991-01-01
New neuromagnetic imaging methods provide spatial information about the functional electrical properties of complex current distributions in the human brain. For practical use in medical diagnosis a combination of the abstract neuromagnetic imaging results with magnetic resonance (MR) or computed tomography (CT) images of the morphology is required. The biomagnetic images can be overlayed onto three-dimensional morphological images with spatially arbitrary selectable slices, calculated from conventional 2D data. For the current reconstruction the 3D images furthermore provide a priori information about the conductor geometry. A combination of current source density calculations and linear estimation methods for handling the inverse magnetic problem allows quick imaging of impressed current source density in arbitrary volume conductors.
A novel data processing technique for image reconstruction of penumbral imaging
NASA Astrophysics Data System (ADS)
Xie, Hongwei; Li, Hongyun; Xu, Zeping; Song, Guzhou; Zhang, Faqiang; Zhou, Lin
2011-06-01
CT image reconstruction technique was applied to the data processing of the penumbral imaging. Compared with other traditional processing techniques for penumbral coded pinhole image such as Wiener, Lucy-Richardson and blind technique, this approach is brand new. In this method, the coded aperture processing method was used for the first time independent to the point spread function of the image diagnostic system. In this way, the technical obstacles was overcome in the traditional coded pinhole image processing caused by the uncertainty of point spread function of the image diagnostic system. Then based on the theoretical study, the simulation of penumbral imaging and image reconstruction was carried out to provide fairly good results. While in the visible light experiment, the point source of light was used to irradiate a 5mm×5mm object after diffuse scattering and volume scattering. The penumbral imaging was made with aperture size of ~20mm. Finally, the CT image reconstruction technique was used for image reconstruction to provide a fairly good reconstruction result.
Comparison of image reconstruction methods for structured illumination microscopy
NASA Astrophysics Data System (ADS)
Lukeš, Tomas; Hagen, Guy M.; Křížek, Pavel; Švindrych, Zdeněk.; Fliegel, Karel; Klíma, Miloš
2014-05-01
Structured illumination microscopy (SIM) is a recent microscopy technique that enables one to go beyond the diffraction limit using patterned illumination. The high frequency information is encoded through aliasing into the observed image. By acquiring multiple images with different illumination patterns aliased components can be separated and a highresolution image reconstructed. Here we investigate image processing methods that perform the task of high-resolution image reconstruction, namely square-law detection, scaled subtraction, super-resolution SIM (SR-SIM), and Bayesian estimation. The optical sectioning and lateral resolution improvement abilities of these algorithms were tested under various noise level conditions on simulated data and on fluorescence microscopy images of a pollen grain test sample and of a cultured cell stained for the actin cytoskeleton. In order to compare the performance of the algorithms, the following objective criteria were evaluated: Signal to Noise Ratio (SNR), Signal to Background Ratio (SBR), circular average of the power spectral density and the S3 sharpness index. The results show that SR-SIM and Bayesian estimation combine illumination patterned images more effectively and provide better lateral resolution in exchange for more complex image processing. SR-SIM requires one to precisely shift the separated spectral components to their proper positions in reciprocal space. High noise levels in the raw data can cause inaccuracies in the shifts of the spectral components which degrade the super-resolved image. Bayesian estimation has proven to be more robust to changes in noise level and illumination pattern frequency.
Super-resolution image reconstruction for ultrasonic nondestructive evaluation.
Li, Shanglei; Chu, Tsuchin Philip
2013-12-01
Ultrasonic testing is one of the most successful nondestructive evaluation (NDE) techniques for the inspection of carbon-fiber-reinforced polymer (CFRP) materials. This paper discusses the application of the iterative backprojection (IBP) super-resolution image reconstruction technique to carbon epoxy laminates with simulated defects to obtain high-resolution images for NDE. Super-resolution image reconstruction is an approach used to overcome the inherent resolution limitations of an existing ultrasonic system. It can greatly improve the image quality and allow more detailed inspection of the region of interest (ROI) with high resolution, improving defect evaluation and accuracy. First, three artificially simulated delamination defects in a CFRP panel were considered to evaluate and validate the application of the IBP method. The results of the validation indicate that both the contrast-tonoise ratio (CNR) and the peak signal-to-noise ratio (PSNR) value of the super-resolution result are better than the bicubic interpolation method. Then, the IBP method was applied to the low-resolution ultrasonic C-scan image sequence with subpixel displacement of two types of defects (delamination and porosity) which were obtained by the micro-scanning imaging technique. The result demonstrated that super-resolution images achieved better visual quality with an improved image resolution compared with raw C-scan images.
NASA Technical Reports Server (NTRS)
Mareboyana, Manohar; Le Moigne-Stewart, Jacqueline; Bennett, Jerome
2016-01-01
In this paper, we demonstrate a simple algorithm that projects low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithm is very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. used in projection yield comparable results. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML), and Maximum a posterior (MAP) algorithms. The algorithm is robust and is not overly sensitive to the registration inaccuracies.
Nagayama, T.; Mancini, R. C.; Mayes, D.; Tommasini, R.; Florido, R.
2015-11-15
Temperature and density asymmetry diagnosis is critical to advance inertial confinement fusion (ICF) science. A multi-monochromatic x-ray imager (MMI) is an attractive diagnostic for this purpose. The MMI records the spectral signature from an ICF implosion core with time resolution, 2-D space resolution, and spectral resolution. While narrow-band images and 2-D space-resolved spectra from the MMI data constrain temperature and density spatial structure of the core, the accuracy of the images and spectra depends not only on the quality of the MMI data but also on the reliability of the post-processing tools. Here, we synthetically quantify the accuracy of images and spectra reconstructed from MMI data. Errors in the reconstructed images are less than a few percent when the space-resolution effect is applied to the modeled images. The errors in the reconstructed 2-D space-resolved spectra are also less than a few percent except those for the peripheral regions. Spectra reconstructed for the peripheral regions have slightly but systematically lower intensities by ∼6% due to the instrumental spatial-resolution effects. However, this does not alter the relative line ratios and widths and thus does not affect the temperature and density diagnostics. We also investigate the impact of the pinhole size variation on the extracted images and spectra. A 10% pinhole size variation could introduce spatial bias to the images and spectra of ∼10%. A correction algorithm is developed, and it successfully reduces the errors to a few percent. It is desirable to perform similar synthetic investigations to fully understand the reliability and limitations of each MMI application.
Imaging of the pancreas: Recent advances
Chaudhary, Vikas; Bano, Shahina
2011-01-01
A wide spectrum of anomalies of pancreas and the pancreatic duct system are commonly encountered at radiological evaluation. Diagnosing pancreatic lesions generally requires a multimodality approach. This review highlights the new advances in pancreatic imaging and their applications in the diagnosis and management of pancreatic pathologies. The mainstay techniques include computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS), radionuclide imaging (RNI) and optical coherence tomography (OCT). PMID:21847450
Scattering calculation and image reconstruction using elevation-focused beams
Duncan, David P.; Astheimer, Jeffrey P.; Waag, Robert C.
2009-01-01
Pressure scattered by cylindrical and spherical objects with elevation-focused illumination and reception has been analytically calculated, and corresponding cross sections have been reconstructed with a two-dimensional algorithm. Elevation focusing was used to elucidate constraints on quantitative imaging of three-dimensional objects with two-dimensional algorithms. Focused illumination and reception are represented by angular spectra of plane waves that were efficiently computed using a Fourier interpolation method to maintain the same angles for all temporal frequencies. Reconstructions were formed using an eigenfunction method with multiple frequencies, phase compensation, and iteration. The results show that the scattered pressure reduces to a two-dimensional expression, and two-dimensional algorithms are applicable when the region of a three-dimensional object within an elevation-focused beam is approximately constant in elevation. The results also show that energy scattered out of the reception aperture by objects contained within the focused beam can result in the reconstructed values of attenuation slope being greater than true values at the boundary of the object. Reconstructed sound speed images, however, appear to be relatively unaffected by the loss in scattered energy. The broad conclusion that can be drawn from these results is that two-dimensional reconstructions require compensation to account for uncaptured three-dimensional scattering. PMID:19425653
Advanced Imaging Optics Utilizing Wavefront Coding.
Scrymgeour, David; Boye, Robert; Adelsberger, Kathleen
2015-06-01
Image processing offers a potential to simplify an optical system by shifting some of the imaging burden from lenses to the more cost effective electronics. Wavefront coding using a cubic phase plate combined with image processing can extend the system's depth of focus, reducing many of the focus-related aberrations as well as material related chromatic aberrations. However, the optimal design process and physical limitations of wavefront coding systems with respect to first-order optical parameters and noise are not well documented. We examined image quality of simulated and experimental wavefront coded images before and after reconstruction in the presence of noise. Challenges in the implementation of cubic phase in an optical system are discussed. In particular, we found that limitations must be placed on system noise, aperture, field of view and bandwidth to develop a robust wavefront coded system.
Polarimetric ISAR: Simulation and image reconstruction
Chambers, David H.
2016-03-21
In polarimetric ISAR the illumination platform, typically airborne, carries a pair of antennas that are directed toward a fixed point on the surface as the platform moves. During platform motion, the antennas maintain their gaze on the point, creating an effective aperture for imaging any targets near that point. The interaction between the transmitted fields and targets (e.g. ships) is complicated since the targets are typically many wavelengths in size. Calculation of the field scattered from the target typically requires solving Maxwell’s equations on a large three-dimensional numerical grid. This is prohibitive to use in any real-world imaging algorithm, so the scattering process is typically simplified by assuming the target consists of a cloud of independent, non-interacting, scattering points (centers). Imaging algorithms based on this scattering model perform well in many applications. Since polarimetric radar is not very common, the scattering model is often derived for a scalar field (single polarization) where the individual scatterers are assumed to be small spheres. However, when polarization is important, we must generalize the model to explicitly account for the vector nature of the electromagnetic fields and its interaction with objects. In this note, we present a scattering model that explicitly includes the vector nature of the fields but retains the assumption that the individual scatterers are small. The response of the scatterers is described by electric and magnetic dipole moments induced by the incident fields. We show that the received voltages in the antennas are linearly related to the transmitting currents through a scattering impedance matrix that depends on the overall geometry of the problem and the nature of the scatterers.
Monte-Carlo simulations and image reconstruction for novel imaging scenarios in emission tomography
NASA Astrophysics Data System (ADS)
Gillam, John E.; Rafecas, Magdalena
2016-02-01
Emission imaging incorporates both the development of dedicated devices for data acquisition as well as algorithms for recovering images from that data. Emission tomography is an indirect approach to imaging. The effect of device modification on the final image can be understood through both the way in which data are gathered, using simulation, and the way in which the image is formed from that data, or image reconstruction. When developing novel devices, systems and imaging tasks, accurate simulation and image reconstruction allow performance to be estimated, and in some cases optimized, using computational methods before or during the process of physical construction. However, there are a vast range of approaches, algorithms and pre-existing computational tools that can be exploited and the choices made will affect the accuracy of the in silico results and quality of the reconstructed images. On the one hand, should important physical effects be neglected in either the simulation or reconstruction steps, specific enhancements provided by novel devices may not be represented in the results. On the other hand, over-modeling of device characteristics in either step leads to large computational overheads that can confound timely results. Here, a range of simulation methodologies and toolkits are discussed, as well as reconstruction algorithms that may be employed in emission imaging. The relative advantages and disadvantages of a range of options are highlighted using specific examples from current research scenarios.
Probe reconstruction for holographic X-ray imaging
Hagemann, Johannes; Robisch, Anna-Lena; Osterhoff, Markus; Salditt, Tim
2017-01-01
In X-ray holographic near-field imaging the resolution and image quality depend sensitively on the beam. Artifacts are often encountered due to the strong focusing required to reach high resolution. Here, two schemes for reconstructing the complex-valued and extended wavefront of X-ray nano-probes, primarily in the planes relevant for imaging (i.e. focus, sample and detection plane), are presented and compared. Firstly, near-field ptychography is used, based on scanning a test pattern laterally as well as longitudinally along the optical axis. Secondly, any test pattern is dispensed of and the wavefront reconstructed only from data recorded for different longitudinal translations of the detector. For this purpose, an optimized multi-plane projection algorithm is presented, which can cope with the numerically very challenging setting of a divergent wavefront emanating from a hard X-ray nanoprobe. The results of both schemes are in very good agreement. The probe retrieval can be used as a tool for optics alignment, in particular at X-ray nanoprobe beamlines. Combining probe retrieval and object reconstruction is also shown to improve the image quality of holographic near-field imaging. PMID:28244446
Analyser-based phase contrast image reconstruction using geometrical optics.
Kitchen, M J; Pavlov, K M; Siu, K K W; Menk, R H; Tromba, G; Lewis, R A
2007-07-21
Analyser-based phase contrast imaging can provide radiographs of exceptional contrast at high resolution (<100 microm), whilst quantitative phase and attenuation information can be extracted using just two images when the approximations of geometrical optics are satisfied. Analytical phase retrieval can be performed by fitting the analyser rocking curve with a symmetric Pearson type VII function. The Pearson VII function provided at least a 10% better fit to experimentally measured rocking curves than linear or Gaussian functions. A test phantom, a hollow nylon cylinder, was imaged at 20 keV using a Si(1 1 1) analyser at the ELETTRA synchrotron radiation facility. Our phase retrieval method yielded a more accurate object reconstruction than methods based on a linear fit to the rocking curve. Where reconstructions failed to map expected values, calculations of the Takagi number permitted distinction between the violation of the geometrical optics conditions and the failure of curve fitting procedures. The need for synchronized object/detector translation stages was removed by using a large, divergent beam and imaging the object in segments. Our image acquisition and reconstruction procedure enables quantitative phase retrieval for systems with a divergent source and accounts for imperfections in the analyser.
Toward 5D image reconstruction for optical interferometry
NASA Astrophysics Data System (ADS)
Baron, Fabien; Kloppenborg, Brian; Monnier, John
2012-07-01
We report on our progress toward a flexible image reconstruction software for optical interferometry capable of "5D imaging" of stellar surfaces. 5D imaging is here defined as the capability to image directly one or several stars in three dimensions, with both the time and wavelength dependencies taken into account during the reconstruction process. Our algorithm makes use of the Healpix (Gorski et al., 2005) sphere partition scheme to tesselate the stellar surface, 3D Open Graphics Language (OpenGL) to model the spheroid geometry, and the Open Compute Language (OpenCL) framework for all other computations. We use the Monte Carlo Markov Chain software SQUEEZE to solve the image reconstruction problem on the surfaces of these stars. Finally, the Compressed Sensing and Bayesian Evidence paradigms are employed to determine the best regularization for spotted stars. Our algorithm makes use of the Healpix (reference needed) sphere partition scheme to tesselate the stellar surface, 3D Open Graphics Language (OpenGL) to model the spheroid, and the Open Compute Language (OpenCL) framework to model the Roche gravitational potential equation.
Atmospheric isoplanatism and astronomical image reconstruction on Mauna Kea
Cowie, L.L.; Songaila, A.
1988-07-01
Atmospheric isoplanatism for visual wavelength image-reconstruction applications was measured on Mauna Kea in Hawaii. For most nights the correlation of the transform functions is substantially wider than the long-exposure transform function at separations up to 30 arcsec. Theoretical analysis shows that this is reasonable if the mean Fried parameter is approximately 30 cm at 5500 A. Reconstructed image quality may be described by a Gaussian with a FWHM of lambda/s/sub 0/. Under average conditions, s/sub 0/ (30 arcsec) exceeds 55 cm at 7000 A. The results show that visual image quality in the 0.1--0.2 arcsec range is obtainable over much of the sky with large ground-based telescopes on this site.
Cone-beam image reconstruction using spherical harmonics.
Taguchi, K; Zeng, G L; Gullberg, G T
2001-06-01
Image reconstruction from cone-beam projections is required for both x-ray computed tomography (CT) and single photon emission computed tomography (SPECT). Grangeat's algorithm accurately performs cone-beam reconstruction provided that Tuy's data sufficiency condition is satisfied and projections are complete. The algorithm consists of three stages: (a) Forming weighted plane integrals by calculating the line integrals on the cone-beam detector, and obtaining the first derivative of the plane integrals (3D Radon transform) by taking the derivative of the weighted plane integrals. (b) Rebinning the data and calculating the second derivative with respect to the normal to the plane. (c) Reconstructing the image using the 3D Radon backprojection. A new method for implementing the first stage of Grangeat's algorithm was developed using spherical harmonics. The method assumes that the detector is large enough to image the whole object without truncation. Computer simulations show that if the trajectory of the cone vertex satisfies Tuy's data sufficiency condition, the proposed algorithm provides an exact reconstruction.
An Inverse Problems Approach to MR-EPT Image Reconstruction.
Borsic, A; Perreard, I; Mahara, A; Halter, R J
2016-01-01
Magnetic Resonance-Electrical Properties Tomography (MR-EPT) is an imaging modality that maps the spatial distribution of the electrical conductivity and permittivity using standard MRI systems. The presence of a body within the scanner alters the RF field, and by mapping these alterations it is possible to recover the electrical properties. The field is time-harmonic, and can be described by the Helmholtz equation. Approximations to this equation have been previously used to estimate conductivity and permittivity in terms of first or second derivatives of RF field data. Using these same approximations, an inverse approach to solving the MR-EPT problem is presented here that leverages a forward model for describing the magnitude and phase of the field within the imaging domain, and a fitting approach for estimating the electrical properties distribution. The advantages of this approach are that 1) differentiation of the measured data is not required, thus reducing noise sensitivity, and 2) different regularization schemes can be adopted, depending on prior knowledge of the distribution of conductivity or permittivity, leading to improved image quality. To demonstrate the developed approach, both Quadratic (QR) and Total Variation (TV) regularization methods were implemented and evaluated through numerical simulation and experimentally acquired data. The proposed inverse approach to MR-EPT reconstruction correctly identifies contrasts and accurately reconstructs the geometry in both simulations and experiments. The TV regularized scheme reconstructs sharp spatial transitions, which are difficult to reconstruct with other, more traditional approaches.
Superiorization-based multi-energy CT image reconstruction
NASA Astrophysics Data System (ADS)
Yang, Q.; Cong, W.; Wang, G.
2017-04-01
The recently-developed superiorization approach is efficient and robust for solving various constrained optimization problems. This methodology can be applied to multi-energy CT image reconstruction with the regularization in terms of the prior rank, intensity and sparsity model (PRISM). In this paper, we propose a superiorized version of the simultaneous algebraic reconstruction technique (SART) based on the PRISM model. Then, we compare the proposed superiorized algorithm with the Split-Bregman algorithm in numerical experiments. The results show that both the Superiorized-SART and the Split-Bregman algorithms generate good results with weak noise and reduced artefacts.
Advanced Imaging for Space Science
NASA Technical Reports Server (NTRS)
Lyon, Richard G.
2008-01-01
Future NASA interferometric missions will realize high-resolution with less mass and volume compared to filled-apertures thus saving in cost over comparable filled-aperture systems. However, interferometeric aperture systems give reduced sensitivity requiring longer integration times to achieve a desired signal-to-noise ratio but is likely the only cost effective path forward for high-resolution space imaging.
Reconstruction of pulse noisy images via stochastic resonance
Han, Jing; Liu, Hongjun; Sun, Qibing; Huang, Nan
2015-01-01
We investigate a practical technology for reconstructing nanosecond pulse noisy images via stochastic resonance, which is based on the modulation instability. A theoretical model of this method for optical pulse signal is built to effectively recover the pulse image. The nanosecond noise-hidden images grow at the expense of noise during the stochastic resonance process in a photorefractive medium. The properties of output images are mainly determined by the input signal-to-noise intensity ratio, the applied voltage across the medium, and the correlation length of noise background. A high cross-correlation gain is obtained by optimizing these parameters. This provides a potential method for detecting low-level or hidden pulse images in various imaging applications. PMID:26067911
The SRT reconstruction algorithm for semiquantification in PET imaging
Kastis, George A.; Gaitanis, Anastasios; Samartzis, Alexandros P.; Fokas, Athanasios S.
2015-10-15
Purpose: The spline reconstruction technique (SRT) is a new, fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The mathematical details of this algorithm and comparisons with filtered backprojection were presented earlier in the literature. In this study, the authors present a comparison between SRT and the ordered-subsets expectation–maximization (OSEM) algorithm for determining contrast and semiquantitative indices of {sup 18}F-FDG uptake. Methods: The authors implemented SRT in the software for tomographic image reconstruction (STIR) open-source platform and evaluated this technique using simulated and real sinograms obtained from the GE Discovery ST positron emission tomography/computer tomography scanner. All simulations and reconstructions were performed in STIR. For OSEM, the authors used the clinical protocol of their scanner, namely, 21 subsets and two iterations. The authors also examined images at one, four, six, and ten iterations. For the simulation studies, the authors analyzed an image-quality phantom with cold and hot lesions. Two different versions of the phantom were employed at two different hot-sphere lesion-to-background ratios (LBRs), namely, 2:1 and 4:1. For each noiseless sinogram, 20 Poisson realizations were created at five different noise levels. In addition to making visual comparisons of the reconstructed images, the authors determined contrast and bias as a function of the background image roughness (IR). For the real-data studies, sinograms of an image-quality phantom simulating the human torso were employed. The authors determined contrast and LBR as a function of the background IR. Finally, the authors present plots of contrast as a function of IR after smoothing each reconstructed image with Gaussian filters of six different sizes. Statistical significance was determined by employing the Wilcoxon rank-sum test. Results: In both simulated and real studies, SRT
LOR-interleaving image reconstruction for PET imaging with fractional-crystal collimation
NASA Astrophysics Data System (ADS)
Li, Yusheng; Matej, Samuel; Karp, Joel S.; Metzler, Scott D.
2015-01-01
Positron emission tomography (PET) has become an important modality in medical and molecular imaging. However, in most PET applications, the resolution is still mainly limited by the physical crystal sizes or the detector’s intrinsic spatial resolution. To achieve images with better spatial resolution in a central region of interest (ROI), we have previously proposed using collimation in PET scanners. The collimator is designed to partially mask detector crystals to detect lines of response (LORs) within fractional crystals. A sequence of collimator-encoded LORs is measured with different collimation configurations. This novel collimated scanner geometry makes the reconstruction problem challenging, as both detector and collimator effects need to be modeled to reconstruct high-resolution images from collimated LORs. In this paper, we present a LOR-interleaving (LORI) algorithm, which incorporates these effects and has the advantage of reusing existing reconstruction software, to reconstruct high-resolution images for PET with fractional-crystal collimation. We also develop a 3D ray-tracing model incorporating both the collimator and crystal penetration for simulations and reconstructions of the collimated PET. By registering the collimator-encoded LORs with the collimator configurations, high-resolution LORs are restored based on the modeled transfer matrices using the non-negative least-squares method and EM algorithm. The resolution-enhanced images are then reconstructed from the high-resolution LORs using the MLEM or OSEM algorithm. For validation, we applied the LORI method to a small-animal PET scanner, A-PET, with a specially designed collimator. We demonstrate through simulated reconstructions with a hot-rod phantom and MOBY phantom that the LORI reconstructions can substantially improve spatial resolution and quantification compared to the uncollimated reconstructions. The LORI algorithm is crucial to improve overall image quality of collimated PET, which
LOR-interleaving image reconstruction for PET imaging with fractional-crystal collimation
Li, Yusheng; Matej, Samuel; Karp, Joel S.; Metzler, Scott D.
2015-01-01
Positron emission tomography (PET) has become an important modality in medical and molecular imaging. However, in most PET applications, the resolution is still mainly limited by the physical crystal sizes or the detector’s intrinsic spatial resolution. To achieve images with better spatial resolution in a central region of interest (ROI), we have previously proposed using collimation in PET scanner. The collimator is designed to partially mask detector crystals to detect lines of response (LORs) within fractional crystals. A sequence of collimator-encoded LORs is measured with different collimation configurations. This novel collimated scanner geometry makes the reconstruction problem challenging, as both detector and collimator effects need to be modeled to reconstruct high-resolution images from collimated LORs. In this paper, we present an LOR-interleaving (LORI) algorithm, which incorporates these effects and has the advantage of reusing existing reconstruction software, to reconstruct high-resolution images for PET with fractional-crystal collimation. We also develop a 3-D ray-tracing model incorporating both the collimator and crystal penetration for simulations and reconstructions of the collimated PET. By registering the collimator-encoded LORs with the collimator configurations, high-resolution LORs are restored based on the modeled transfer matrices using the nonnegative least-squares method and EM algorithm. The resolution-enhanced images are then reconstructed from the high-resolution LORs using the MLEM or OSEM algorithm. For validation, we applied the LORI method to a small-animal PET scanner, A-PET, with a specially designed collimator. We demonstrate through simulated reconstructions with a hot-rod phantom and MOBY phantom that the LORI reconstructions can substantially improve spatial resolution and quantification compared to the uncollimated reconstructions. The LORI algorithm is crucial to improve overall image quality of collimated PET, which
Light field display and 3D image reconstruction
NASA Astrophysics Data System (ADS)
Iwane, Toru
2016-06-01
Light field optics and its applications become rather popular in these days. With light field optics or light field thesis, real 3D space can be described in 2D plane as 4D data, which we call as light field data. This process can be divided in two procedures. First, real3D scene is optically reduced with imaging lens. Second, this optically reduced 3D image is encoded into light field data. In later procedure we can say that 3D information is encoded onto a plane as 2D data by lens array plate. This transformation is reversible and acquired light field data can be decoded again into 3D image with the arrayed lens plate. "Refocusing" (focusing image on your favorite point after taking a picture), light-field camera's most popular function, is some kind of sectioning process from encoded 3D data (light field data) to 2D image. In this paper at first I show our actual light field camera and our 3D display using acquired and computer-simulated light field data, on which real 3D image is reconstructed. In second I explain our data processing method whose arithmetic operation is performed not in Fourier domain but in real domain. Then our 3D display system is characterized by a few features; reconstructed image is of finer resolutions than density of arrayed lenses and it is not necessary to adjust lens array plate to flat display on which light field data is displayed.
Accuracy of quantitative reconstructions in SPECT/CT imaging
NASA Astrophysics Data System (ADS)
Shcherbinin, S.; Celler, A.; Belhocine, T.; van der Werf, R.; Driedger, A.
2008-09-01
The goal of this study was to determine the quantitative accuracy of our OSEM-APDI reconstruction method based on SPECT/CT imaging for Tc-99m, In-111, I-123, and I-131 isotopes. Phantom studies were performed on a SPECT/low-dose multislice CT system (Infinia-Hawkeye-4 slice, GE Healthcare) using clinical acquisition protocols. Two radioactive sources were centrally and peripherally placed inside an anthropometric Thorax phantom filled with non-radioactive water. Corrections for attenuation, scatter, collimator blurring and collimator septal penetration were applied and their contribution to the overall accuracy of the reconstruction was evaluated. Reconstruction with the most comprehensive set of corrections resulted in activity estimation with error levels of 3-5% for all the isotopes.
Advances in noninvasive functional imaging of bone.
Lan, Sheng-Min; Wu, Ya-Na; Wu, Ping-Ching; Sun, Chi-Kuang; Shieh, Dar-Bin; Lin, Ruey-Mo
2014-02-01
The demand for functional imaging in clinical medicine is comprehensive. Although the gold standard for the functional imaging of human bones in clinical settings is still radionuclide-based imaging modalities, nonionizing noninvasive imaging technology in small animals has greatly advanced in recent decades, especially the diffuse optical imaging to which Britton Chance made tremendous contributions. The evolution of imaging probes, instruments, and computation has facilitated exploration in the complicated biomedical research field by allowing longitudinal observation of molecular events in live cells and animals. These research-imaging tools are being used for clinical applications in various specialties, such as oncology, neuroscience, and dermatology. The Bone, a deeply located mineralized tissue, presents a challenge for noninvasive functional imaging in humans. Using nanoparticles (NP) with multiple favorable properties as bioimaging probes has provided orthopedics an opportunity to benefit from these noninvasive bone-imaging techniques. This review highlights the historical evolution of radionuclide-based imaging, computed tomography, positron emission tomography, and magnetic resonance imaging, diffuse optics-enabled in vivo technologies, vibrational spectroscopic imaging, and a greater potential for using NPs for biomedical imaging.
Advanced MR Imaging in Neuro-oncology.
Radbruch, A; Bendszus, M
2015-10-01
The value of magnetic resonance (MR) imaging for the clinical management of brain tumour patients has greatly increased in recent years through the introduction of functional MR sequences. Previously, MR imaging for brain tumours relied for the most part on contrast-enhanced T1-weighted MR sequences but today with the help of advanced functional MR sequences, the pathophysiological aspects of tumour growth can be directly visualised and investigated. This article will present the pathophysiological background of the MR sequences relevant to neuro-oncological imaging as well as potential clinical applications. Ultimately, we take a look at possible future developments for ultra-high-field MR imaging.
Advances in Small Animal Imaging Systems
NASA Astrophysics Data System (ADS)
Loudos, George K.
2007-11-01
The rapid growth in genetics and molecular biology combined with the development of techniques for genetically engineering small animals has led to an increased interest in in vivo laboratory animal imaging during the past few years. For this purpose, new instrumentation, data acquisition strategies, and image processing and reconstruction techniques are being developed, researched and evaluated. The aim of this article is to give a short overview of the state of the art technologies for high resolution and high sensitivity molecular imaging techniques, primarily positron emission tomography (PET) and single photon emission computed tomography (SPECT). The basic needs of small animal imaging will be described. The evolution in instrumentation in the past two decades, as well as the commercially available systems will be overviewed. Finally, the new trends in detector technology and preliminary results from challenging applications will be presented. For more details a number of references are provided.
Advanced Microwave/Millimeter-Wave Imaging Technology
NASA Astrophysics Data System (ADS)
Shen, Zuowei; Yang, Lu; Luhmann, N. C., Jr.; Domier, C. W.; Ito, N.; Kogi, Y.; Liang, Y.; Mase, A.; Park, H.; Sakata, E.; Tsai, W.; Xia, Z. G.; Zhang, P.
Millimeter wave technology advances have made possible active and passive millimeter wave imaging for a variety of applications including advanced plasma diagnostics, radio astronomy, atmospheric radiometry, concealed weapon detection, all-weather aircraft landing, contraband goods detection, harbor navigation/surveillance in fog, highway traffic monitoring in fog, helicopter and automotive collision avoidance in fog, and environmental remote sensing data associated with weather, pollution, soil moisture, oil spill detection, and monitoring of forest fires, to name but a few. The primary focus of this paper is on technology advances which have made possible advanced imaging and visualization of magnetohydrodynamic (MHD) fluctuations and microturbulence in fusion plasmas. Topics of particular emphasis include frequency selective surfaces, planar Schottky diode mixer arrays, electronically controlled beam shaping/steering arrays, and high power millimeter wave local oscillator and probe sources.
Edge-preserving PET image reconstruction using trust optimization transfer.
Wang, Guobao; Qi, Jinyi
2015-04-01
Iterative image reconstruction for positron emission tomography can improve image quality by using spatial regularization. The most commonly used quadratic penalty often oversmoothes sharp edges and fine features in reconstructed images, while nonquadratic penalties can preserve edges and achieve higher contrast recovery. Existing optimization algorithms such as the expectation maximization (EM) and preconditioned conjugate gradient (PCG) algorithms work well for the quadratic penalty, but are less efficient for high-curvature or nonsmooth edge-preserving regularizations. This paper proposes a new algorithm to accelerate edge-preserving image reconstruction by using two strategies: trust surrogate and optimization transfer descent. Trust surrogate approximates the original penalty by a smoother function at each iteration, but guarantees the algorithm to descend monotonically; Optimization transfer descent accelerates a conventional optimization transfer algorithm by using conjugate gradient and line search. Results of computer simulations and real 3-D data show that the proposed algorithm converges much faster than the conventional EM and PCG for smooth edge-preserving regularization and can also be more efficient than the current state-of-art algorithms for the nonsmooth l1 regularization.
Missing data reconstruction using Gaussian mixture models for fingerprint images
NASA Astrophysics Data System (ADS)
Agaian, Sos S.; Yeole, Rushikesh D.; Rao, Shishir P.; Mulawka, Marzena; Troy, Mike; Reinecke, Gary
2016-05-01
Publisher's Note: This paper, originally published on 25 May 2016, was replaced with a revised version on 16 June 2016. If you downloaded the original PDF, but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. One of the most important areas in biometrics is matching partial fingerprints in fingerprint databases. Recently, significant progress has been made in designing fingerprint identification systems for missing fingerprint information. However, a dependable reconstruction of fingerprint images still remains challenging due to the complexity and the ill-posed nature of the problem. In this article, both binary and gray-level images are reconstructed. This paper also presents a new similarity score to evaluate the performance of the reconstructed binary image. The offered fingerprint image identification system can be automated and extended to numerous other security applications such as postmortem fingerprints, forensic science, investigations, artificial intelligence, robotics, all-access control, and financial security, as well as for the verification of firearm purchasers, driver license applicants, etc.
Comparison of power spectra for tomosynthesis projections and reconstructed images
Engstrom, Emma; Reiser, Ingrid; Nishikawa, Robert
2009-05-15
Burgess et al. [Med. Phys. 28, 419-437 (2001)] showed that the power spectrum of mammographic breast background follows a power law and that lesion detectability is affected by the power-law exponent {beta} which measures the amount of structure in the background. Following the study of Burgess et al., the authors measured and compared the power-law exponent of mammographic backgrounds in tomosynthesis projections and reconstructed slices to investigate the effect of tomosynthesis imaging on background structure. Our data set consisted of 55 patient cases. For each case, regions of interest (ROIs) were extracted from both projection images and reconstructed slices. The periodogram of each ROI was computed by taking the squared modulus of the Fourier transform of the ROI. The power-law exponent was determined for each periodogram and averaged across all ROIs extracted from all projections or reconstructed slices for each patient data set. For the projections, the mean {beta} averaged across the 55 cases was 3.06 (standard deviation of 0.21), while it was 2.87 (0.24) for the corresponding reconstructions. The difference in {beta} for a given patient between the projection ROIs and the reconstructed ROIs averaged across the 55 cases was 0.194, which was statistically significant (p<0.001). The 95% CI for the difference between the mean value of {beta} for the projections and reconstructions was [0.170, 0.218]. The results are consistent with the observation that the amount of breast structure in the tomosynthesis slice is reduced compared to projection mammography and that this may lead to improved lesion detectability.
Comparison of power spectra for tomosynthesis projections and reconstructed images.
Engstrom, Emma; Reiser, Ingrid; Nishikawa, Robert
2009-05-01
Burgess et al. [Med. Phys. 28, 419-437 (2001)] showed that the power spectrum of mammographic breast background follows a power law and that lesion detectability is affected by the power-law exponent beta which measures the amount of structure in the background. Following the study of Burgess et al., the authors measured and compared the power-law exponent of mammographic backgrounds in tomosynthesis projections and reconstructed slices to investigate the effect of tomosynthesis imaging on background structure. Our data set consisted of 55 patient cases. For each case, regions of interest (ROIs) were extracted from both projection images and reconstructed slices. The periodogram of each ROI was computed by taking the squared modulus of the Fourier transform of the ROI. The power-law exponent was determined for each periodogram and averaged across all ROIs extracted from all projections or reconstructed slices for each patient data set. For the projections, the mean beta averaged across the 55 cases was 3.06 (standard deviation of 0.21), while it was 2.87 (0.24) for the corresponding reconstructions. The difference in beta for a given patient between the projection ROIs and the reconstructed ROIs averaged across the 55 cases was 0.194, which was statistically significant (p < 0.001). The 95% CI for the difference between the mean value of beta for the projections and reconstructions was [0.170, 0.218]. The results are consistent with the observation that the amount of breast structure in the tomosynthesis slice is reduced compared to projection mammography and that this may lead to improved lesion detectability.
A dual oxygenation and fluorescence imaging platform for reconstructive surgery
NASA Astrophysics Data System (ADS)
Ashitate, Yoshitomo; Nguyen, John N.; Venugopal, Vivek; Stockdale, Alan; Neacsu, Florin; Kettenring, Frank; Lee, Bernard T.; Frangioni, John V.; Gioux, Sylvain
2013-03-01
There is a pressing clinical need to provide image guidance during surgery. Currently, assessment of tissue that needs to be resected or avoided is performed subjectively, leading to a large number of failures, patient morbidity, and increased healthcare costs. Because near-infrared (NIR) optical imaging is safe, noncontact, inexpensive, and can provide relatively deep information (several mm), it offers unparalleled capabilities for providing image guidance during surgery. These capabilities are well illustrated through the clinical translation of fluorescence imaging during oncologic surgery. In this work, we introduce a novel imaging platform that combines two complementary NIR optical modalities: oxygenation imaging and fluorescence imaging. We validated this platform during facial reconstructive surgery on large animals approaching the size of humans. We demonstrate that NIR fluorescence imaging provides identification of perforator arteries, assesses arterial perfusion, and can detect thrombosis, while oxygenation imaging permits the passive monitoring of tissue vital status, as well as the detection and origin of vascular compromise simultaneously. Together, the two methods provide a comprehensive approach to identifying problems and intervening in real time during surgery before irreparable damage occurs. Taken together, this novel platform provides fully integrated and clinically friendly endogenous and exogenous NIR optical imaging for improved image-guided intervention during surgery.
[Design of the 2D-FFT image reconstruction software based on Matlab].
Xu, Hong-yu; Wang, Hong-zhi
2008-09-01
This paper presents a Matlab's implementation for 2D-FFT image reconstruction algorithm of magnetic resonance imaging, with the universal COM component that Windows system can identify. This allows to segregate the 2D-FFT image reconstruction algorithm from the business magnetic resonance imaging closed system, providing the ability for initial data processing before reconstruction, which would be important for improving the image quality, diagnostic value and image post-processing.
NASA Astrophysics Data System (ADS)
Sharp, J. H.; Barnard, J. S.; Kaneko, K.; Higashida, K.; Midgley, P. A.
2008-08-01
After previous work producing a successful 3D tomographic reconstruction of dislocations in GaN from conventional weak-beam dark-field (WBDF) images, we have reconstructed a cascade of dislocations in deformed and annealed silicon to a comparable standard using the more experimentally straightforward technique of STEM annular dark-field imaging (STEM ADF). In this mode, image contrast was much more consistent over the specimen tilt range than in conventional weak-beam dark-field imaging. Automatic acquisition software could thus restore the correct dislocation array to the field of view at each tilt angle, though manual focusing was still required. Reconstruction was carried out by sequential iterative reconstruction technique using FEI's Inspect3D software. Dislocations were distributed non-uniformly along cascades, with sparse areas between denser clumps in which individual dislocations of in-plane image width 24 nm could be distinguished in images and reconstruction. Denser areas showed more complicated stacking-fault contrast, hampering tomographic reconstruction. The general three-dimensional form of the denser areas was reproduced well, showing the dislocation array to be planar and not parallel to the foil surfaces.
Parallel MR image reconstruction using augmented Lagrangian methods.
Ramani, Sathish; Fessler, Jeffrey A
2011-03-01
Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity-based regularization criteria can improve image quality, but they demand computation-intensive nonlinear optimization. In this paper, we present novel methods for regularized MRI reconstruction from undersampled sensitivity encoded data--SENSE-reconstruction--using the augmented Lagrangian (AL) framework for solving large-scale constrained optimization problems. We first formulate regularized SENSE-reconstruction as an unconstrained optimization task and then convert it to a set of (equivalent) constrained problems using variable splitting. We then attack these constrained versions in an AL framework using an alternating minimization method, leading to algorithms that can be implemented easily. The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., l(1)-norm of wavelet coefficients) criteria and combinations thereof. Numerical experiments with synthetic and in vivo human data illustrate that the proposed AL algorithms converge faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA.
Bayesian image reconstruction with space-variant noise suppression
NASA Astrophysics Data System (ADS)
Nunez, J.; Llacer, J.
1998-07-01
In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.
Technology and application advancements of uncooled imagers
NASA Astrophysics Data System (ADS)
Norton, Peter W.; Kohin, Margaret
2005-05-01
Having delivered over 30,000 uncooled microbolometer based thermal imaging engines, BAE Systems is the world's leading producer. Advancements in technology include the demonstration of broadband microbolometers on a 46 μm pixel pitch which have excellent sensitivity in the MWIR (NETD ~180 mK, 3-5 μm) and LWIR (NETD ~ 15 mK, 8-12 μm) wavebands. Application advancements include the development of a family of thermal weapons sights for the military which will replace current cooled systems with lighter, lower power systems and the introduction of a new generation of handheld and pole mounted thermal imagers for commercial markets.
NASA Technical Reports Server (NTRS)
Newman, Timothy; Santhanam, Naveen; Zhang, Huijuan; Gallagher, Dennis
2003-01-01
A new method for reconstructing the global 3D distribution of plasma densities in the plasmasphere from a limited number of 2D views is presented. The method is aimed at using data from the Extreme Ultra Violet (EUV) sensor on NASA s Imager for Magnetopause-to-Aurora Global Exploration (IMAGE) satellite. Physical properties of the plasmasphere are exploited by the method to reduce the level of inaccuracy imposed by the limited number of views. The utility of the method is demonstrated on synthetic data.
Stokes image reconstruction for two-color microgrid polarization imaging systems.
Lemaster, Daniel A
2011-07-18
The Air Force Research Laboratory has developed a new microgrid polarization imaging system capable of simultaneously reconstructing linear Stokes parameter images in two colors on a single focal plane array. In this paper, an effective method for extracting Stokes images is presented for this type of camera system. It is also shown that correlations between the color bands can be exploited to significantly increase overall spatial resolution. Test data is used to show the advantages of this approach over bilinear interpolation. The bounds (in terms of available reconstruction bandwidth) on image resolution are also provided.
Optimizing modelling in iterative image reconstruction for preclinical pinhole PET
NASA Astrophysics Data System (ADS)
Goorden, Marlies C.; van Roosmalen, Jarno; van der Have, Frans; Beekman, Freek J.
2016-05-01
The recently developed versatile emission computed tomography (VECTor) technology enables high-energy SPECT and simultaneous SPECT and PET of small animals at sub-mm resolutions. VECTor uses dedicated clustered pinhole collimators mounted in a scanner with three stationary large-area NaI(Tl) gamma detectors. Here, we develop and validate dedicated image reconstruction methods that compensate for image degradation by incorporating accurate models for the transport of high-energy annihilation gamma photons. Ray tracing software was used to calculate photon transport through the collimator structures and into the gamma detector. Input to this code are several geometric parameters estimated from system calibration with a scanning 99mTc point source. Effects on reconstructed images of (i) modelling variable depth-of-interaction (DOI) in the detector, (ii) incorporating photon paths that go through multiple pinholes (‘multiple-pinhole paths’ (MPP)), and (iii) including various amounts of point spread function (PSF) tail were evaluated. Imaging 18F in resolution and uniformity phantoms showed that including large parts of PSFs is essential to obtain good contrast-noise characteristics and that DOI modelling is highly effective in removing deformations of small structures, together leading to 0.75 mm resolution PET images of a hot-rod Derenzo phantom. Moreover, MPP modelling reduced the level of background noise. These improvements were also clearly visible in mouse images. Performance of VECTor can thus be significantly improved by accurately modelling annihilation gamma photon transport.
Advances in Molecular Imaging with Ultrasound
Gessner, Ryan; Dayton, Paul A.
2010-01-01
Ultrasound imaging has long demonstrated utility in the study and measurement of anatomic features and noninvasive observation of blood flow. Within the last decade, advances in molecular biology and contrast agents have allowed researchers to use ultrasound to detect changes in the expression of molecular markers on the vascular endothelium and other intravascular targets. This new technology, referred to as ultrasonic molecular imaging, is still in its infancy. However, in preclinical studies, ultrasonic molecular imaging has shown promise in assessing angiogenesis, inflammation, and thrombus. In this review, we discuss recent advances in microbubble-type contrast agent development, ultrasound technology, and signal processing strategies that have the potential to substantially improve the capabilities and utility of ultrasonic molecular imaging. PMID:20487678
Image reconstruction and optimization using a terahertz scanned imaging system
NASA Astrophysics Data System (ADS)
Yıldırım, İhsan Ozan; Özkan, Vedat A.; Idikut, Fırat; Takan, Taylan; Şahin, Asaf B.; Altan, Hakan
2014-10-01
Due to the limited number of array detection architectures in the millimeter wave to terahertz region of the electromagnetic spectrum, imaging schemes with scan architectures are typically employed. In these configurations the interplay between the frequencies used to illuminate the scene and the optics used play an important role in the quality of the formed image. Using a multiplied Schottky-diode based terahertz transceiver operating at 340 GHz, in a stand-off detection scheme; the effect of image quality of a metal target was assessed based on the scanning speed of the galvanometer mirrors as well as the optical system that was constructed. Background effects such as leakage on the receiver were minimized by conditioning the signal at the output of the transceiver. Then, the image of the target was simulated based on known parameters of the optical system and the measured images were compared to the simulation. By using an image quality index based on χ2 algorithm the simulated and measured images were found to be in good agreement with a value of χ2 = 0 .14. The measurements as shown here will aid in the future development of larger stand-off imaging systems that work in the terahertz frequency range.
Joint image reconstruction and segmentation using the Potts model
NASA Astrophysics Data System (ADS)
Storath, Martin; Weinmann, Andreas; Frikel, Jürgen; Unser, Michael
2015-02-01
We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford-Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficiently. Our method does not require a priori knowledge on the gray levels nor on the number of segments of the reconstruction. Further, it avoids anisotropic artifacts such as geometric staircasing. We demonstrate the suitability of our method for joint image reconstruction and segmentation. We focus on Radon data, where we in particular consider limited data situations. For instance, our method is able to recover all segments of the Shepp-Logan phantom from seven angular views only. We illustrate the practical applicability on a real positron emission tomography dataset. As further applications, we consider spherical Radon data as well as blurred data.
A maximum entropy reconstruction technique for tomographic particle image velocimetry
NASA Astrophysics Data System (ADS)
Bilsky, A. V.; Lozhkin, V. A.; Markovich, D. M.; Tokarev, M. P.
2013-04-01
This paper studies a novel approach for reducing tomographic PIV computational complexity. The proposed approach is an algebraic reconstruction technique, termed MENT (maximum entropy). This technique computes the three-dimensional light intensity distribution several times faster than SMART, using at least ten times less memory. Additionally, the reconstruction quality remains nearly the same as with SMART. This paper presents the theoretical computation performance comparison for MENT, SMART and MART, followed by validation using synthetic particle images. Both the theoretical assessment and validation of synthetic images demonstrate significant computational time reduction. The data processing accuracy of MENT was compared to that of SMART in a slot jet experiment. A comparison of the average velocity profiles shows a high level of agreement between the results obtained with MENT and those obtained with SMART.
NASA Astrophysics Data System (ADS)
Helou, E. S.; Zibetti, M. V. W.; Miqueles, E. X.
2017-04-01
We propose the superiorization of incremental algorithms for tomographic image reconstruction. The resulting methods follow a better path in its way to finding the optimal solution for the maximum likelihood problem in the sense that they are closer to the Pareto optimal curve than the non-superiorized techniques. A new scaled gradient iteration is proposed and three superiorization schemes are evaluated. Theoretical analysis of the methods as well as computational experiments with both synthetic and real data are provided.
Fan beam image reconstruction with generalized Fourier slice theorem.
Zhao, Shuangren; Yang, Kang; Yang, Kevin
2014-01-01
For parallel beam geometry the Fourier reconstruction works via the Fourier slice theorem (or central slice theorem, projection slice theorem). For fan beam situation, Fourier slice can be extended to a generalized Fourier slice theorem (GFST) for fan-beam image reconstruction. We have briefly introduced this method in a conference. This paper reintroduces the GFST method for fan beam geometry in details. The GFST method can be described as following: the Fourier plane is filled by adding up the contributions from all fanbeam projections individually; thereby the values in the Fourier plane are directly calculated for Cartesian coordinates such avoiding the interpolation from polar to Cartesian coordinates in the Fourier domain; inverse fast Fourier transform is applied to the image in Fourier plane and leads to a reconstructed image in spacial domain. The reconstructed image is compared between the result of the GFST method and the result from the filtered backprojection (FBP) method. The major differences of the GFST and the FBP methods are: (1) The interpolation process are at different data sets. The interpolation of the GFST method is at projection data. The interpolation of the FBP method is at filtered projection data. (2) The filtering process are done in different places. The filtering process of the GFST is at Fourier domain. The filtering process of the FBP method is the ramp filter which is done at projections. The resolution of ramp filter is variable with different location but the filter in the Fourier domain lead to resolution invariable with location. One advantage of the GFST method over the FBP method is in short scan situation, an exact solution can be obtained with the GFST method, but it can not be obtained with the FBP method. The calculation of both the GFST and the FBP methods are at O(N
An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging
Valente, Solivan A.; Zibetti, Marcelo V. W.; Pipa, Daniel R.; Maia, Joaquim M.; Schneider, Fabio K.
2017-01-01
Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general sparse signal reconstruction, are also suited for imaging. In this paper, a discrete acquisition model is assessed by solving a linear system of equations by an ℓ1-regularized least-squares minimization, where the solution sparsity may be adjusted as desired. The paper surveys 11 variants of four well-known algorithms for sparse reconstruction, and assesses their optimization parameters with the goal of finding the best approach for iterative ultrasound imaging. The strategy for the model evaluation consists of using two distinct datasets. We first generate data from a synthetic phantom that mimics real targets inside a professional ultrasound phantom device. This dataset is contaminated with Gaussian noise with an estimated SNR, and all methods are assessed by their resulting images and performances. The model and methods are then assessed with real data collected by a research ultrasound platform when scanning the same phantom device, and results are compared with beamforming. A distinct real dataset is finally used to further validate the proposed modeling. Although high computational effort is required by iterative methods, results show that the discrete model may lead to images closer to ground-truth than traditional beamforming. However, computing capabilities of current platforms need to evolve before frame rates currently delivered by ultrasound equipments are achievable. PMID:28282862
Neutron source reconstruction from pinhole imaging at National Ignition Facility
NASA Astrophysics Data System (ADS)
Volegov, P.; Danly, C. R.; Fittinghoff, D. N.; Grim, G. P.; Guler, N.; Izumi, N.; Ma, T.; Merrill, F. E.; Warrick, A. L.; Wilde, C. H.; Wilson, D. C.
2014-02-01
The neutron imaging system at the National Ignition Facility (NIF) is an important diagnostic tool for measuring the two-dimensional size and shape of the neutrons produced in the burning deuterium-tritium plasma during the ignition stage of inertial confinement fusion (ICF) implosions at NIF. Since the neutron source is small (˜100 μm) and neutrons are deeply penetrating (>3 cm) in all materials, the apertures used to achieve the desired 10-μm resolution are 20-cm long, single-sided tapers in gold. These apertures, which have triangular cross sections, produce distortions in the image, and the extended nature of the pinhole results in a non-stationary or spatially varying point spread function across the pinhole field of view. In this work, we have used iterative Maximum Likelihood techniques to remove the non-stationary distortions introduced by the aperture to reconstruct the underlying neutron source distributions. We present the detailed algorithms used for these reconstructions, the stopping criteria used and reconstructed sources from data collected at NIF with a discussion of the neutron imaging performance in light of other diagnostics.
Neutron source reconstruction from pinhole imaging at National Ignition Facility.
Volegov, P; Danly, C R; Fittinghoff, D N; Grim, G P; Guler, N; Izumi, N; Ma, T; Merrill, F E; Warrick, A L; Wilde, C H; Wilson, D C
2014-02-01
The neutron imaging system at the National Ignition Facility (NIF) is an important diagnostic tool for measuring the two-dimensional size and shape of the neutrons produced in the burning deuterium-tritium plasma during the ignition stage of inertial confinement fusion (ICF) implosions at NIF. Since the neutron source is small (∼100 μm) and neutrons are deeply penetrating (>3 cm) in all materials, the apertures used to achieve the desired 10-μm resolution are 20-cm long, single-sided tapers in gold. These apertures, which have triangular cross sections, produce distortions in the image, and the extended nature of the pinhole results in a non-stationary or spatially varying point spread function across the pinhole field of view. In this work, we have used iterative Maximum Likelihood techniques to remove the non-stationary distortions introduced by the aperture to reconstruct the underlying neutron source distributions. We present the detailed algorithms used for these reconstructions, the stopping criteria used and reconstructed sources from data collected at NIF with a discussion of the neutron imaging performance in light of other diagnostics.
Specialized Color Targets for Spectral Reflectance Reconstruction of Magnified Images
NASA Astrophysics Data System (ADS)
Kruschwitz, Jennifer D. T.
Digital images are used almost exclusively instead of film to capture visual information across many scientific fields. The colorimetric color representation within these digital images can be relayed from the digital counts produced by the camera with the use of a known color target. In image capture of magnified images, there is currently no reliable color target that can be used at multiple magnifications and give the user a solid understanding of the color ground truth within those images. The first part of this dissertation included the design, fabrication, and testing of a color target produced with optical interference coated microlenses for use in an off-axis illumination, compound microscope. An ideal target was designed to increase the color gamut for colorimetric imaging and provide the necessary "Block Dye" spectral reflectance profiles across the visible spectrum to reduce the number of color patches necessary for multiple filter imaging systems that rely on statistical models for spectral reflectance reconstruction. There are other scientific disciplines that can benefit from a specialized color target to determine the color ground truth in their magnified images and perform spectral estimation. Not every discipline has the luxury of having a multi-filter imaging system. The second part of this dissertation developed two unique ways of using an interference coated color mirror target: one that relies on multiple light-source angles, and one that leverages a dynamic color change with time. The source multi-angle technique would be used for the microelectronic discipline where the reconstructed spectral reflectance would be used to determine a dielectric film thickness on a silicon substrate, and the time varying technique would be used for a biomedical example to determine the thickness of human tear film.
Jung, Jae-Hyun; Hong, Keehoon; Park, Gilbae; Chung, Indeok; Park, Jae-Hyeung; Lee, Byoungho
2010-12-06
We proposed a reconstruction method for the occluded region of three-dimensional (3D) object using the depth extraction based on the optical flow and triangular mesh reconstruction in integral imaging. The depth information of sub-images from the acquired elemental image set is extracted using the optical flow with sub-pixel accuracy, which alleviates the depth quantization problem. The extracted depth maps of sub-image array are segmented by the depth threshold from the histogram based segmentation, which is represented as the point clouds. The point clouds are projected to the viewpoint of center sub-image and reconstructed by the triangular mesh reconstruction. The experimental results support the validity of the proposed method with high accuracy of peak signal-to-noise ratio and normalized cross-correlation in 3D image recognition.
NASA Astrophysics Data System (ADS)
Ou-Yang, Mang; Jeng, Wei-De; Wu, Yin-Yi; Dung, Lan-Rong; Wu, Hsien-Ming; Weng, Ping-Kuo; Huang, Ker-Jer; Chiu, Luan-Jiau
2012-05-01
This study investigates image processing using the radial imaging capsule endoscope (RICE) system. First, an experimental environment is established in which a simulated object has a shape that is similar to a cylinder, such that a triaxial platform can be used to push the RICE into the sample and capture radial images. Then four algorithms (mean absolute error, mean square error, Pearson correlation coefficient, and deformation processing) are used to stitch the images together. The Pearson correlation coefficient method is the most effective algorithm because it yields the highest peak signal-to-noise ratio, higher than 80.69 compared to the original image. Furthermore, a living animal experiment is carried out. Finally, the Pearson correlation coefficient method and vector deformation processing are used to stitch the images that were captured in the living animal experiment. This method is very attractive because unlike the other methods, in which two lenses are required to reconstruct the geometrical image, RICE uses only one lens and one mirror.
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.
A study of image reconstruction algorithms for hybrid intensity interferometers
NASA Astrophysics Data System (ADS)
Crabtree, Peter N.; Murray-Krezan, Jeremy; Picard, Richard H.
2011-09-01
Phase retrieval is explored for image reconstruction using outputs from both a simulated intensity interferometer (II) and a hybrid system that combines the II outputs with partially resolved imagery from a traditional imaging telescope. Partially resolved imagery provides an additional constraint for the iterative phase retrieval process, as well as an improved starting point. The benefits of this additional a priori information are explored and include lower residual phase error for SNR values above 0.01, increased sensitivity, and improved image quality. Results are also presented for image reconstruction from II measurements alone, via current state-of-the-art phase retrieval techniques. These results are based on the standard hybrid input-output (HIO) algorithm, as well as a recent enhancement to HIO that optimizes step lengths in addition to step directions. The additional step length optimization yields a reduction in residual phase error, but only for SNR values greater than about 10. Image quality for all algorithms studied is quite good for SNR>=10, but it should be noted that the studied phase-recovery techniques yield useful information even for SNRs that are much lower.
Three-dimensional freehand ultrasound: image reconstruction and volume analysis.
Barry, C D; Allott, C P; John, N W; Mellor, P M; Arundel, P A; Thomson, D S; Waterton, J C
1997-01-01
A system is described that rapidly produces a regular 3-dimensional (3-D) data block suitable for processing by conventional image analysis and volume measurement software. The system uses electromagnetic spatial location of 2-dimensional (2-D) freehand-scanned ultrasound B-mode images, custom-built signal-conditioning hardware, UNIX-based computer processing and an efficient 3-D reconstruction algorithm. Utilisation of images from multiple angles of insonation, "compounding," reduces speckle contrast, improves structure coherence within the reconstructed grey-scale image and enhances the ability to detect structure boundaries and to segment and quantify features. Volume measurements using a series of water-filled latex and cylindrical foam rubber phantoms with volumes down to 0.7 mL show that a high degree of accuracy, precision and reproducibility can be obtained. Extension of the technique to handle in vivo data sets by allowing physiological criteria to be taken into account in selecting the images used for construction is also illustrated.
Super-resolution reconstruction for tongue MR images
NASA Astrophysics Data System (ADS)
Woo, Jonghye; Bai, Ying; Roy, Snehashis; Murano, Emi Z.; Stone, Maureen; Prince, Jerry L.
2012-02-01
Magnetic resonance (MR) images of the tongue have been used in both clinical medicine and scientific research to reveal tongue structure and motion. In order to see different features of the tongue and its relation to the vocal tract it is beneficial to acquire three orthogonal image stacks-e.g., axial, sagittal and coronal volumes. In order to maintain both low noise and high visual detail, each set of images is typically acquired with in-plane resolution that is much better than the through-plane resolution. As a result, any one data set, by itself, is not ideal for automatic volumetric analyses such as segmentation and registration or even for visualization when oblique slices are required. This paper presents a method of super-resolution reconstruction of the tongue that generates an isotropic image volume using the three orthogonal image stacks. The method uses preprocessing steps that include intensity matching and registration and a data combination approach carried out by Markov random field optimization. The performance of the proposed method was demonstrated on five clinical datasets, yielding superior results when compared with conventional reconstruction methods.
Task-based optimization of image reconstruction in breast CT
NASA Astrophysics Data System (ADS)
Sanchez, Adrian A.; Sidky, Emil Y.; Pan, Xiaochuan
2014-03-01
We demonstrate a task-based assessment of image quality in dedicated breast CT in order to optimize the number of projection views acquired. The methodology we employ is based on the Hotelling Observer (HO) and its associated metrics. We consider two tasks: the Rayleigh task of discerning between two resolvable objects and a single larger object, and the signal detection task of classifying an image as belonging to either a signalpresent or signal-absent hypothesis. HO SNR values are computed for 50, 100, 200, 500, and 1000 projection view images, with the total imaging radiation dose held constant. We use the conventional fan-beam FBP algorithm and investigate the effect of varying the width of a Hanning window used in the reconstruction, since this affects both the noise properties of the image and the under-sampling artifacts which can arise in the case of sparse-view acquisitions. Our results demonstrate that fewer projection views should be used in order to increase HO performance, which in this case constitutes an upper-bound on human observer performance. However, the impact on HO SNR of using fewer projection views, each with a higher dose, is not as significant as the impact of employing regularization in the FBP reconstruction through a Hanning filter.
Advances in the calibration of atom probe tomographic reconstruction
Gault, Baptiste; Moody, Michael P.; La Fontaine, Alexandre; Stephenson, Leigh T.; Haley, Daniel; Ringer, Simon P.; Geuser, Frederic de; Tsafnat, Guy
2009-02-01
Modern wide field-of-view atom probes permit observation of a wide range of crystallographic features that can be used to calibrate the tomographic reconstruction of the analyzed volume. In this study, methodologies to determine values of the geometric parameters involved in the tomographic reconstruction of atom probe data sets are presented and discussed. The influence of the tip to electrode distance and specimen temperature on these parameters is explored. Significantly, their influence is demonstrated to be very limited, indicating a relatively wide regime of experimental parameters space for sound atom probe tomography (APT) experiments. These methods have been used on several specimens and material types, and the results indicate that the reconstruction parameters are specific to each specimen. Finally, it is shown how an accurate calibration of the reconstruction enables improvements to the quality and reliability of the microscopy and microanalysis capabilities of the atom probe.
Advanced seismic imaging for geothermal development
Louie, John; Pullammanappallil, Satish; Honjas, Bill
2016-08-01
J. N. Louie, Pullammanappallil, S., and Honjas, W., 2011, Advanced seismic imaging for geothermal development: Proceedings of the New Zealand Geothermal Workshop 2011, Nov. 21-23, Auckland, paper 32, 7 pp. Preprint available at http://crack.seismo.unr.edu/geothermal/Louie-NZGW11.pdf
Uncooled thermal imaging sensor and application advances
NASA Astrophysics Data System (ADS)
Norton, Peter W.; Cox, Stephen; Murphy, Bob; Grealish, Kevin; Joswick, Mike; Denley, Brian; Feda, Frank; Elmali, Loriann; Kohin, Margaret
2006-05-01
BAE Systems continues to advance the technology and performance of microbolometer-based thermal imaging modules and systems. 640x480 digital uncooled infrared focal plane arrays are in full production, illustrated by recent production line test data for two thousand focal plane arrays. This paper presents a snapshot of microbolometer technology at BAE Systems and an overview of two of the most important thermal imaging sensor programs currently in production: a family of thermal weapons sights for the United States Army and a thermal imager for the remote weapons station on the Stryker vehicle.
Iterative Self-Dual Reconstruction on Radar Image Recovery
Martins, Charles; Medeiros, Fatima; Ushizima, Daniela; Bezerra, Francisco; Marques, Regis; Mascarenhas, Nelson
2010-05-21
Imaging systems as ultrasound, sonar, laser and synthetic aperture radar (SAR) are subjected to speckle noise during image acquisition. Before analyzing these images, it is often necessary to remove the speckle noise using filters. We combine properties of two mathematical morphology filters with speckle statistics to propose a signal-dependent noise filter to multiplicative noise. We describe a multiscale scheme that preserves sharp edges while it smooths homogeneous areas, by combining local statistics with two mathematical morphology filters: the alternating sequential and the self-dual reconstruction algorithms. The experimental results show that the proposed approach is less sensitive to varying window sizes when applied to simulated and real SAR images in comparison with standard filters.
3D Lunar Terrain Reconstruction from Apollo Images
NASA Technical Reports Server (NTRS)
Broxton, Michael J.; Nefian, Ara V.; Moratto, Zachary; Kim, Taemin; Lundy, Michael; Segal, Alkeksandr V.
2009-01-01
Generating accurate three dimensional planetary models is becoming increasingly important as NASA plans manned missions to return to the Moon in the next decade. This paper describes a 3D surface reconstruction system called the Ames Stereo Pipeline that is designed to produce such models automatically by processing orbital stereo imagery. We discuss two important core aspects of this system: (1) refinement of satellite station positions and pose estimates through least squares bundle adjustment; and (2) a stochastic plane fitting algorithm that generalizes the Lucas-Kanade method for optimal matching between stereo pair images.. These techniques allow us to automatically produce seamless, highly accurate digital elevation models from multiple stereo image pairs while significantly reducing the influence of image noise. Our technique is demonstrated on a set of 71 high resolution scanned images from the Apollo 15 mission
Li, Ang; Zhang, Quan; Culver, Joseph P; Miller, Eric L; Boas, David A
2004-02-01
We present an algorithm to reconstruct chromosphere concentration images directly rather than following the traditional two-step process of reconstructing wavelength-dependent absorption coefficient images and then calculating chromosphere concentration images. This procedure imposes prior spectral information into the image reconstruction that results in a dramatic improvement in the image contrast-to-noise ratio of better than 100%. We demonstrate this improvement with simulations and a dynamic blood phantom experiment.
X-ray imaging and 3D reconstruction of in-flight exploding foil initiator flyers
Willey, T. M.; Champley, K.; Hodgin, R.; ...
2016-06-17
Exploding foil initiators (EFIs), also known as slapper initiators or detonators, offer clear safety and timing advantages over other means of initiating detonation in high explosives. The work described here outlines a new capability for imaging and reconstructing three-dimensional images of operating EFIs. Flyer size and intended velocity were chosen based on parameters of the imaging system. The EFI metal plasma and plastic flyer traveling at 2.5 km/s were imaged with short ~80 ps pulses spaced 153.4 ns apart. A four-camera system acquired 4 images from successive x-ray pulses from each shot. The first frame was prior to bridge burst,more » the 2nd images the flyer about 0.16 mm above the surface but edges of the foil and/or flyer are still attached to the substrate. The 3rd frame captures the flyer in flight, while the 4th shows a completely detached flyer in a position that is typically beyond where slappers strike initiating explosives. Multiple acquisitions at different incident angles and advanced computed tomography reconstruction algorithms were used to produce a 3-dimensional image of the flyer at 0.16 and 0.53 mm above the surface. Both the x-ray images and the 3D reconstruction show a strong anisotropy in the shape of the flyer and underlying foil parallel vs. perpendicular to the initiating current and electrical contacts. These results provide detailed flyer morphology during the operation of the EFI.« less
X-ray imaging and 3D reconstruction of in-flight exploding foil initiator flyers
Willey, T. M.; Champley, K.; Hodgin, R.; Lauderbach, L.; Bagge-Hansen, M.; May, C.; Sanchez, N.; Jensen, B. J.; Iverson, A.; van Buuren, T.
2016-06-17
Exploding foil initiators (EFIs), also known as slapper initiators or detonators, offer clear safety and timing advantages over other means of initiating detonation in high explosives. The work described here outlines a new capability for imaging and reconstructing three-dimensional images of operating EFIs. Flyer size and intended velocity were chosen based on parameters of the imaging system. The EFI metal plasma and plastic flyer traveling at 2.5 km/s were imaged with short ~80 ps pulses spaced 153.4 ns apart. A four-camera system acquired 4 images from successive x-ray pulses from each shot. The first frame was prior to bridge burst, the 2^{nd} images the flyer about 0.16 mm above the surface but edges of the foil and/or flyer are still attached to the substrate. The 3^{rd} frame captures the flyer in flight, while the 4^{th} shows a completely detached flyer in a position that is typically beyond where slappers strike initiating explosives. Multiple acquisitions at different incident angles and advanced computed tomography reconstruction algorithms were used to produce a 3-dimensional image of the flyer at 0.16 and 0.53 mm above the surface. Both the x-ray images and the 3D reconstruction show a strong anisotropy in the shape of the flyer and underlying foil parallel vs. perpendicular to the initiating current and electrical contacts. These results provide detailed flyer morphology during the operation of the EFI.
Anthropometric body measurements based on multi-view stereo image reconstruction.
Li, Zhaoxin; Jia, Wenyan; Mao, Zhi-Hong; Li, Jie; Chen, Hsin-Chen; Zuo, Wangmeng; Wang, Kuanquan; Sun, Mingui
2013-01-01
Anthropometric measurements, such as the circumferences of the hip, arm, leg and waist, waist-to-hip ratio, and body mass index, are of high significance in obesity and fitness evaluation. In this paper, we present a home based imaging system capable of conducting anthropometric measurements. Body images are acquired at different angles using a home camera and a simple rotating disk. Advanced image processing algorithms are utilized for 3D body surface reconstruction. A coarse body shape model is first established from segmented body silhouettes. Then, this model is refined through an inter-image consistency maximization process based on an energy function. Our experimental results using both a mannequin surrogate and a real human body validate the feasibility of the proposed system.
A reconstruction algorithm for photoacoustic imaging based on the nonuniform FFT.
Haltmeier, Markus; Scherzer, Otmar; Zangerl, Gerhard
2009-11-01
Fourier reconstruction algorithms significantly outperform conventional backprojection algorithms in terms of computation time. In photoacoustic imaging, these methods require interpolation in the Fourier space domain, which creates artifacts in reconstructed images. We propose a novel reconstruction algorithm that applies the one-dimensional nonuniform fast Fourier transform to photoacoustic imaging. It is shown theoretically and numerically that our algorithm avoids artifacts while preserving the computational effectiveness of Fourier reconstruction.
Isotope specific resolution recovery image reconstruction in high resolution PET imaging
Kotasidis, Fotis A.; Angelis, Georgios I.; Anton-Rodriguez, Jose; Matthews, Julian C.; Reader, Andrew J.; Zaidi, Habib
2014-05-15
Purpose: Measuring and incorporating a scanner-specific point spread function (PSF) within image reconstruction has been shown to improve spatial resolution in PET. However, due to the short half-life of clinically used isotopes, other long-lived isotopes not used in clinical practice are used to perform the PSF measurements. As such, non-optimal PSF models that do not correspond to those needed for the data to be reconstructed are used within resolution modeling (RM) image reconstruction, usually underestimating the true PSF owing to the difference in positron range. In high resolution brain and preclinical imaging, this effect is of particular importance since the PSFs become more positron range limited and isotope-specific PSFs can help maximize the performance benefit from using resolution recovery image reconstruction algorithms. Methods: In this work, the authors used a printing technique to simultaneously measure multiple point sources on the High Resolution Research Tomograph (HRRT), and the authors demonstrated the feasibility of deriving isotope-dependent system matrices from fluorine-18 and carbon-11 point sources. Furthermore, the authors evaluated the impact of incorporating them within RM image reconstruction, using carbon-11 phantom and clinical datasets on the HRRT. Results: The results obtained using these two isotopes illustrate that even small differences in positron range can result in different PSF maps, leading to further improvements in contrast recovery when used in image reconstruction. The difference is more pronounced in the centre of the field-of-view where the full width at half maximum (FWHM) from the positron range has a larger contribution to the overall FWHM compared to the edge where the parallax error dominates the overall FWHM. Conclusions: Based on the proposed methodology, measured isotope-specific and spatially variant PSFs can be reliably derived and used for improved spatial resolution and variance performance in resolution
Advanced endoscopic imaging to improve adenoma detection
Neumann, Helmut; Nägel, Andreas; Buda, Andrea
2015-01-01
Advanced endoscopic imaging is revolutionizing our way on how to diagnose and treat colorectal lesions. Within recent years a variety of modern endoscopic imaging techniques was introduced to improve adenoma detection rates. Those include high-definition imaging, dye-less chromoendoscopy techniques and novel, highly flexible endoscopes, some of them equipped with balloons or multiple lenses in order to improve adenoma detection rates. In this review we will focus on the newest developments in the field of colonoscopic imaging to improve adenoma detection rates. Described techniques include high-definition imaging, optical chromoendoscopy techniques, virtual chromoendoscopy techniques, the Third Eye Retroscope and other retroviewing devices, the G-EYE endoscope and the Full Spectrum Endoscopy-system. PMID:25789092
Kim, D; Kang, S; Kim, T; Suh, T; Kim, S
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 studies 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)
NASA Astrophysics Data System (ADS)
Chuang, Ching-Cheng; Tsai, Jui-che; Chen, Chung-Ming; Yu, Zong-Han; Sun, Chia-Wei
2012-04-01
Diffuse optical tomography (DOT) is an emerging technique for functional biological imaging. The imaging quality of DOT depends on the imaging reconstruction algorithm. The SIRT has been widely used for DOT image reconstruction but there is no criterion to truncate based on any kind of residual parameter. The iteration loops will always be decided by experimental rule. This work presents the CR calculation that can be great help for SIRT optimization. In this paper, four inhomogeneities with various shapes of absorption distributions are simulated as imaging targets. The images are reconstructed and analyzed based on the simultaneous iterative reconstruction technique (SIRT) method. For optimization between time consumption and imaging accuracy in reconstruction process, the numbers of iteration loop needed to be optimized with a criterion in algorithm, that is, the root mean square error (RMSE) should be minimized in limited iterations. For clinical applications of DOT, the RMSE cannot be obtained because the measured targets are unknown. Thus, the correlations between the RMSE and the convergence rate (CR) in SIRT algorithm are analyzed in this paper. From the simulation results, the parameter CR reveals the related RMSE value of reconstructed images. The CR calculation offers an optimized criterion of iteration process in SIRT algorithm for DOT imaging. Based on the result, the SIRT can be modified with CR calculation for self-optimization. CR reveals an indicator of SIRT image reconstruction in clinical DOT measurement. Based on the comparison result between RMSE and CR, a threshold value of CR (CRT) can offer an optimized number of iteration steps for DOT image reconstruction. This paper shows the feasibility study by utilizing CR criterion for SIRT in simulation and the clinical application of DOT measurement relies on further investigation.
Reconstruction of major fibers using 7T multi-shell Hybrid Diffusion Imaging in mice
NASA Astrophysics Data System (ADS)
Daianu, Madelaine; Jacobs, Russell E.; Zlokovic, Berislav V.; Montagne, Axel; Thompson, Paul M.
2015-12-01
Diffusion weighted imaging (DWI) can reveal the orientation of the underlying fiber populations in the brain. High angular resolution diffusion imaging (HARDI) is increasingly used to better resolve the orientation and mixing of fibers. Here, we assessed the added value of multi-shell q-space sampling on the reconstruction of major fibers using mathematical frameworks from q-ball imaging (QBI) and generalized q-sampling imaging (GQI), as compared to diffusion tensor imaging (DTI). We scanned a healthy mouse brain using 7-Tesla 5-shell HARDI (b=1000, 3000, 4000, 8000, 12000 s/mm2), also known as hybrid diffusion imaging (HYDI). We found that QBI may provide greater reconstruction accuracy for major fibers, which improves with the addition of higher b-value shells, unlike GQI or DTI (as expected). Although QBI is a special case of GQI, the major fiber orientation in QBI was more closely related to the orientation in DTI, rather than GQI. HYDI can aid the clinical outcomes of research and especially - more advanced human and animal connectomics projects to map the brain's neural pathways and networks.
Nonlinear Dual Reconstruction of SPECT Activity and Attenuation Images
Liu, Huafeng; Guo, Min; Hu, Zhenghui; Shi, Pengcheng; Hu, Hongjie
2014-01-01
In single photon emission computed tomography (SPECT), accurate attenuation maps are needed to perform essential attenuation compensation for high quality radioactivity estimation. Formulating the SPECT activity and attenuation reconstruction tasks as coupled signal estimation and system parameter identification problems, where the activity distribution and the attenuation parameter are treated as random variables with known prior statistics, we present a nonlinear dual reconstruction scheme based on the unscented Kalman filtering (UKF) principles. In this effort, the dynamic changes of the organ radioactivity distribution are described through state space evolution equations, while the photon-counting SPECT projection data are measured through the observation equations. Activity distribution is then estimated with sub-optimal fixed attenuation parameters, followed by attenuation map reconstruction given these activity estimates. Such coupled estimation processes are iteratively repeated as necessary until convergence. The results obtained from Monte Carlo simulated data, physical phantom, and real SPECT scans demonstrate the improved performance of the proposed method both from visual inspection of the images and a quantitative evaluation, compared to the widely used EM-ML algorithms. The dual estimation framework has the potential to be useful for estimating the attenuation map from emission data only and thus benefit the radioactivity reconstruction. PMID:25225796
Nonlinear dual reconstruction of SPECT activity and attenuation images.
Liu, Huafeng; Guo, Min; Hu, Zhenghui; Shi, Pengcheng; Hu, Hongjie
2014-01-01
In single photon emission computed tomography (SPECT), accurate attenuation maps are needed to perform essential attenuation compensation for high quality radioactivity estimation. Formulating the SPECT activity and attenuation reconstruction tasks as coupled signal estimation and system parameter identification problems, where the activity distribution and the attenuation parameter are treated as random variables with known prior statistics, we present a nonlinear dual reconstruction scheme based on the unscented Kalman filtering (UKF) principles. In this effort, the dynamic changes of the organ radioactivity distribution are described through state space evolution equations, while the photon-counting SPECT projection data are measured through the observation equations. Activity distribution is then estimated with sub-optimal fixed attenuation parameters, followed by attenuation map reconstruction given these activity estimates. Such coupled estimation processes are iteratively repeated as necessary until convergence. The results obtained from Monte Carlo simulated data, physical phantom, and real SPECT scans demonstrate the improved performance of the proposed method both from visual inspection of the images and a quantitative evaluation, compared to the widely used EM-ML algorithms. The dual estimation framework has the potential to be useful for estimating the attenuation map from emission data only and thus benefit the radioactivity reconstruction.
Advanced technologies for remote sensing imaging applications
Wood, L.L.
1993-06-07
Generating and returning imagery from great distances has been generally associated with national security activities, with emphasis on reliability of system operation. (While the introduction of such capabilities was usually characterized by high levels of innovation, the evolution of such systems has followed the classical track of proliferation of ``standardized items`` expressing ever more incremental technological advances.) Recent focusing of interest on the use of remote imaging systems for commercial and scientific purposes can be expected to induce comparatively rapid advances along the axes of efficiency and technological sophistication, respectively. This paper reviews the most basic reasons for expecting the next decade of advances to dwarf the impressive accomplishments of the past ten years. The impact of these advances clearly will be felt in all major areas of large-scale human endeavor, commercial, military and scientific.
The Pixon Method for Data Compression Image Classification, and Image Reconstruction
NASA Technical Reports Server (NTRS)
Puetter, Richard; Yahil, Amos
2002-01-01
As initially proposed, this program had three goals: (1) continue to develop the highly successful Pixon method for image reconstruction and support other scientist in implementing this technique for their applications; (2) develop image compression techniques based on the Pixon method; and (3) develop artificial intelligence algorithms for image classification based on the Pixon approach for simplifying neural networks. Subsequent to proposal review the scope of the program was greatly reduced and it was decided to investigate the ability of the Pixon method to provide superior restorations of images compressed with standard image compression schemes, specifically JPEG-compressed images.
Adaptive photoacoustic imaging quality optimization with EMD and reconstruction
NASA Astrophysics Data System (ADS)
Guo, Chengwen; Ding, Yao; Yuan, Jie; Xu, Guan; Wang, Xueding; Carson, Paul L.
2016-10-01
Biomedical photoacoustic (PA) signal is characterized with extremely low signal to noise ratio which will yield significant artifacts in photoacoustic tomography (PAT) images. Since PA signals acquired by ultrasound transducers are non-linear and non-stationary, traditional data analysis methods such as Fourier and wavelet method cannot give useful information for further research. In this paper, we introduce an adaptive method to improve the quality of PA imaging based on empirical mode decomposition (EMD) and reconstruction. Data acquired by ultrasound transducers are adaptively decomposed into several intrinsic mode functions (IMFs) after a sifting pre-process. Since noise is randomly distributed in different IMFs, depressing IMFs with more noise while enhancing IMFs with less noise can effectively enhance the quality of reconstructed PAT images. However, searching optimal parameters by means of brute force searching algorithms will cost too much time, which prevent this method from practical use. To find parameters within reasonable time, heuristic algorithms, which are designed for finding good solutions more efficiently when traditional methods are too slow, are adopted in our method. Two of the heuristic algorithms, Simulated Annealing Algorithm, a probabilistic method to approximate the global optimal solution, and Artificial Bee Colony Algorithm, an optimization method inspired by the foraging behavior of bee swarm, are selected to search optimal parameters of IMFs in this paper. The effectiveness of our proposed method is proved both on simulated data and PA signals from real biomedical tissue, which might bear the potential for future clinical PA imaging de-noising.
Four dimensional reconstruction and analysis of plume images
NASA Astrophysics Data System (ADS)
Dhawan, Atam P.; Peck, Charles, III; Disimile, Peter
1991-05-01
A number of methods have been investigated and are under current investigation for monitoring the health of the Space Shuttle Main Engine (SSME). Plume emission analysis has recently emerged as a potential technique for correlating the emission characteristics with the health of an engine. In order to correlate the visual and spectral signatures of the plume emission with the characteristic health monitoring features of the engine, the plume emission data must be acquired, stored, and analyzed in a manner similar to flame emission spectroscopy. The characteristic visual and spectral signatures of the elements vaporized in exhaust plume along with the features related to their temperature, pressure, and velocity can be analyzed once the images of plume emission are effectively acquired, digitized, and stored on a computer. Since the emission image varies with respect to time at a specified planar location, four dimensional visual and spectral analysis need to be performed on the plume emission data. In order to achieve this objective, feasibility research was conducted to digitize, store, analyze, and visualize the images of a subsonic jet in a cross flow. The jet structure was made visible using a direct injection flow visualization technique. The results of time-history based three dimensional reconstruction of the cross sectional images corresponding to a specific planar location of the jet structure are presented. The experimental set-up to acquire such data is described and three dimensional displays of time-history based reconstructions of the jet structure are discussed.
Application of DIRI dynamic infrared imaging in reconstructive surgery
NASA Astrophysics Data System (ADS)
Pawlowski, Marek; Wang, Chengpu; Jin, Feng; Salvitti, Matthew; Tenorio, Xavier
2006-04-01
We have developed the BioScanIR System based on QWIP (Quantum Well Infrared Photodetector). Data collected by this sensor are processed using the DIRI (Dynamic Infrared Imaging) algorithms. The combination of DIRI data processing methods with the unique characteristics of the QWIP sensor permit the creation of a new imaging modality capable of detecting minute changes in temperature at the surface of the tissue and organs associated with blood perfusion due to certain diseases such as cancer, vascular disease and diabetes. The BioScanIR System has been successfully applied in reconstructive surgery to localize donor flap feeding vessels (perforators) during the pre-surgical planning stage. The device is also used in post-surgical monitoring of skin flap perfusion. Since the BioScanIR is mobile; it can be moved to the bedside for such monitoring. In comparison to other modalities, the BioScanIR can localize perforators in a single, 20 seconds scan with definitive results available in minutes. The algorithms used include (FFT) Fast Fourier Transformation, motion artifact correction, spectral analysis and thermal image scaling. The BioScanIR is completely non-invasive and non-toxic, requires no exogenous contrast agents and is free of ionizing radiation. In addition to reconstructive surgery applications, the BioScanIR has shown promise as a useful functional imaging modality in neurosurgery, drug discovery in pre-clinical animal models, wound healing and peripheral vascular disease management.
Image reconstruction methods for the PBX-M pinhole camera.
Holland, A; Powell, E T; Fonck, R J
1991-09-10
We describe two methods that have been used to reconstruct the soft x-ray emission profile of the PBX-M tokamak from the projected images recorded by the PBX-M pinhole camera [Proc. Soc. Photo-Opt. Instrum. Eng. 691, 111 (1986)]. Both methods must accurately represent the shape of the reconstructed profile while also providing a degree of immunity to noise in the data. The first method is a simple least-squares fit to the data. This has the advantage of being fast and small and thus easily implemented on the PDP-11 computer used to control the video digitizer for the pinhole camera. The second method involves the application of a maximum entropy algorithm to an overdetermined system. This has the advantage of allowing the use of a default profile. This profile contains additional knowledge about the plasma shape that can be obtained from equilibrium fits to the external magnetic measurements. Additionally the reconstruction is guaranteed positive, and the fit to the data can be relaxed by specifying both the amount and distribution of noise in the image. The algorithm described has the advantage of being considerably faster for an overdetermined system than the usual Lagrange multiplier approach to finding the maximum entropy solution [J. Opt. Soc. Am. 62, 511 (1972); Rev. Sci. Instrum. 57, 1557 (1986)].
Computation of high overrelaxation parameters in iterative image reconstruction
Schmidlin, P.; Brix, G.; Bellemann, M.E.; Lorenz, W.J.
1996-12-31
Single-projection iterative reconstruction allows the useful of high over relaxation parameters which can be determined by postulating maximum increase of image quality during an iteration step. Further speeding is achieved if maximum increase of quality is postulated during two or more steps. This postulate leads to high initial over relaxation values which drop down during the following steps. The use of parameters obtained by optimization during two steps requires about half the number of iteration steps compared to one-step parameters. Estimates are used to find the optimum parameters rapidly. Optimization within three or more steps provides only minor convergence improvement, but it demonstrates that different combinations of parameters may achieve images of similar quality. It follows that parameters derived from one data set may be widely used for other data. However, the optimizing procedure is expected to be helpful if data from new equipment or with new characteristics have to be reconstructed. Especially in three-dimensional iterative reconstruction, rapid convergence using high initial over relaxation is important.
Simultaneous reconstruction and segmentation for dynamic SPECT imaging
NASA Astrophysics Data System (ADS)
Burger, Martin; Rossmanith, Carolin; Zhang, Xiaoqun
2016-10-01
This work deals with the reconstruction of dynamic images that incorporate characteristic dynamics in certain subregions, as arising for the kinetics of many tracers in emission tomography (SPECT, PET). We make use of a basis function approach for the unknown tracer concentration by assuming that the region of interest can be divided into subregions with spatially constant concentration curves. Applying a regularised variational framework reminiscent of the Chan-Vese model for image segmentation we simultaneously reconstruct both the labelling functions of the subregions as well as the subconcentrations within each region. Our particular focus is on applications in SPECT with the Poisson noise model, resulting in a Kullback-Leibler data fidelity in the variational approach. We present a detailed analysis of the proposed variational model and prove existence of minimisers as well as error estimates. The latter apply to a more general class of problems and generalise existing results in literature since we deal with a nonlinear forward operator and a nonquadratic data fidelity. A computational algorithm based on alternating minimisation and splitting techniques is developed for the solution of the problem and tested on appropriately designed synthetic data sets. For those we compare the results to those of standard EM reconstructions and investigate the effects of Poisson noise in the data.
Improved proton computed tomography by dual modality image reconstruction
Hansen, David C. Bassler, Niels; Petersen, Jørgen Breede Baltzer; Sørensen, Thomas Sangild
2014-03-15
Purpose: Proton computed tomography (CT) is a promising image modality for improving the stopping power estimates and dose calculations for particle therapy. However, the finite range of about 33 cm of water of most commercial proton therapy systems limits the sites that can be scanned from a full 360° rotation. In this paper the authors propose a method to overcome the problem using a dual modality reconstruction (DMR) combining the proton data with a cone-beam x-ray prior. Methods: A Catphan 600 phantom was scanned using a cone beam x-ray CT scanner. A digital replica of the phantom was created in the Monte Carlo code Geant4 and a 360° proton CT scan was simulated, storing the entrance and exit position and momentum vector of every proton. Proton CT images were reconstructed using a varying number of angles from the scan. The proton CT images were reconstructed using a constrained nonlinear conjugate gradient algorithm, minimizing total variation and the x-ray CT prior while remaining consistent with the proton projection data. The proton histories were reconstructed along curved cubic-spline paths. Results: The spatial resolution of the cone beam CT prior was retained for the fully sampled case and the 90° interval case, with the MTF = 0.5 (modulation transfer function) ranging from 5.22 to 5.65 linepairs/cm. In the 45° interval case, the MTF = 0.5 dropped to 3.91 linepairs/cm For the fully sampled DMR, the maximal root mean square (RMS) error was 0.006 in units of relative stopping power. For the limited angle cases the maximal RMS error was 0.18, an almost five-fold improvement over the cone beam CT estimate. Conclusions: Dual modality reconstruction yields the high spatial resolution of cone beam x-ray CT while maintaining the improved stopping power estimation of proton CT. In the case of limited angles, the use of prior image proton CT greatly improves the resolution and stopping power estimate, but does not fully achieve the quality of a 360
Advances in retinal ganglion cell imaging
Balendra, S I; Normando, E M; Bloom, P A; Cordeiro, M F
2015-01-01
Glaucoma is one of the leading causes of blindness worldwide and will affect 79.6 million people worldwide by 2020. It is caused by the progressive loss of retinal ganglion cells (RGCs), predominantly via apoptosis, within the retinal nerve fibre layer and the corresponding loss of axons of the optic nerve head. One of its most devastating features is its late diagnosis and the resulting irreversible visual loss that is often predictable. Current diagnostic tools require significant RGC or functional visual field loss before the threshold for detection of glaucoma may be reached. To propel the efficacy of therapeutics in glaucoma, an earlier diagnostic tool is required. Recent advances in retinal imaging, including optical coherence tomography, confocal scanning laser ophthalmoscopy, and adaptive optics, have propelled both glaucoma research and clinical diagnostics and therapeutics. However, an ideal imaging technique to diagnose and monitor glaucoma would image RGCs non-invasively with high specificity and sensitivity in vivo. It may confirm the presence of healthy RGCs, such as in transgenic models or retrograde labelling, or detect subtle changes in the number of unhealthy or apoptotic RGCs, such as detection of apoptosing retinal cells (DARC). Although many of these advances have not yet been introduced to the clinical arena, their successes in animal studies are enthralling. This review will illustrate the challenges of imaging RGCs, the main retinal imaging modalities, the in vivo techniques to augment these as specific RGC-imaging tools and their potential for translation to the glaucoma clinic. PMID:26293138
Surface reconstruction from structured-light images for radiation therapy
NASA Astrophysics Data System (ADS)
Peshko, Olesya; Anand, Christopher K.; Terlaky, Tamás
2005-09-01
To design and deliver proper radiation treatment for cancer patients, knowledge of the body's surface in the affected area is required. Currently, surface information is obtained by using a manually operated tracer. The drawbacks of this contact method include slow operation, and errors in repositioning the patient in an x-ray machine. Utilization of MRI or CT is also possible but expensive. We propose a non-contact, quick, inexpensive method to reconstruct the surface. In our non-contact method, a mask with transparent circular coloured spots and a black background, and an incoherent light source are used to create structured-light images. Colour coding is necessary to establish the correspondence between the projected and the observed patterns, which is essential for surface reconstruction. The deformed light pattern is photographed by an offset camera and analyzed. First, noise reduction is performed because images are noisy due to the low-light conditions and low sensitivity of an off-the-shelf camera. Then, pattern elements (light elliptical spots) are found in the image. We use an inverse polynomial to model the intensity of a light spot, which results in a non-convex, least-squares optimization problem. Next, spots are assigned to a grid according to their colours and location, and errors are corrected using the relative position of the spots. Finally, spatial coordinates of the surface points are computed and surface reconstruction is performed. The described algorithms are implemented as a MATLAB package, which converts the acquired images into a three-dimensional surface. The developed system is inexpensive, and it can easily be mounted on an x-ray machine. The software package can run on any standard PC.
Image and Data-analysis Tools For Paleoclimatic Reconstructions
NASA Astrophysics Data System (ADS)
Pozzi, M.
It comes here proposed a directory of instruments and computer science resources chosen in order to resolve the problematic ones that regard the paleoclimatic recon- structions. They will come discussed in particular the following points: 1) Numerical analysis of paleo-data (fossils abundances, species analyses, isotopic signals, chemical-physical parameters, biological data): a) statistical analyses (uni- variate, diversity, rarefaction, correlation, ANOVA, F and T tests, Chi^2) b) multidi- mensional analyses (principal components, corrispondence, cluster analysis, seriation, discriminant, autocorrelation, spectral analysis) neural analyses (backpropagation net, kohonen feature map, hopfield net genetic algorithms) 2) Graphical analysis (visu- alization tools) of paleo-data (quantitative and qualitative fossils abundances, species analyses, isotopic signals, chemical-physical parameters): a) 2-D data analyses (graph, histogram, ternary, survivorship) b) 3-D data analyses (direct volume rendering, iso- surfaces, segmentation, surface reconstruction, surface simplification,generation of tetrahedral grids). 3) Quantitative and qualitative digital image analysis (macro and microfossils image analysis, Scanning Electron Microscope. and Optical Polarized Microscope images capture and analysis, morphometric data analysis, 3-D reconstruc- tions): a) 2D image analysis (correction of image defects, enhancement of image de- tail, converting texture and directionality to grey scale or colour differences, visual enhancement using pseudo-colour, pseudo-3D, thresholding of image features, binary image processing, measurements, stereological measurements, measuring features on a white background) b) 3D image analysis (basic stereological procedures, two dimen- sional structures; area fraction from the point count, volume fraction from the point count, three dimensional structures: surface area and the line intercept count, three dimensional microstructures; line length and the
Adaptive Kaczmarz Method for Image Reconstruction in Electrical Impedance Tomography
Li, Taoran; Kao, Tzu-Jen; Isaacson, David; Newell, Jonathan C.; Saulnier, Gary J.
2013-01-01
We present an adaptive Kaczmarz method for solving the inverse problem in electrical impedance tomography and determining the conductivity distribution inside an object from electrical measurements made on the surface. To best characterize an unknown conductivity distribution and avoid inverting the Jacobian-related term JTJ which could be expensive in terms of computation cost and memory in large scale problems, we propose solving the inverse problem by applying the optimal current patterns for distinguishing the actual conductivity from the conductivity estimate between each iteration of the block Kaczmarz algorithm. With a novel subset scheme, the memory-efficient reconstruction algorithm which appropriately combines the optimal current pattern generation with the Kaczmarz method can produce more accurate and stable solutions adaptively as compared to traditional Kaczmarz and Gauss-Newton type methods. Choices of initial current pattern estimates are discussed in the paper. Several reconstruction image metrics are used to quantitatively evaluate the performance of the simulation results. PMID:23718952
3D x-ray reconstruction using lightfield imaging
NASA Astrophysics Data System (ADS)
Saha, Sajib; Tahtali, Murat; Lambert, Andrew; Pickering, Mark R.
2014-09-01
Existing Computed Tomography (CT) systems require full 360° rotation projections. Using the principles of lightfield imaging, only 4 projections under ideal conditions can be sufficient when the object is illuminated with multiple-point Xray sources. The concept was presented in a previous work with synthetically sampled data from a synthetic phantom. Application to real data requires precise calibration of the physical set up. This current work presents the calibration procedures along with experimental findings for the reconstruction of a physical 3D phantom consisting of simple geometric shapes. The crucial part of this process is to determine the effective distances of the X-ray paths, which are not possible or very difficult by direct measurements. Instead, they are calculated by tracking the positions of fiducial markers under prescribed source and object movements. Iterative algorithms are used for the reconstruction. Customized backprojection is used to ensure better initial guess for the iterative algorithms to start with.
Single face image reconstruction for super resolution using support vector regression
NASA Astrophysics Data System (ADS)
Lin, Haijie; Yuan, Qiping; Chen, Zhihong; Yang, Xiaoping
2016-10-01
In recent years, we have witnessed the prosperity of the face image super-resolution (SR) reconstruction, especially the learning-based technology. In this paper, a novel super-resolution face reconstruction framework based on support vector regression (SVR) about a single image is presented. Given some input data, SVR can precisely predict output class labels. We regard the SR problem as the estimation of pixel labels in its high resolution version. It's effective to put local binary pattern (LBP) codes and partial pixels into input vectors during training models in our work, and models are learnt from a set of high and low resolution face image. By optimizing vector pairs which are used for learning model, the final reconstructed results were advanced. Especially to deserve to be mentioned, we can get more high frequency information by exploiting the cyclical scan actions in the process of both training and prediction. A large number of experimental data and visual observation have shown that our method outperforms bicubic interpolation and some stateof- the-art super-resolution algorithms.
General Structure of Regularization Procedures in Image Reconstruction
NASA Astrophysics Data System (ADS)
Titterington, D. M.
1985-03-01
Regularization procedures are portrayed as compromises between the conflicting aims of fidelity with the observed image and perfect smoothness. The selection of an estimated image involves the choice of a prescription, indicating the manner of smoothing, and of a smoothing parameter, which defines the degree of smoothing. Prescriptions of the minimum-penalized- distance type are considered and are shown to be equivalent to maximum-penalized-smoothness prescriptions. These include, therefore, constrained least-squares and constrained maximum entropy methods. The formal link with Bayesian statistical analysis is pointed out. Two important methods of choosing the degree of smoothing are described, one based on criteria of consistency with the data and one based on minimizing a risk function. The latter includes minimum mean-squared error criteria. Although the maximum entropy method has some practical advantages, there seems no case for it to hold a special place on philosophical grounds, in the context of image reconstruction.
Spectral image reconstruction by a tunable LED illumination
NASA Astrophysics Data System (ADS)
Lin, Meng-Chieh; Tsai, Chen-Wei; Tien, Chung-Hao
2013-09-01
Spectral reflectance estimation of an object via low-dimensional snapshot requires both image acquisition and a post numerical estimation analysis. In this study, we set up a system incorporating a homemade cluster of LEDs with spectral modulation for scene illumination, and a multi-channel CCD to acquire multichannel images by means of fully digital process. Principal component analysis (PCA) and pseudo inverse transformation were used to reconstruct the spectral reflectance in a constrained training set, such as Munsell and Macbeth Color Checker. The average reflectance spectral RMS error from 34 patches of a standard color checker were 0.234. The purpose is to investigate the use of system in conjunction with the imaging analysis for industry or medical inspection in a fast and acceptable accuracy, where the approach was preliminary validated.
Image reconstruction algorithm for two-phase flow electrical capacitance tomography system
NASA Astrophysics Data System (ADS)
Zheng, Guibin; Chen, Deyun; Yu, Xiaoyang
2002-10-01
A new image reconstruction algorithm based on the genetic algorithms is proposed for two-component flow electrical capacitance system in this paper. Two times reconstructions are performed in once tomography, the first step is reconstructing the image of fewer pixel blocks and the second step is reconstructing image using genetic algorithm with the result of the first step is used in population initialization in order to improve the speed and accuracy of genetic tomography. With this method, cross-section image of two-component flow can be reconstructed with better quality and better accuracy in component concentration than SIRT algorithm.
Computational and design methods for advanced imaging
NASA Astrophysics Data System (ADS)
Birch, Gabriel C.
This dissertation merges the optical design and computational aspects of imaging systems to create novel devices that solve engineering problems in optical science and attempts to expand the solution space available to the optical designer. This dissertation is divided into two parts: the first discusses a new active illumination depth sensing modality, while the second part discusses a passive illumination system called plenoptic, or lightfield, imaging. The new depth sensing modality introduced in part one is called depth through controlled aberration. This technique illuminates a target with a known, aberrated projected pattern and takes an image using a traditional, unmodified imaging system. Knowing how the added aberration in the projected pattern changes as a function of depth, we are able to quantitatively determine depth of a series of points from the camera. A major advantage this method permits is the ability for illumination and imaging axes to be coincident. Plenoptic cameras capture both spatial and angular data simultaneously. This dissertation present a new set of parameters that permit the design and comparison of plenoptic devices outside the traditionally published plenoptic 1.0 and plenoptic 2.0 configurations. Additionally, a series of engineering advancements are presented, including full system raytraces of raw plenoptic images, Zernike compression techniques of raw image files, and non-uniform lenslet arrays to compensate for plenoptic system aberrations. Finally, a new snapshot imaging spectrometer is proposed based off the plenoptic configuration.
A generalized Fourier penalty in prior-image-based reconstruction for cross-platform imaging
NASA Astrophysics Data System (ADS)
Pourmorteza, A.; Siewerdsen, J. H.; Stayman, J. W.
2016-03-01
Sequential CT studies present an excellent opportunity to apply prior-image-based reconstruction (PIBR) methods that leverage high-fidelity prior imaging studies to improve image quality and/or reduce x-ray exposure in subsequent studies. One major obstacle in using PIBR is that the initial and subsequent studies are often performed on different scanners (e.g. diagnostic CT followed by CBCT for interventional guidance); this results in mismatch in attenuation values due to hardware and software differences. While improved artifact correction techniques can potentially mitigate such differences, the correction is often incomplete. Here, we present an alternate strategy where the PIBR itself is used to mitigate these differences. We define a new penalty for the previously introduced PIBR called Reconstruction of Difference (RoD). RoD differs from many other PIBRs in that it reconstructs only changes in the anatomy (vs. reconstructing the current anatomy). Direct regularization of the difference image in RoD provides an opportunity to selectively penalize spatial frequencies of the difference image (e.g. low frequency differences associated with attenuation offsets and shading artifacts) without interfering with the variations in unchanged background image. We leverage this flexibility and introduce a novel regularization strategy using a generalized Fourier penalty within the RoD framework and develop the modified reconstruction algorithm. We evaluate the performance of the new approach in both simulation studies and in physical CBCT test-bench data. We find that generalized Fourier penalty can be highly effective in reducing low-frequency x-ray artifacts through selective suppression of spatial frequencies in the reconstructed difference image.
A hybrid ECT image reconstruction based on Tikhonov regularization theory and SIRT algorithm
NASA Astrophysics Data System (ADS)
Lei, Wang; Xiaotong, Du; Xiaoyin, Shao
2007-07-01
Electrical Capacitance Tomography (ECT) image reconstruction is a key problem that is not well solved due to the influence of soft-field in the ECT system. In this paper, a new hybrid ECT image reconstruction algorithm is proposed by combining Tikhonov regularization theory and Simultaneous Reconstruction Technique (SIRT) algorithm. Tikhonov regularization theory is used to solve ill-posed image reconstruction problem to obtain a stable original reconstructed image in the region of the optimized solution aggregate. Then, SIRT algorithm is used to improve the quality of the final reconstructed image. In order to satisfy the industrial requirement of real-time computation, the proposed algorithm is further been modified to improve the calculation speed. Test results show that the quality of reconstructed image is better than that of the well-known Filter Linear Back Projection (FLBP) algorithm and the time consumption of the new algorithm is less than 0.1 second that satisfies the online requirements.
3D Sorghum Reconstructions from Depth Images Identify QTL Regulating Shoot Architecture1[OPEN
2016-01-01
Dissecting the genetic basis of complex traits is aided by frequent and nondestructive measurements. Advances in range imaging technologies enable the rapid acquisition of three-dimensional (3D) data from an imaged scene. A depth camera was used to acquire images of sorghum (Sorghum bicolor), an important grain, forage, and bioenergy crop, at multiple developmental time points from a greenhouse-grown recombinant inbred line population. A semiautomated software pipeline was developed and used to generate segmented, 3D plant reconstructions from the images. Automated measurements made from 3D plant reconstructions identified quantitative trait loci for standard measures of shoot architecture, such as shoot height, leaf angle, and leaf length, and for novel composite traits, such as shoot compactness. The phenotypic variability associated with some of the quantitative trait loci displayed differences in temporal prevalence; for example, alleles closely linked with the sorghum Dwarf3 gene, an auxin transporter and pleiotropic regulator of both leaf inclination angle and shoot height, influence leaf angle prior to an effect on shoot height. Furthermore, variability in composite phenotypes that measure overall shoot architecture, such as shoot compactness, is regulated by loci underlying component phenotypes like leaf angle. As such, depth imaging is an economical and rapid method to acquire shoot architecture phenotypes in agriculturally important plants like sorghum to study the genetic basis of complex traits. PMID:27528244
Terahertz Tools Advance Imaging for Security, Industry
NASA Technical Reports Server (NTRS)
2010-01-01
Picometrix, a wholly owned subsidiary of Advanced Photonix Inc. (API), of Ann Arbor, Michigan, invented the world s first commercial terahertz system. The company improved the portability and capabilities of their systems through Small Business Innovation Research (SBIR) agreements with Langley Research Center to provide terahertz imaging capabilities for inspecting the space shuttle external tanks and orbiters. Now API s systems make use of the unique imaging capacity of terahertz radiation on manufacturing floors, for thickness measurements of coatings, pharmaceutical tablet production, and even art conservation.
Rheumatoid Arthritis Revisited - Advanced Imaging Review.
Vyas, Surabhi; Bhalla, Ashu Seith; Ranjan, Piyush; Kumar, Sandeep; Kumar, Uma; Gupta, Arun Kumar
2016-01-01
Rheumatoid Arthritis (RA) is a multisystem disorder, which causes significant morbidity. An early diagnosis of RA is essential to prevent the development of irreversible bone and joint changes. The disease has characteristic clinical features, but an early evaluation of the quantum of disease may be difficult with plain radiography alone. Recent developments in the imaging of RA have contributed significantly to an early diagnosis of the disease. In this article, we review the role and current status of various imaging modalities including recent advances in the evaluation and follow-up of early RA.
Rheumatoid Arthritis Revisited – Advanced Imaging Review
Vyas, Surabhi; Bhalla, Ashu Seith; Ranjan, Piyush; Kumar, Sandeep; Kumar, Uma; Gupta, Arun Kumar
2016-01-01
Summary Rheumatoid Arthritis (RA) is a multisystem disorder, which causes significant morbidity. An early diagnosis of RA is essential to prevent the development of irreversible bone and joint changes. The disease has characteristic clinical features, but an early evaluation of the quantum of disease may be difficult with plain radiography alone. Recent developments in the imaging of RA have contributed significantly to an early diagnosis of the disease. In this article, we review the role and current status of various imaging modalities including recent advances in the evaluation and follow-up of early RA. PMID:28105245
Single particle maximum likelihood reconstruction from superresolution microscopy images.
Verdier, Timothée; Gunzenhauser, Julia; Manley, Suliana; Castelnovo, Martin
2017-01-01
Point localization superresolution microscopy enables fluorescently tagged molecules to be imaged beyond the optical diffraction limit, reaching single molecule localization precisions down to a few nanometers. For small objects whose sizes are few times this precision, localization uncertainty prevents the straightforward extraction of a structural model from the reconstructed images. We demonstrate in the present work that this limitation can be overcome at the single particle level, requiring no particle averaging, by using a maximum likelihood reconstruction (MLR) method perfectly suited to the stochastic nature of such superresolution imaging. We validate this method by extracting structural information from both simulated and experimental PALM data of immature virus-like particles of the Human Immunodeficiency Virus (HIV-1). MLR allows us to measure the radii of individual viruses with precision of a few nanometers and confirms the incomplete closure of the viral protein lattice. The quantitative results of our analysis are consistent with previous cryoelectron microscopy characterizations. Our study establishes the framework for a method that can be broadly applied to PALM data to determine the structural parameters for an existing structural model, and is particularly well suited to heterogeneous features due to its single particle implementation.
Adaptive reconstruction of millimeter-wave radiometric images.
Sarkis, Michel
2012-09-01
We present a robust method to reconstruct a millimeter-wave image from a passive sensor. The method operates directly on the raw samples from the radiometer. It allocates for each pixel to be estimated a patch in the space formed by all the raw samples of the image. It then estimates the noise in the patch by measuring some distances that reflect how far the samples are from forming a piecewise smooth surface. It then allocates a weight for each sample that defines its contribution in the pixel reconstruction. This is done via a smoothing Kernel that enforces the distances to have a piecewise smooth variation inside the patch. Results on real datasets show that our scheme leads to more contrast and less noise and the shape of an object is better preserved in a constructed image compared to state-of-the-art schemes. The proposed scheme produces better results even with low integration times, i.e., 10% of the total integration time used in our experiments.
A Combined Reconstruction Algorithm for Limited-View Multi-Element Photoacoustic Imaging
NASA Astrophysics Data System (ADS)
Yang, Di-Wu; Xing, Da; Zhao, Xue-Hui; Pan, Chang-Ning; Fang, Jian-Shu
2010-05-01
We present a photoacoustic imaging system with a linear transducer array scanning in limited-view fields and develop a combined reconstruction algorithm, which is a combination of the limited-field filtered back projection (LFBP) algorithm and the simultaneous iterative reconstruction technique (SIRT) algorithm, to reconstruct the optical absorption distribution. In this algorithm, the LFBP algorithm is exploited to reconstruct the original photoacoustic image, and then the SIRT algorithm is used to improve the quality of the final reconstructed photoacoustic image. Numerical simulations with calculated incomplete data validate the reliability of this algorithm and the reconstructed experimental results further demonstrate that the combined reconstruction algorithm effectively reduces the artifacts and blurs and yields better quality of reconstruction image than that with the LFBP algorithm.
Maiti, Abhik; Chakravarty, Debashish
2016-01-01
3D reconstruction of geo-objects from their digital images is a time-efficient and convenient way of studying the structural features of the object being modelled. This paper presents a 3D reconstruction methodology which can be used to generate photo-realistic 3D watertight surface of different irregular shaped objects, from digital image sequences of the objects. The 3D reconstruction approach described here is robust, simplistic and can be readily used in reconstructing watertight 3D surface of any object from its digital image sequence. Here, digital images of different objects are used to build sparse, followed by dense 3D point clouds of the objects. These image-obtained point clouds are then used for generation of photo-realistic 3D surfaces, using different surface reconstruction algorithms such as Poisson reconstruction and Ball-pivoting algorithm. Different control parameters of these algorithms are identified, which affect the quality and computation time of the reconstructed 3D surface. The effects of these control parameters in generation of 3D surface from point clouds of different density are studied. It is shown that the reconstructed surface quality of Poisson reconstruction depends on Samples per node (SN) significantly, greater SN values resulting in better quality surfaces. Also, the quality of the 3D surface generated using Ball-Pivoting algorithm is found to be highly depend upon Clustering radius and Angle threshold values. The results obtained from this study give the readers of the article a valuable insight into the effects of different control parameters on determining the reconstructed surface quality.
Advanced magnetic resonance imaging of neurodegenerative diseases.
Agosta, Federica; Galantucci, Sebastiano; Filippi, Massimo
2017-01-01
Magnetic resonance imaging (MRI) is playing an increasingly important role in the study of neurodegenerative diseases, delineating the structural and functional alterations determined by these conditions. Advanced MRI techniques are of special interest for their potential to characterize the signature of each neurodegenerative condition and aid both the diagnostic process and the monitoring of disease progression. This aspect will become crucial when disease-modifying (personalized) therapies will be established. MRI techniques are very diverse and go from the visual inspection of MRI scans to more complex approaches, such as manual and automatic volume measurements, diffusion tensor MRI, and functional MRI. All these techniques allow us to investigate the different features of neurodegeneration. In this review, we summarize the most recent advances concerning the use of MRI in some of the most important neurodegenerative conditions, putting an emphasis on the advanced techniques.
Fast Multigrid Techniques in Total Variation-Based Image Reconstruction
NASA Technical Reports Server (NTRS)
Oman, Mary Ellen
1996-01-01
Existing multigrid techniques are used to effect an efficient method for reconstructing an image from noisy, blurred data. Total Variation minimization yields a nonlinear integro-differential equation which, when discretized using cell-centered finite differences, yields a full matrix equation. A fixed point iteration is applied with the intermediate matrix equations solved via a preconditioned conjugate gradient method which utilizes multi-level quadrature (due to Brandt and Lubrecht) to apply the integral operator and a multigrid scheme (due to Ewing and Shen) to invert the differential operator. With effective preconditioning, the method presented seems to require Omicron(n) operations. Numerical results are given for a two-dimensional example.
Image reconstruction by the speckle-masking method.
Weigelt, G; Wirnitzer, B
1983-07-01
Speckle masking is a method for reconstructing high-resolution images of general astronomical objects from stellar speckle interferograms. In speckle masking no unresolvable star is required within the isoplanatic patch of the object. We present digital applications of speckle masking to close spectroscopic double stars. The speckle interferograms were recorded with the European Southern Observatory's 3.6-m telescope. Diffraction-limited resolution (0.03 arc see) was achieved, which is about 30 times higher than the resolution of conventional astrophotography.
Imaging Tumor Hypoxia to Advance Radiation Oncology
Lee, Chen-Ting; Boss, Mary-Keara
2014-01-01
Abstract Significance: Most solid tumors contain regions of low oxygenation or hypoxia. Tumor hypoxia has been associated with a poor clinical outcome and plays a critical role in tumor radioresistance. Recent Advances: Two main types of hypoxia exist in the tumor microenvironment: chronic and cycling hypoxia. Chronic hypoxia results from the limited diffusion distance of oxygen, and cycling hypoxia primarily results from the variation in microvessel red blood cell flux and temporary disturbances in perfusion. Chronic hypoxia may cause either tumor progression or regressive effects depending on the tumor model. However, there is a general trend toward the development of a more aggressive phenotype after cycling hypoxia. With advanced hypoxia imaging techniques, spatiotemporal characteristics of tumor hypoxia and the changes to the tumor microenvironment can be analyzed. Critical Issues: In this review, we focus on the biological and clinical consequences of chronic and cycling hypoxia on radiation treatment. We also discuss the advanced non-invasive imaging techniques that have been developed to detect and monitor tumor hypoxia in preclinical and clinical studies. Future Directions: A better understanding of the mechanisms of tumor hypoxia with non-invasive imaging will provide a basis for improved radiation therapeutic practices. Antioxid. Redox Signal. 21, 313–337. PMID:24329000
Image reconstruction for coherent imaging for space surveillance and directed energy applications
NASA Astrophysics Data System (ADS)
Holmes, Richard; Gudimetla, V. S. Rao
2016-09-01
Imaging of distant objects in a terrestrial environment involves propagation of light through significant turbulence. Conventional methods for imaging for these applications are radar or focal plane imaging. Both of these methods have limitations, such as object rotation rate and post-processing. A different class of imaging approaches involving coherent illumination has several advantages (a) reduced sensitivity to illumination conditions, (b) reduced sensitivity to object rotation, (c) the use of arrayed receivers that are lighter and lower in cost, and (d) snapshot reconstruction of aberrated images with just one or a few frames. These advantages must be balanced by the challenges of speckle noise in the image reconstructions, and more difficult algorithms. Coherent pupil-plane and focal-plane techniques are investigated for image formation. The associated algorithms include root-reconstruction techniques, phase smoothing methods, polynomial fit approaches, blind iterative deconvolution, and multi-frame blind deconvolution (MFBD). These techniques and algorithms are surveyed for speed and quality of image formation.
Zhang, Xuezhu; Zhou, Jian; Cherry, Simon R; Badawi, Ramsey D; Qi, Jinyi
2017-03-21
The EXPLORER project aims to build a 2 meter long total-body PET scanner, which will provide extremely high sensitivity for imaging the entire human body. It will possess a range of capabilities currently unavailable to state-of-the-art clinical PET scanners with a limited axial field-of-view. The huge number of lines-of-response (LORs) of the EXPLORER poses a challenge to the data handling and image reconstruction. The objective of this study is to develop a quantitative image reconstruction method for the EXPLORER and compare its performance with current whole-body scanners. Fully 3D image reconstruction was performed using time-of-flight list-mode data with parallel computation. To recover the resolution loss caused by the parallax error between crystal pairs at a large axial ring difference or transaxial radial offset, we applied an image domain resolution model estimated from point source data. To evaluate the image quality, we conducted computer simulations using the SimSET Monte-Carlo toolkit and XCAT 2.0 anthropomorphic phantom to mimic a 20 min whole-body PET scan with an injection of 25 MBq (18)F-FDG. We compare the performance of the EXPLORER with a current clinical scanner that has an axial FOV of 22 cm. The comparison results demonstrated superior image quality from the EXPLORER with a 6.9-fold reduction in noise standard deviation comparing with multi-bed imaging using the clinical scanner.
Quantitative image reconstruction for total-body PET imaging using the 2-meter long EXPLORER scanner
NASA Astrophysics Data System (ADS)
Zhang, Xuezhu; Zhou, Jian; Cherry, Simon R.; Badawi, Ramsey D.; Qi, Jinyi
2017-03-01
The EXPLORER project aims to build a 2 meter long total-body PET scanner, which will provide extremely high sensitivity for imaging the entire human body. It will possess a range of capabilities currently unavailable to state-of-the-art clinical PET scanners with a limited axial field-of-view. The huge number of lines-of-response (LORs) of the EXPLORER poses a challenge to the data handling and image reconstruction. The objective of this study is to develop a quantitative image reconstruction method for the EXPLORER and compare its performance with current whole-body scanners. Fully 3D image reconstruction was performed using time-of-flight list-mode data with parallel computation. To recover the resolution loss caused by the parallax error between crystal pairs at a large axial ring difference or transaxial radial offset, we applied an image domain resolution model estimated from point source data. To evaluate the image quality, we conducted computer simulations using the SimSET Monte–Carlo toolkit and XCAT 2.0 anthropomorphic phantom to mimic a 20 min whole-body PET scan with an injection of 25 MBq 18F-FDG. We compare the performance of the EXPLORER with a current clinical scanner that has an axial FOV of 22 cm. The comparison results demonstrated superior image quality from the EXPLORER with a 6.9-fold reduction in noise standard deviation comparing with multi-bed imaging using the clinical scanner.
Jang, Jae-Young; Cho, Myungjin
2015-06-20
We propose a new approach for depth conversion of three-dimensional (3D) reconstruction from pseudoscopic to orthoscopic real images in resolution priority integral imaging. In integral imaging, depth of field is recorded in an elemental image array. In the proposed method, the depth information is converted by a 180° rotation of each elemental image in an elemental image array based on a reference point of conversion, which is caused by a reference point of object space. Orthoscopic real images can be reconstructed in 3D space by using the depth conversion of an elemental image array. The feasibility of the proposed method has been confirmed through preliminary experiments as well as ray optical analysis.
Exogenous specific fluorescence marker location reconstruction using surface fluorescence imaging
NASA Astrophysics Data System (ADS)
Avital, Garashi; Gannot, Israel; Chernomordik, Victor V.; Gannot, Gallya; Gandjbakhche, Amir H.
2003-07-01
Diseased tissue may be specifically marked by an exogenous fluorescent marker and then, following laser activation of the marker, optically and non-invasively detected through fluorescence imaging. Interaction of a fluorophore, conjugated to an appropriate antibody, with the antigen expressed by the diseased tissue, can indicate the presence of a specific disease. Using an optical detection system and a reconstruction algorithm, we were able to determine the fluorophore"s position in the tissue. We present 3D reconstructions of the location of a fluorescent marker, FITC, in the tongues of mice. One group of BALB/c mice was injected with squamous cell carcinoma (SqCC) cell line to the tongue, while another group served as the control. After tumor development, the mice"s tongues were injected with FITC conjugated to anti-CD3 and anti-CD 19 antibodies. An Argon laser excited the marker at 488 nm while a high precision fluorescent camera collected the emitted fluorescence. Measurements were performed with the fluorescent marker embedded at various simulated depths. The simulation was performed using agarose-based gel slabs applied to the tongue as tissue-like phantoms. A biopsy was taken from every mouse after the procedure and the excised tissue was histologically evaluated. We reconstruct the fluorescent marker"s location in 3D using an algorithm based on the random walk theory.
Wang, Kun; Huang, Chao; Kao, Yu-Jiun; Chou, Cheng-Ying; Oraevsky, Alexander A.; Anastasio, Mark A.
2013-01-01
Purpose: Optoacoustic tomography (OAT) is inherently a three-dimensional (3D) inverse problem. However, most studies of OAT image reconstruction still employ two-dimensional imaging models. One important reason is because 3D image reconstruction is computationally burdensome. The aim of this work is to accelerate existing image reconstruction algorithms for 3D OAT by use of parallel programming techniques. Methods: Parallelization strategies are proposed to accelerate a filtered backprojection (FBP) algorithm and two different pairs of projection/backprojection operations that correspond to two different numerical imaging models. The algorithms are designed to fully exploit the parallel computing power of graphics processing units (GPUs). In order to evaluate the parallelization strategies for the projection/backprojection pairs, an iterative image reconstruction algorithm is implemented. Computer simulation and experimental studies are conducted to investigate the computational efficiency and numerical accuracy of the developed algorithms. Results: The GPU implementations improve the computational efficiency by factors of 1000, 125, and 250 for the FBP algorithm and the two pairs of projection/backprojection operators, respectively. Accurate images are reconstructed by use of the FBP and iterative image reconstruction algorithms from both computer-simulated and experimental data. Conclusions: Parallelization strategies for 3D OAT image reconstruction are proposed for the first time. These GPU-based implementations significantly reduce the computational time for 3D image reconstruction, complementing our earlier work on 3D OAT iterative image reconstruction. PMID:23387778
Bayesian Super-Resolved Surface Reconstruction From Multiple Images
NASA Technical Reports Server (NTRS)
Smelyanskiy, V. N.; Cheesman, P.; Maluf, D. A.; Morris, R. D.; Swanson, Keith (Technical Monitor)
1999-01-01
Bayesian inference has been wed successfully for many problems where the aim is to infer the parameters of a model of interest. In this paper we formulate the three dimensional reconstruction problem as the problem of inferring the parameters of a surface model from image data, and show how Bayesian methods can be used to estimate the parameters of this model given the image data. Thus we recover the three dimensional description of the scene. This approach also gives great flexibility. We can specify the geometrical properties of the model to suit our purpose, and can also use different models for how the surface reflects the light incident upon it. In common with other Bayesian inference problems, the estimation methodology requires that we can simulate the data that would have been recoded for any values of the model parameters. In this application this means that if we have image data we must be able to render the surface model. However it also means that we can infer the parameters of a model whose resolution can be chosen irrespective of the resolution of the images, and may be super-resolved. We present results of the inference of surface models from simulated aerial photographs for the case of super-resolution, where many surface elements project into a single pixel in the low-resolution images.
A High Precision Terahertz Wave Image Reconstruction Algorithm
Guo, Qijia; Chang, Tianying; Geng, Guoshuai; Jia, Chengyan; Cui, Hong-Liang
2016-01-01
With the development of terahertz (THz) technology, the applications of this spectrum have become increasingly wide-ranging, in areas such as non-destructive testing, security applications and medical scanning, in which one of the most important methods is imaging. Unlike remote sensing applications, THz imaging features sources of array elements that are almost always supposed to be spherical wave radiators, including single antennae. As such, well-developed methodologies such as Range-Doppler Algorithm (RDA) are not directly applicable in such near-range situations. The Back Projection Algorithm (BPA) can provide products of high precision at the the cost of a high computational burden, while the Range Migration Algorithm (RMA) sacrifices the quality of images for efficiency. The Phase-shift Migration Algorithm (PMA) is a good alternative, the features of which combine both of the classical algorithms mentioned above. In this research, it is used for mechanical scanning, and is extended to array imaging for the first time. In addition, the performances of PMA are studied in detail in contrast to BPA and RMA. It is demonstrated in our simulations and experiments described herein that the algorithm can reconstruct images with high precision. PMID:27455269
Poisson image reconstruction with Hessian Schatten-norm regularization.
Lefkimmiatis, Stamatios; Unser, Michael
2013-11-01
Poisson inverse problems arise in many modern imaging applications, including biomedical and astronomical ones. The main challenge is to obtain an estimate of the underlying image from a set of measurements degraded by a linear operator and further corrupted by Poisson noise. In this paper, we propose an efficient framework for Poisson image reconstruction, under a regularization approach, which depends on matrix-valued regularization operators. In particular, the employed regularizers involve the Hessian as the regularization operator and Schatten matrix norms as the potential functions. For the solution of the problem, we propose two optimization algorithms that are specifically tailored to the Poisson nature of the noise. These algorithms are based on an augmented-Lagrangian formulation of the problem and correspond to two variants of the alternating direction method of multipliers. Further, we derive a link that relates the proximal map of an l(p) norm with the proximal map of a Schatten matrix norm of order p. This link plays a key role in the development of one of the proposed algorithms. Finally, we provide experimental results on natural and biological images for the task of Poisson image deblurring and demonstrate the practical relevance and effectiveness of the proposed framework.
Supercontinent Reconstructions as of 2014: Advances and Challenges
NASA Astrophysics Data System (ADS)
Evans, D. A.
2014-12-01
Dynamic models of supercontinent assembly and dispersal should strive to emulate kinematic histories obtained from the geologic record; but the breakup of Pangea and initial assembly of Amasia, at 200-0 Ma, are the only phases of an alleged "cycle" that are known with good precision. Pre-Mesozoic reconstructions (i.e., 95% of Earth history) must rely on typically non-unique geological comparisons between extant continental fragments, guided by sparse paleomagnetic data from those blocks. Pangea assembly and evolution (500-200 Ma) are known in broad terms, but uncertainties of order 10-20° arc distance are common. The transition from Rodinia breakup to incipient Pangea assembly (Gondwana amalgamation; 700-500 Ma) is the least understood interval of global kinematics from the past two billion years, with uncertainties occasionally nearing 90°, and raising the geophysical specters of non-dipole geomagnetic field geometries and/or rapid true polar wander oscillations. Various Rodinia reconstructions (900-700 Ma) share many common features, but they are upheld by a rather lean dataset, by comparison to Pangea. The assembly stage of Rodinia (1300-900 Ma) derives its substantial uncertainty from questions concerning not only the supercontinent's end-configuration as noted above, but also the reconstruction of its predecessor Nuna (1600-1300 Ma), which has made much recent progress though likewise based on relatively limited data. Nuna's assembly phase (2000-1600 Ma) is a fruitful frontier for global reconstruction hypotheses at their earliest stages of development. Older intervals (2700-2000 Ma) will likely soon yield some insights into whether an earlier possible supercontinent, Kenorland, may have existed; but gaining even a broad outline of its reconstruction will be hard-fought, probably over decades, against dwindling Archean preservation. Today's geodynamic modelers of supercontinental episodicity should consider these noteworthy uncertainties as they assess
Reconstruction of mechanically recorded sound by image processing
Fadeyev, Vitaliy; Haber, Carl
2003-03-26
Audio information stored in the undulations of grooves in a medium such as a phonograph record may be reconstructed, with no or minimal contact, by measuring the groove shape using precision metrology methods and digital image processing. The effects of damage, wear, and contamination may be compensated, in many cases, through image processing and analysis methods. The speed and data handling capacity of available computing hardware make this approach practical. Various aspects of this approach are discussed. A feasibility test is reported which used a general purpose optical metrology system to study a 50 year old 78 r.p.m. phonograph record. Comparisons are presented with stylus playback of the record and with a digitally re-mastered version of the original magnetic recording. A more extensive implementation of this approach, with dedicated hardware and software, is considered.
Image reconstruction with acoustic radiation force induced shear waves
NASA Astrophysics Data System (ADS)
McAleavey, Stephen A.; Nightingale, Kathryn R.; Stutz, Deborah L.; Hsu, Stephen J.; Trahey, Gregg E.
2003-05-01
Acoustic radiation force may be used to induce localized displacements within tissue. This phenomenon is used in Acoustic Radiation Force Impulse Imaging (ARFI), where short bursts of ultrasound deliver an impulsive force to a small region. The application of this transient force launches shear waves which propagate normally to the ultrasound beam axis. Measurements of the displacements induced by the propagating shear wave allow reconstruction of the local shear modulus, by wave tracking and inversion techniques. Here we present in vitro, ex vivo and in vivo measurements and images of shear modulus. Data were obtained with a single transducer, a conventional ultrasound scanner and specialized pulse sequences. Young's modulus values of 4 kPa, 13 kPa and 14 kPa were observed for fat, breast fibroadenoma, and skin. Shear modulus anisotropy in beef muscle was observed.
Lee, S; Kim, H-J
2014-05-01
X-ray computed tomography (CT) images obtained with a kilo-voltage (kV) on-board imaging (OBI) system improve the accuracy of patient setup and treatment planning. The use of iterative reconstruction techniques (IRTs) for CT imaging can also reduce radiation dose compared to analytic reconstruction techniques. Despite these improvements, the image quality varies with IRTs, and the noise structure of reconstructed images can be distorted by IRTs. In this study, the noise properties and spatial resolution of the images reconstructed by IRTs were evaluated in terms of conventional noise metrics, high-order statistics, noise spectral density (NSD) and modulation transfer function (MTF) at different radiation doses. A kV OBI system mounted on a Varian Trilogy machine and a CATPHAN600 phantom were used to obtain projections, and the projections were reconstructed by Feldkamp (FDK), algebraic reconstruction technique (ART), maximum-likelihood expectation-maximization (MLEM) and total variation (TV) minimization algorithms. The reconstructed images were compared according to mean, standard deviation, skewness, kurtosis, NSD and MTF at different radiation doses. The results demonstrated that the noise properties and spatial resolution of reconstructed images depend on the type of IRT and the radiation dose. The noise structures are altered by IRTs and can be characterized by high-order statistics and NSD, as well as conventional noise metrics. In conclusion, high-order statistics and NSD should be considered in order to provide detailed information for the images reconstructed by IRTs. Also, trade-off among noise properties, spatial resolution and contrast is important to optimize image quality obtained using IRTs.
Modeling of polychromatic attenuation using computed tomography reconstructed images.
Yan, C H; Whalen, R T; Beaupré, G S; Yen, S Y; Napel, S
1999-04-01
This paper presents a procedure for estimating an accurate model of the CT imaging process including spectral effects. As raw projection data are typically unavailable to the end-user, we adopt a post-processing approach that utilizes the reconstructed images themselves. This approach includes errors from x-ray scatter and the nonidealities of the built-in soft tissue correction into the beam characteristics, which is crucial to beam hardening correction algorithms that are designed to be applied directly to CT reconstructed images. We formulate this approach as a quadratic programming problem and propose two different methods, dimension reduction and regularization, to overcome ill conditioning in the model. For the regularization method we use a statistical procedure, Cross Validation, to select the regularization parameter. We have constructed step-wedge phantoms to estimate the effective beam spectrum of a GE CT-I scanner. Using the derived spectrum, we computed the attenuation ratios for the wedge phantoms and found that the worst case modeling error is less than 3% of the corresponding attenuation ratio. We have also built two test (hybrid) phantoms to evaluate the effective spectrum. Based on these test phantoms, we have shown that the effective beam spectrum provides an accurate model for the CT imaging process. Last, we used a simple beam hardening correction experiment to demonstrate the effectiveness of the estimated beam profile for removing beam hardening artifacts. We hope that this estimation procedure will encourage more independent research on beam hardening corrections and will lead to the development of application-specific beam hardening correction algorithms.
Model-based image reconstruction for four-dimensional PET
Li Tianfang; Thorndyke, Brian; Schreibmann, Eduard; Yang Yong; Xing Lei
2006-05-15
Positron emission tonography (PET) is useful in diagnosis and radiation treatment planning for a variety of cancers. For patients with cancers in thoracic or upper abdominal region, the respiratory motion produces large distortions in the tumor shape and size, affecting the accuracy in both diagnosis and treatment. Four-dimensional (4D) (gated) PET aims to reduce the motion artifacts and to provide accurate measurement of the tumor volume and the tracer concentration. A major issue in 4D PET is the lack of statistics. Since the collected photons are divided into several frames in the 4D PET scan, the quality of each reconstructed frame degrades as the number of frames increases. The increased noise in each frame heavily degrades the quantitative accuracy of the PET imaging. In this work, we propose a method to enhance the performance of 4D PET by developing a new technique of 4D PET reconstruction with incorporation of an organ motion model derived from 4D-CT images. The method is based on the well-known maximum-likelihood expectation-maximization (ML-EM) algorithm. During the processes of forward- and backward-projection in the ML-EM iterations, all projection data acquired at different phases are combined together to update the emission map with the aid of deformable model, the statistics is therefore greatly improved. The proposed algorithm was first evaluated with computer simulations using a mathematical dynamic phantom. Experiment with a moving physical phantom was then carried out to demonstrate the accuracy of the proposed method and the increase of signal-to-noise ratio over three-dimensional PET. Finally, the 4D PET reconstruction was applied to a patient case.
Optoelectronic image scanning with high spatial resolution and reconstruction fidelity
NASA Astrophysics Data System (ADS)
Craubner, Siegfried I.
2002-02-01
In imaging systems the detector arrays deliver at the output time-discrete signals, where the spatial frequencies of the object scene are mapped into the electrical signal frequencies. Since the spatial frequency spectrum cannot be bandlimited by the front optics, the usual detector arrays perform a spatial undersampling and as a consequence aliasing occurs. A means to partially suppress the backfolded alias band is bandwidth limitation in the reconstruction low-pass, at the price of resolution loss. By utilizing a bilinear detector array in a pushbroom-type scanner, undersampling and aliasing can be overcome. For modeling the perception, the theory of discrete systems and multirate digital filter banks is applied, where aliasing cancellation and perfect reconstruction play an important role. The discrete transfer function of a bilinear array can be imbedded into the scheme of a second-order filter bank. The detector arrays already build the analysis bank and the overall filter bank is completed with the synthesis bank, for which stabilized inverse filters are proposed, to compensate for the low-pass characteristics and to approximate perfect reconstruction. The synthesis filter branch can be realized in a so-called `direct form,' or the `polyphase form,' where the latter is an expenditure-optimal solution, which gives advantages when implemented in a signal processor. This paper attempts to introduce well-established concepts of the theory of multirate filter banks into the analysis of scanning imagers, which is applicable in a much broader sense than for the problems addressed here. To the author's knowledge this is also a novelty.
Three dimensional reconstruction of neuron morphology from confocal microscopy images
NASA Astrophysics Data System (ADS)
Fanti, Zian; Martinez-Perez, M. Elena
2010-05-01
In recent years it has been more common to see 3D visualization of objects applied in many different areas. In neuroscience research, 3D visualization of neurons acquired at different depth views (i.e. image stacks) by means of confocal microscopy are of increase use. However in the best case, these visualizations only help to have a qualitative description of the neuron shape. Since it is well know that neuronal function is intimately related to its morphology. Having a precise characterization of neuronal structures such as axons and dendrites is critical to perform a quantitative analysis and thus it allows to design neuronal functional models based on neuron morphology. Currently there exists different commercial software to reconstruct neuronal arbors, however these processes are labor intensive since in most of the cases they are manually made. In this paper we propose a new software capable to reconstruct 3D neurons from confocal microscopy views in a more efficient way, with minimal user intervention. The propose algorithm is based on finding the tubular structures present in the stack of images using a modify version of the minimal graph cut algorithm. The model is generated from the segmented stack with a modified version of the Marching Cubes algorithm to generate de 3D isosurface. Herein we describe the principles of our 3D segmentation technique and the preliminary results.
A Convex Formulation for Magnetic Particle Imaging X-Space Reconstruction.
Konkle, Justin J; Goodwill, Patrick W; Hensley, Daniel W; Orendorff, Ryan D; Lustig, Michael; Conolly, Steven M
2015-01-01
Magnetic Particle Imaging (mpi) is an emerging imaging modality with exceptional promise for clinical applications in rapid angiography, cell therapy tracking, cancer imaging, and inflammation imaging. Recent publications have demonstrated quantitative mpi across rat sized fields of view with x-space reconstruction methods. Critical to any medical imaging technology is the reliability and accuracy of image reconstruction. Because the average value of the mpi signal is lost during direct-feedthrough signal filtering, mpi reconstruction algorithms must recover this zero-frequency value. Prior x-space mpi recovery techniques were limited to 1d approaches which could introduce artifacts when reconstructing a 3d image. In this paper, we formulate x-space reconstruction as a 3d convex optimization problem and apply robust a priori knowledge of image smoothness and non-negativity to reduce non-physical banding and haze artifacts. We conclude with a discussion of the powerful extensibility of the presented formulation for future applications.
Pre-calculation of the image quality of the simultaneous iterative reconstruction technique
NASA Astrophysics Data System (ADS)
Kunze, Holger; Härer, Wolfgang; Stierstorfer, Karl
2007-03-01
Iterative reconstruction methods possess many advantages over analytical reconstruction methods especially if constraints can be used to regularize the reconstruction. However the main problem of iterative reconstruction algorithms is to decide when to stop the iteration. For the Simultaneous Iterative Reconstruction Technique (SIRT) without constraints we derived a mathematical formula with which the quality of the reconstruction after a given number of iterations can be calculated. The image quality is expressed here by a special filter kernel for a FBP reconstruction which creates images with the same sharpness and noise properties as SIRT. Further on the formula can be used to analyze the numerical stability of a certain implementation of SIRT. Experiments show the validity of these "iteration-equivalent"-kernels with respect to sharpness and noise properties of the reconstructed images.
Liu, Jiulong; Ding, Huanjun; Molloi, Sabee; Zhang, Xiaoqun; Gao, Hao
2016-12-01
This work develops a material reconstruction method for spectral CT, namely Total Image Constrained Material Reconstruction (TICMR), to maximize the utility of projection data in terms of both spectral information and high signal-to-noise ratio (SNR). This is motivated by the following fact: when viewed as a spectrally-integrated measurement, the projection data can be used to reconstruct a total image without spectral information, which however has a relatively high SNR; when viewed as a spectrally-resolved measurement, the projection data can be utilized to reconstruct the material composition, which however has a relatively low SNR. The material reconstruction synergizes material decomposition and image reconstruction, i.e., the direct reconstruction of material compositions instead of a two-step procedure that first reconstructs images and then decomposes images. For material reconstruction with high SNR, we propose TICMR with nonlocal total variation (NLTV) regularization. That is, first we reconstruct a total image using spectrally-integrated measurement without spectral binning, and build the NLTV weights from this image that characterize nonlocal image features; then the NLTV weights are incorporated into a NLTV-based iterative material reconstruction scheme using spectrally-binned projection data, so that these weights serve as a high-SNR reference to regularize material reconstruction. Note that the nonlocal property of NLTV is essential for material reconstruction, since material compositions may have significant local intensity variations although their structural information is often similar. In terms of solution algorithm, TICMR is formulated as an iterative reconstruction method with the NLTV regularization, in which the nonlocal divergence is utilized based on the adjoint relationship. The alternating direction method of multipliers is developed to solve this sparsity optimization problem. The proposed TICMR method was validated using both simulated
NASA Astrophysics Data System (ADS)
Okawa, Shinpei; Hirasawa, Takeshi; Kushibiki, Toshihiro; Ishihara, Miya
2013-09-01
An image reconstruction algorithm for biomedical photoacoustic imaging is discussed. The algorithm solves the inverse problem of the photoacoustic phenomenon in biological media and images the distribution of large optical absorption coefficients, which can indicate diseased tissues such as cancers with angiogenesis and the tissues labeled by exogenous photon absorbers. The linearized forward problem, which relates the absorption coefficients to the detected photoacoustic signals, is formulated by using photon diffusion and photoacoustic wave equations. Both partial differential equations are solved by a finite element method. The inverse problem is solved by truncated singular value decomposition, which reduces the effects of the measurement noise and the errors between forward modeling and actual measurement systems. The spatial resolution and the robustness to various factors affecting the image reconstruction are evaluated by numerical experiments with 2D geometry.
TU-EF-207-00: Advances in Breast Imaging
2015-06-15
mode due to lower photon fluence per projection. This may require fast-frame acquisition and symmetric or asymmetric pixel binning in some systems. Recent studies investigated the performance of increased conversion layer thickness for contrast-enhanced imaging of the breast in dual-energy acquisition mode. In other direct conversion detectors operating in the avalanche mode, sensitivities close to the single photon response are also explored for mammography and breast tomosynthesis. The potential advantages and challenges of this approach are described. Dedicated breast CT brings x-ray imaging of the breast to true tomographic 3D imaging. It can eliminate the tissue superposition problem and does not require physical compression of the breast. Using cone beam geometry and a flat-panel detector, several hundred projections are acquired and reconstructed to near isotropic voxels. Multiplanar reconstruction facilitates viewing the breast volume in any desired orientation. Ongoing clinical studies, the current state-of-the art, and research to advance the technology are described. Learning Objectives: To understand the ongoing developments in x-ray imaging of the breast To understand the approaches and applications of spectral mammography To understand the potential advantages of distributed x-ray source arrays for digital breast tomosynthesis To understand the ongoing developments in detector technology for digital mammography and breast tomosynthesis To understand the current state-of-the-art for dedicated cone-beam breast CT and research to advance the technology. Research collaboration with Koning Corporation.
The robotic-assisted laparoscopic pyeloplasty: gateway to advanced reconstruction.
Tasian, Gregory E; Casale, Pasquale
2015-02-01
Robotic pyeloplasty is now commonly performed for children with ureteropelvic junction obstruction. Because surgical robotics is a tool that facilitates pyeloplasty and other reconstructive urologic operations, the indications for robotic-assisted laparoscopic pyeloplasty are the same as those for an open pyeloplasty but offer distinct advantages with respect to visualization, range of motion, and ease of laparoscopic suturing. In this review, the authors discuss the operative approach for robotic pyeloplasty in children and the extensions of the basic techniques to challenging clinical scenarios.
3D image reconstruction algorithms for cryo-electron-microscopy images of virus particles
NASA Astrophysics Data System (ADS)
Doerschuk, Peter C.; Johnson, John E.
2000-11-01
A statistical model for the object and the complete image formation process in cryo electron microscopy of viruses is presented. Using this model, maximum likelihood reconstructions of the 3D structure of viruses are computed using the expectation maximization algorithm and an example based on Cowpea mosaic virus is provided.
Advanced imaging in valvular heart disease.
Bax, Jeroen J; Delgado, Victoria
2017-04-01
Although echocardiography remains the mainstay imaging technique for the evaluation of patients with valvular heart disease (VHD), innovations in noninvasive imaging in the past few years have provided new insights into the pathophysiology and quantification of VHD, early detection of left ventricular (LV) dysfunction, and advanced prognostic assessment. The severity grading of valve dysfunction has been refined with the use of Doppler echocardiography, cardiac magnetic resonance (CMR), and CT imaging. LV ejection fraction remains an important criterion when deciding whether patients should be referred for surgery. However, echocardiographic strain imaging can now detect impaired LV systolic function before LV ejection fraction reduces, thus provoking the debate on whether patients with severe VHD should be referred for surgery at an earlier stage (before symptom onset). Impaired LV strain correlates with the amount of myocardial fibrosis detected with CMR techniques. Furthermore, accumulating data show that the extent of fibrosis associated with severe VHD has important prognostic implications. The present Review focuses on using these novel imaging modalities to assess pathophysiology, early LV dysfunction, and prognosis of major VHDs, including aortic stenosis, mitral regurgitation, and aortic regurgitation.
Patch-based image reconstruction for PET using prior-image derived dictionaries
NASA Astrophysics Data System (ADS)
Tahaei, Marzieh S.; Reader, Andrew J.
2016-09-01
In PET image reconstruction, regularization is often needed to reduce the noise in the resulting images. Patch-based image processing techniques have recently been successfully used for regularization in medical image reconstruction through a penalized likelihood framework. Re-parameterization within reconstruction is another powerful regularization technique in which the object in the scanner is re-parameterized using coefficients for spatially-extensive basis vectors. In this work, a method for extracting patch-based basis vectors from the subject’s MR image is proposed. The coefficients for these basis vectors are then estimated using the conventional MLEM algorithm. Furthermore, using the alternating direction method of multipliers, an algorithm for optimizing the Poisson log-likelihood while imposing sparsity on the parameters is also proposed. This novel method is then utilized to find sparse coefficients for the patch-based basis vectors extracted from the MR image. The results indicate the superiority of the proposed methods to patch-based regularization using the penalized likelihood framework.
Earth Observing-1 Advanced Land Imager: Imaging Performance On-Orbit
NASA Technical Reports Server (NTRS)
Hearn, D. R.
2002-01-01
This report analyzes the on-orbit imaging performance of the Advanced Land Imager (ALI) on the Earth Observing-1 satellite. The pre-flight calibrations are first summarized. The methods used to reconstruct and geometrically correct the image data from this push-broom sensor are described. The method used here does not refer to the position and attitude telemetry from the spacecraft. Rather, it is assumed that the image of the scene moves across the focal plane with a constant velocity, which can be ascertained from the image data itself. Next, an assortment of the images so reconstructed is presented. Color images sharpened with the 10-m panchromatic band data are shown, and the algorithm for producing them from the 30-m multispectral data is described. The approach taken for assessing spatial resolution is to compare the sharpness of features in the on-orbit image data with profiles predicted on the basis of the pre-flight calibrations. A large assortment of bridge profiles is analyzed, and very good fits to the predicted shapes are obtained. Lunar calibration scans are analyzed to examine the sharpness of the edge-spread function at the limb of the moon. The darkness of the space beyond the limb is better for this purpose than anything that could be simulated on the ground. From these scans, we find clear evidence of scattering in the optical system, as well as some weak ghost images. Scans of planets and stars are also analyzed. Stars are useful point sources of light at all wavelengths, and delineate the point-spread functions of the system. From a quarter-speed scan over the Pleiades, we find that the ALI can detect 6th magnitude stars. The quality of the reconstructed images verifies the capability of the ALI to produce Landsat-type multi spectral data. The signal-to-noise and panchromatic spatial resolution are considerably superior to those of the existing Landsat sensors. The spatial resolution is confirmed to be as good as it was designed to be.
Pesavento, J B; Morgan, D; Bermingham, R; Zamora, D; Chromy, B; Segelke, B; Coleman, M; Xing, L; Cheng, H; Bench, G; Hoeprich, P
2007-06-07
Nanolipoprotein particles (NLPs) are small 10-20 nm diameter assemblies of apolipoproteins and lipids. At Lawrence Livermore National Laboratory (LLNL), they have constructed multiple variants of these assemblies. NLPs have been generated from a variety of lipoproteins, including apolipoprotein Al, apolipophorin III, apolipoprotein E4 22K, and MSP1T2 (nanodisc, Inc.). Lipids used included DMPC (bulk of the bilayer material), DMPE (in various amounts), and DPPC. NLPs were made in either the absence or presence of the detergent cholate. They have collected electron microscopy data as a part of the characterization component of this research. Although purified by size exclusion chromatography (SEC), samples are somewhat heterogeneous when analyzed at the nanoscale by negative stained cryo-EM. Images reveal a broad range of shape heterogeneity, suggesting variability in conformational flexibility, in fact, modeling studies point to dynamics of inter-helical loop regions within apolipoproteins as being a possible source for observed variation in NLP size. Initial attempts at three-dimensional reconstructions have proven to be challenging due to this size and shape disparity. They are pursuing a strategy of computational size exclusion to group particles into subpopulations based on average particle diameter. They show here results from their ongoing efforts at statistically and computationally subdividing NLP populations to realize greater homogeneity and then generate 3D reconstructions.
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.
An adaptive total variation image reconstruction method for speckles through disordered media
NASA Astrophysics Data System (ADS)
Gong, Changmei; Shao, Xiaopeng; Wu, Tengfei
2013-09-01
Multiple scattering of light in highly disordered medium can break the diffraction limit of conventional optical system combined with image reconstruction method. Once the transmission matrix of the imaging system is obtained, the target image can be reconstructed from its speckle pattern by image reconstruction algorithm. Nevertheless, the restored image attained by common image reconstruction algorithms such as Tikhonov regularization has a relatively low signal-tonoise ratio (SNR) due to the experimental noise and reconstruction noise, greatly reducing the quality of the result image. In this paper, the speckle pattern of the test image is simulated by the combination of light propagation theories and statistical optics theories. Subsequently, an adaptive total variation (ATV) algorithm—the TV minimization by augmented Lagrangian and alternating direction algorithms (TVAL3), which is based on augmented Lagrangian and alternating direction algorithm, is utilized to reconstruct the target image. Numerical simulation experimental results show that, the TVAL3 algorithm can effectively suppress the noise of the restored image and preserve more image details, thus greatly boosts the SNR of the restored image. It also indicates that, compared with the image directly formed by `clean' system, the reconstructed results can overcoming the diffraction limit of the `clean' system, therefore being conductive to the observation of cells and protein molecules in biological tissues and other structures in micro/nano scale.
Advancement of 31P Magnetic Resonance Spectroscopy Using GRAPPA Reconstruction on a 3D Volume
NASA Astrophysics Data System (ADS)
Clevenger, Tony
The overall objective of this research is to improve currently available metabolic imaging techniques for clinical use in monitoring and predicting treatment response to radiation therapy in liver cancer. Liver metabolism correlates with inflammatory and neoplastic liver diseases, which alter the intracellular concentration of phosphorus- 31 (31P) metabolites [1]. It is assumed that such metabolic changes occur prior to physical changes of the tissue. Therefore, information on regional changes of 31P metabolites in the liver, obtained by Magnetic Resonance Spectroscopic Imaging (MRSI) [1,2], can help in diagnosis and follow-up of various liver diseases. Specifically, there appears to be an immediate need of this technology for both the assessment of tumor response in patients with Hepatocellular Carcinoma (HCC) treated with Stereotactic Body Radiation Therapy (SBRT) [3--5], as well as assessment of radiation toxicity, which can result in worsening liver dysfunction [6]. Pilot data from our lab has shown that 31P MRSI has the potential to identify treatment response five months sooner than conventional methods [7], and to assess the biological response of liver tissue to radiation 24 hours post radiation therapy [8]. While this data is very promising, commonly occurring drawbacks for 31P MRSI are patient discomfort due to long scan times and prone positioning within the scanner, as well as reduced data quality due to patient motion and respiration. To further advance the full potential of 31P MRSI as a clinical diagnostic tool in the management of liver cancer, this PhD research project had the following aims: I) Reduce the long acquisition time of 3D 31P MRS by formulating and imple- menting an appropriate GRAPPA undersampling scheme and reconstruction on a clinical MRI scanner II) Testing and quantitative validation of GRAPPA reconstruction on 3D 31P MRSI on developmental phantoms and healthy volunteers At completion, this work should considerably advance 31P MRSI
Bindu, G; Semenov, S
2013-01-01
This paper describes an efficient two-dimensional fused image reconstruction approach for Microwave Tomography (MWT). Finite Difference Time Domain (FDTD) models were created for a viable MWT experimental system having the transceivers modelled using thin wire approximation with resistive voltage sources. Born Iterative and Distorted Born Iterative methods have been employed for image reconstruction with the extremity imaging being done using a differential imaging technique. The forward solver in the imaging algorithm employs the FDTD method of solving the time domain Maxwell's equations with the regularisation parameter computed using a stochastic approach. The algorithm is tested with 10% noise inclusion and successful image reconstruction has been shown implying its robustness.
Xie, Huiqiao; Yang, Yi; Tang, Xiangyang; Niu, Tianye; Ren, Yi
2015-06-15
Purpose: Optimization-based reconstruction has been proposed and investigated for reconstructing CT images from sparse views, as such the radiation dose can be substantially reduced while maintaining acceptable image quality. The investigation has so far focused on reconstruction from evenly distributed sparse views. Recognizing the clinical situations wherein only unevenly sparse views are available, e.g., image guided radiation therapy, CT perfusion and multi-cycle cardiovascular imaging, we investigate the performance of optimization-based image reconstruction from unevenly sparse projection views in this work. Methods: The investigation is carried out using the FORBILD and an anthropomorphic head phantoms. In the study, 82 views, which are evenly sorted out from a full (360°) axial CT scan consisting of 984 views, form sub-scan I. Another 82 views are sorted out in a similar manner to form sub-scan II. As such, a CT scan with sparse (164) views at 1:6 ratio are formed. By shifting the two sub-scans relatively in view angulation, a CT scan with unevenly distributed sparse (164) views at 1:6 ratio are formed. An optimization-based method is implemented to reconstruct images from the unevenly distributed views. By taking the FBP reconstruction from the full scan (984 views) as the reference, the root mean square (RMS) between the reference and the optimization-based reconstruction is used to evaluate the performance quantitatively. Results: In visual inspection, the optimization-based method outperforms the FBP substantially in the reconstruction from unevenly distributed, which are quantitatively verified by the RMS gauged globally and in ROIs in both the FORBILD and anthropomorphic head phantoms. The RMS increases with increasing severity in the uneven angular distribution, especially in the case of anthropomorphic head phantom. Conclusion: The optimization-based image reconstruction can save radiation dose up to 12-fold while providing acceptable image quality
Recent advances in imaging preterm brain injury.
Boardman, J P; Dyet, L E
2007-08-01
Survivors of preterm birth are at high risk of neurocognitive impairment in childhood, but the disturbances to brain growth and function that underlie impairment are not completely understood. Improvements in perinatal care have led to a reduction in the major destructive parenchymal brain lesions that are associated with motor impairment, such as cystic periventricular leucomalacia and haemorrhagic parenchymal infarction. However, with the application of advanced magnetic resonance (MR) imaging and processing techniques in the neonatal period, subtle alterations in brain development have become apparent. These changes occur with similar frequency to long-term neurocognitive impairment, and may therefore represent candidate neural substrates for this group of disorders. Here we review the range of lesions and associated outcomes that are seen in the current era of perinatal care, and discuss how state of the art MR imaging techniques have helped to define the neural systems affected by preterm birth, and have provided insights into understanding mechanisms of injury.
Advanced methods in synthetic aperture radar imaging
NASA Astrophysics Data System (ADS)
Kragh, Thomas
2012-02-01
For over 50 years our world has been mapped and measured with synthetic aperture radar (SAR). A SAR system operates by transmitting a series of wideband radio-frequency pulses towards the ground and recording the resulting backscattered electromagnetic waves as the system travels along some one-dimensional trajectory. By coherently processing the recorded backscatter over this extended aperture, one can form a high-resolution 2D intensity map of the ground reflectivity, which we call a SAR image. The trajectory, or synthetic aperture, is achieved by mounting the radar on an aircraft, spacecraft, or even on the roof of a car traveling down the road, and allows for a diverse set of applications and measurement techniques for remote sensing applications. It is quite remarkable that the sub-centimeter positioning precision and sub-nanosecond timing precision required to make this work properly can in fact be achieved under such real-world, often turbulent, vibrationally intensive conditions. Although the basic principles behind SAR imaging and interferometry have been known for decades, in recent years an explosion of data exploitation techniques enabled by ever-faster computational horsepower have enabled some remarkable advances. Although SAR images are often viewed as simple intensity maps of ground reflectivity, SAR is also an exquisitely sensitive coherent imaging modality with a wealth of information buried within the phase information in the image. Some of the examples featured in this presentation will include: (1) Interferometric SAR, where by comparing the difference in phase between two SAR images one can measure subtle changes in ground topography at the wavelength scale. (2) Change detection, in which carefully geolocated images formed from two different passes are compared. (3) Multi-pass 3D SAR tomography, where multiple trajectories can be used to form 3D images. (4) Moving Target Indication (MTI), in which Doppler effects allow one to detect and
Image reconstruction for a Positron Emission Tomograph optimized for breast cancer imaging
Virador, Patrick R.G.
2000-04-01
The author performs image reconstruction for a novel Positron Emission Tomography camera that is optimized for breast cancer imaging. This work addresses for the first time, the problem of fully-3D, tomographic reconstruction using a septa-less, stationary, (i.e. no rotation or linear motion), and rectangular camera whose Field of View (FOV) encompasses the entire volume enclosed by detector modules capable of measuring Depth of Interaction (DOI) information. The camera is rectangular in shape in order to accommodate breasts of varying sizes while allowing for soft compression of the breast during the scan. This non-standard geometry of the camera exacerbates two problems: (a) radial elongation due to crystal penetration and (b) reconstructing images from irregularly sampled data. Packing considerations also give rise to regions in projection space that are not sampled which lead to missing information. The author presents new Fourier Methods based image reconstruction algorithms that incorporate DOI information and accommodate the irregular sampling of the camera in a consistent manner by defining lines of responses (LORs) between the measured interaction points instead of rebinning the events into predefined crystal face LORs which is the only other method to handle DOI information proposed thus far. The new procedures maximize the use of the increased sampling provided by the DOI while minimizing interpolation in the data. The new algorithms use fixed-width evenly spaced radial bins in order to take advantage of the speed of the Fast Fourier Transform (FFT), which necessitates the use of irregular angular sampling in order to minimize the number of unnormalizable Zero-Efficiency Bins (ZEBs). In order to address the persisting ZEBs and the issue of missing information originating from packing considerations, the algorithms (a) perform nearest neighbor smoothing in 2D in the radial bins (b) employ a semi-iterative procedure in order to estimate the unsampled data
Investigation of optimization-based reconstruction with an image-total-variation constraint in PET
NASA Astrophysics Data System (ADS)
Zhang, Zheng; Ye, Jinghan; Chen, Buxin; Perkins, Amy E.; Rose, Sean; Sidky, Emil Y.; Kao, Chien-Min; Xia, Dan; Tung, Chi-Hua; Pan, Xiaochuan
2016-08-01
Interest remains in reconstruction-algorithm research and development for possible improvement of image quality in current PET imaging and for enabling innovative PET systems to enhance existing, and facilitate new, preclinical and clinical applications. Optimization-based image reconstruction has been demonstrated in recent years of potential utility for CT imaging applications. In this work, we investigate tailoring the optimization-based techniques to image reconstruction for PET systems with standard and non-standard scan configurations. Specifically, given an image-total-variation (TV) constraint, we investigated how the selection of different data divergences and associated parameters impacts the optimization-based reconstruction of PET images. The reconstruction robustness was explored also with respect to different data conditions and activity up-takes of practical relevance. A study was conducted particularly for image reconstruction from data collected by use of a PET configuration with sparsely populated detectors. Overall, the study demonstrates the robustness of the TV-constrained, optimization-based reconstruction for considerably different data conditions in PET imaging, as well as its potential to enable PET configurations with reduced numbers of detectors. Insights gained in the study may be exploited for developing algorithms for PET-image reconstruction and for enabling PET-configuration design of practical usefulness in preclinical and clinical applications.
Current profile reconstruction using electron temperature imaging diagnostics
Tritz, K.; Stutman, D.; Delgado-Aparicio, L.F.; Finkenthal, M.; Pacella, D.; Kaita, R.; Stratton, B.; Sabbagh, S.
2004-10-01
Flux surface shape information can be used to constrain the current profile for reconstruction of the plasma equilibrium. One method of inferring flux surface shape relies on plasma x-ray emission; however, deviations from the flux surfaces due to impurity and density asymmetries complicate the interpretation. Electron isotherm surfaces should correspond well to the plasma flux surfaces, and equilibrium constraint modeling using this isotherm information constrains the current profile. The KFIT code is used to assess the profile uncertainty and to optimize the number, location and SNR required for the Te detectors. As Te imaging detectors we consider tangentially viewing, vertically spaced, linear gas electron multiplier arrays operated in pulse height analysis (PHA) mode and multifoil soft x-ray arrays. Isoflux coordinate sets provided by T{sub e} measurements offer a strong constraint on the equilibrium reconstruction in both a stacked horizontal array configuration and a crossed horizontal and vertical beam system, with q{sub 0} determined to within {+-}4%. The required SNR can be provided with either PHA or multicolor diagnostic techniques, though the multicolor system requires {approx}x4 better statistics for comparable final errors.
Acceleration of iterative image reconstruction for x-ray imaging for security applications
NASA Astrophysics Data System (ADS)
Degirmenci, Soysal; Politte, David G.; Bosch, Carl; Tricha, Nawfel; O'Sullivan, Joseph A.
2015-03-01
Three-dimensional image reconstruction for scanning baggage in security applications is becoming increasingly important. Compared to medical x-ray imaging, security imaging systems must be designed for a greater variety of objects. There is a lot of variation in attenuation and nearly every bag scanned has metal present, potentially yielding significant artifacts. Statistical iterative reconstruction algorithms are known to reduce metal artifacts and yield quantitatively more accurate estimates of attenuation than linear methods. For iterative image reconstruction algorithms to be deployed at security checkpoints, the images must be quantitatively accurate and the convergence speed must be increased dramatically. There are many approaches for increasing convergence; two approaches are described in detail in this paper. The first approach includes a scheduled change in the number of ordered subsets over iterations and a reformulation of convergent ordered subsets that was originally proposed by Ahn, Fessler et. al.1 The second approach is based on varying the multiplication factor in front of the additive step in the alternating minimization (AM) algorithm, resulting in more aggressive updates in iterations. Each approach is implemented on real data from a SureScanTM x 1000 Explosive Detection System∗ and compared to straightforward implementations of the alternating minimization algorithm of O'Sullivan and Benac2 with a Huber-type edge-preserving penalty, originally proposed by Lange.3
NASA Astrophysics Data System (ADS)
Zhao, Hui; Zong, Caihui; Wei, Jingxuan; Xie, Xiaopeng
2016-10-01
Wave-front coding, proposed by Dowski and Cathey in 1995, is widely known to be capable of extending the depth of focus (DOF) of incoherent imaging systems. However, benefiting from its very large point spread function (PSF) generated by a suitably designed phase mask that is added to the aperture plane, wave-front coding could also be used to achieve super-resolution without replacing the current sensor with one of smaller pitch size. An image amplification based super-resolution reconstruction procedure has been specifically designed for wave-front coded imaging systems and its effectiveness has been tested by experiment. For instance, for a focal length of 50 mm and f-number 4.5, objects within the range [5 m, ∞] are clearly imaged with the help of wave-front coding, which indicates a DOF extension ratio of approximately 20. The proposed super-resolution reconstruction procedure produces at least 3× resolution improvement, with the quality of the reconstructed super-resolution image approaching the diffraction limit.
Image reconstruction and image quality evaluation for a dual source CT scanner
Flohr, T. G.; Bruder, H.; Stierstorfer, K.; Petersilka, M.; Schmidt, B.; McCollough, C. H.
2008-01-01
The authors present and evaluate concepts for image reconstruction in dual source CT (DSCT). They describe both standard spiral (helical) DSCT image reconstruction and electrocardiogram (ECG)-synchronized image reconstruction. For a compact mechanical design of the DSCT, one detector (A) can cover the full scan field of view, while the other detector (B) has to be restricted to a smaller, central field of view. The authors develop an algorithm for scan data completion, extrapolating truncated data of detector (B) by using data of detector (A). They propose a unified framework for convolution and simultaneous 3D backprojection of both (A) and (B) data, with similar treatment of standard spiral, ECG-gated spiral, and sequential (axial) scan data. In ECG-synchronized image reconstruction, a flexible scan data range per measurement system can be used to trade off temporal resolution for reduced image noise. Both data extrapolation and image reconstruction are evaluated by means of computer simulated data of anthropomorphic phantoms, by phantom measurements and patient studies. The authors show that a consistent filter direction along the spiral tangent on both detectors is essential to reduce cone-beam artifacts, requiring truncation of the extrapolated (B) data after convolution in standard spiral scans. Reconstructions of an anthropomorphic thorax phantom demonstrate good image quality and dose accumulation as theoretically expected for simultaneous 3D backprojection of the filtered (A) data and the truncated filtered (B) data into the same 3D image volume. In ECG-gated spiral modes, spiral slice sensitivity profiles (SSPs) show only minor dependence on the patient’s heart rate if the spiral pitch is properly adapted. Measurements with a thin gold plate phantom result in effective slice widths (full width at half maximum of the SSP) of 0.63–0.69mm for the nominal 0.6mm slice and 0.82–0.87mm for the nominal 0.75mm slice. The visually determined through-plane (z
Harris, C.L.
1990-01-01
This report describes progress on work to develop a cost effective, rapid response system for measuring momentum and kinetic energy of spall for the Advanced Technology Assessment Center (ATAC) Armor/Anti-Armor (A{sup 3}) program at Los Alamos National Laboratory. The system will exploit data contained in two sets of simultaneous co-planar flash radiographs taken along the center line of anticipated spall motion. Data contained in each set (which is proportional to the mass and z- number of the spall material intersected by the exposing x-ray at each point) is digitized and used to construct a three dimensional model (called the reconstructed spall image) that approximates the original spall cloud. From the model the mass of spall fragments is computed. The two sets of radiographs, separated in time, represent the spall configuration at two instants of time. Spall fragments from the first instant are matched with those from the second instant to determine velocity. Evaluation of the fidelity of candidate reconstruction algorithms is the highest priority task in this development program for the obvious reason that the efficacy of the projected spall analysis system depends upon the fidelity of the reconstruction techniques. The purpose of this document is to report the results of analysis of the fidelity of best reconstruction procedure (for one radiograph set) investigated to date. The reconstruction procedure uses data from four simultaneous radiographs representing two sides and two diagonals of a cube. The procedure makes use of an available space algorithm, two probabilistic devices (a mass placement probability heuristic, and a mass clumping heuristic), and a stochastic procedure for mass that cannot be placed by the algorithm or either of the heuristics. The procedure is fully described in the body of the report.
3D imaging reconstruction and impacted third molars: case reports
Tuzi, Andrea; Di Bari, Roberto; Cicconetti, Andrea
2012-01-01
Summary There is a debate in the literature about the need for Computed Tomagraphy (CT) before removing third molars, even if positive radiographic signs are present. In few cases, the third molar is so close to the inferior alveolar nerve that its extraction might expose patients to the risk of post-operative neuro-sensitive alterations of the skin and the mucosa of the homolateral lower lip and chin. Thus, the injury of the inferior alveolar nerve may represent a serious, though infrequent, neurologic complication in the surgery of the third molars rendering necessary a careful pre-operative evaluation of their anatomical relationship with the inferior alveolar nerve by means of radiographic imaging techniques. This contribution presents two case reports showing positive radiographic signs, which are the hallmarks of a possible close relationship between the inferior alveolar nerve and the third molars. We aim at better defining the relationship between third molars and the mandibular canal using Dental CT Scan, DICOM image acquisition and 3D reconstruction with a dedicated software. By our study we deduce that 3D images are not indispensable, but they can provide a very agreeable assistance in the most complicated cases. PMID:23386934
Reconstructing Images in Astrophysics, an Inverse Problem Point of View
NASA Astrophysics Data System (ADS)
Theys, Céline; Aime, Claude
2016-04-01
After a short introduction, a first section provides a brief tutorial to the physics of image formation and its detection in the presence of noises. The rest of the chapter focuses on the resolution of the inverse problem
Iterative reconstruction of images from incomplete spectral data
NASA Astrophysics Data System (ADS)
Rhebergen, Jan B.; van den Berg, Peter M.; Habashy, Tarek M.
1997-06-01
In various branches of engineering and science, one is confronted with measurements resulting in incomplete spectral data. The problem of the reconstruction of an image from such a data set can be formulated in terms of an integral equation of the first kind. Consequently, this equation can be converted into an equivalent integral equation of the second kind which can be solved by a Neumann-type iterative method. It is shown that this Neumann expansion is an error-reducing method and that it is equivalent to the Papoulis - Gerchberg algorithm for band-limited signal extrapolation. The integral equation can also be solved by employing a conjugate gradient iterative scheme. Again, convergence of this scheme is demonstrated. Finally a number of illustrative numerical examples are presented and discussed.
Terrain reconstruction from Chang'e-3 PCAM images
NASA Astrophysics Data System (ADS)
Wang, Wen-Rui; Ren, Xin; Wang, Fen-Fei; Liu, Jian-Jun; Li, Chun-Lai
2015-07-01
The existing terrain models that describe the local lunar surface have limited resolution and accuracy, which can hardly meet the needs of rover navigation, positioning and geological analysis. China launched the lunar probe Chang'e-3 in December, 2013. Chang'e-3 encompassed a lander and a lunar rover called “Yutu” (Jade Rabbit). A set of panoramic cameras were installed on the rover mast. After acquiring panoramic images of four sites that were explored, the terrain models of the local lunar surface with resolution of 0.02m were reconstructed. Compared with other data sources, the models derived from Chang'e-3 data were clear and accurate enough that they could be used to plan the route of Yutu. Supported by the National Natural Science Foundation of China.
Optimization-based image reconstruction with artifact reduction in C-arm CBCT
NASA Astrophysics Data System (ADS)
Xia, Dan; Langan, David A.; Solomon, Stephen B.; Zhang, Zheng; Chen, Buxin; Lai, Hao; Sidky, Emil Y.; Pan, Xiaochuan
2016-10-01
We investigate an optimization-based reconstruction, with an emphasis on image-artifact reduction, from data collected in C-arm cone-beam computed tomography (CBCT) employed in image-guided interventional procedures. In the study, an image to be reconstructed is formulated as a solution to a convex optimization program in which a weighted data divergence is minimized subject to a constraint on the image total variation (TV); a data-derivative fidelity is introduced in the program specifically for effectively suppressing dominant, low-frequency data artifact caused by, e.g. data truncation; and the Chambolle-Pock (CP) algorithm is tailored to reconstruct an image through solving the program. Like any other reconstructions, the optimization-based reconstruction considered depends upon numerous parameters. We elucidate the parameters, illustrate their determination, and demonstrate their impact on the reconstruction. The optimization-based reconstruction, when applied to data collected from swine and patient subjects, yields images with visibly reduced artifacts in contrast to the reference reconstruction, and it also appears to exhibit a high degree of robustness against distinctively different anatomies of imaged subjects and scanning conditions of clinical significance. Knowledge and insights gained in the study may be exploited for aiding in the design of practical reconstructions of truly clinical-application utility.
Photoacoustic image reconstruction from ultrasound post-beamformed B-mode image
NASA Astrophysics Data System (ADS)
Zhang, Haichong K.; Guo, Xiaoyu; Kang, Hyun Jae; Boctor, Emad M.
2016-03-01
A requirement to reconstruct photoacoustic (PA) image is to have a synchronized channel data acquisition with laser firing. Unfortunately, most clinical ultrasound (US) systems don't offer an interface to obtain synchronized channel data. To broaden the impact of clinical PA imaging, we propose a PA image reconstruction algorithm utilizing US B-mode image, which is readily available from clinical scanners. US B-mode image involves a series of signal processing including beamforming, followed by envelope detection, and end with log compression. Yet, it will be defocused when PA signals are input due to incorrect delay function. Our approach is to reverse the order of image processing steps and recover the original US post-beamformed radio-frequency (RF) data, in which a synthetic aperture based PA rebeamforming algorithm can be further applied. Taking B-mode image as the input, we firstly recovered US postbeamformed RF data by applying log decompression and convoluting an acoustic impulse response to combine carrier frequency information. Then, the US post-beamformed RF data is utilized as pre-beamformed RF data for the adaptive PA beamforming algorithm, and the new delay function is applied by taking into account that the focus depth in US beamforming is at the half depth of the PA case. The feasibility of the proposed method was validated through simulation, and was experimentally demonstrated using an acoustic point source. The point source was successfully beamformed from a US B-mode image, and the full with at the half maximum of the point improved 3.97 times. Comparing this result to the ground-truth reconstruction using channel data, the FWHM was slightly degraded with 1.28 times caused by information loss during envelope detection and convolution of the RF information.
Lutzweiler, Christian; Razansky, Daniel
2013-01-01
This paper comprehensively reviews the emerging topic of optoacoustic imaging from the image reconstruction and quantification perspective. Optoacoustic imaging combines highly attractive features, including rich contrast and high versatility in sensing diverse biological targets, excellent spatial resolution not compromised by light scattering, and relatively low cost of implementation. Yet, living objects present a complex target for optoacoustic imaging due to the presence of a highly heterogeneous tissue background in the form of strong spatial variations of scattering and absorption. Extracting quantified information on the actual distribution of tissue chromophores and other biomarkers constitutes therefore a challenging problem. Image quantification is further compromised by some frequently-used approximated inversion formulae. In this review, the currently available optoacoustic image reconstruction and quantification approaches are assessed, including back-projection and model-based inversion algorithms, sparse signal representation, wavelet-based approaches, methods for reduction of acoustic artifacts as well as multi-spectral methods for visualization of tissue bio-markers. Applicability of the different methodologies is further analyzed in the context of real-life performance in small animal and clinical in-vivo imaging scenarios. PMID:23736854
Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.
Lai, Zongying; Qu, Xiaobo; Liu, Yunsong; Guo, Di; Ye, Jing; Zhan, Zhifang; Chen, Zhong
2016-01-01
Compressed sensing magnetic resonance imaging has shown great capacity for accelerating magnetic resonance imaging if an image can be sparsely represented. How the image is sparsified seriously affects its reconstruction quality. In the present study, a graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions. With this transform, image patches is viewed as vertices and their differences as edges, and the shortest path on the graph minimizes the total difference of all image patches. Using the l1 norm regularized formulation of the problem solved by an alternating-direction minimization with continuation algorithm, the experimental results demonstrate that the proposed method outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.
Real-time maximum a-posteriori image reconstruction for fluorescence microscopy
NASA Astrophysics Data System (ADS)
Jabbar, Anwar A.; Dilipkumar, Shilpa; C K, Rasmi; Rajan, K.; Mondal, Partha P.
2015-08-01
Rapid reconstruction of multidimensional image is crucial for enabling real-time 3D fluorescence imaging. This becomes a key factor for imaging rapidly occurring events in the cellular environment. To facilitate real-time imaging, we have developed a graphics processing unit (GPU) based real-time maximum a-posteriori (MAP) image reconstruction system. The parallel processing capability of GPU device that consists of a large number of tiny processing cores and the adaptability of image reconstruction algorithm to parallel processing (that employ multiple independent computing modules called threads) results in high temporal resolution. Moreover, the proposed quadratic potential based MAP algorithm effectively deconvolves the images as well as suppresses the noise. The multi-node multi-threaded GPU and the Compute Unified Device Architecture (CUDA) efficiently execute the iterative image reconstruction algorithm that is ≈200-fold faster (for large dataset) when compared to existing CPU based systems.
Image Reconstruction in SNR Units: A General Method for SNR Measurement
Kellman, Peter; McVeigh, Elliot R.
2007-01-01
The method for phased array image reconstruction of uniform noise images may be used in conjunction with proper image scaling as a means of reconstructing images directly in SNR units. This facilitates accurate and precise SNR measurement on a per pixel basis. This method is applicable to root-sum-of-squares magnitude combining, B1-weighted combining, and parallel imaging such as SENSE. A procedure for image reconstruction and scaling is presented, and the method for SNR measurement is validated with phantom data. Alternative methods that rely on noise only regions are not appropriate for parallel imaging where the noise level is highly variable across the field-of-view. The purpose of this article is to provide a nuts and bolts procedure for calculating scale factors used for reconstructing images directly in SNR units. The procedure includes scaling for noise equivalent bandwidth of digital receivers, FFTs and associated window functions (raw data filters), and array combining. PMID:16261576
NASA Astrophysics Data System (ADS)
He, Jinping; Ruan, Ningjuan; Zhao, Haibo; Liu, Yuchen
2016-10-01
Remote sensing features are varied and complicated. There is no comprehensive coverage dictionary for reconstruction. The reconstruction precision is not guaranteed. Aiming at the above problems, a novel reconstruction method with multiple compressed sensing data based on energy compensation is proposed in this paper. The multiple measured data and multiple coding matrices compose the reconstruction equation. It is locally solved through the Orthogonal Matching Pursuit (OMP) algorithm. Then the initial reconstruction image is obtained. Further assuming the local image patches have the same compensation gray value, the mathematical model of compensation value is constructed by minimizing the error of multiple estimated measured values and actual measured values. After solving the minimization, the compensation values are added to the initial reconstruction image. Then the final energy compensation image is obtained. The experiments prove that the energy compensation method is superior to those without compensation. Our method is more suitable for remote sensing features.
MR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation
Gopi, Varun P.; Palanisamy, P.; Wahid, Khan A.; Babyn, Paul
2013-01-01
This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled k-space data. The nonlocal total variation is taken as the L1-regularization functional and solved using Split Bregman iteration. The proposed algorithm is compared with previous methods in terms of the reconstruction accuracy and computational complexity. The comparison results demonstrate the superiority of the proposed algorithm for compressed MR image reconstruction. PMID:23997810
A MATLAB package for the EIDORS project to reconstruct two-dimensional EIT images.
Vauhkonen, M; Lionheart, W R; Heikkinen, L M; Vauhkonen, P J; Kaipio, J P
2001-02-01
The EIDORS (electrical impedance and diffuse optical reconstruction software) project aims to produce a software system for reconstructing images from electrical or diffuse optical data. MATLAB is a software that is used in the EIDORS project for rapid prototyping, graphical user interface construction and image display. We have written a MATLAB package (http://venda.uku.fi/ vauhkon/) which can be used for two-dimensional mesh generation, solving the forward problem and reconstructing and displaying the reconstructed images (resistivity or admittivity). In this paper we briefly describe the mathematical theory on which the codes are based on and also give some examples of the capabilities of the package.
Research on super-resolution image reconstruction based on an improved POCS algorithm
NASA Astrophysics Data System (ADS)
Xu, Haiming; Miao, Hong; Yang, Chong; Xiong, Cheng
2015-07-01
Super-resolution image reconstruction (SRIR) can improve the fuzzy image's resolution; solve the shortage of the spatial resolution, excessive noise, and low-quality problem of the image. Firstly, we introduce the image degradation model to reveal the essence of super-resolution reconstruction process is an ill-posed inverse problem in mathematics. Secondly, analysis the blurring reason of optical imaging process - light diffraction and small angle scattering is the main reason for the fuzzy; propose an image point spread function estimation method and an improved projection onto convex sets (POCS) algorithm which indicate effectiveness by analyzing the changes between the time domain and frequency domain algorithm in the reconstruction process, pointed out that the improved POCS algorithms based on prior knowledge have the effect to restore and approach the high frequency of original image scene. Finally, we apply the algorithm to reconstruct synchrotron radiation computer tomography (SRCT) image, and then use these images to reconstruct the three-dimensional slice images. Comparing the differences between the original method and super-resolution algorithm, it is obvious that the improved POCS algorithm can restrain the noise and enhance the image resolution, so it is indicated that the algorithm is effective. This study and exploration to super-resolution image reconstruction by improved POCS algorithm is proved to be an effective method. It has important significance and broad application prospects - for example, CT medical image processing and SRCT ceramic sintering analyze of microstructure evolution mechanism.
Shan, Xiao-Feng; Li, Ru-Huang; Lu, Xu-Guang; Cai, Zhi-Gang; Zhang, Jie; Zhang, Jian-Guo
2015-03-01
Fibular osteoseptocutaneous flap has been widely used for unilateral mandibular reconstruction. However, reports about the effects of fibular osteoseptocutaneous flap for the reconstruction of bilateral mandibular defects are limited. In this study, we used free vascularized fibular flaps to successfully manage bilateral mandibular osteoradionecrosis(ORN) in 5 patients. Functional aspects were evaluated during the reconstruction process. All 5 patients had bilateral refractory ORN of the mandible and underwent radical resection between 2003 and 2011. The reconstruction surgery was performed in 2 stages using 2 free fibular flaps in 3 patients. In the other 2 patients, reconstruction was performed in a single stage using 2 separate flaps prepared from a single fibula. All patients had a healthy mandibular symphysis and meniscus of the temporomandibular joint, and these structures were preserved during the reconstruction.Of the 10 defects involving the mandible sides, 9 were successfully reconstructed. One microvascular composite flap failed because of radiation injury to the arterial endothelium at the recipient site. After the treatments, all patients had good esthetic and functional outcomes. Preoperative clinical features such as trismus and dysphagia were also markedly improved. Our surgical method may be an effective alternative for the clinical management of advanced bilateral mandibular ORN.
Iterative image reconstruction for planar small animal imaging with a multipinhole collimator
Smith, Mark
2002-07-01
A method to reconstruct images from planar multipinhole single photon projection data using the iterative maximum likelihood expectation maximization algorithm has been developed. The projection and backprojection steps in the algorithm are accomplished using raytracing through each of the pinholes. The method was applied to a Tc-99m syringe and box phantom study and to a Tc- 99m sestamibi cardiac study in a Sprague-Dawley rat. Projection data were acquired using a custom-built detector whose key components were a pixellated array of 1x1x5 mm' NaI(Tl) scintillation crystals, a position-sensitive photomultiplier tube and a four pinhole array. For the phantom study the syringe:box activity concentration ratio was 5:1. Syringe contrast with background was 0.22 in the projection image and 0.59 in the reconstructed image. The reconstructed rat cardiac image shows improved visualization of the left ventricular myocardium. These results are a step toward the practicalapplication of high re
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.
Advanced Airborne Hyperspectral Imaging System (AAHIS)
NASA Astrophysics Data System (ADS)
Topping, Miles Q.; Pfeiffer, Joel E.; Sparks, Andrew W.; Jim, Kevin T. C.; Yoon, Dugan
2002-11-01
The design, operation, and performance of the fourth generation of Science and Technology International's Advanced Airborne Hyperspectral Imaging Sensors (AAHIS) are described. These imaging spectrometers have a variable bandwidth ranging from 390-840 nm. A three-axis image stabilization provides spatially and spectrally coherent imagery by damping most of the airborne platform's random motion. A wide 40-degree field of view coupled with sub-pixel detection allows for a large area coverage rate. A software controlled variable aperture, spectral shaping filters, and high quantum efficiency, back-illuminated CCD's contribute to the excellent sensitivity of the sensors. AAHIS sensors have been operated on a variety of fixed and rotary wing platforms, achieving ground-sampling distances ranging from 6.5 cm to 2 m. While these sensors have been primarily designed for use over littoral zones, they are able to operate over both land and water. AAHIS has been used for detecting and locating submarines, mines, tanks, divers, camouflage and disturbed earth. Civilian applications include search and rescue on land and at sea, agricultural analysis, environmental time-series, coral reef assessment, effluent plume detection, coastal mapping, damage assessment, and seasonal whale population monitoring
NASA Astrophysics Data System (ADS)
Dang, H.; Wang, A. S.; Sussman, Marc S.; Siewerdsen, J. H.; Stayman, J. W.
2014-09-01
Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration, and
Dang, H.; Wang, A. S.; Sussman, Marc S.; Siewerdsen, J. H.; Stayman, J. W.
2014-01-01
Sequential imaging studies are conducted in many clinical scenarios. Prior images from previous studies contain a great deal of patient-specific anatomical information and can be used in conjunction with subsequent imaging acquisitions to maintain image quality while enabling radiation dose reduction (e.g., through sparse angular sampling, reduction in fluence, etc.). However, patient motion between images in such sequences results in misregistration between the prior image and current anatomy. Existing prior-image-based approaches often include only a simple rigid registration step that can be insufficient for capturing complex anatomical motion, introducing detrimental effects in subsequent image reconstruction. In this work, we propose a joint framework that estimates the 3D deformation between an unregistered prior image and the current anatomy (based on a subsequent data acquisition) and reconstructs the current anatomical image using a model-based reconstruction approach that includes regularization based on the deformed prior image. This framework is referred to as deformable prior image registration, penalized-likelihood estimation (dPIRPLE). Central to this framework is the inclusion of a 3D B-spline-based free-form-deformation model into the joint registration-reconstruction objective function. The proposed framework is solved using a maximization strategy whereby alternating updates to the registration parameters and image estimates are applied allowing for improvements in both the registration and reconstruction throughout the optimization process. Cadaver experiments were conducted on a cone-beam CT testbench emulating a lung nodule surveillance scenario. Superior reconstruction accuracy and image quality were demonstrated using the dPIRPLE algorithm as compared to more traditional reconstruction methods including filtered backprojection, penalized-likelihood estimation (PLE), prior image penalized-likelihood estimation (PIPLE) without registration
Eck, Brendan L.; Fahmi, Rachid; Brown, Kevin M.; Zabic, Stanislav; Raihani, Nilgoun; Miao, Jun; Wilson, David L.
2015-01-01
model complexity according to AICc. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments. PMID:26429285
Eck, Brendan L.; Fahmi, Rachid; Miao, Jun; Brown, Kevin M.; Zabic, Stanislav; Raihani, Nilgoun; Wilson, David L.
2015-10-15
and model complexity according to AIC{sub c}. With parameters fixed, the model reasonably predicted detectability of human observers in blended FBP-IMR images. Semianalytic internal noise computation gave results equivalent to Monte Carlo, greatly speeding parameter estimation. Using Model-k4, the authors found an average detectability improvement of 2.7 ± 0.4 times that of FBP. IMR showed greater improvements in detectability with larger signals and relatively consistent improvements across signal contrast and x-ray dose. In the phantom tested, Model-k4 predicted an 82% dose reduction compared to FBP, verified with physical CT scans at 80% reduced dose. Conclusions: IMR improves detectability over FBP and may enable significant dose reductions. A channelized Hotelling observer with internal noise proportional to channel output standard deviation agreed well with human observers across a wide range of variables, even across reconstructions with drastically different image characteristics. Utility of the model observer was demonstrated by predicting the effect of image processing (blending), analyzing detectability improvements with IMR across dose, size, and contrast, and in guiding real CT scan dose reduction experiments. Such a model observer can be applied in optimizing parameters in advanced iterative reconstruction algorithms as well as guiding dose reduction protocols in physical CT experiments.
Simultaneous reconstruction of the activity image and registration of the CT image in TOF-PET
NASA Astrophysics Data System (ADS)
Rezaei, Ahmadreza; Michel, Christian; Casey, Michael E.; Nuyts, Johan
2016-02-01
Previously, maximum-likelihood methods have been proposed to jointly estimate the activity image and the attenuation image or the attenuation sinogram from time-of-flight (TOF) positron emission tomography (PET) data. In this contribution, we propose a method that addresses the possible alignment problem of the TOF-PET emission data and the computed tomography (CT) attenuation data, by combining reconstruction and registration. The method, called MLRR, iteratively reconstructs the activity image while registering the available CT-based attenuation image, so that the pair of activity and attenuation images maximise the likelihood of the TOF emission sinogram. The algorithm is slow to converge, but some acceleration could be achieved by using Nesterov’s momentum method and by applying a multi-resolution scheme for the non-rigid displacement estimation. The latter also helps to avoid local optima, although convergence to the global optimum cannot be guaranteed. The results are evaluated on 2D and 3D simulations as well as a respiratory gated clinical scan. Our experiments indicate that the proposed method is able to correct for possible misalignment of the CT-based attenuation image, and is therefore a very promising approach to suppressing attenuation artefacts in clinical PET/CT. When applied to respiratory gated data of a patient scan, it produced deformations that are compatible with breathing motion and which reduced the well known attenuation artefact near the dome of the liver. Since the method makes use of the energy-converted CT attenuation image, the scale problem of joint reconstruction is automatically solved.
NASA Astrophysics Data System (ADS)
Field, Jeffrey J.; Bartels, Randy A.
2016-03-01
Coherent holographic image reconstruction by phase transfer (CHIRPT) is an imaging method that permits digital holographic propagation of fluorescent light. The image formation process in CHIRPT is based on illuminating the specimen with a precisely controlled spatio-temporally varying intensity pattern. This pattern is formed by focusing a spatially coherent illumination beam to a line focus on a spinning modulation mask, and image relaying the mask plane to the focal plane of an objective lens. Deviations from the designed spatio-temporal illumination pattern due to imperfect mounting of the circular modulation mask onto the rotation motor induce aberrations in the recovered image. Here we show that these aberrations can be measured and removed non-iteratively by measuring the disk aberration phase externally. We also demonstrate measurement and correction of systematic optical aberrations in the CHIRPT microscope.
Bayer patterned high dynamic range image reconstruction using adaptive weighting function
NASA Astrophysics Data System (ADS)
Kang, Hee; Lee, Suk Ho; Song, Ki Sun; Kang, Moon Gi
2014-12-01
It is not easy to acquire a desired high dynamic range (HDR) image directly from a camera due to the limited dynamic range of most image sensors. Therefore, generally, a post-process called HDR image reconstruction is used, which reconstructs an HDR image from a set of differently exposed images to overcome the limited dynamic range. However, conventional HDR image reconstruction methods suffer from noise factors and ghost artifacts. This is due to the fact that the input images taken with a short exposure time contain much noise in the dark regions, which contributes to increased noise in the corresponding dark regions of the reconstructed HDR image. Furthermore, since input images are acquired at different times, the images contain different motion information, which results in ghost artifacts. In this paper, we propose an HDR image reconstruction method which reduces the impact of the noise factors and prevents ghost artifacts. To reduce the influence of the noise factors, the weighting function, which determines the contribution of a certain input image to the reconstructed HDR image, is designed to adapt to the exposure time and local motions. Furthermore, the weighting function is designed to exclude ghosting regions by considering the differences of the luminance and the chrominance values between several input images. Unlike conventional methods, which generally work on a color image processed by the image processing module (IPM), the proposed method works directly on the Bayer raw image. This allows for a linear camera response function and also improves the efficiency in hardware implementation. Experimental results show that the proposed method can reconstruct high-quality Bayer patterned HDR images while being robust against ghost artifacts and noise factors.
Preoperative digital mammography imaging in conservative mastectomy and immediate reconstruction
Angrigiani, Claudio; Hammond, Dennis; Nava, Maurizio; Gonzalez, Eduardo; Rostagno, Roman; Gercovich, Gustavo
2016-01-01
Background Digital mammography clearly distinguishes gland tissue density from the overlying non-glandular breast tissue coverage, which corresponds to the existing tissue between the skin and the Cooper’s ligaments surrounding the gland (i.e., dermis and subcutaneous fat). Preoperative digital imaging can determine the thickness of this breast tissue coverage, thus facilitating planning of the most adequate surgical techniques and reconstructive procedures for each case. Methods This study aimed to describe the results of a retrospective study of 352 digital mammograms in 176 patients with different breast volumes who underwent preoperative conservative mastectomies. The breast tissue coverage thickness and its relationship with the breast volume were evaluated. Results The breast tissue coverage thickness ranged from 0.233 to 4.423 cm, with a mean value of 1.952 cm. A comparison of tissue coverage and breast volume revealed a non-direct relationship between these factors. Conclusions Preoperative planning should not depend only on breast volume. Flap evaluations based on preoperative imaging measurements might be helpful when planning a conservative mastectomy. Accordingly, we propose a breast tissue coverage classification (BTCC). PMID:26855903
Multiframe image point matching and 3-d surface reconstruction.
Tsai, R Y
1983-02-01
This paper presents two new methods, the Joint Moment Method (JMM) and the Window Variance Method (WVM), for image matching and 3-D object surface reconstruction using multiple perspective views. The viewing positions and orientations for these perspective views are known a priori, as is usually the case for such applications as robotics and industrial vision as well as close range photogrammetry. Like the conventional two-frame correlation method, the JMM and WVM require finding the extrema of 1-D curves, which are proved to theoretically approach a delta function exponentially as the number of frames increases for the JMM and are much sharper than the two-frame correlation function for both the JMM and the WVM, even when the image point to be matched cannot be easily distinguished from some of the other points. The theoretical findings have been supported by simulations. It is also proved that JMM and WVM are not sensitive to certain radiometric effects. If the same window size is used, the computational complexity for the proposed methods is about n - 1 times that for the two-frame method where n is the number of frames. Simulation results show that the JMM and WVM require smaller windows than the two-frame correlation method with better accuracy, and therefore may even be more computationally feasible than the latter since the computational complexity increases quadratically as a function of the window size.
Advanced MR Imaging in Pediatric Brain Tumors, Clinical Applications.
Lequin, Maarten; Hendrikse, Jeroen
2017-02-01
Advanced MR imaging techniques, such as spectroscopy, perfusion, diffusion, and functional imaging, have improved the diagnosis of brain tumors in children and also play an important role in defining surgical as well as therapeutic responses in these patients. In addition to the anatomic or structural information gained with conventional MR imaging sequences, advanced MR imaging techniques also provide physiologic information about tumor morphology, metabolism, and hemodynamics. This article reviews the physiology, techniques, and clinical applications of diffusion-weighted and diffusion tensor imaging, MR spectroscopy, perfusion MR imaging, susceptibility-weighted imaging, and functional MR imaging in the setting of neuro-oncology.
Chen, Shuo; Wang, Gang; Cui, Xiaoyu; Liu, Quan
2017-01-23
Raman spectroscopy has demonstrated great potential in biomedical applications. However, spectroscopic Raman imaging is limited in the investigation of fast changing phenomena because of slow data acquisition. Our previous studies have indicated that spectroscopic Raman imaging can be significantly sped up using the approach of narrow-band imaging followed by spectral reconstruction. A multi-channel system was built to demonstrate the feasibility of fast wide-field spectroscopic Raman imaging using the approach of simultaneous narrow-band image acquisition followed by spectral reconstruction based on Wiener estimation in phantoms. To further improve the accuracy of reconstructed Raman spectra, we propose a stepwise spectral reconstruction method in this study, which can be combined with the earlier developed sequential weighted Wiener estimation to improve spectral reconstruction accuracy. The stepwise spectral reconstruction method first reconstructs the fluorescence background spectrum from narrow-band measurements and then the pure Raman narrow-band measurements can be estimated by subtracting the estimated fluorescence background from the overall narrow-band measurements. Thereafter, the pure Raman spectrum can be reconstructed from the estimated pure Raman narrow-band measurements. The result indicates that the stepwise spectral reconstruction method can improve spectral reconstruction accuracy significantly when combined with sequential weighted Wiener estimation, compared with the traditional Wiener estimation. In addition, qualitatively accurate cell Raman spectra were successfully reconstructed using the stepwise spectral reconstruction method from the narrow-band measurements acquired by a four-channel wide-field Raman spectroscopic imaging system. This method can potentially facilitate the adoption of spectroscopic Raman imaging to the investigation of fast changing phenomena.
NASA Astrophysics Data System (ADS)
Müller, K.; Maier, A. K.; Schwemmer, C.; Lauritsch, G.; De Buck, S.; Wielandts, J.-Y.; Hornegger, J.; Fahrig, R.
2014-06-01
The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical
Recent advances in imaging subcellular processes.
Myers, Kenneth A; Janetopoulos, Christopher
2016-01-01
Cell biology came about with the ability to first visualize cells. As microscopy techniques advanced, the early microscopists became the first cell biologists to observe the inner workings and subcellular structures that control life. This ability to see organelles within a cell provided scientists with the first understanding of how cells function. The visualization of the dynamic architecture of subcellular structures now often drives questions as researchers seek to understand the intricacies of the cell. With the advent of fluorescent labeling techniques, better and new optical techniques, and more sensitive and faster cameras, a whole array of questions can now be asked. There has been an explosion of new light microscopic techniques, and the race is on to build better and more powerful imaging systems so that we can further our understanding of the spatial and temporal mechanisms controlling molecular cell biology.
Recent advances in imaging subcellular processes
Myers, Kenneth A.; Janetopoulos, Christopher
2016-01-01
Cell biology came about with the ability to first visualize cells. As microscopy techniques advanced, the early microscopists became the first cell biologists to observe the inner workings and subcellular structures that control life. This ability to see organelles within a cell provided scientists with the first understanding of how cells function. The visualization of the dynamic architecture of subcellular structures now often drives questions as researchers seek to understand the intricacies of the cell. With the advent of fluorescent labeling techniques, better and new optical techniques, and more sensitive and faster cameras, a whole array of questions can now be asked. There has been an explosion of new light microscopic techniques, and the race is on to build better and more powerful imaging systems so that we can further our understanding of the spatial and temporal mechanisms controlling molecular cell biology. PMID:27408708
Rahmat, Mohd Fua'ad; Isa, Mohd Daud; Rahim, Ruzairi Abdul; Hussin, Tengku Ahmad Raja
2009-01-01
Electrical charge tomography (EChT) is a non-invasive imaging technique that is aimed to reconstruct the image of materials being conveyed based on data measured by an electrodynamics sensor installed around the pipe. Image reconstruction in electrical charge tomography is vital and has not been widely studied before. Three methods have been introduced before, namely the linear back projection method, the filtered back projection method and the least square method. These methods normally face ill-posed problems and their solutions are unstable and inaccurate. In order to ensure the stability and accuracy, a special solution should be applied to obtain a meaningful image reconstruction result. In this paper, a new image reconstruction method - Least squares with regularization (LSR) will be introduced to reconstruct the image of material in a gravity mode conveyor pipeline for electrical charge tomography. Numerical analysis results based on simulation data indicated that this algorithm efficiently overcomes the numerical instability. The results show that the accuracy of the reconstruction images obtained using the proposed algorithm was enhanced and similar to the image captured by a CCD Camera. As a result, an efficient method for electrical charge tomography image reconstruction has been introduced.
Online 4d Reconstruction Using Multi-Images Available Under Open Access
NASA Astrophysics Data System (ADS)
Ioannides, M.; Hadjiprocopi, A.; Doulamis, N.; Doulamis, A.; Protopapadakis, E.; Makantasis, K.; Santos, P.; Fellner, D.; Stork, A.; Balet, O.; Julien, M.; Weinlinger, G.; Johnson, P. S.; Klein, M.; Fritsch, D.
2013-07-01
The advent of technology in digital cameras and their incorporation into virtually any smart mobile device has led to an explosion of the number of photographs taken every day. Today, the number of images stored online and available freely has reached unprecedented levels. It is estimated that in 2011, there were over 100 billion photographs stored in just one of the major social media sites. This number is growing exponentially. Moreover, advances in the fields of Photogrammetry and Computer Vision have led to significant breakthroughs such as the Structure from Motion algorithm which creates 3D models of objects using their twodimensional photographs. The existence of powerful and affordable computational machinery not only the reconstruction of complex structures but also entire cities. This paper illustrates an overview of our methodology for producing 3D models of Cultural Heritage structures such as monuments and artefacts from 2D data (pictures, video), available on Internet repositories, social media, Google Maps, Bing, etc. We also present new approaches to semantic enrichment of the end results and their subsequent export to Europeana, the European digital library, for integrated, interactive 3D visualisation within regular web browsers using WebGl and X3D. Our main goal is to enable historians, architects, archaeologists, urban planners and affiliated professionals to reconstruct views of historical structures from millions of images floating around the web and interact with them.
On multigrid methods for image reconstruction from projections
Henson, V.E.; Robinson, B.T.; Limber, M.
1994-12-31
The sampled Radon transform of a 2D function can be represented as a continuous linear map R : L{sup 1} {yields} R{sup N}. The image reconstruction problem is: given a vector b {element_of} R{sup N}, find an image (or density function) u(x, y) such that Ru = b. Since in general there are infinitely many solutions, the authors pick the solution with minimal 2-norm. Numerous proposals have been made regarding how best to discretize this problem. One can, for example, select a set of functions {phi}{sub j} that span a particular subspace {Omega} {contained_in} L{sup 1}, and model R : {Omega} {yields} R{sup N}. The subspace {Omega} may be chosen as a member of a sequence of subspaces whose limit is dense in L{sup 1}. One approach to the choice of {Omega} gives rise to a natural pixel discretization of the image space. Two possible choices of the set {phi}{sub j} are the set of characteristic functions of finite-width `strips` representing energy transmission paths and the set of intersections of such strips. The authors have studied the eigenstructure of the matrices B resulting from these choices and the effect of applying a Gauss-Seidel iteration to the problem Bw = b. There exists a near null space into which the error vectors migrate with iteration, after which Gauss-Seidel iteration stalls. The authors attempt to accelerate convergence via a multilevel scheme, based on the principles of McCormick`s Multilevel Projection Method (PML). Coarsening is achieved by thickening the rays which results in a much smaller discretization of an optimal grid, and a halving of the number of variables. This approach satisfies all the requirements of the PML scheme. They have observed that a multilevel approach based on this idea accelerates convergence at least to the point where noise in the data dominates.
Image reconstruction method IRBis for optical/infrared long-baseline interferometry
NASA Astrophysics Data System (ADS)
Hofmann, Karl-Heinz; Heininger, Matthias; Schertl, Dieter; Weigelt, Gerd; Millour, Florentin; Berio, Philippe
2016-07-01
IRBis is an image reconstruction method for optical/infrared long-baseline interferometry. IRBis can reconstruct images from (a) measured visibilities and closure phases, or from (b) measured complex visibilities (i.e. the Fourier phases and visibilities). The applied optimization routine ASA CG is based on conjugate gradients. The method allows the user to implement different regularizers, as for example, maximum entropy, smoothness, total variation, etc., and apply residual ratios as an additional metric for goodness-of-fit. In addition, IRBis allows the user to change the following reconstruction parameters: (a) FOV of the area to be reconstructed, (b) the size of the pixel-grid used, (c) size of a binary mask in image space allowing reconstructed intensities < 0 within the binary mask only, (d) the strength of the regularization, etc. The two main reconstruction parameters are the size of the binary mask in image space (c) and the strength of the regularization (d). Several values of these two parameters are tested within the algorithm. The quality of the different reconstructions obtained is roughly estimated by evaluation of the differences between the measured data and the reconstructed image (using the reduced χ2 values and the residual ratios). The best-quality reconstruction and a few reconstructions sorted according to their quality are provided to the user as resulting reconstructions. We describe the theory of IRBis and will present several applications to simulated interferometric data and data of real astronomical objects: (a) We have investigated image reconstruction experiments of MATISSE target candidates by computer simulations. We have modeled gaps in a disk of a young stellar object and have simulated interferometric data (squared visibilities and closure phases) with a signal-to-noise ratio as expected for MATISSE observations. We have performed image reconstruction experiments with this model for different flux levels of the target and
Ceritoglu, Can; Wang, Lei; Selemon, Lynn D.; Csernansky, John G.; Miller, Michael I.; Ratnanather, J. Tilak
2009-01-01
Our current understanding of neuroanatomical abnormalities in neuropsychiatric diseases is based largely on magnetic resonance imaging (MRI) and post mortem histological analyses of the brain. Further advances in elucidating altered brain structure in these human conditions might emerge from combining MRI and histological methods. We propose a multistage method for registering 3D volumes reconstructed from histological sections to corresponding in vivo MRI volumes from the same subjects: (1) manual segmentation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) compartments in histological sections, (2) alignment of consecutive histological sections using 2D rigid transformation to construct a 3D histological image volume from the aligned sections, (3) registration of reconstructed 3D histological volumes to the corresponding 3D MRI volumes using 3D affine transformation, (4) intensity normalization of images via histogram matching, and (5) registration of the volumes via intensity based large deformation diffeomorphic metric (LDDMM) image matching algorithm. Here we demonstrate the utility of our method in the transfer of cytoarchitectonic information from histological sections to identify regions of interest in MRI scans of nine adult macaque brains for morphometric analyses. LDDMM improved the accuracy of the registration via decreased distances between GM/CSF surfaces after LDDMM (0.39 ± 0.13 mm) compared to distances after affine registration (0.76 ± 0.41 mm). Similarly, WM/GM distances decreased to 0.28 ± 0.16 mm after LDDMM compared to 0.54 ± 0.39 mm after affine registration. The multistage registration method may find broad application for mapping histologically based information, for example, receptor distributions, gene expression, onto MRI volumes. PMID:20577633
Optimization-based reconstruction of sparse images from few-view projections
NASA Astrophysics Data System (ADS)
Han, Xiao; Bian, Junguo; Ritman, Erik L.; Sidky, Emil Y.; Pan, Xiaochuan
2012-08-01
In this work, we investigate optimization-based image reconstruction from few-view (i.e. less than ten views) projections of sparse objects such as coronary-artery specimens. Using optimization programs as a guide, we formulate constraint programs as reconstruction programs and develop algorithms to reconstruct images through solving the reconstruction programs. Characterization studies are carried out for elucidating the algorithm properties of ‘convergence’ (relative to designed solutions) and ‘utility’ (relative to desired solutions) by using simulated few-view data calculated from a discrete FORBILD coronary-artery phantom, and real few-view data acquired from a human coronary-artery specimen. Study results suggest that carefully designed reconstruction programs and algorithms can yield accurate reconstructions of sparse images from few-view projections.
Image reconstruction from phased-array data based on multichannel blind deconvolution.
She, Huajun; Chen, Rong-Rong; Liang, Dong; Chang, Yuchou; Ying, Leslie
2015-11-01
In this paper we consider image reconstruction from fully sampled multichannel phased array MRI data without knowledge of the coil sensitivities. To overcome the non-uniformity of the conventional sum-of-square reconstruction, a new framework based on multichannel blind deconvolution (MBD) is developed for joint estimation of the image function and the sensitivity functions in image domain. The proposed approach addresses the non-uniqueness of the MBD problem by exploiting the smoothness of both functions in the image domain through regularization. Results using simulation, phantom and in vivo experiments demonstrate that the reconstructions by the proposed algorithm are more uniform than those by the existing methods.
Image reconstruction and image quality evaluation for a 16-slice CT scanner.
Flohr, Th; Stierstorfer, K; Bruder, H; Simon, J; Polacin, A; Schaller, S
2003-05-01
We present a theoretical overview and a performance evaluation of a novel approximate reconstruction algorithm for cone-beam spiral CT, the adaptive multiple plane reconstruction (AMPR), which has been introduced by Schaller, Flohr et al. [Proc. SPIE Int. Symp. Med. Imag. 4322, 113-127 (2001)] AMPR has been implemented in a recently introduced 16-slice CT scanner. We present a detailed algorithmic description of AMPR which allows for a free selection of the spiral pitch. We show that dose utilization is better than 90% independent of the pitch. We give an overview on the z-reformation functions chosen to allow for a variable selection of the spiral slice width at arbitrary pitch values. To investigate AMPR image quality we present images of anthropomorphic phantoms and initial patient results. We present measurements of spiral slice sensitivity profiles (SSPs) and measurements of the maximum achievable transverse resolution, both in the isocenter and off-center. We discuss the pitch dependence of image noise measured in a centered 20 cm water phantom. Using the AMPR approach, cone-beam artifacts are considerably reduced for the 16-slice scanner investigated. Image quality in MPRs is independent of the pitch and equivalent to a single-slice CT system at pitch p approximately 1.5. The full width at half-maximum (FWHM) of the spiral SSPs shows only minor variations as a function of the pitch, nominal, and measured values differ by less than 0.2 mm. With 16 x 0.75 mm collimation, the measured FWHM of the smallest reconstructed slice is about 0.9 mm. Using this slice width and overlapping image reconstruction, cylindrical holes with 0.6 mm diameter can be resolved in a z-resolution phantom. Image noise for constant effective mAs is nearly independent of the pitch. Measured and theoretically expected dose utilization are in good agreement. Meanwhile, clinical practice has demonstrated the excellent image quality and the increased diagnostic capability that is obtained
NASA Astrophysics Data System (ADS)
Choi, Joonsung; Kim, Dongchan; Oh, Changhyun; Han, Yeji; Park, HyunWook
2013-05-01
In MRI (magnetic resonance imaging), signal sampling along a radial k-space trajectory is preferred in certain applications due to its distinct advantages such as robustness to motion, and the radial sampling can be beneficial for reconstruction algorithms such as parallel MRI (pMRI) due to the incoherency. For radial MRI, the image is usually reconstructed from projection data using analytic methods such as filtered back-projection or Fourier reconstruction after gridding. However, the quality of the reconstructed image from these analytic methods can be degraded when the number of acquired projection views is insufficient. In this paper, we propose a novel reconstruction method based on the expectation maximization (EM) method, where the EM algorithm is remodeled for MRI so that complex images can be reconstructed. Then, to optimize the proposed method for radial pMRI, a reconstruction method that uses coil sensitivity information of multichannel RF coils is formulated. Experiment results from synthetic and in vivo data show that the proposed method introduces better reconstructed images than the analytic methods, even from highly subsampled data, and provides monotonic convergence properties compared to the conjugate gradient based reconstruction method.
Recent advances in human viruses imaging studies.
Florian, Paula Ecaterina; Rouillé, Yves; Ruta, Simona; Nichita, Norica; Roseanu, Anca
2016-06-01
Microscopy techniques are often exploited by virologists to investigate molecular details of critical steps in viruses' life cycles such as host cell recognition and entry, genome replication, intracellular trafficking, and release of mature virions. Fluorescence microscopy is the most attractive tool employed to detect intracellular localizations of various stages of the viral infection and monitor the pathogen-host interactions associated with them. Super-resolution microscopy techniques have overcome the technical limitations of conventional microscopy and offered new exciting insights into the formation and trafficking of human viruses. In addition, the development of state-of-the art electron microscopy techniques has become particularly important in studying virus morphogenesis by revealing ground-braking ultrastructural details of this process. This review provides recent advances in human viruses imaging in both, in vitro cell culture systems and in vivo, in the animal models recently developed. The newly available imaging technologies bring a major contribution to our understanding of virus pathogenesis and will become an important tool in early diagnosis of viral infection and the development of novel therapeutics to combat the disease.
Iterative reconstruction in image space (IRIS) in cardiac computed tomography: initial experience.
Bittencourt, Márcio Sommer; Schmidt, Bernhard; Seltmann, Martin; Muschiol, Gerd; Ropers, Dieter; Daniel, Werner Günther; Achenbach, Stephan
2011-10-01
Improvements in image quality in cardiac computed tomography may be achieved through iterative image reconstruction techniques. We evaluated the ability of "Iterative Reconstruction in Image Space" (IRIS) reconstruction to reduce image noise and improve subjective image quality. 55 consecutive patients undergoing coronary CT angiography to rule out coronary artery stenosis were included. A dual source CT system and standard protocols were used. Images were reconstructed using standard filtered back projection and IRIS. Image noise, attenuation within the coronary arteries, contrast, signal to noise and contrast to noise parameters as well as subjective classification of image quality (using a scale with four categories) were evaluated and compared between the two image reconstruction protocols. Subjective image quality (2.8 ± 0.4 in filtered back projection and 2.8 ± 0.4 in iterative reconstruction) and the number of "evaluable" segments per patient 14.0 ± 1.2 in filtered back projection and 14.1 ± 1.1 in iterative reconstruction) were not significant different between the two methods. However iterative reconstruction had a lower image noise (22.6 ± 4.5 HU vs. 28.6 ± 5.1 HU) and higher signal to noise and image to noise ratios in the proximal coronary arteries. IRIS reduces image noise and contrast-to-noise ratio in coronary CT angiography, thus providing potential for reducing radiation exposure.
Texture-preserving Bayesian image reconstruction for low-dose CT
NASA Astrophysics Data System (ADS)
Zhang, Hao; Han, Hao; Hu, Yifan; Liu, Yan; Ma, Jianhua; Li, Lihong; Moore, William; Liang, Zhengrong
2016-03-01
Markov random field (MRF) model has been widely used in Bayesian image reconstruction to reconstruct piecewise smooth images in the presence of noise, such as in low-dose X-ray computed tomography (LdCT). While it can preserve edge sharpness via edge-preserving potential function, its regional smoothing may sacrifice tissue image textures, which have been recognized as useful imaging biomarkers, and thus it compromises clinical tasks such as differentiating malignant vs. benign lesions, e.g., lung nodule or colon polyp. This study aims to shift the edge preserving regional noise smoothing paradigm to texture-preserving framework for LdCT image reconstruction while retaining the advantage of MRF's neighborhood system on edge preservation. Specifically, we adapted the MRF model to incorporate the image textures of lung, bone, fat, muscle, etc. from previous full-dose CT scan as a priori knowledge for texture-preserving Bayesian reconstruction of current LdCT images. To show the feasibility of proposed reconstruction framework, experiments using clinical patient scans (with lung nodule or colon polyp) were conducted. The experimental outcomes showed noticeable gain by the a priori knowledge for LdCT image reconstruction with the well-known Haralick texture measures. Thus, it is conjectured that texture-preserving LdCT reconstruction has advantages over edge-preserving regional smoothing paradigm for texture-specific clinical applications.
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.
Sato, T; Norton, S J; Linzer, M; Ikeda, O; Hirama, M
1981-02-01
An iterative technique is proposed for improving the quality of reconstructions from projections when the number of projections is small or the angular range of projections is limited. The technique consists of transforming repeatedly between image and transform spaces and applying a priori object information at each iteration. The approach is a generalization of the Gerchberg-Papoulis algorithm, a technique for extrapolating in the Fourier domain by imposing a space-limiting constraint on the object in the spatial domain. A priori object data that may be applied, in addition to truncating the image beyond the known boundaries of the object, include limiting the maximum range of variation of the physical parameter being imaged. The results of computer simulations show clearly how the process of forcing the image to conform to a priori object data reduces artifacts arising from limited data available in the Fourier domain.
NASA Astrophysics Data System (ADS)
Zhu, Liren; Chen, Yujia; Liang, Jinyang; Gao, Liang; Ma, Cheng; Wang, Lihong V.
2016-03-01
The single-shot compressed ultrafast photography (CUP) camera is the fastest receive-only camera in the world. In this work, we introduce an external CCD camera and a space- and intensity-constrained (SIC) reconstruction algorithm to improve the image quality of CUP. The CCD camera takes a time-unsheared image of the dynamic scene. Unlike the previously used unconstrained algorithm, the proposed algorithm incorporates both spatial and intensity constraints, based on the additional prior information provided by the external CCD camera. First, a spatial mask is extracted from the time-unsheared image to define the zone of action. Second, an intensity threshold constraint is determined based on the similarity between the temporally projected image of the reconstructed datacube and the time-unsheared image taken by the external CCD. Both simulation and experimental studies showed that the SIC reconstruction improves the spatial resolution, contrast, and general quality of the reconstructed image.
An Lq-Lp optimization framework for image reconstruction of electrical resistance tomography
NASA Astrophysics Data System (ADS)
Zhao, Jia; Xu, Yanbin; Dong, Feng
2014-12-01
Image reconstruction in electrical resistance tomography (ERT) is an ill-posed and nonlinear problem, which is easily affected by measurement noise. The regularization method with L2 constraint term or L1 constraint term is often used to solve the inverse problem of ERT. It shows that the reconstruction method with L2 regularization puts smoothness to obtain stability in the image reconstruction process, which is blurry at the interface of different conductivities. The regularization method with L1 norm is powerful at dealing with the over-smoothing effects, which is beneficial in obtaining a sharp transaction in conductivity distribution. To find the reason for these effects, an Lq-Lp optimization framework (1 ⩽ q ⩽ 2, 1 ⩽ p ⩽ 2) for the image reconstruction of ERT is presented in this paper. The Lq-Lp optimization framework is solved based on an approximation handling with Gauss-Newton iteration algorithm. The optimization framework is tested for image reconstruction of ERT with different models and the effects of the Lp regularization term on the quality of the reconstructed images are discussed with both simulation and experiment. By comparing the reconstructed results with different p in the regularization term, it is found that a large penalty is implemented on small data in the solution when p is small and a lesser penalty is implemented on small data in the solution when p is larger. It also makes the reconstructed images smoother and more easily affected by noise when p is larger.
NASA Astrophysics Data System (ADS)
Althobaiti, Murad; Vavadi, Hamed; Zhu, Quing
2017-02-01
Ultrasound-guided diffuse optical tomography (DOT) is a promising imaging technique that maps hemoglobin concentrations of breast lesions to assist ultrasound (US) for cancer diagnosis and treatment monitoring. The accurate recovery of breast lesion optical properties requires an effective image reconstruction method. We introduce a reconstruction approach in which US images are encoded as prior information for regularization of the inversion matrix. The framework of this approach is based on image reconstruction package "NIRFAST." We compare this approach to the US-guided dual-zone mesh reconstruction method, which is based on Born approximation and conjugate gradient optimization developed in our laboratory. Results were evaluated using phantoms and clinical data. This method improves classification of malignant and benign lesions by increasing malignant to benign lesion absorption contrast. The results also show improvements in reconstructed lesion shapes and the spatial distribution of absorption maps.
Advances in diagnostic imaging for pathologic conditions of the jaws.
Benson, Byron W; Flint, Diane J; Liang, Hui; Opatowsky, Michael J
2014-12-01
Advances in dental and maxillofacial imaging are delineated along with the advantages and disadvantages of each imaging modality. The imaging modalities that are included are intraoral radiography, panoramic radiography, cone-beam computed tomography, multidetector computed tomography, magnetic resonance imaging, nuclear medicine, and ultrasound.
Region-of-interest image reconstruction in circular cone-beam microCT
Cho, Seungryong; Bian, Junguo; Pelizzari, Charles A.; Chen, C.-T.; He, T.-C.; Pan Xiaochuan
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 resolution 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.
NASA Astrophysics Data System (ADS)
Chou, C. S.; Tang, Y. P.; Chu, F. S.; Huang, W. C.; Liu, R. G.; Gau, T. S.
2012-03-01
Calibration of mask images on wafer becomes more important as features shrink. Two major types of metrology have been commonly adopted. One is to measure the mask image with scanning electron microscope (SEM) to obtain the contours on mask and then simulate the wafer image with optical simulator. The other is to use an optical imaging tool Aerial Image Measurement System (AIMSTM) to emulate the image on wafer. However, the SEM method is indirect. It just gathers planar contours on a mask with no consideration of optical characteristics such as 3D topography structures. Hence, the image on wafer is not predicted precisely. Though the AIMSTM method can be used to directly measure the intensity at the near field of a mask but the image measured this way is not quite the same as that on the wafer due to reflections and refractions in the films on wafer. Here, a new approach is proposed to emulate the image on wafer more precisely. The behavior of plane waves with different oblique angles is well known inside and between planar film stacks. In an optical microscope imaging system, plane waves can be extracted from the pupil plane with a coherent point source of illumination. Once plane waves with a specific coherent illumination are analyzed, the partially coherent component of waves could be reconstructed with a proper transfer function, which includes lens aberration, polarization, reflection and refraction in films. It is a new method that we can transfer near light field of a mask into an image on wafer without the disadvantages of indirect SEM measurement such as neglecting effects of mask topography, reflections and refractions in the wafer film stacks. Furthermore, with this precise latent image, a separated resist model also becomes more achievable.
3-D Reconstruction From 2-D Radiographic Images and Its Application to Clinical Veterinary Medicine
NASA Astrophysics Data System (ADS)
Hamamoto, Kazuhiko; Sato, Motoyoshi
3D imaging technique is very important and indispensable in diagnosis. The main stream of the technique is one in which 3D image is reconstructed from a set of slice images, such as X-ray CT and MRI. However, these systems require large space and high costs. On the other hand, a low cost and small size 3D imaging system is needed in clinical veterinary medicine, for example, in the case of diagnosis in X-ray car or pasture area. We propose a novel 3D imaging technique using 2-D X-ray radiographic images. This system can be realized by cheaper system than X-ray CT and enables to get 3D image in X-ray car or portable X-ray equipment. In this paper, a 3D visualization technique from 2-D radiographic images is proposed and several reconstructions are shown. These reconstructions are evaluated by veterinarians.
Parametric image reconstruction using the discrete cosine transform for optical tomography.
Gu, Xuejun; Ren, Kui; Masciotti, James; Hielscher, Andreas H
2009-01-01
It is well known that the inverse problem in optical tomography is highly ill-posed. The image reconstruction process is often unstable and nonunique, because the number of the boundary measurements data is far fewer than the number of the unknown parameters to be reconstructed. To overcome this problem, one can either increase the number of measurement data (e.g., multispectral or multifrequency methods), or reduce the number of unknowns (e.g., using prior structural information from other imaging modalities). We introduce a novel approach for reducing the unknown parameters in the reconstruction process. The discrete cosine transform (DCT), which has long been used in image compression, is here employed to parameterize the reconstructed image. In general, only a few DCT coefficients are needed to describe the main features in an optical tomographic image. Thus, the number of unknowns in the image reconstruction process can be drastically reduced. We show numerical and experimental examples that illustrate the performance of the new algorithm as compared to a standard model-based iterative image reconstructions scheme. We especially focus on the influence of initial guesses and noise levels on the reconstruction results.
NASA Astrophysics Data System (ADS)
Song, Xizi; Xu, Yanbin; Dong, Feng
2016-11-01
A new image reconstruction framework based on boundary voltages is presented for ultrasound modulated electrical impedance tomography (UMEIT). Combining the electric and acoustic modalities, UMEIT reconstructs the conductivity distribution with more measurements with position information. The proposed image reconstruction framework begins with approximately constructing the sensitivity matrix of the imaging object with inclusion. Then the conductivity is recovered from the boundary voltages of the imaging object. To solve the nonlinear inverse problem, an optimization method is adopted and the iterative method is tested. Compared with that for electrical resistance tomography (ERT), the newly constructed sensitivity matrix is more sensitive to the inclusion, even in the center of the imaging object, and it contains more effective information about the inclusions. Finally, image reconstruction is carried out by the conjugate gradient algorithm, and results show that reconstructed images with higher quality can be obtained for UMEIT with a faster convergence rate. Both theory and image reconstruction results validate the feasibility of the proposed framework for UMEIT and confirm that UMEIT is a potential imaging technique.
Efficient content-based low-altitude images correlated network and strips reconstruction
NASA Astrophysics Data System (ADS)
He, Haiqing; You, Qi; Chen, Xiaoyong
2017-01-01
The manual intervention method is widely used to reconstruct strips for further aerial triangulation in low-altitude photogrammetry. Clearly the method for fully automatic photogrammetric data processing is not an expected way. In this paper, we explore a content-based approach without manual intervention or external information for strips reconstruction. Feature descriptors in the local spatial patterns are extracted by SIFT to construct vocabulary tree, in which these features are encoded in terms of TF-IDF numerical statistical algorithm to generate new representation for each low-altitude image. Then images correlated network is reconstructed by similarity measure, image matching and geometric graph theory. Finally, strips are reconstructed automatically by tracing straight lines and growing adjacent images gradually. Experimental results show that the proposed approach is highly effective in automatically rearranging strips of lowaltitude images and can provide rough relative orientation for further aerial triangulation.
Nielsen, Tim; Brendel, Bernhard; Ziegler, Ronny; van Beek, Michiel; Uhlemann, Falk; Bontus, Claas; Koehler, Thomas
2009-04-01
Diffuse optical tomography (DOT) is a potential new imaging modality to detect or monitor breast lesions. Recently, Philips developed a new DOT system capable of transmission and fluorescence imaging, where the investigated breast is hanging freely into the measurement cup containing scattering fluid. We present a fast and robust image reconstruction algorithm that is used for the transmission measurements. The algorithm is based on the Rytov approximation. We show that this algorithm can be used over a wide range of tissue optical properties if the reconstruction is adapted to each patient. We use estimates of the breast shape and average tissue optical properties to initialize the reconstruction, which improves the image quality significantly. We demonstrate the capability of the measurement system and reconstruction to image breast lesions by clinical examples.
Tomographic reconstruction of damage images in hollow cylinders using Lamb waves.
Hu, Bin; Hu, Ning; Li, Leilei; Li, Weiguo; Tang, Shan; Li, Yuan; Peng, Xianghe; Homma, Atsushi; Liu, Yaolu; Wu, Liangke; Ning, Huiming
2014-09-01
Lamb wave tomography (LWT) is a potential and efficient technique for non-destructive tomographic reconstruction of damage images in structural components or materials. A two-stage inverse algorithm proposed by the authors for quickly reconstructing the damage images was applied to hollow cylinders. An aluminum hollow cylinder with an internal surface pit and a Carbon Fiber Reinforced Plastic (CFRP) laminated hollow cylinder with an artificial internal surface damage were used to validate the proposed method. The results show that the present method is capable of successfully reconstructing the images of the above damages in a larger inspection area with much less experimental data compared to some conventional ultrasonic tomography techniques.
Feature-based face representations and image reconstruction from behavioral and neural data
Nestor, Adrian; Plaut, David C.; Behrmann, Marlene
2016-01-01
The reconstruction of images from neural data can provide a unique window into the content of human perceptual representations. Although recent efforts have established the viability of this enterprise using functional magnetic resonance imaging (MRI) patterns, these efforts have relied on a variety of prespecified image features. Here, we take on the twofold task of deriving features directly from empirical data and of using these features for facial image reconstruction. First, we use a method akin to reverse correlation to derive visual features from functional MRI patterns elicited by a large set of homogeneous face exemplars. Then, we combine these features to reconstruct novel face images from the corresponding neural patterns. This approach allows us to estimate collections of features associated with different cortical areas as well as to successfully match image reconstructions to corresponding face exemplars. Furthermore, we establish the robustness and the utility of this approach by reconstructing images from patterns of behavioral data. From a theoretical perspective, the current results provide key insights into the nature of high-level visual representations, and from a practical perspective, these findings make possible a broad range of image-reconstruction applications via a straightforward methodological approach. PMID:26711997
Methodological advances in imaging intravital axonal transport
Sleigh, James N.; Vagnoni, Alessio; Twelvetrees, Alison E.; Schiavo, Giampietro
2017-01-01
Axonal transport is the active process whereby neurons transport cargoes such as organelles and proteins anterogradely from the cell body to the axon terminal and retrogradely in the opposite direction. Bi-directional transport in axons is absolutely essential for the functioning and survival of neurons and appears to be negatively impacted by both aging and diseases of the nervous system, such as Alzheimer’s disease and amyotrophic lateral sclerosis. The movement of individual cargoes along axons has been studied in vitro in live neurons and tissue explants for a number of years; however, it is currently unclear as to whether these systems faithfully and consistently replicate the in vivo situation. A number of intravital techniques originally developed for studying diverse biological events have recently been adapted to monitor axonal transport in real-time in a range of live organisms and are providing novel insight into this dynamic process. Here, we highlight these methodological advances in intravital imaging of axonal transport, outlining key strengths and limitations while discussing findings, possible improvements, and outstanding questions. PMID:28344778
Methodological advances in imaging intravital axonal transport.
Sleigh, James N; Vagnoni, Alessio; Twelvetrees, Alison E; Schiavo, Giampietro
2017-01-01
Axonal transport is the active process whereby neurons transport cargoes such as organelles and proteins anterogradely from the cell body to the axon terminal and retrogradely in the opposite direction. Bi-directional transport in axons is absolutely essential for the functioning and survival of neurons and appears to be negatively impacted by both aging and diseases of the nervous system, such as Alzheimer's disease and amyotrophic lateral sclerosis. The movement of individual cargoes along axons has been studied in vitro in live neurons and tissue explants for a number of years; however, it is currently unclear as to whether these systems faithfully and consistently replicate the in vivo situation. A number of intravital techniques originally developed for studying diverse biological events have recently been adapted to monitor axonal transport in real-time in a range of live organisms and are providing novel insight into this dynamic process. Here, we highlight these methodological advances in intravital imaging of axonal transport, outlining key strengths and limitations while discussing findings, possible improvements, and outstanding questions.
Three-dimensional reconstruction of light microscopy image sections: present and future.
Wang, Yuzhen; Xu, Rui; Luo, Gaoxing; Wu, Jun
2015-03-01
Three-dimensional (3D) image reconstruction technologies can reveal previously hidden microstructures in human tissue. However, the lack of ideal, non-destructive cross-sectional imaging techniques is still a problem. Despite some drawbacks, histological sectioning remains one of the most powerful methods for accurate high-resolution representation of tissue structures. Computer technologies can produce 3D representations of interesting human tissue and organs that have been serial-sectioned, dyed or stained, imaged, and segmented for 3D visualization. 3D reconstruction also has great potential in the fields of tissue engineering and 3D printing. This article outlines the most common methods for 3D tissue section reconstruction. We describe the most important academic concepts in this field, and provide critical explanations and comparisons. We also note key steps in the reconstruction procedures, and highlight recent progress in the development of new reconstruction methods.
Iterative image reconstruction for limited-angle inverse helical cone-beam computed tomography.
Yu, Wei; Zeng, Li
2016-01-01
Helical trajectory satisfying the condition of exact reconstruction, has been widely utilized in the commercial computed tomography (CT). While limited by the scanning environment in some practical applications, the conventional helical cone-beam CT imaging is hard to complete, thus, developing an imaging system suited for long-object may be valuable. Three-dimensional C-arm CT is an innovative imaging technique which has been greatly concerned. Since there is a high degree of freedom of C-arm, more flexible image acquisition trajectories for 3D imaging can be achieved. In this work, a fast iterative reconstruction algorithm based on total variation minimization is developed for a trajectory of limited-angle inverse helical cone-beam CT, which can be applied to detect long-object without slip-ring technology. The experimental results show that the developed algorithm can yield reconstructed images of low noise level and high image quality.
Bischel, Alexander; Stratis, Andreas; Kakar, Apoorv; Bosmans, Hilde; Jacobs, Reinhilde; Gassner, Eva-Maria; Puelacher, Wolfgang; Pauwels, Ruben
2016-01-01
Objective: The aim of this study was to evaluate whether application of ultralow dose protocols and iterative reconstruction technology (IRT) influence quantitative Hounsfield units (HUs) and contrast-to-noise ratio (CNR) in dentomaxillofacial CT imaging. Methods: A phantom with inserts of five types of materials was scanned using protocols for (a) a clinical reference for navigated surgery (CT dose index volume 36.58 mGy), (b) low-dose sinus imaging (18.28 mGy) and (c) four ultralow dose imaging (4.14, 2.63, 0.99 and 0.53 mGy). All images were reconstructed using: (i) filtered back projection (FBP); (ii) IRT: adaptive statistical iterative reconstruction-50 (ASIR-50), ASIR-100 and model-based iterative reconstruction (MBIR); and (iii) standard (std) and bone kernel. Mean HU, CNR and average HU error after recalibration were determined. Each combination of protocols was compared using Friedman analysis of variance, followed by Dunn's multiple comparison test. Results: Pearson's sample correlation coefficients were all >0.99. Ultralow dose protocols using FBP showed errors of up to 273 HU. Std kernels had less HU variability than bone kernels. MBIR reduced the error value for the lowest dose protocol to 138 HU and retained the highest relative CNR. ASIR could not demonstrate significant advantages over FBP. Conclusions: Considering a potential dose reduction as low as 1.5% of a std protocol, ultralow dose protocols and IRT should be further tested for clinical dentomaxillofacial CT imaging. Advances in knowledge: HU as a surrogate for bone density may vary significantly in CT ultralow dose imaging. However, use of std kernels and MBIR technology reduce HU error values and may retain the highest CNR. PMID:26859336
High resolution reconstruction of solar prominence images observed by the New Vacuum Solar Telescope
NASA Astrophysics Data System (ADS)
Xiang, Yong-yuan; Liu, Zhong; Jin, Zhen-yu
2016-11-01
A high resolution image showing fine structures is crucial for understanding the nature of solar prominence. In this paper, high resolution imaging of solar prominence on the New Vacuum Solar Telescope (NVST) is introduced, using speckle masking. Each step of the data reduction especially the image alignment is discussed. Accurate alignment of all frames and the non-isoplanatic calibration of each image are the keys for a successful reconstruction. Reconstructed high resolution images from NVST also indicate that under normal seeing condition, it is feasible to carry out high resolution observations of solar prominence by a ground-based solar telescope, even in the absence of adaptive optics.
Yang, Alice C; Kretzler, Madison; Sudarski, Sonja; Gulani, Vikas; Seiberlich, Nicole
2016-06-01
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
NASA Astrophysics Data System (ADS)
Zhang, Zhaohui; Liu, Anran; Lei, Qian
2015-12-01
In this paper, we propose a method for single image super-resolution(SR). Given the training set produced from large amount of high-low resolution image patches, an over-complete joint dictionary is firstly learned from a pair of high-low resolution image feature space based on Restricted Boltzmann Machines (RBM). Then for each low resolution image patch densely extracted from an up-scaled low resolution input image , its high resolution image patch can be reconstructed based on sparse representation. Finally, the reconstructed image patches are overlapped to form a large image, and a high resolution image can be achieved by means of iterated residual image compensation. Experimental results verify the effectiveness of the proposed method.
Hein, L R
2001-10-01
A set of NIH Image macro programs was developed to make qualitative and quantitative analyses from digital stereo pictures produced by scanning electron microscopes. These tools were designed for image alignment, anaglyph representation, animation, reconstruction of true elevation surfaces, reconstruction of elevation profiles, true-scale elevation mapping and, for the quantitative approach, surface area and roughness calculations. Limitations on time processing, scanning techniques and programming concepts are also discussed.
Pelt, Daniël M; Gürsoy, Dogˇa; Palenstijn, Willem Jan; Sijbers, Jan; De Carlo, Francesco; Batenburg, Kees Joost
2016-05-01
The processing of tomographic synchrotron data requires advanced and efficient software to be able to produce accurate results in reasonable time. In this paper, the integration of two software toolboxes, TomoPy and the ASTRA toolbox, which, together, provide a powerful framework for processing tomographic data, is presented. The integration combines the advantages of both toolboxes, such as the user-friendliness and CPU-efficient methods of TomoPy and the flexibility and optimized GPU-based reconstruction methods of the ASTRA toolbox. It is shown that both toolboxes can be easily installed and used together, requiring only minor changes to existing TomoPy scripts. Furthermore, it is shown that the efficient GPU-based reconstruction methods of the ASTRA toolbox can significantly decrease the time needed to reconstruct large datasets, and that advanced reconstruction methods can improve reconstruction quality compared with TomoPy's standard reconstruction method.
Pelt, Daniël M.; Gürsoy, Doǧa; Palenstijn, Willem Jan; Sijbers, Jan; De Carlo, Francesco; Batenburg, Kees Joost
2016-01-01
The processing of tomographic synchrotron data requires advanced and efficient software to be able to produce accurate results in reasonable time. In this paper, the integration of two software toolboxes, TomoPy and the ASTRA toolbox, which, together, provide a powerful framework for processing tomographic data, is presented. The integration combines the advantages of both toolboxes, such as the user-friendliness and CPU-efficient methods of TomoPy and the flexibility and optimized GPU-based reconstruction methods of the ASTRA toolbox. It is shown that both toolboxes can be easily installed and used together, requiring only minor changes to existing TomoPy scripts. Furthermore, it is shown that the efficient GPU-based reconstruction methods of the ASTRA toolbox can significantly decrease the time needed to reconstruct large datasets, and that advanced reconstruction methods can improve reconstruction quality compared with TomoPy’s standard reconstruction method. PMID:27140167
Achromatic light patterning and improved image reconstruction for parallelized RESOLFT nanoscopy
Chmyrov, Andriy; Leutenegger, Marcel; Grotjohann, Tim; Schönle, Andreas; Keller-Findeisen, Jan; Kastrup, Lars; Jakobs, Stefan; Donnert, Gerald; Sahl, Steffen J.; Hell, Stefan W.
2017-01-01
Fluorescence microscopy is rapidly turning into nanoscopy. Among the various nanoscopy methods, the STED/RESOLFT super-resolution family has recently been expanded to image even large fields of view within a few seconds. This advance relies on using light patterns featuring substantial arrays of intensity minima for discerning features by switching their fluorophores between ‘on’ and ‘off’ states of fluorescence. Here we show that splitting the light with a grating and recombining it in the focal plane of the objective lens renders arrays of minima with wavelength-independent periodicity. This colour-independent creation of periodic patterns facilitates coaligned on- and off-switching and readout with combinations chosen from a range of wavelengths. Applying up to three such periodic patterns on the switchable fluorescent proteins Dreiklang and rsCherryRev1.4, we demonstrate highly parallelized, multicolour RESOLFT nanoscopy in living cells for ~100 × 100 μm2 fields of view. Individual keratin filaments were rendered at a FWHM of ~60–80 nm, with effective resolution for the filaments of ~80–100 nm. We discuss the impact of novel image reconstruction algorithms featuring background elimination by spatial bandpass filtering, as well as strategies that incorporate complete image formation models. PMID:28317930
Achromatic light patterning and improved image reconstruction for parallelized RESOLFT nanoscopy
NASA Astrophysics Data System (ADS)
Chmyrov, Andriy; Leutenegger, Marcel; Grotjohann, Tim; Schönle, Andreas; Keller-Findeisen, Jan; Kastrup, Lars; Jakobs, Stefan; Donnert, Gerald; Sahl, Steffen J.; Hell, Stefan W.
2017-03-01
Fluorescence microscopy is rapidly turning into nanoscopy. Among the various nanoscopy methods, the STED/RESOLFT super-resolution family has recently been expanded to image even large fields of view within a few seconds. This advance relies on using light patterns featuring substantial arrays of intensity minima for discerning features by switching their fluorophores between ‘on’ and ‘off’ states of fluorescence. Here we show that splitting the light with a grating and recombining it in the focal plane of the objective lens renders arrays of minima with wavelength-independent periodicity. This colour-independent creation of periodic patterns facilitates coaligned on- and off-switching and readout with combinations chosen from a range of wavelengths. Applying up to three such periodic patterns on the switchable fluorescent proteins Dreiklang and rsCherryRev1.4, we demonstrate highly parallelized, multicolour RESOLFT nanoscopy in living cells for ~100 × 100 μm2 fields of view. Individual keratin filaments were rendered at a FWHM of ~60–80 nm, with effective resolution for the filaments of ~80–100 nm. We discuss the impact of novel image reconstruction algorithms featuring background elimination by spatial bandpass filtering, as well as strategies that incorporate complete image formation models.
Achromatic light patterning and improved image reconstruction for parallelized RESOLFT nanoscopy.
Chmyrov, Andriy; Leutenegger, Marcel; Grotjohann, Tim; Schönle, Andreas; Keller-Findeisen, Jan; Kastrup, Lars; Jakobs, Stefan; Donnert, Gerald; Sahl, Steffen J; Hell, Stefan W
2017-03-20
Fluorescence microscopy is rapidly turning into nanoscopy. Among the various nanoscopy methods, the STED/RESOLFT super-resolution family has recently been expanded to image even large fields of view within a few seconds. This advance relies on using light patterns featuring substantial arrays of intensity minima for discerning features by switching their fluorophores between 'on' and 'off' states of fluorescence. Here we show that splitting the light with a grating and recombining it in the focal plane of the objective lens renders arrays of minima with wavelength-independent periodicity. This colour-independent creation of periodic patterns facilitates coaligned on- and off-switching and readout with combinations chosen from a range of wavelengths. Applying up to three such periodic patterns on the switchable fluorescent proteins Dreiklang and rsCherryRev1.4, we demonstrate highly parallelized, multicolour RESOLFT nanoscopy in living cells for ~100 × 100 μm(2) fields of view. Individual keratin filaments were rendered at a FWHM of ~60-80 nm, with effective resolution for the filaments of ~80-100 nm. We discuss the impact of novel image reconstruction algorithms featuring background elimination by spatial bandpass filtering, as well as strategies that incorporate complete image formation models.
Three-dimensional mammography reconstruction using low-dose projection images
NASA Astrophysics Data System (ADS)
Wu, Tao
A method is described for the reconstruction of three-dimensional distribution of attenuation coefficient of the breast using a limited number of low dose projection images. This method uses the cone beam x-ray geometry, a digital detector and a constrained iterative reconstruction algorithm. The method has been tested on a digital Tomosynthesis mammography system. The total radiation dose to the patient is comparable to that used for one conventional mammogram. The reconstructed image has intrinsically high resolution (˜0.1mm) in two dimensions and lower resolution in the third dimension (˜1mm). Using this method, a breast that is projected into one two-dimensional image in conventional mammography is separated into layers parallel to the two high-resolution dimensions. The thickness of the layer is in the low-resolution dimension. The three-dimensional reconstruction increases the conspicuity of features that is often obscured by overlapping tissues in a single projection. Factors affecting the quality of reconstruction have been investigated by computer simulations. These factors include the scatter, the projection angular range, the shape of the breast and the x-ray energy. Non-uniform distribution of x-ray exposures among projection images and non-uniform-resolution image-acquisition are explored to optimize the image quality within an x-ray dose limit. The method is validated with reconstruction images of mammography phantoms, mastectomy specimens, computer simulations and volunteer patients.
[MRI image reconstruction using polar-coordinates conversion of k-space data].
Tachibana, Atsushi; Hashimoto, Takeyuki; Sakaguchi, Kazuya; Obata, Takayuki; Shinohara, Hiroyuki
2012-01-01
In this study, we proposed the new reconstruction techniques for magnetic resonance imaging (MRI) using filtered back projection (FBP) or simultaneous reconstruction technique (SIRT). We converted the k-space which was acquired by conventional phase-encoding schemes from Cartesian coordinates to polar coordinates and created the projection. The linear interpolation and the sinc interpolation were used in the conversion. The accuracy of the reconstructed image using projection was evaluated by the relative error in comparison with the standard image which was reconstructed by the two-dimensional Fourier transform (2DFT) with conventional Cartesian k-space. The relative error reconstructed both FBP and SIRT from projection with sinc interpolation is 0.013. The maximum value of standard image is 1.501451, FBP is 1.47921, and SIRT with iteration 100 is 1.44858 and with iteration 200 is 1.579442. The minimum value of both the standard image and the others is about 0. Visually, there is no margin between the standard image and the reconstructed image from projection with FBP or SIRT.
NASA Astrophysics Data System (ADS)
Archer, Glen E.; Bos, Jeremy P.; Roggemann, Michael C.
2013-08-01
All optical systems that operate in or through the atmosphere suffer from turbulence induced image blur. Both military and civilian surveillance, gun sighting, and target identification systems are interested in terrestrial imaging over very long horizontal paths, but atmospheric turbulence can blur the resulting images beyond usefulness. This work explores the mean square error (MSE) performance of a multiframe blind deconvolution (MFBD) technique applied under anisoplanatic conditions for both Gaussian and Poisson noise model assumptions. The technique is evaluated for use in reconstructing images of scenes corrupted by turbulence in long horizontal-path imaging scenarios. Performance is evaluated via the reconstruction of a common object from three sets of simulated turbulence degraded imagery representing low, moderate, and severe turbulence conditions. Each set consisted of 1000 simulated turbulence degraded images. The MSE performance of the estimator is evaluated as a function of the number of images, and the number of Zernike polynomial terms used to characterize the point spread function. A Gaussian noise model-based MFBD algorithm reconstructs objects that showed as much as 40% improvement in MSE with as few as 14 frames and 30 Zernike coefficients used in the reconstruction, despite the presence of anisoplanatism in the data. An MFBD algorithm based on the Poisson noise model required a minimum of 50 frames to achieve significant improvement over the average MSE for the data set. Reconstructed objects show as much as 38% improvement in MSE using 175 frames and 30 Zernike coefficients in the reconstruction.
Grosser, Oliver S.; Kupitz, Dennis; Ruf, Juri; Czuczwara, Damian; Steffen, Ingo G.; Furth, Christian; Thormann, Markus; Loewenthal, David; Ricke, Jens; Amthauer, Holger
2015-01-01
Background Hybrid imaging combines nuclear medicine imaging such as single photon emission computed tomography (SPECT) or positron emission tomography (PET) with computed tomography (CT). Through this hybrid design, scanned patients accumulate radiation exposure from both applications. Imaging modalities have been the subject of long-term optimization efforts, focusing on diagnostic applications. It was the aim of this study to investigate the influence of an iterative CT image reconstruction algorithm (ASIR) on the image quality of the low-dose CT images. Methodology/Principal Findings Examinations were performed with a SPECT-CT scanner with standardized CT and SPECT-phantom geometries and CT protocols with systematically reduced X-ray tube currents. Analyses included image quality with respect to photon flux. Results were compared to the standard FBP reconstructed images. The general impact of the CT-based attenuation maps used during SPECT reconstruction was examined for two SPECT phantoms. Using ASIR for image reconstructions, image noise was reduced compared to FBP reconstructions for the same X-ray tube current. The Hounsfield unit (HU) values reconstructed by ASIR were correlated to the FBP HU values(R2 ≥ 0.88) and the contrast-to-noise ratio (CNR) was improved by ASIR. However, for a phantom with increased attenuation, the HU values shifted for low X-ray tube currents I ≤ 60 mA (p ≤ 0.04). In addition, the shift of the HU values was observed within the attenuation corrected SPECT images for very low X-ray tube currents (I ≤ 20 mA, p ≤ 0.001). Conclusion/Significance In general, the decrease in X-ray tube current up to 30 mA in combination with ASIR led to a reduction of CT-related radiation exposure without a significant decrease in image quality. PMID:26390216
PET Image Reconstruction and Deformable Motion Correction Using Unorganized Point Clouds.
Klyuzhin, Ivan; Sossi, Vesna
2017-03-02
Quantitative PET imaging often requires correcting the image data for deformable motion. With cyclic motion, this is traditionally achieved by separating the coincidence data into a relatively small number of gates, and incorporating the inter-gate image transformation matrices into the reconstruction algorithm. In the presence of non-cyclic deformable motion, this approach may be impractical due to a large number of required gates. In this work, we propose an alternative approach to iterative image reconstruction with correction for deformable motion, wherein unorganized point clouds are used to model the imaged objects in the image space, and motion is corrected for explicitly by introducing a time-dependency into the point coordinates. The image function is represented using constant basis functions with finite support determined by the boundaries of the Voronoi cells in the point cloud. We validate the quantitative accuracy and stability of the proposed approach by reconstructing noise-free and noisy projection data from digital and physical phantoms. The pointcloud based MLEM and one-pass list-mode OSEM algorithms are validated. The results demonstrate that images reconstructed using the proposed method are quantitatively stable, with noise and convergence properties comparable to image reconstruction based on the use of rectangular and radially-symmetric basis functions.
High-resolution iris image reconstruction from low-resolution imagery
NASA Astrophysics Data System (ADS)
Barnard, R.; Pauca, V. P.; Torgersen, T. C.; Plemmons, R. J.; Prasad, S.; van der Gracht, J.; Nagy, J.; Chung, J.; Behrmann, G.; Mathews, S.; Mirotznik, M.
2006-08-01
We investigate the use of a novel multi-lens imaging system in the context of biometric identification, and more specifically, for iris recognition. Multi-lenslet cameras offer a number of significant advantages over standard single-lens camera systems, including thin form-factor and wide angle of view. By using appropriate lenslet spacing relative to the detector pixel pitch, the resulting ensemble of images implicitly contains subject information at higher spatial frequencies than those present in a single image. Additionally, a multi-lenslet approach enables the use of observational diversity, including phase, polarization, neutral density, and wavelength diversities. For example, post-processing multiple observations taken with differing neutral density filters yields an image having an extended dynamic range. Our research group has developed several multi-lens camera prototypes for the investigation of such diversities. In this paper, we present techniques for computing a high-resolution reconstructed image from an ensemble of low-resolution images containing sub-pixel level displacements. The quality of a reconstructed image is measured by computing the Hamming distance between the Daugman 4 iris code of a conventional reference iris image, and the iris code of a corresponding reconstructed image. We present numerical results concerning the effect of noise and defocus blur in the reconstruction process using simulated data and report preliminary work on the reconstruction of actual iris data obtained with our camera prototypes.
Advances in superresolution optical fluctuation imaging (SOFI)
Dertinger, Thomas; Pallaoro, Alessia; Braun, Gary; Ly, Sonny; Laurence, Ted A.; Weiss, Shimon
2013-01-01
We review the concept of superresolution optical fluctuation imaging (SOFI), discuss its attributes and trade-offs (in comparison with other superresolution methods), and present superresolved images taken on samples stained with quantum dots, organic dyes, and plasmonic metal nanoparticles. We also discuss the prospects of SOFI for live cell superresolution imaging and for imaging with other (non-fluorescent) contrasts. PMID:23672771
Qian Weixin; Qi Shuangxi; Wang Wanli; Cheng Jinming; Liu Dongbing
2011-09-15
Neutron penumbral imaging is a significant diagnostic technique in laser-driven inertial confinement fusion experiment. It is very important to develop a new reconstruction method to improve the resolution of neutron penumbral imaging. A new nonlinear reconstruction method based on total variation (TV) regularization is proposed in this paper. A TV-norm is used as regularized term to construct a smoothing functional for penumbral image reconstruction in the new method, in this way, the problem of penumbral image reconstruction is transformed to the problem of a functional minimization. In addition, a fixed point iteration scheme is introduced to solve the problem of functional minimization. The numerical experimental results show that, compared to linear reconstruction method based on Wiener filter, the TV regularized nonlinear reconstruction method is beneficial to improve the quality of reconstructed image with better performance of noise smoothing and edge preserving. Meanwhile, it can also obtain the spatial resolution with 5 {mu}m which is higher than the Wiener method.
Zhang, Tiankui; Hu, Huasi; Jia, Qinggang; Zhang, Fengna; Chen, Da; Li, Zhenghong; Wu, Yuelei; Liu, Zhihua; Hu, Guang; Guo, Wei
2012-11-01
Monte-Carlo simulation of neutron coded imaging based on encoding aperture for Z-pinch of large field-of-view with 5 mm radius has been investigated, and then the coded image has been obtained. Reconstruction method of source image based on genetic algorithms (GA) has been established. "Residual watermark," which emerges unavoidably in reconstructed image, while the peak normalization is employed in GA fitness calculation because of its statistical fluctuation amplification, has been discovered and studied. Residual watermark is primarily related to the shape and other parameters of the encoding aperture cross section. The properties and essential causes of the residual watermark were analyzed, while the identification on equivalent radius of aperture was provided. By using the equivalent radius, the reconstruction can also be accomplished without knowing the point spread function (PSF) of actual aperture. The reconstruction result is close to that by using PSF of the actual aperture.
Zhang Tiankui; Hu Huasi; Jia Qinggang; Zhang Fengna; Liu Zhihua; Hu Guang; Guo Wei; Chen Da; Li Zhenghong; Wu Yuelei
2012-11-15
Monte-Carlo simulation of neutron coded imaging based on encoding aperture for Z-pinch of large field-of-view with 5 mm radius has been investigated, and then the coded image has been obtained. Reconstruction method of source image based on genetic algorithms (GA) has been established. 'Residual watermark,' which emerges unavoidably in reconstructed image, while the peak normalization is employed in GA fitness calculation because of its statistical fluctuation amplification, has been discovered and studied. Residual watermark is primarily related to the shape and other parameters of the encoding aperture cross section. The properties and essential causes of the residual watermark were analyzed, while the identification on equivalent radius of aperture was provided. By using the equivalent radius, the reconstruction can also be accomplished without knowing the point spread function (PSF) of actual aperture. The reconstruction result is close to that by using PSF of the actual aperture.
An improved POCS super-resolution infrared image reconstruction algorithm based on visual mechanism
NASA Astrophysics Data System (ADS)
Liu, Jinsong; Dai, Shaosheng; Guo, Zhongyuan; Zhang, Dezhou
2016-09-01
The traditional projection onto convex sets (POCS) super-resolution (SR) reconstruction algorithm can only get reconstructed images with poor contrast, low signal-to-noise ratio and blurring edges. In order to solve the above disadvantages, an improved POCS SR infrared image reconstruction algorithm based on visual mechanism is proposed, which introduces data consistency constraint with variable correction thresholds to highlight the target edges and filter out background noises; further, the algorithm introduces contrast constraint considering the resolving ability of human eyes into the traditional algorithm, enhancing the contrast of the image reconstructed adaptively. The experimental results show that the improved POCS algorithm can acquire high quality infrared images whose contrast, average gradient and peak signal to noise ratio are improved many times compared with traditional algorithm.
Advanced x-ray imaging spectrometer
NASA Technical Reports Server (NTRS)
Callas, John L. (Inventor); Soli, George A. (Inventor)
1998-01-01
An x-ray spectrometer that also provides images of an x-ray source. Coded aperture imaging techniques are used to provide high resolution images. Imaging position-sensitive x-ray sensors with good energy resolution are utilized to provide excellent spectroscopic performance. The system produces high resolution spectral images of the x-ray source which can be viewed in any one of a number of specific energy bands.
Zhao, Bo; Ding, Huanjun; Lu, Yang; Wang, Ge; Zhao, Jun; Molloi, Sabee
2015-01-01
In this study, we investigated the effectiveness of a novel Iterative Reconstruction (IR) method coupled with Dual-Dictionary Learning (DDL) for image reconstruction in a dedicated breast Computed Tomography (CT) system based on a Cadmium-Zinc-Telluride (CZT) photon-counting detector and compared it to the Filtered-Back-Projection (FBP) method with the ultimate goal of reducing the number of projections necessary for reconstruction without sacrificing image quality. Postmortem breast samples were scanned in a fan-beam CT system and were reconstructed from 100–600 projections with both IR and FBP methods. The Contrast-to-Noise Ratio (CNR) between the glandular and adipose tissues of the postmortem breast samples was calculated to compare the quality of images reconstructed from IR and FBP. The spatial resolution of the two reconstruction techniques was evaluated using Aluminum (Al) wires with diameters of 643, 813, 1020, 1290 and 1630 µm in a plastic epoxy resin phantom with diameter of 13 cm. Both the spatial resolution and the CNR were improved with IR compared to FBP for the images reconstructed from the same number of projections. In comparison with FBP reconstruction, the CNR was improved from 3.4 to 7.5 by using the IR method with 6-fold fewer projections while maintaining the same spatial resolution. The study demonstrated that the IR method coupled with DDL could significantly reduce the required number of projections for a CT reconstruction compared to FBP method while achieving a much better CNR and maintaining the same spatial resolution. From this, the radiation dose and scanning time can potentially be reduced by a factor of approximately 6 by using this IR method for image reconstruction in a CZT-based breast CT system. PMID:23192234
Fujioka, Shinsuke; Shiraga, Hiroyuki; Azechi, Hiroshi; Nishimura, Hiroaki; Izawa, Yasukazu; Nozaki, Shinya; Chen, Yen-wei
2004-10-01
Temporal resolved x-ray penumbral imaging has been developed using an image reconstruction procedure of the heuristic method and a wide dynamic range x-ray streak camera (XSC). Reconstruction procedure of the penumbral imaging is inherently intolerant to noise, a reconstructed image is strongly distorted by artifacts caused by noise in a penumbral image. Statistical fluctuation in the number of detected photon is the dominant source of noise in an x-ray image, however acceptable brightness of an image is limited by dynamic range of an XSC. The wide dynamic range XSC was used to obtain penumbral images bright enough to be reconstructed. Additionally, the heuristic method was introduced in the penumbral image reconstruction procedure. Distortion of reconstructed images is sufficiently suppressed by these improvements. Density profiles of laser driven brominated plastic and tin plasma were measured with this technique.
NASA Astrophysics Data System (ADS)
Majeed, Abdul; Piah, Abd Rahni Mt
2015-10-01
Spline has been used extensively in engineering design and modelling for representation, analysis and manufacturing purposes. This paper presents an application of spline methods in bio-medical modelling. We reconstruct craniofacial fractured skull bone images using rational cubic Ball interpolant with two free parameters. The free parameters are optimized with the help of genetic algorithm. Our emphasis is placed on the accuracy and smoothness of the reconstructed images.
NASA Astrophysics Data System (ADS)
Hong, Daeki; Cho, Hyosung; Lee, Seonhwa; Oh, Jieun; Lee, Minsik; Kim, Hyojeong; Je, Uikyu; Park, Yeonok; Choi, Sungil; Koo, Yangseo; Cho, Heemoon
2013-01-01
As a continuation of our dental imaging R&D, we have proposed a novel idea that is capable of implementing cost-effective, low-dose, volumetric image reconstruction directly onto a dental panoramic imaging system. In the proposed geometry, a linear-type panoramic detector is rotated 90° from the orientation for panoramic imaging and scanned along a limited-angle zigzag trajectory in the axial direction to cover the whole imaging volume thickness. We used an effective reconstruction algorithm based on the total-variation (TV) minimization approach for the proposed geometry and performed systematic simulation work to demonstrate the viability of our proposed approach and its effectiveness for three-dimensional (3D) dental X-ray imaging. We have successfully reconstructed images of substantially high image accuracy from the proposed geometry and evaluated the reconstruction quality by using an image similarity metric, the universal-quality index (UQI). We expect the proposed method to be applicable to developing a cost-effective, low-dose, all-in-one dental X-ray imaging system.
CT x-ray tube voltage optimisation and image reconstruction evaluation using visual grading analysis
NASA Astrophysics Data System (ADS)
Zheng, Xiaoming; Kim, Ted M.; Davidson, Rob; Lee, Seongju; Shin, Cheongil; Yang, Sook
2014-03-01
The purposes of this work were to find an optimal x-ray voltage for CT imaging and to determine the diagnostic effectiveness of image reconstruction techniques by using the visual grading analysis (VGA). Images of the PH-5 CT abdomen phantom (Kagaku Co, Kyoto) were acquired by the Toshiba Aquillion One 320 slices CT system with various exposures (from 10 to 580 mAs) under different tube peak voltages (80, 100 and 120 kVp). The images were reconstructed by employing the FBP and the AIDR 3D iterative reconstructions with Mild, Standard and Strong FBP blending. Image quality was assessed by measuring noise, contrast to noise ratio and human observer's VGA scores. The CT dose index CTDIv was obtained from the values displayed on the images. The best fit for the curves of the image quality VGA vs dose CTDIv is a logistic function from the SPSS estimation. A threshold dose Dt is defined as the CTDIv at the just acceptable for diagnostic image quality and a figure of merit (FOM) is defined as the slope of the standardised logistic function. The Dt and FOM were found to be 5.4, 8.1 and 9.1 mGy and 0.47, 0.51 and 0.38 under the tube voltages of 80, 100 and 120 kVp, respectively, from images reconstructed by the FBP technique. The Dt and FOM values were lower from the images reconstructed by the AIDR 3D in comparison with the FBP technique. The optimal xray peak voltage for the imaging of the PH-5 abdomen phantom by the Aquillion One CT system was found to be at 100 kVp. The images reconstructed by the FBP are more diagnostically effective than that by the AIDR 3D but with a higher dose Dt to the patients.
Dictionary-based image reconstruction for superresolution in integrated circuit imaging.
Cilingiroglu, T Berkin; Uyar, Aydan; Tuysuzoglu, Ahmet; Karl, W Clem; Konrad, Janusz; Goldberg, Bennett B; Ünlü, M Selim
2015-06-01
Resolution improvement through signal processing techniques for integrated circuit imaging is becoming more crucial as the rapid decrease in integrated circuit dimensions continues. Although there is a significant effort to push the limits of optical resolution for backside fault analysis through the use of solid immersion lenses, higher order laser beams, and beam apodization, signal processing techniques are required for additional improvement. In this work, we propose a sparse image reconstruction framework which couples overcomplete dictionary-based representation with a physics-based forward model to improve resolution and localization accuracy in high numerical aperture confocal microscopy systems for backside optical integrated circuit analysis. The effectiveness of the framework is demonstrated on experimental data.
Bilateral bad pixel and Stokes image reconstruction for microgrid polarimetric imagers
NASA Astrophysics Data System (ADS)
LeMaster, Daniel A.; Ratliff, Bradley M.
2015-09-01
Uncorrected or poorly corrected bad pixels reduce the effectiveness of polarimetric clutter suppression. In conventional microgrid processing, bad pixel correction is accomplished as a separate step from Stokes image reconstruction. Here, these two steps are combined to speed processing and provide better estimates of the entire image, including missing samples. A variation on the bilateral filter enables both edge preservation in the Stokes imagery and bad pixel suppression. Understanding the newly presented filter requires two key insights. First, the adaptive nature of the bilateral filter is extended to correct for bad pixels by simply incorporating a bad pixel mask. Second, the bilateral filter for Stokes estimation is the sum of the normalized bilateral filters for estimating each analyzer channel individually. This paper describes the new approach and compares it to our legacy method using simulated imagery.
Acceleration of the direct reconstruction of linear parametric images using nested algorithms.
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.
Phase-selective image reconstruction of the lungs in small animals using micro-CT
NASA Astrophysics Data System (ADS)
Johnston, S. M.; Perez, B. A.; Kirsch, D. G.; Badea, C. T.
2010-04-01
Gating in small animal imaging can compensate for artifacts due to physiological motion. This paper presents a strategy for sampling and image reconstruction in the rodent lung using micro-CT. The approach involves rapid sampling of freebreathing mice without any additional hardware to detect respiratory motion. The projection images are analyzed postacquisition to derive a respiratory signal, which is used to provide weighting factors for each projection that favor a selected phase of the respiration (e.g. end-inspiration or end-expiration) for the reconstruction. Since the sampling cycle and the respiratory cycle are uncorrelated, the sets of projections corresponding to any of the selected respiratory phases do not have a regular angular distribution. This drastically affects the image quality of reconstructions based on simple filtered backprojection. To address this problem, we use an iterative reconstruction algorithm that combines the Simultaneous Algebraic Reconstruction Technique with Total Variation minimization (SART-TV). At each SART-TV iteration, backprojection is performed with a set of weighting factors that favor the desired respiratory phase. To reduce reconstruction time, the algorithm is implemented on a graphics processing unit. The performance of the proposed approach was investigated in simulations and in vivo scans of mice with primary lung cancers imaged with our in-house developed dual tube/detector micro-CT system. We note that if the ECG signal is acquired during sampling, the same approach could be used for phase-selective cardiac imaging.
Texture enhanced optimization-based image reconstruction (TxE-OBIR) from sparse projection views
NASA Astrophysics Data System (ADS)
Xie, Huiqiao; Niu, Tianye; Yang, Yi; Ren, Yi; Tang, Xiangyang
2016-03-01
The optimization-based image reconstruction (OBIR) has been proposed and investigated in recent years to reduce radiation dose in X-ray computed tomography (CT) through acquiring sparse projection views. However, the OBIR usually generates images with a quite different noise texture compared to the clinical widely used reconstruction method (i.e. filtered back-projection - FBP). This may make the radiologists/physicians less confident while they are making clinical decisions. Recognizing the fact that the X-ray photon noise statistics is relatively uniform across the detector cells, which is enabled by beam forming devices (e.g. bowtie filters), we propose and evaluate a novel and practical texture enhancement method in this work. In the texture enhanced optimization-based image reconstruction (TxEOBIR), we first reconstruct a texture image with the FBP algorithm from a full set of synthesized projection views of noise. Then, the TxE-OBIR image is generated by adding the texture image into the OBIR reconstruction. As qualitatively confirmed by visual inspection and quantitatively by noise power spectrum (NPS) evaluation, the proposed method can produce images with textures that are visually identical to those of the gold standard FBP images.
Line Integral Alternating Minimization Algorithm for Dual-Energy X-Ray CT Image Reconstruction.
Chen, Yaqi; O'Sullivan, Joseph A; Politte, David G; Evans, Joshua D; Han, Dong; Whiting, Bruce R; Williamson, Jeffrey F
2016-02-01
We propose a new algorithm, called line integral alternating minimization (LIAM), for dual-energy X-ray CT image reconstruction. Instead of obtaining component images by minimizing the discrepancy between the data and the mean estimates, LIAM allows for a tunable discrepancy between the basis material projections and the basis sinograms. A parameter is introduced that controls the size of this discrepancy, and with this parameter the new algorithm can continuously go from a two-step approach to the joint estimation approach. LIAM alternates between iteratively updating the line integrals of the component images and reconstruction of the component images using an image iterative deblurring algorithm. An edge-preserving penalty function can be incorporated in the iterative deblurring step to decrease the roughness in component images. Images from both simulated and experimentally acquired sinograms from a clinical scanner were reconstructed by LIAM while varying the regularization parameters to identify good choices. The results from the dual-energy alternating minimization algorithm applied to the same data were used for comparison. Using a small fraction of the computation time of dual-energy alternating minimization, LIAM achieves better accuracy of the component images in the presence of Poisson noise for simulated data reconstruction and achieves the same level of accuracy for real data reconstruction.
Ahmad, Munir; Shahzad, Tasawar; Masood, Khalid; Rashid, Khalid; Tanveer, Muhammad; Iqbal, Rabail; Hussain, Nasir; Shahid, Abubakar; Fazal-E-Aleem
2016-06-01
Emission tomographic image reconstruction is an ill-posed problem due to limited and noisy data and various image-degrading effects affecting the data and leads to noisy reconstructions. Explicit regularization, through iterative reconstruction methods, is considered better to compensate for reconstruction-based noise. Local smoothing and edge-preserving regularization methods can reduce reconstruction-based noise. However, these methods produce overly smoothed images or blocky artefacts in the final image because they can only exploit local image properties. Recently, non-local regularization techniques have been introduced, to overcome these problems, by incorporating geometrical global continuity and connectivity present in the objective image. These techniques can overcome drawbacks of local regularization methods; however, they also have certain limitations, such as choice of the regularization function, neighbourhood size or calibration of several empirical parameters involved. This work compares different local and non-local regularization techniques used in emission tomographic imaging in general and emission computed tomography in specific for improved quality of the resultant images.
GPU accelerated generation of digitally reconstructed radiographs for 2-D/3-D image registration.
Dorgham, Osama M; Laycock, Stephen D; Fisher, Mark H
2012-09-01
Recent advances in programming languages for graphics processing units (GPUs) provide developers with a convenient way of implementing applications which can be executed on the CPU and GPU interchangeably. GPUs are becoming relatively cheap, powerful, and widely available hardware components, which can be used to perform intensive calculations. The last decade of hardware performance developments shows that GPU-based computation is progressing significantly faster than CPU-based computation, particularly if one considers the execution of highly parallelisable algorithms. Future predictions illustrate that this trend is likely to continue. In this paper, we introduce a way of accelerating 2-D/3-D image registration by developing a hybrid system which executes on the CPU and utilizes the GPU for parallelizing the generation of digitally reconstructed radiographs (DRRs). Based on the advancements of the GPU over the CPU, it is timely to exploit the benefits of many-core GPU technology by developing algorithms for DRR generation. Although some previous work has investigated the rendering of DRRs using the GPU, this paper investigates approximations which reduce the computational overhead while still maintaining a quality consistent with that needed for 2-D/3-D registration with sufficient accuracy to be clinically acceptable in certain applications of radiation oncology. Furthermore, by comparing implementations of 2-D/3-D registration on the CPU and GPU, we investigate current performance and propose an optimal framework for PC implementations addressing the rigid registration problem. Using this framework, we are able to render DRR images from a 256×256×133 CT volume in ~24 ms using an NVidia GeForce 8800 GTX and in ~2 ms using NVidia GeForce GTX 580. In addition to applications requiring fast automatic patient setup, these levels of performance suggest image-guided radiation therapy at video frame rates is technically feasible using relatively low cost PC
NASA Astrophysics Data System (ADS)
Kole, J. S.; Beekman, F. J.
2006-02-01
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.
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.
NASA Astrophysics Data System (ADS)
Van de Casteele, Elke; Parizel, Paul; Sijbers, Jan
2012-03-01
Adaptive statistical iterative reconstruction (ASiR) is a new reconstruction algorithm used in the field of medical X-ray imaging. This new reconstruction method combines the idealized system representation, as we know it from the standard Filtered Back Projection (FBP) algorithm, and the strength of iterative reconstruction by including a noise model in the reconstruction scheme. It studies how noise propagates through the reconstruction steps, feeds this model back into the loop and iteratively reduces noise in the reconstructed image without affecting spatial resolution. In this paper the effect of ASiR on the contrast to noise ratio is studied using the low contrast module of the Catphan phantom. The experiments were done on a GE LightSpeed VCT system at different voltages and currents. The results show reduced noise and increased contrast for the ASiR reconstructions compared to the standard FBP method. For the same contrast to noise ratio the images from ASiR can be obtained using 60% less current, leading to a reduction in dose of the same amount.
Advances in quantitative nanoscale subsurface imaging by mode-synthesizing atomic force microscopy
Vitry, P.; Bourillot, E.; Plassard, C.; Lacroute, Y.; Lesniewska, E.; Tetard, L.
2014-08-04
This paper reports on advances toward quantitative non-destructive nanoscale subsurface investigation of a nanofabricated sample based on mode synthesizing atomic force microscopy with heterodyne detection, addressing the need to correlate the role of actuation frequencies of the probe f{sub p} and the sample f{sub s} with depth resolution for 3D tomography reconstruction. Here, by developing a simple model and validating the approach experimentally through the study of the nanofabricated calibration depth samples consisting of buried metallic patterns, we demonstrate avenues for quantitative nanoscale subsurface imaging. Our findings enable the reconstruction of the sample depth profile and allow high fidelity resolution of the buried nanostructures. Non-destructive quantitative nanoscale subsurface imaging offers great promise in the study of the structures and properties of complex systems at the nanoscale.
Advance of Molecular Imaging Technology and Targeted Imaging Agent in Imaging and Therapy
Chen, Zhi-Yi; Wang, Yi-Xiang; Lin, Yan; Zhang, Jin-Shan; Yang, Feng; Zhou, Qiu-Lan; Liao, Yang-Ying
2014-01-01
Molecular imaging is an emerging field that integrates advanced imaging technology with cellular and molecular biology. It can realize noninvasive and real time visualization, measurement of physiological or pathological process in the living organism at the cellular and molecular level, providing an effective method of information acquiring for diagnosis, therapy, and drug development and evaluating treatment of efficacy. Molecular imaging requires high resolution and high sensitive instruments and specific imaging agents that link the imaging signal with molecular event. Recently, the application of new emerging chemical technology and nanotechnology has stimulated the development of imaging agents. Nanoparticles modified with small molecule, peptide, antibody, and aptamer have been extensively applied for preclinical studies. Therapeutic drug or gene is incorporated into nanoparticles to construct multifunctional imaging agents which allow for theranostic applications. In this review, we will discuss the characteristics of molecular imaging, the novel imaging agent including targeted imaging agent and multifunctional imaging agent, as well as cite some examples of their application in molecular imaging and therapy. PMID:24689058
Quan, Tingwei; Lv, Xiaohua; Liu, Xiuli; Zeng, Shaoqun
2016-01-01
Calcium imaging is becoming an increasingly popular technology to indirectly measure activity patterns in local neuronal networks. Based on the dependence of calcium fluorescence on neuronal spiking, two-photon calcium imaging affords single-cell resolution of neuronal population activity. However, it is still difficult to reconstruct neuronal activity from complex calcium fluorescence traces, particularly for traces contaminated by noise. Here, we describe a robust and efficient neuronal-activity reconstruction method that utilizes Lq minimization and interval screening (IS), which we refer to as LqIS. The simulation results show that LqIS performs satisfactorily in terms of both accuracy and speed of reconstruction. Reconstruction of simulation and experimental data also shows that LqIS has advantages in terms of the recall rate, precision rate, and timing error. Finally, LqIS is demonstrated to effectively reconstruct neuronal burst activity from calcium fluorescence traces recorded from large-size neuronal population. PMID:27375930
3-D reconstruction of neurons from multichannel confocal laser scanning image series.
Wouterlood, Floris G
2005-08-01
A confocal laser scanning microscope (CLSM) collects information from a thin, focal plane and ignores out-of-focus information. The operator configures separate channels (laser, filters, detector settings) for each fluorochrome used in a particular experiment. Then, 3-D reconstructions are made from Z-series of confocal images: one series per channel. Channel signal separation is extremely important and measures to avoid bleaching are vital. Post-acquisition deconvolution of the image series is then performed to increase resolution. In the 3-D reconstruction program described in this unit, reconstructions can be inspected in real time from any viewing angle. By altering viewing angles and by switching channels off and on, the spatial relationship of 3-D-reconstructed structures with respect to structures seen in other channels can be studied. Since each brand of CLSM, computer program, and 3-D reconstruction package has its own proprietary set of procedures, a general approach is provided wherever possible.
NASA Astrophysics Data System (ADS)
Shin, Y.; Kim, G.; Lee, G.
2017-01-01
A rotating modulation collimator (RMC) is an indirect imaging technique that has proven useful for remote radiation source detection. While it was initially invented for detecting sources in a far field, a recent development by Kowash has shown the feasibility of the RMC for detecting mid-range sources. However, their image reconstruction algorithm often produces spurious source estimates in pixels where no source exists. In this paper, we propose to improve the RMC image quality using a variance stabilizing transform. The transform reduces the inhomogeneous Poisson noise in the RMC data. In our simulation study, the proposed algorithm could reconstruct RMC images without misleading artifacts.
Adaptive Forward Modeling Method for Analysis and Reconstructions of Orientation Image Map
Frankie Li, Shiu Fai
2014-06-01
IceNine is a MPI-parallel orientation reconstruction and microstructure analysis code. It's primary purpose is to reconstruct a spatially resolved orientation map given a set of diffraction images from a high energy x-ray diffraction microscopy (HEDM) experiment (1). In particular, IceNine implements the adaptive version of the forward modeling method (2, 3). Part of IceNine is a library used to for conbined analysis of the microstructure with the experimentally measured diffraction signal. The libraries is also designed for tapid prototyping of new reconstruction and analysis algorithms. IceNine is also built with a simulator of diffraction images with an input microstructure.
ℓ0 Gradient Minimization Based Image Reconstruction for Limited-Angle Computed Tomography
Yu, Wei; Zeng, Li
2015-01-01
In medical and industrial applications of computed tomography (CT) imaging, limited by the scanning environment and the risk of excessive X-ray radiation exposure imposed to the patients, reconstructing high quality CT images from limited projection data has become a hot topic. X-ray imaging in limited scanning angular range is an effective imaging modality to reduce the radiation dose to the patients. As the projection data available in this modality are incomplete, limited-angle CT image reconstruction is actually an ill-posed inverse problem. To solve the problem, image reconstructed by conventional filtered back projection (FBP) algorithm frequently results in conspicuous streak artifacts and gradual changed artifacts nearby edges. Image reconstruction based on total variation minimization (TVM) can significantly reduce streak artifacts in few-view CT, but it suffers from the gradual changed artifacts nearby edges in limited-angle CT. To suppress this kind of artifacts, we develop an image reconstruction algorithm based on ℓ0 gradient minimization for limited-angle CT in this paper. The ℓ0-norm of the image gradient is taken as the regularization function in the framework of developed reconstruction model. We transformed the optimization problem into a few optimization sub-problems and then, solved these sub-problems in the manner of alternating iteration. Numerical experiments are performed to validate the efficiency and the feasibility of the developed algorithm. From the statistical analysis results of the performance evaluations peak signal-to-noise ratio (PSNR) and normalized root mean square distance (NRMSD), it shows that there are significant statistical differences between different algorithms from different scanning angular ranges (p<0.0001). From the experimental results, it also indicates that the developed algorithm outperforms classical reconstruction algorithms in suppressing the streak artifacts and the gradual changed artifacts nearby edges
Neural network algorithm for image reconstruction using the "grid-friendly" projections.
Cierniak, Robert
2011-09-01
The presented paper describes a development of original approach to the reconstruction problem using a recurrent neural network. Particularly, the "grid-friendly" angles of performed projections are selected according to the discrete Radon transform (DRT) concept to decrease the number of projections required. The methodology of our approach is consistent with analytical reconstruction algorithms. Reconstruction problem is reformulated in our approach to optimization problem. This problem is solved in present concept using method based on the maximum likelihood methodology. The reconstruction algorithm proposed in this work is consequently adapted for more practical discrete fan beam projections. Computer simulation results show that the neural network reconstruction algorithm designed to work in this way improves obtained results and outperforms conventional methods in reconstructed image quality.
NASA Astrophysics Data System (ADS)
Wang, Qi; Lian, Zhijie; Wang, Jianming; Chen, Qingliang; Sun, Yukuan; Li, Xiuyan; Duan, Xiaojie; Cui, Ziqiang; Wang, Huaxiang
2016-11-01
Electrical impedance tomography (EIT) reconstruction is a nonlinear and ill-posed problem. Exact reconstruction of an EIT image inverts a high dimensional mathematical model to calculate the conductivity field, which causes significant problems regarding that the computational complexity will reduce the achievable frame rate, which is considered as a major advantage of EIT imaging. The single-step method, state estimation method, and projection method were always used to accelerate reconstruction process. The basic principle of these methods is to reduce computational complexity. However, maintaining high resolution in space together with not much cost is still challenging, especially for complex conductivity distribution. This study proposes an idea to accelerate image reconstruction of EIT based on compressive sensing (CS) theory, namely, CSEIT method. The novel CSEIT method reduces the sampling rate through minimizing redundancy in measurements, so that detailed information of reconstruction is not lost. In order to obtain sparse solution, which is the prior condition of signal recovery required by CS theory, a novel image reconstruction algorithm based on patch-based sparse representation is proposed. By applying the new framework of CSEIT, the data acquisition time, or the sampling rate, is reduced by more than two times, while the accuracy of reconstruction is significantly improved.
GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume
Zhao, Xing; Hu, Jing-jing; Zhang, Peng
2009-01-01
Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation. PMID:19730744
Technology Transfer Automated Retrieval System (TEKTRAN)
Reconstruction of 3D images from a series of 2D images has been restricted by the limited capacity to decrease the opacity of surrounding tissue. Commercial software that allows color-keying and manipulation of 2D images in true 3D space allowed us to produce 3D reconstructions from pixel based imag...
Optics for Advanced Neutron Imaging and Scattering
Moncton, David E.; Khaykovich, Boris
2016-03-30
During the report period, we continued the work as outlined in the original proposal. We have analyzed potential optical designs of Wolter mirrors for the neutron-imaging instrument VENUS, which is under construction at SNS. In parallel, we have conducted the initial polarized imaging experiment at Helmholtz Zentrum, Berlin, one of very few of currently available polarized-imaging facilities worldwide.
Li, Jing; Quan, Tingwei; Li, Shiwei; Zhou, Hang; Luo, Qingming; Gong, Hui; Zeng, Shaoqun
2015-01-01
Brain surface profile is essential for brain studies, including registration, segmentation of brain structure and drawing neuronal circuits. Recent advances in high-throughput imaging techniques enable imaging whole mouse brain at micron spatial resolution and provide a basis for more fine quantitative studies in neuroscience. However, reconstructing micron resolution brain surface from newly produced neuronal dataset still faces challenges. Most current methods apply global analysis, which are neither applicable to a large imaging dataset nor to a brain surface with an inhomogeneous signal intensity. Here, we proposed a resampling-based variational model for this purpose. In this model, the movement directions of the initial boundary elements are fixed, the final positions of the initial boundary elements that form the brain surface are determined by the local signal intensity. These features assure an effective reconstruction of the brain surface from a new brain dataset. Compared with conventional typical methods, such as level set based method and active contour method, our method significantly increases the recall and precision rates above 97% and is approximately hundreds-fold faster. We demonstrated a fast reconstruction at micron level of the whole brain surface from a large dataset of hundreds of GB in size within 6 hours. PMID:26245266
Siewerdsen, Jeffrey
2016-06-01
At least three major trends in surgical intervention have emerged over the last decade: a move toward more minimally invasive (or non-invasive) approach to the surgical target; the development of high-precision treatment delivery techniques; and the increasing role of multi-modality intraoperative imaging in support of such procedures. This symposium includes invited presentations on recent advances in each of these areas and the emerging role for medical physics research in the development and translation of high-precision interventional techniques. The four speakers are: (1) Keyvan Farahani, "Image-guided focused ultrasound surgery and therapy" (2) Jeffrey H. Siewerdsen, "Advances in image registration and reconstruction for image-guided neurosurgery" (3) Tina Kapur, "Image-guided surgery and interventions in the advanced multimodality image-guided operating (AMIGO) suite" (4) Raj Shekhar, "Multimodality image-guided interventions: Multimodality for the rest of us" Learning Objectives: 1. Understand the principles and applications of HIFU in surgical ablation. 2. Learn about recent advances in 3D-2D and 3D deformable image registration in support of surgical safety and precision. 3. Learn about recent advances in model-based 3D image reconstruction in application to intraoperative 3D imaging. 4. Understand the multi-modality imaging technologies and clinical applications investigated in the AMIGO suite. 5. Understand the emerging need and techniques to implement multi-modality image guidance in surgical applications such as neurosurgery, orthopaedic surgery, vascular surgery, and interventional radiology. Research supported by the NIH and Siemens Healthcare.; J. Siewerdsen; Grant Support - National Institutes of Health; Grant Support - Siemens Healthcare; Grant Support - Carestream Health; Advisory Board - Carestream Health; Licensing Agreement - Carestream Health; Licensing Agreement - Elekta Oncology.; T. Kapur, P41EB015898; R. Shekhar, Funding: R42CA137886 and
Digital reconstructed radiography with multiple color image overlay for image-guided radiotherapy
Yoshino, Shinichi; Miki, Kentaro; Sakata, Kozo; Nakayama, Yuko; Shibayama, Kouichi; Mori, Shinichiro
2015-01-01
Registration of patient anatomical structures to the reference position is a basic part of the patient set-up procedure. Registration of anatomical structures between the si