Application Of Iterative Reconstruction Techniques To Conventional Circular Tomography

NASA Astrophysics Data System (ADS)

Ghosh Roy, D. N.; Kruger, R. A.; Yih, B. C.; Del Rio, S. P.; Power, R. L.

1985-06-01

Two "point-by-point" iteration procedures, namely, Iterative Least Square Technique (ILST) and Simultaneous Iterative Reconstructive Technique (SIRT) were applied to classical circular tomographic reconstruction. The technique of tomosynthetic DSA was used in forming the tomographic images. Reconstructions of a dog's renal and neck anatomy are presented.

Emerging Techniques for Dose Optimization in Abdominal CT

PubMed Central

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

2014-01-01

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

Non-iterative volumetric particle reconstruction near moving bodies

NASA Astrophysics Data System (ADS)

Mendelson, Leah; Techet, Alexandra

2017-11-01

When multi-camera 3D PIV experiments are performed around a moving body, the body often obscures visibility of regions of interest in the flow field in a subset of cameras. We evaluate the performance of non-iterative particle reconstruction algorithms used for synthetic aperture PIV (SAPIV) in these partially-occluded regions. We show that when partial occlusions are present, the quality and availability of 3D tracer particle information depends on the number of cameras and reconstruction procedure used. Based on these findings, we introduce an improved non-iterative reconstruction routine for SAPIV around bodies. The reconstruction procedure combines binary masks, already required for reconstruction of the body's 3D visual hull, and a minimum line-of-sight algorithm. This approach accounts for partial occlusions without performing separate processing for each possible subset of cameras. We combine this reconstruction procedure with three-dimensional imaging on both sides of the free surface to reveal multi-fin wake interactions generated by a jumping archer fish. Sufficient particle reconstruction in near-body regions is crucial to resolving the wake structures of upstream fins (i.e., dorsal and anal fins) before and during interactions with the caudal tail.

AIR-MRF: Accelerated iterative reconstruction for magnetic resonance fingerprinting.

PubMed

Cline, Christopher C; Chen, Xiao; Mailhe, Boris; Wang, Qiu; Pfeuffer, Josef; Nittka, Mathias; Griswold, Mark A; Speier, Peter; Nadar, Mariappan S

2017-09-01

Existing approaches for reconstruction of multiparametric maps with magnetic resonance fingerprinting (MRF) are currently limited by their estimation accuracy and reconstruction time. We aimed to address these issues with a novel combination of iterative reconstruction, fingerprint compression, additional regularization, and accelerated dictionary search methods. The pipeline described here, accelerated iterative reconstruction for magnetic resonance fingerprinting (AIR-MRF), was evaluated with simulations as well as phantom and in vivo scans. We found that the AIR-MRF pipeline provided reduced parameter estimation errors compared to non-iterative and other iterative methods, particularly at shorter sequence lengths. Accelerated dictionary search methods incorporated into the iterative pipeline reduced the reconstruction time at little cost of quality. Copyright © 2017 Elsevier Inc. All rights reserved.

3D algebraic iterative reconstruction for cone-beam x-ray differential phase-contrast computed tomography.

PubMed

Fu, Jian; Hu, Xinhua; Velroyen, Astrid; Bech, Martin; Jiang, Ming; Pfeiffer, Franz

2015-01-01

Due to the potential of compact imaging systems with magnified spatial resolution and contrast, cone-beam x-ray differential phase-contrast computed tomography (DPC-CT) has attracted significant interest. The current proposed FDK reconstruction algorithm with the Hilbert imaginary filter will induce severe cone-beam artifacts when the cone-beam angle becomes large. In this paper, we propose an algebraic iterative reconstruction (AIR) method for cone-beam DPC-CT and report its experiment results. This approach considers the reconstruction process as the optimization of a discrete representation of the object function to satisfy a system of equations that describes the cone-beam DPC-CT imaging modality. Unlike the conventional iterative algorithms for absorption-based CT, it involves the derivative operation to the forward projections of the reconstructed intermediate image to take into account the differential nature of the DPC projections. This method is based on the algebraic reconstruction technique, reconstructs the image ray by ray, and is expected to provide better derivative estimates in iterations. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured with a three-grating interferometer and a mini-focus x-ray tube source. It is shown that the proposed method can reduce the cone-beam artifacts and performs better than FDK under large cone-beam angles. This algorithm is of interest for future cone-beam DPC-CT applications.

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

PubMed

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

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

Optimization and validation of accelerated golden-angle radial sparse MRI reconstruction with self-calibrating GRAPPA operator gridding.

PubMed

Benkert, Thomas; Tian, Ye; Huang, Chenchan; DiBella, Edward V R; Chandarana, Hersh; Feng, Li

2018-07-01

Golden-angle radial sparse parallel (GRASP) MRI reconstruction requires gridding and regridding to transform data between radial and Cartesian k-space. These operations are repeatedly performed in each iteration, which makes the reconstruction computationally demanding. This work aimed to accelerate GRASP reconstruction using self-calibrating GRAPPA operator gridding (GROG) and to validate its performance in clinical imaging. GROG is an alternative gridding approach based on parallel imaging, in which k-space data acquired on a non-Cartesian grid are shifted onto a Cartesian k-space grid using information from multicoil arrays. For iterative non-Cartesian image reconstruction, GROG is performed only once as a preprocessing step. Therefore, the subsequent iterative reconstruction can be performed directly in Cartesian space, which significantly reduces computational burden. Here, a framework combining GROG with GRASP (GROG-GRASP) is first optimized and then compared with standard GRASP reconstruction in 22 prostate patients. GROG-GRASP achieved approximately 4.2-fold reduction in reconstruction time compared with GRASP (∼333 min versus ∼78 min) while maintaining image quality (structural similarity index ≈ 0.97 and root mean square error ≈ 0.007). Visual image quality assessment by two experienced radiologists did not show significant differences between the two reconstruction schemes. With a graphics processing unit implementation, image reconstruction time can be further reduced to approximately 14 min. The GRASP reconstruction can be substantially accelerated using GROG. This framework is promising toward broader clinical application of GRASP and other iterative non-Cartesian reconstruction methods. Magn Reson Med 80:286-293, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Iterative image reconstruction for PROPELLER-MRI using the nonuniform fast fourier transform.

PubMed

Tamhane, Ashish A; Anastasio, Mark A; Gui, Minzhi; Arfanakis, Konstantinos

2010-07-01

To investigate an iterative image reconstruction algorithm using the nonuniform fast Fourier transform (NUFFT) for PROPELLER (Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction) MRI. Numerical simulations, as well as experiments on a phantom and a healthy human subject were used to evaluate the performance of the iterative image reconstruction algorithm for PROPELLER, and compare it with that of conventional gridding. The trade-off between spatial resolution, signal to noise ratio, and image artifacts, was investigated for different values of the regularization parameter. The performance of the iterative image reconstruction algorithm in the presence of motion was also evaluated. It was demonstrated that, for a certain range of values of the regularization parameter, iterative reconstruction produced images with significantly increased signal to noise ratio, reduced artifacts, for similar spatial resolution, compared with gridding. Furthermore, the ability to reduce the effects of motion in PROPELLER-MRI was maintained when using the iterative reconstruction approach. An iterative image reconstruction technique based on the NUFFT was investigated for PROPELLER MRI. For a certain range of values of the regularization parameter, the new reconstruction technique may provide PROPELLER images with improved image quality compared with conventional gridding. (c) 2010 Wiley-Liss, Inc.

Iterative Image Reconstruction for PROPELLER-MRI using the NonUniform Fast Fourier Transform

PubMed Central

Tamhane, Ashish A.; Anastasio, Mark A.; Gui, Minzhi; Arfanakis, Konstantinos

2013-01-01

Purpose To investigate an iterative image reconstruction algorithm using the non-uniform fast Fourier transform (NUFFT) for PROPELLER (Periodically Rotated Overlapping parallEL Lines with Enhanced Reconstruction) MRI. Materials and Methods Numerical simulations, as well as experiments on a phantom and a healthy human subject were used to evaluate the performance of the iterative image reconstruction algorithm for PROPELLER, and compare it to that of conventional gridding. The trade-off between spatial resolution, signal to noise ratio, and image artifacts, was investigated for different values of the regularization parameter. The performance of the iterative image reconstruction algorithm in the presence of motion was also evaluated. Results It was demonstrated that, for a certain range of values of the regularization parameter, iterative reconstruction produced images with significantly increased SNR, reduced artifacts, for similar spatial resolution, compared to gridding. Furthermore, the ability to reduce the effects of motion in PROPELLER-MRI was maintained when using the iterative reconstruction approach. Conclusion An iterative image reconstruction technique based on the NUFFT was investigated for PROPELLER MRI. For a certain range of values of the regularization parameter the new reconstruction technique may provide PROPELLER images with improved image quality compared to conventional gridding. PMID:20578028

Computed inverse resonance imaging for magnetic susceptibility map reconstruction.

PubMed

Chen, Zikuan; Calhoun, Vince

2012-01-01

This article reports a computed inverse magnetic resonance imaging (CIMRI) model for reconstructing the magnetic susceptibility source from MRI data using a 2-step computational approach. The forward T2*-weighted MRI (T2*MRI) process is broken down into 2 steps: (1) from magnetic susceptibility source to field map establishment via magnetization in the main field and (2) from field map to MR image formation by intravoxel dephasing average. The proposed CIMRI model includes 2 inverse steps to reverse the T2*MRI procedure: field map calculation from MR-phase image and susceptibility source calculation from the field map. The inverse step from field map to susceptibility map is a 3-dimensional ill-posed deconvolution problem, which can be solved with 3 kinds of approaches: the Tikhonov-regularized matrix inverse, inverse filtering with a truncated filter, and total variation (TV) iteration. By numerical simulation, we validate the CIMRI model by comparing the reconstructed susceptibility maps for a predefined susceptibility source. Numerical simulations of CIMRI show that the split Bregman TV iteration solver can reconstruct the susceptibility map from an MR-phase image with high fidelity (spatial correlation ≈ 0.99). The split Bregman TV iteration solver includes noise reduction, edge preservation, and image energy conservation. For applications to brain susceptibility reconstruction, it is important to calibrate the TV iteration program by selecting suitable values of the regularization parameter. The proposed CIMRI model can reconstruct the magnetic susceptibility source of T2*MRI by 2 computational steps: calculating the field map from the phase image and reconstructing the susceptibility map from the field map. The crux of CIMRI lies in an ill-posed 3-dimensional deconvolution problem, which can be effectively solved by the split Bregman TV iteration algorithm.

Computed inverse MRI for magnetic susceptibility map reconstruction

PubMed Central

Chen, Zikuan; Calhoun, Vince

2015-01-01

Objective This paper reports on a computed inverse magnetic resonance imaging (CIMRI) model for reconstructing the magnetic susceptibility source from MRI data using a two-step computational approach. Methods The forward T2*-weighted MRI (T2*MRI) process is decomposed into two steps: 1) from magnetic susceptibility source to fieldmap establishment via magnetization in a main field, and 2) from fieldmap to MR image formation by intravoxel dephasing average. The proposed CIMRI model includes two inverse steps to reverse the T2*MRI procedure: fieldmap calculation from MR phase image and susceptibility source calculation from the fieldmap. The inverse step from fieldmap to susceptibility map is a 3D ill-posed deconvolution problem, which can be solved by three kinds of approaches: Tikhonov-regularized matrix inverse, inverse filtering with a truncated filter, and total variation (TV) iteration. By numerical simulation, we validate the CIMRI model by comparing the reconstructed susceptibility maps for a predefined susceptibility source. Results Numerical simulations of CIMRI show that the split Bregman TV iteration solver can reconstruct the susceptibility map from a MR phase image with high fidelity (spatial correlation≈0.99). The split Bregman TV iteration solver includes noise reduction, edge preservation, and image energy conservation. For applications to brain susceptibility reconstruction, it is important to calibrate the TV iteration program by selecting suitable values of the regularization parameter. Conclusions The proposed CIMRI model can reconstruct the magnetic susceptibility source of T2*MRI by two computational steps: calculating the fieldmap from the phase image and reconstructing the susceptibility map from the fieldmap. The crux of CIMRI lies in an ill-posed 3D deconvolution problem, which can be effectively solved by the split Bregman TV iteration algorithm. PMID:22446372

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Influence of iterative reconstruction on coronary calcium scores at multiple heart rates: a multivendor phantom study on state-of-the-art CT systems.

PubMed

van der Werf, N R; Willemink, M J; Willems, T P; Greuter, M J W; Leiner, T

2017-12-28

The objective of this study was to evaluate the influence of iterative reconstruction on coronary calcium scores (CCS) at different heart rates for four state-of-the-art CT systems. Within an anthropomorphic chest phantom, artificial coronary arteries were translated in a water-filled compartment. The arteries contained three different calcifications with low (38 mg), medium (80 mg) and high (157 mg) mass. Linear velocities were applied, corresponding to heart rates of 0, < 60, 60-75 and > 75 bpm. Data were acquired on four state-of-the-art CT systems (CT1-CT4) with routinely used CCS protocols. Filtered back projection (FBP) and three increasing levels of iterative reconstruction (L1-L3) were used for reconstruction. CCS were quantified as Agatston score and mass score. An iterative reconstruction susceptibility (IRS) index was used to assess susceptibility of Agatston score (IRS AS ) and mass score (IRS MS ) to iterative reconstruction. IRS values were compared between CT systems and between calcification masses. For each heart rate, differences in CCS of iterative reconstructed images were evaluated with CCS of FBP images as reference, and indicated as small (< 5%), medium (5-10%) or large (> 10%). Statistical analysis was performed with repeated measures ANOVA tests. While subtle differences were found for Agatston scores of low mass calcification, medium and high mass calcifications showed increased CCS up to 77% with increasing heart rates. IRS AS of CT1-T4 were 17, 41, 130 and 22% higher than IRS MS . Not only were IRS significantly different between all CT systems, but also between calcification masses. Up to a fourfold increase in IRS was found for the low mass calcification in comparison with the high mass calcification. With increasing iterative reconstruction strength, maximum decreases of 21 and 13% for Agatston and mass score were found. In total, 21 large differences between Agatston scores from FBP and iterative reconstruction were found, while only five large differences were found between FBP and iterative reconstruction mass scores. Iterative reconstruction results in reduced CCS. The effect of iterative reconstruction on CCS is more prominent with low-density calcifications, high heart rates and increasing iterative reconstruction strength.

SU-F-BRCD-09: Total Variation (TV) Based Fast Convergent Iterative CBCT Reconstruction with GPU Acceleration.

PubMed

Xu, Q; Yang, D; Tan, J; Anastasio, M

2012-06-01

To improve image quality and reduce imaging dose in CBCT for radiation therapy applications and to realize near real-time image reconstruction based on use of a fast convergence iterative algorithm and acceleration by multi-GPUs. An iterative image reconstruction that sought to minimize a weighted least squares cost function that employed total variation (TV) regularization was employed to mitigate projection data incompleteness and noise. To achieve rapid 3D image reconstruction (< 1 min), a highly optimized multiple-GPU implementation of the algorithm was developed. The convergence rate and reconstruction accuracy were evaluated using a modified 3D Shepp-Logan digital phantom and a Catphan-600 physical phantom. The reconstructed images were compared with the clinical FDK reconstruction results. Digital phantom studies showed that only 15 iterations and 60 iterations are needed to achieve algorithm convergence for 360-view and 60-view cases, respectively. The RMSE was reduced to 10-4 and 10-2, respectively, by using 15 iterations for each case. Our algorithm required 5.4s to complete one iteration for the 60-view case using one Tesla C2075 GPU. The few-view study indicated that our iterative algorithm has great potential to reduce the imaging dose and preserve good image quality. For the physical Catphan studies, the images obtained from the iterative algorithm possessed better spatial resolution and higher SNRs than those obtained from by use of a clinical FDK reconstruction algorithm. We have developed a fast convergence iterative algorithm for CBCT image reconstruction. The developed algorithm yielded images with better spatial resolution and higher SNR than those produced by a commercial FDK tool. In addition, from the few-view study, the iterative algorithm has shown great potential for significantly reducing imaging dose. We expect that the developed reconstruction approach will facilitate applications including IGART and patient daily CBCT-based treatment localization. © 2012 American Association of Physicists in Medicine.

Propagation-based x-ray phase contrast imaging using an iterative phase diversity technique

NASA Astrophysics Data System (ADS)

Carroll, Aidan J.; van Riessen, Grant A.; Balaur, Eugeniu; Dolbnya, Igor P.; Tran, Giang N.; Peele, Andrew G.

2018-03-01

Through the use of a phase diversity technique, we demonstrate a near-field in-line x-ray phase contrast algorithm that provides improved object reconstruction when compared to our previous iterative methods for a homogeneous sample. Like our previous methods, the new technique uses the sample refractive index distribution during the reconstruction process. The technique complements existing monochromatic and polychromatic methods and is useful in situations where experimental phase contrast data is affected by noise.

Varying-energy CT imaging method based on EM-TV

NASA Astrophysics Data System (ADS)

Chen, Ping; Han, Yan

2016-11-01

For complicated structural components with wide x-ray attenuation ranges, conventional fixed-energy computed tomography (CT) imaging cannot obtain all the structural information. This limitation results in a shortage of CT information because the effective thickness of the components along the direction of x-ray penetration exceeds the limit of the dynamic range of the x-ray imaging system. To address this problem, a varying-energy x-ray CT imaging method is proposed. In this new method, the tube voltage is adjusted several times with the fixed lesser interval. Next, the fusion of grey consistency and logarithm demodulation are applied to obtain full and lower noise projection with a high dynamic range (HDR). In addition, for the noise suppression problem of the analytical method, EM-TV (expectation maximization-total Jvariation) iteration reconstruction is used. In the process of iteration, the reconstruction result obtained at one x-ray energy is used as the initial condition of the next iteration. An accompanying experiment demonstrates that this EM-TV reconstruction can also extend the dynamic range of x-ray imaging systems and provide a higher reconstruction quality relative to the fusion reconstruction method.

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

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

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

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

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

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

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

PubMed

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

2016-04-01

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

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

PubMed Central

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

2016-01-01

Purpose: The development of iterative image reconstruction algorithms for cone-beam computed tomography (CBCT) remains an active and important research area. Even with hardware acceleration, the overwhelming majority of the available 3D iterative algorithms that implement nonsmooth regularizers remain computationally burdensome and have not been translated for routine use in time-sensitive applications such as image-guided radiation therapy (IGRT). In this work, two variants of the fast iterative shrinkage thresholding algorithm (FISTA) are proposed and investigated for accelerated iterative image reconstruction in CBCT. Methods: Algorithm acceleration was achieved by replacing the original gradient-descent step in the FISTAs by a subproblem that is solved by use of the ordered subset simultaneous algebraic reconstruction technique (OS-SART). Due to the preconditioning matrix adopted in the OS-SART method, two new weighted proximal problems were introduced and corresponding fast gradient projection-type algorithms were developed for solving them. We also provided efficient numerical implementations of the proposed algorithms that exploit the massive data parallelism of multiple graphics processing units. Results: The improved rates of convergence of the proposed algorithms were quantified in computer-simulation studies and by use of clinical projection data corresponding to an IGRT study. The accelerated FISTAs were shown to possess dramatically improved convergence properties as compared to the standard FISTAs. For example, the number of iterations to achieve a specified reconstruction error could be reduced by an order of magnitude. Volumetric images reconstructed from clinical data were produced in under 4 min. Conclusions: The FISTA achieves a quadratic convergence rate and can therefore potentially reduce the number of iterations required to produce an image of a specified image quality as compared to first-order methods. We have proposed and investigated accelerated FISTAs for use with two nonsmooth penalty functions that will lead to further reductions in image reconstruction times while preserving image quality. Moreover, with the help of a mixed sparsity-regularization, better preservation of soft-tissue structures can be potentially obtained. The algorithms were systematically evaluated by use of computer-simulated and clinical data sets. PMID:27036582

Inverse imaging of the breast with a material classification technique.

PubMed

Manry, C W; Broschat, S L

1998-03-01

In recent publications [Chew et al., IEEE Trans. Blomed. Eng. BME-9, 218-225 (1990); Borup et al., Ultrason. Imaging 14, 69-85 (1992)] the inverse imaging problem has been solved by means of a two-step iterative method. In this paper, a third step is introduced for ultrasound imaging of the breast. In this step, which is based on statistical pattern recognition, classification of tissue types and a priori knowledge of the anatomy of the breast are integrated into the iterative method. Use of this material classification technique results in more rapid convergence to the inverse solution--approximately 40% fewer iterations are required--as well as greater accuracy. In addition, tumors are detected early in the reconstruction process. Results for reconstructions of a simple two-dimensional model of the human breast are presented. These reconstructions are extremely accurate when system noise and variations in tissue parameters are not too great. However, for the algorithm used, degradation of the reconstructions and divergence from the correct solution occur when system noise and variations in parameters exceed threshold values. Even in this case, however, tumors are still identified within a few iterations.

ADART: an adaptive algebraic reconstruction algorithm for discrete tomography.

PubMed

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

2011-08-01

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

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods

PubMed Central

Smith, David S.; Gore, John C.; Yankeelov, Thomas E.; Welch, E. Brian

2012-01-01

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images. PMID:22481908

Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods.

PubMed

Smith, David S; Gore, John C; Yankeelov, Thomas E; Welch, E Brian

2012-01-01

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 4096(2) or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 1024(2) and smaller took ~0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images.

Further investigation on "A multiplicative regularization for force reconstruction"

NASA Astrophysics Data System (ADS)

Aucejo, M.; De Smet, O.

2018-05-01

We have recently proposed a multiplicative regularization to reconstruct mechanical forces acting on a structure from vibration measurements. This method does not require any selection procedure for choosing the regularization parameter, since the amount of regularization is automatically adjusted throughout an iterative resolution process. The proposed iterative algorithm has been developed with performance and efficiency in mind, but it is actually a simplified version of a full iterative procedure not described in the original paper. The present paper aims at introducing the full resolution algorithm and comparing it with its simplified version in terms of computational efficiency and solution accuracy. In particular, it is shown that both algorithms lead to very similar identified solutions.

Adaptive Statistical Iterative Reconstruction-V Versus Adaptive Statistical Iterative Reconstruction: Impact on Dose Reduction and Image Quality in Body Computed Tomography.

PubMed

Gatti, Marco; Marchisio, Filippo; Fronda, Marco; Rampado, Osvaldo; Faletti, Riccardo; Bergamasco, Laura; Ropolo, Roberto; Fonio, Paolo

The aim of this study was to evaluate the impact on dose reduction and image quality of the new iterative reconstruction technique: adaptive statistical iterative reconstruction (ASIR-V). Fifty consecutive oncologic patients acted as case controls undergoing during their follow-up a computed tomography scan both with ASIR and ASIR-V. Each study was analyzed in a double-blinded fashion by 2 radiologists. Both quantitative and qualitative analyses of image quality were conducted. Computed tomography scanner radiation output was 38% (29%-45%) lower (P < 0.0001) for the ASIR-V examinations than for the ASIR ones. The quantitative image noise was significantly lower (P < 0.0001) for ASIR-V. Adaptive statistical iterative reconstruction-V had a higher performance for the subjective image noise (P = 0.01 for 5 mm and P = 0.009 for 1.25 mm), the other parameters (image sharpness, diagnostic acceptability, and overall image quality) being similar (P > 0.05). Adaptive statistical iterative reconstruction-V is a new iterative reconstruction technique that has the potential to provide image quality equal to or greater than ASIR, with a dose reduction around 40%.

Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography

PubMed Central

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

2012-01-01

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

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

PubMed

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

2018-06-01

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

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

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

Gingold, E; Dave, J

2014-06-01

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

Low-light-level image super-resolution reconstruction based on iterative projection photon localization algorithm

NASA Astrophysics Data System (ADS)

Ying, Changsheng; Zhao, Peng; Li, Ye

2018-01-01

The intensified charge-coupled device (ICCD) is widely used in the field of low-light-level (LLL) imaging. The LLL images captured by ICCD suffer from low spatial resolution and contrast, and the target details can hardly be recognized. Super-resolution (SR) reconstruction of LLL images captured by ICCDs is a challenging issue. The dispersion in the double-proximity-focused image intensifier is the main factor that leads to a reduction in image resolution and contrast. We divide the integration time into subintervals that are short enough to get photon images, so the overlapping effect and overstacking effect of dispersion can be eliminated. We propose an SR reconstruction algorithm based on iterative projection photon localization. In the iterative process, the photon image is sliced by projection planes, and photons are screened under the constraints of regularity. The accurate position information of the incident photons in the reconstructed SR image is obtained by the weighted centroids calculation. The experimental results show that the spatial resolution and contrast of our SR image are significantly improved.

A fast method to emulate an iterative POCS image reconstruction algorithm.

PubMed

Zeng, Gengsheng L

2017-10-01

Iterative image reconstruction algorithms are commonly used to optimize an objective function, especially when the objective function is nonquadratic. Generally speaking, the iterative algorithms are computationally inefficient. This paper presents a fast algorithm that has one backprojection and no forward projection. This paper derives a new method to solve an optimization problem. The nonquadratic constraint, for example, an edge-preserving denoising constraint is implemented as a nonlinear filter. The algorithm is derived based on the POCS (projections onto projections onto convex sets) approach. A windowed FBP (filtered backprojection) algorithm enforces the data fidelity. An iterative procedure, divided into segments, enforces edge-enhancement denoising. Each segment performs nonlinear filtering. The derived iterative algorithm is computationally efficient. It contains only one backprojection and no forward projection. Low-dose CT data are used for algorithm feasibility studies. The nonlinearity is implemented as an edge-enhancing noise-smoothing filter. The patient studies results demonstrate its effectiveness in processing low-dose x ray CT data. This fast algorithm can be used to replace many iterative algorithms. © 2017 American Association of Physicists in Medicine.

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets.

PubMed

Bicer, Tekin; Gürsoy, Doğa; Andrade, Vincent De; Kettimuthu, Rajkumar; Scullin, William; Carlo, Francesco De; Foster, Ian T

2017-01-01

Modern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis. We present Trace, a data-intensive computing engine that we have developed to enable high-performance implementation of iterative tomographic reconstruction algorithms for parallel computers. Trace provides fine-grained reconstruction of tomography datasets using both (thread-level) shared memory and (process-level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations that we apply to the replicated reconstruction objects and evaluate them using tomography datasets collected at the Advanced Photon Source. Our experimental evaluations show that our optimizations and parallelization techniques can provide 158× speedup using 32 compute nodes (384 cores) over a single-core configuration and decrease the end-to-end processing time of a large sinogram (with 4501 × 1 × 22,400 dimensions) from 12.5 h to <5 min per iteration. The proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.

Non-homogeneous updates for the iterative coordinate descent algorithm

NASA Astrophysics Data System (ADS)

Yu, Zhou; Thibault, Jean-Baptiste; Bouman, Charles A.; Sauer, Ken D.; Hsieh, Jiang

2007-02-01

Statistical reconstruction methods show great promise for improving resolution, and reducing noise and artifacts in helical X-ray CT. In fact, statistical reconstruction seems to be particularly valuable in maintaining reconstructed image quality when the dosage is low and the noise is therefore high. However, high computational cost and long reconstruction times remain as a barrier to the use of statistical reconstruction in practical applications. Among the various iterative methods that have been studied for statistical reconstruction, iterative coordinate descent (ICD) has been found to have relatively low overall computational requirements due to its fast convergence. This paper presents a novel method for further speeding the convergence of the ICD algorithm, and therefore reducing the overall reconstruction time for statistical reconstruction. The method, which we call nonhomogeneous iterative coordinate descent (NH-ICD) uses spatially non-homogeneous updates to speed convergence by focusing computation where it is most needed. Experimental results with real data indicate that the method speeds reconstruction by roughly a factor of two for typical 3D multi-slice geometries.

SU-E-I-01: Iterative CBCT Reconstruction with a Feature-Preserving Penalty

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

Lyu, Q; Li, B; Southern Medical University, Guangzhou

2015-06-15

Purpose: Low-dose CBCT is desired in various clinical applications. Iterative image reconstruction algorithms have shown advantages in suppressing noise in low-dose CBCT. However, due to the smoothness constraint enforced during the reconstruction process, edges may be blurred and image features may lose in the reconstructed image. In this work, we proposed a new penalty design to preserve image features in the image reconstructed by iterative algorithms. Methods: Low-dose CBCT is reconstructed by minimizing the penalized weighted least-squares (PWLS) objective function. Binary Robust Independent Elementary Features (BRIEF) of the image were integrated into the penalty of PWLS. BRIEF is a generalmore » purpose point descriptor that can be used to identify important features of an image. In this work, BRIEF distance of two neighboring pixels was used to weigh the smoothing parameter in PWLS. For pixels of large BRIEF distance, weaker smooth constraint will be enforced. Image features will be better preserved through such a design. The performance of the PWLS algorithm with BRIEF penalty was evaluated by a CatPhan 600 phantom. Results: The image quality reconstructed by the proposed PWLS-BRIEF algorithm is superior to that by the conventional PWLS method and the standard FDK method. At matched noise level, edges in PWLS-BRIEF reconstructed image are better preserved. Conclusion: This study demonstrated that the proposed PWLS-BRIEF algorithm has great potential on preserving image features in low-dose CBCT.« less

Development of an iterative reconstruction method to overcome 2D detector low resolution limitations in MLC leaf position error detection for 3D dose verification in IMRT.

PubMed

Visser, R; Godart, J; Wauben, D J L; Langendijk, J A; Van't Veld, A A; Korevaar, E W

2016-05-21

The objective of this study was to introduce a new iterative method to reconstruct multi leaf collimator (MLC) positions based on low resolution ionization detector array measurements and to evaluate its error detection performance. The iterative reconstruction method consists of a fluence model, a detector model and an optimizer. Expected detector response was calculated using a radiotherapy treatment plan in combination with the fluence model and detector model. MLC leaf positions were reconstructed by minimizing differences between expected and measured detector response. The iterative reconstruction method was evaluated for an Elekta SLi with 10.0 mm MLC leafs in combination with the COMPASS system and the MatriXX Evolution (IBA Dosimetry) detector with a spacing of 7.62 mm. The detector was positioned in such a way that each leaf pair of the MLC was aligned with one row of ionization chambers. Known leaf displacements were introduced in various field geometries ranging from  -10.0 mm to 10.0 mm. Error detection performance was tested for MLC leaf position dependency relative to the detector position, gantry angle dependency, monitor unit dependency, and for ten clinical intensity modulated radiotherapy (IMRT) treatment beams. For one clinical head and neck IMRT treatment beam, influence of the iterative reconstruction method on existing 3D dose reconstruction artifacts was evaluated. The described iterative reconstruction method was capable of individual MLC leaf position reconstruction with millimeter accuracy, independent of the relative detector position within the range of clinically applied MU's for IMRT. Dose reconstruction artifacts in a clinical IMRT treatment beam were considerably reduced as compared to the current dose verification procedure. The iterative reconstruction method allows high accuracy 3D dose verification by including actual MLC leaf positions reconstructed from low resolution 2D measurements.

Combined iterative reconstruction and image-domain decomposition for dual energy CT using total-variation regularization

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

Dong, Xue; Niu, Tianye; Zhu, Lei, E-mail: leizhu@gatech.edu

2014-05-15

Purpose: Dual-energy CT (DECT) is being increasingly used for its capability of material decomposition and energy-selective imaging. A generic problem of DECT, however, is that the decomposition process is unstable in the sense that the relative magnitude of decomposed signals is reduced due to signal cancellation while the image noise is accumulating from the two CT images of independent scans. Direct image decomposition, therefore, leads to severe degradation of signal-to-noise ratio on the resultant images. Existing noise suppression techniques are typically implemented in DECT with the procedures of reconstruction and decomposition performed independently, which do not explore the statistical propertiesmore » of decomposed images during the reconstruction for noise reduction. In this work, the authors propose an iterative approach that combines the reconstruction and the signal decomposition procedures to minimize the DECT image noise without noticeable loss of resolution. Methods: The proposed algorithm is formulated as an optimization problem, which balances the data fidelity and total variation of decomposed images in one framework, and the decomposition step is carried out iteratively together with reconstruction. The noise in the CT images from the proposed algorithm becomes well correlated even though the noise of the raw projections is independent on the two CT scans. Due to this feature, the proposed algorithm avoids noise accumulation during the decomposition process. The authors evaluate the method performance on noise suppression and spatial resolution using phantom studies and compare the algorithm with conventional denoising approaches as well as combined iterative reconstruction methods with different forms of regularization. Results: On the Catphan©600 phantom, the proposed method outperforms the existing denoising methods on preserving spatial resolution at the same level of noise suppression, i.e., a reduction of noise standard deviation by one order of magnitude. This improvement is mainly attributed to the high noise correlation in the CT images reconstructed by the proposed algorithm. Iterative reconstruction using different regularization, including quadratic orq-generalized Gaussian Markov random field regularization, achieves similar noise suppression from high noise correlation. However, the proposed TV regularization obtains a better edge preserving performance. Studies of electron density measurement also show that our method reduces the average estimation error from 9.5% to 7.1%. On the anthropomorphic head phantom, the proposed method suppresses the noise standard deviation of the decomposed images by a factor of ∼14 without blurring the fine structures in the sinus area. Conclusions: The authors propose a practical method for DECT imaging reconstruction, which combines the image reconstruction and material decomposition into one optimization framework. Compared to the existing approaches, our method achieves a superior performance on DECT imaging with respect to decomposition accuracy, noise reduction, and spatial resolution.« less

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

PubMed Central

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

2009-01-01

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

Statistical iterative reconstruction for streak artefact reduction when using multidetector CT to image the dento-alveolar structures.

PubMed

Dong, J; Hayakawa, Y; Kober, C

2014-01-01

When metallic prosthetic appliances and dental fillings exist in the oral cavity, the appearance of metal-induced streak artefacts is not avoidable in CT images. The aim of this study was to develop a method for artefact reduction using the statistical reconstruction on multidetector row CT images. Adjacent CT images often depict similar anatomical structures. Therefore, reconstructed images with weak artefacts were attempted using projection data of an artefact-free image in a neighbouring thin slice. Images with moderate and strong artefacts were continuously processed in sequence by successive iterative restoration where the projection data was generated from the adjacent reconstructed slice. First, the basic maximum likelihood-expectation maximization algorithm was applied. Next, the ordered subset-expectation maximization algorithm was examined. Alternatively, a small region of interest setting was designated. Finally, the general purpose graphic processing unit machine was applied in both situations. The algorithms reduced the metal-induced streak artefacts on multidetector row CT images when the sequential processing method was applied. The ordered subset-expectation maximization and small region of interest reduced the processing duration without apparent detriments. A general-purpose graphic processing unit realized the high performance. A statistical reconstruction method was applied for the streak artefact reduction. The alternative algorithms applied were effective. Both software and hardware tools, such as ordered subset-expectation maximization, small region of interest and general-purpose graphic processing unit achieved fast artefact correction.

Development of a GNSS water vapour tomography system using algebraic reconstruction techniques

NASA Astrophysics Data System (ADS)

Bender, Michael; Dick, Galina; Ge, Maorong; Deng, Zhiguo; Wickert, Jens; Kahle, Hans-Gert; Raabe, Armin; Tetzlaff, Gerd

2011-05-01

A GNSS water vapour tomography system developed to reconstruct spatially resolved humidity fields in the troposphere is described. The tomography system was designed to process the slant path delays of about 270 German GNSS stations in near real-time with a temporal resolution of 30 min, a horizontal resolution of 40 km and a vertical resolution of 500 m or better. After a short introduction to the GPS slant delay processing the framework of the GNSS tomography is described in detail. Different implementations of the iterative algebraic reconstruction techniques (ART) used to invert the linear inverse problem are discussed. It was found that the multiplicative techniques (MART) provide the best results with least processing time, i.e., a tomographic reconstruction of about 26,000 slant delays on a 8280 cell grid can be obtained in less than 10 min. Different iterative reconstruction techniques are compared with respect to their convergence behaviour and some numerical parameters. The inversion can be considerably stabilized by using additional non-GNSS observations and implementing various constraints. Different strategies for initialising the tomography and utilizing extra information are discussed. At last an example of a reconstructed field of the wet refractivity is presented and compared to the corresponding distribution of the integrated water vapour, an analysis of a numerical weather model (COSMO-DE) and some radiosonde profiles.

Statistical shape model-based reconstruction of a scaled, patient-specific surface model of the pelvis from a single standard AP x-ray radiograph

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

Zheng Guoyan

2010-04-15

Purpose: The aim of this article is to investigate the feasibility of using a statistical shape model (SSM)-based reconstruction technique to derive a scaled, patient-specific surface model of the pelvis from a single standard anteroposterior (AP) x-ray radiograph and the feasibility of estimating the scale of the reconstructed surface model by performing a surface-based 3D/3D matching. Methods: Data sets of 14 pelvises (one plastic bone, 12 cadavers, and one patient) were used to validate the single-image based reconstruction technique. This reconstruction technique is based on a hybrid 2D/3D deformable registration process combining a landmark-to-ray registration with a SSM-based 2D/3D reconstruction.more » The landmark-to-ray registration was used to find an initial scale and an initial rigid transformation between the x-ray image and the SSM. The estimated scale and rigid transformation were used to initialize the SSM-based 2D/3D reconstruction. The optimal reconstruction was then achieved in three stages by iteratively matching the projections of the apparent contours extracted from a 3D model derived from the SSM to the image contours extracted from the x-ray radiograph: Iterative affine registration, statistical instantiation, and iterative regularized shape deformation. The image contours are first detected by using a semiautomatic segmentation tool based on the Livewire algorithm and then approximated by a set of sparse dominant points that are adaptively sampled from the detected contours. The unknown scales of the reconstructed models were estimated by performing a surface-based 3D/3D matching between the reconstructed models and the associated ground truth models that were derived from a CT-based reconstruction method. Such a matching also allowed for computing the errors between the reconstructed models and the associated ground truth models. Results: The technique could reconstruct the surface models of all 14 pelvises directly from the landmark-based initialization. Depending on the surface-based matching techniques, the reconstruction errors were slightly different. When a surface-based iterative affine registration was used, an average reconstruction error of 1.6 mm was observed. This error was increased to 1.9 mm, when a surface-based iterative scaled rigid registration was used. Conclusions: It is feasible to reconstruct a scaled, patient-specific surface model of the pelvis from single standard AP x-ray radiograph using the present approach. The unknown scale of the reconstructed model can be estimated by performing a surface-based 3D/3D matching.« less

Iterative CT reconstruction using coordinate descent with ordered subsets of data

NASA Astrophysics Data System (ADS)

Noo, F.; Hahn, K.; Schöndube, H.; Stierstorfer, K.

2016-04-01

Image reconstruction based on iterative minimization of a penalized weighted least-square criteria has become an important topic of research in X-ray computed tomography. This topic is motivated by increasing evidence that such a formalism may enable a significant reduction in dose imparted to the patient while maintaining or improving image quality. One important issue associated with this iterative image reconstruction concept is slow convergence and the associated computational effort. For this reason, there is interest in finding methods that produce approximate versions of the targeted image with a small number of iterations and an acceptable level of discrepancy. We introduce here a novel method to produce such approximations: ordered subsets in combination with iterative coordinate descent. Preliminary results demonstrate that this method can produce, within 10 iterations and using only a constant image as initial condition, satisfactory reconstructions that retain the noise properties of the targeted image.

Fast, Accurate and Shift-Varying Line Projections for Iterative Reconstruction Using the GPU

PubMed Central

Pratx, Guillem; Chinn, Garry; Olcott, Peter D.; Levin, Craig S.

2013-01-01

List-mode processing provides an efficient way to deal with sparse projections in iterative image reconstruction for emission tomography. An issue often reported is the tremendous amount of computation required by such algorithm. Each recorded event requires several back- and forward line projections. We investigated the use of the programmable graphics processing unit (GPU) to accelerate the line-projection operations and implement fully-3D list-mode ordered-subsets expectation-maximization for positron emission tomography (PET). We designed a reconstruction approach that incorporates resolution kernels, which model the spatially-varying physical processes associated with photon emission, transport and detection. Our development is particularly suitable for applications where the projection data is sparse, such as high-resolution, dynamic, and time-of-flight PET reconstruction. The GPU approach runs more than 50 times faster than an equivalent CPU implementation while image quality and accuracy are virtually identical. This paper describes in details how the GPU can be used to accelerate the line projection operations, even when the lines-of-response have arbitrary endpoint locations and shift-varying resolution kernels are used. A quantitative evaluation is included to validate the correctness of this new approach. PMID:19244015

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

PubMed

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

2016-01-01

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

Quantitative evaluation of ASiR image quality: an adaptive statistical iterative reconstruction technique

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.

GPU-accelerated iterative reconstruction from Compton scattered data using a matched pair of conic projector and backprojector.

PubMed

Nguyen, Van-Giang; Lee, Soo-Jin

2016-07-01

Iterative reconstruction from Compton scattered data is known to be computationally more challenging than that from conventional line-projection based emission data in that the gamma rays that undergo Compton scattering are modeled as conic projections rather than line projections. In conventional tomographic reconstruction, to parallelize the projection and backprojection operations using the graphics processing unit (GPU), approximated methods that use an unmatched pair of ray-tracing forward projector and voxel-driven backprojector have been widely used. In this work, we propose a new GPU-accelerated method for Compton camera reconstruction which is more accurate by using exactly matched pair of projector and backprojector. To calculate conic forward projection, we first sample the cone surface into conic rays and accumulate the intersecting chord lengths of the conic rays passing through voxels using a fast ray-tracing method (RTM). For conic backprojection, to obtain the true adjoint of the conic forward projection, while retaining the computational efficiency of the GPU, we use a voxel-driven RTM which is essentially the same as the standard RTM used for the conic forward projector. Our simulation results show that, while the new method is about 3 times slower than the approximated method, it is still about 16 times faster than the CPU-based method without any loss of accuracy. The net conclusion is that our proposed method is guaranteed to retain the reconstruction accuracy regardless of the number of iterations by providing a perfectly matched projector-backprojector pair, which makes iterative reconstruction methods for Compton imaging faster and more accurate. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Mohan, K. Aditya

2017-10-01

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

Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework

PubMed Central

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

2016-01-01

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

Analytic TOF PET reconstruction algorithm within DIRECT data partitioning framework

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Blob-enhanced reconstruction technique

NASA Astrophysics Data System (ADS)

Castrillo, Giusy; Cafiero, Gioacchino; Discetti, Stefano; Astarita, Tommaso

2016-09-01

A method to enhance the quality of the tomographic reconstruction and, consequently, the 3D velocity measurement accuracy, is presented. The technique is based on integrating information on the objects to be reconstructed within the algebraic reconstruction process. A first guess intensity distribution is produced with a standard algebraic method, then the distribution is rebuilt as a sum of Gaussian blobs, based on location, intensity and size of agglomerates of light intensity surrounding local maxima. The blobs substitution regularizes the particle shape allowing a reduction of the particles discretization errors and of their elongation in the depth direction. The performances of the blob-enhanced reconstruction technique (BERT) are assessed with a 3D synthetic experiment. The results have been compared with those obtained by applying the standard camera simultaneous multiplicative reconstruction technique (CSMART) to the same volume. Several blob-enhanced reconstruction processes, both substituting the blobs at the end of the CSMART algorithm and during the iterations (i.e. using the blob-enhanced reconstruction as predictor for the following iterations), have been tested. The results confirm the enhancement in the velocity measurements accuracy, demonstrating a reduction of the bias error due to the ghost particles. The improvement is more remarkable at the largest tested seeding densities. Additionally, using the blobs distributions as a predictor enables further improvement of the convergence of the reconstruction algorithm, with the improvement being more considerable when substituting the blobs more than once during the process. The BERT process is also applied to multi resolution (MR) CSMART reconstructions, permitting simultaneously to achieve remarkable improvements in the flow field measurements and to benefit from the reduction in computational time due to the MR approach. Finally, BERT is also tested on experimental data, obtaining an increase of the signal-to-noise ratio in the reconstructed flow field and a higher value of the correlation factor in the velocity measurements with respect to the volume to which the particles are not replaced.

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

PubMed

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

2004-06-01

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

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

PubMed

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

2015-01-01

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

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

PubMed

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

2016-03-01

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

MO-DE-207A-07: Filtered Iterative Reconstruction (FIR) Via Proximal Forward-Backward Splitting: A Synergy of Analytical and Iterative Reconstruction Method for CT

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

Gao, H

Purpose: This work is to develop a general framework, namely filtered iterative reconstruction (FIR) method, to incorporate analytical reconstruction (AR) method into iterative reconstruction (IR) method, for enhanced CT image quality. Methods: FIR is formulated as a combination of filtered data fidelity and sparsity regularization, and then solved by proximal forward-backward splitting (PFBS) algorithm. As a result, the image reconstruction decouples data fidelity and image regularization with a two-step iterative scheme, during which an AR-projection step updates the filtered data fidelity term, while a denoising solver updates the sparsity regularization term. During the AR-projection step, the image is projected tomore » the data domain to form the data residual, and then reconstructed by certain AR to a residual image which is in turn weighted together with previous image iterate to form next image iterate. Since the eigenvalues of AR-projection operator are close to the unity, PFBS based FIR has a fast convergence. Results: The proposed FIR method is validated in the setting of circular cone-beam CT with AR being FDK and total-variation sparsity regularization, and has improved image quality from both AR and IR. For example, AIR has improved visual assessment and quantitative measurement in terms of both contrast and resolution, and reduced axial and half-fan artifacts. Conclusion: FIR is proposed to incorporate AR into IR, with an efficient image reconstruction algorithm based on PFBS. The CBCT results suggest that FIR synergizes AR and IR with improved image quality and reduced axial and half-fan artifacts. The authors was partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000), and the Shanghai Pujiang Talent Program (#14PJ1404500).« less

Low-dose CT image reconstruction using gain intervention-based dictionary learning

NASA Astrophysics Data System (ADS)

Pathak, Yadunath; Arya, K. V.; Tiwari, Shailendra

2018-05-01

Computed tomography (CT) approach is extensively utilized in clinical diagnoses. However, X-ray residue in human body may introduce somatic damage such as cancer. Owing to radiation risk, research has focused on the radiation exposure distributed to patients through CT investigations. Therefore, low-dose CT has become a significant research area. Many researchers have proposed different low-dose CT reconstruction techniques. But, these techniques suffer from various issues such as over smoothing, artifacts, noise, etc. Therefore, in this paper, we have proposed a novel integrated low-dose CT reconstruction technique. The proposed technique utilizes global dictionary-based statistical iterative reconstruction (GDSIR) and adaptive dictionary-based statistical iterative reconstruction (ADSIR)-based reconstruction techniques. In case the dictionary (D) is predetermined, then GDSIR can be used and if D is adaptively defined then ADSIR is appropriate choice. The gain intervention-based filter is also used as a post-processing technique for removing the artifacts from low-dose CT reconstructed images. Experiments have been done by considering the proposed and other low-dose CT reconstruction techniques on well-known benchmark CT images. Extensive experiments have shown that the proposed technique outperforms the available approaches.

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Sinogram-based adaptive iterative reconstruction for sparse view x-ray computed tomography

NASA Astrophysics Data System (ADS)

Trinca, D.; Zhong, Y.; Wang, Y.-Z.; Mamyrbayev, T.; Libin, E.

2016-10-01

With the availability of more powerful computing processors, iterative reconstruction algorithms have recently been successfully implemented as an approach to achieving significant dose reduction in X-ray CT. In this paper, we propose an adaptive iterative reconstruction algorithm for X-ray CT, that is shown to provide results comparable to those obtained by proprietary algorithms, both in terms of reconstruction accuracy and execution time. The proposed algorithm is thus provided for free to the scientific community, for regular use, and for possible further optimization.

Comparative assessment of pressure field reconstructions from particle image velocimetry measurements and Lagrangian particle tracking

NASA Astrophysics Data System (ADS)

van Gent, P. L.; Michaelis, D.; van Oudheusden, B. W.; Weiss, P.-É.; de Kat, R.; Laskari, A.; Jeon, Y. J.; David, L.; Schanz, D.; Huhn, F.; Gesemann, S.; Novara, M.; McPhaden, C.; Neeteson, N. J.; Rival, D. E.; Schneiders, J. F. G.; Schrijer, F. F. J.

2017-04-01

A test case for pressure field reconstruction from particle image velocimetry (PIV) and Lagrangian particle tracking (LPT) has been developed by constructing a simulated experiment from a zonal detached eddy simulation for an axisymmetric base flow at Mach 0.7. The test case comprises sequences of four subsequent particle images (representing multi-pulse data) as well as continuous time-resolved data which can realistically only be obtained for low-speed flows. Particle images were processed using tomographic PIV processing as well as the LPT algorithm `Shake-The-Box' (STB). Multiple pressure field reconstruction techniques have subsequently been applied to the PIV results (Eulerian approach, iterative least-square pseudo-tracking, Taylor's hypothesis approach, and instantaneous Vortex-in-Cell) and LPT results (FlowFit, Vortex-in-Cell-plus, Voronoi-based pressure evaluation, and iterative least-square pseudo-tracking). All methods were able to reconstruct the main features of the instantaneous pressure fields, including methods that reconstruct pressure from a single PIV velocity snapshot. Highly accurate reconstructed pressure fields could be obtained using LPT approaches in combination with more advanced techniques. In general, the use of longer series of time-resolved input data, when available, allows more accurate pressure field reconstruction. Noise in the input data typically reduces the accuracy of the reconstructed pressure fields, but none of the techniques proved to be critically sensitive to the amount of noise added in the present test case.

A methodology for finding the optimal iteration number of the SIRT algorithm for quantitative Electron Tomography.

PubMed

Okariz, Ana; Guraya, Teresa; Iturrondobeitia, Maider; Ibarretxe, Julen

2017-02-01

The SIRT (Simultaneous Iterative Reconstruction Technique) algorithm is commonly used in Electron Tomography to calculate the original volume of the sample from noisy images, but the results provided by this iterative procedure are strongly dependent on the specific implementation of the algorithm, as well as on the number of iterations employed for the reconstruction. In this work, a methodology for selecting the iteration number of the SIRT reconstruction that provides the most accurate segmentation is proposed. The methodology is based on the statistical analysis of the intensity profiles at the edge of the objects in the reconstructed volume. A phantom which resembles a a carbon black aggregate has been created to validate the methodology and the SIRT implementations of two free software packages (TOMOJ and TOMO3D) have been used. Copyright © 2016 Elsevier B.V. All rights reserved.

Dose reduction with adaptive statistical iterative reconstruction for paediatric CT: phantom study and clinical experience on chest and abdomen CT.

PubMed

Gay, F; Pavia, Y; Pierrat, N; Lasalle, S; Neuenschwander, S; Brisse, H J

2014-01-01

To assess the benefit and limits of iterative reconstruction of paediatric chest and abdominal computed tomography (CT). The study compared adaptive statistical iterative reconstruction (ASIR) with filtered back projection (FBP) on 64-channel MDCT. A phantom study was first performed using variable tube potential, tube current and ASIR settings. The assessed image quality indices were the signal-to-noise ratio (SNR), the noise power spectrum, low contrast detectability (LCD) and spatial resolution. A clinical retrospective study of 26 children (M:F = 14/12, mean age: 4 years, range: 1-9 years) was secondarily performed allowing comparison of 18 chest and 14 abdominal CT pairs, one with a routine CT dose and FBP reconstruction, and the other with 30 % lower dose and 40 % ASIR reconstruction. Two radiologists independently compared the images for overall image quality, noise, sharpness and artefacts, and measured image noise. The phantom study demonstrated a significant increase in SNR without impairment of the LCD or spatial resolution, except for tube current values below 30-50 mA. On clinical images, no significant difference was observed between FBP and reduced dose ASIR images. Iterative reconstruction allows at least 30 % dose reduction in paediatric chest and abdominal CT, without impairment of image quality. • Iterative reconstruction helps lower radiation exposure levels in children undergoing CT. • Adaptive statistical iterative reconstruction (ASIR) significantly increases SNR without impairing spatial resolution. • For abdomen and chest CT, ASIR allows at least a 30 % dose reduction.

Compressively sampled MR image reconstruction using generalized thresholding iterative algorithm

NASA Astrophysics Data System (ADS)

Elahi, Sana; kaleem, Muhammad; Omer, Hammad

2018-01-01

Compressed sensing (CS) is an emerging area of interest in Magnetic Resonance Imaging (MRI). CS is used for the reconstruction of the images from a very limited number of samples in k-space. This significantly reduces the MRI data acquisition time. One important requirement for signal recovery in CS is the use of an appropriate non-linear reconstruction algorithm. It is a challenging task to choose a reconstruction algorithm that would accurately reconstruct the MR images from the under-sampled k-space data. Various algorithms have been used to solve the system of non-linear equations for better image quality and reconstruction speed in CS. In the recent past, iterative soft thresholding algorithm (ISTA) has been introduced in CS-MRI. This algorithm directly cancels the incoherent artifacts produced because of the undersampling in k -space. This paper introduces an improved iterative algorithm based on p -thresholding technique for CS-MRI image reconstruction. The use of p -thresholding function promotes sparsity in the image which is a key factor for CS based image reconstruction. The p -thresholding based iterative algorithm is a modification of ISTA, and minimizes non-convex functions. It has been shown that the proposed p -thresholding iterative algorithm can be used effectively to recover fully sampled image from the under-sampled data in MRI. The performance of the proposed method is verified using simulated and actual MRI data taken at St. Mary's Hospital, London. The quality of the reconstructed images is measured in terms of peak signal-to-noise ratio (PSNR), artifact power (AP), and structural similarity index measure (SSIM). The proposed approach shows improved performance when compared to other iterative algorithms based on log thresholding, soft thresholding and hard thresholding techniques at different reduction factors.

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

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

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

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

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

PubMed

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

2015-11-01

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

Solving ill-posed inverse problems using iterative deep neural networks

NASA Astrophysics Data System (ADS)

Adler, Jonas; Öktem, Ozan

2017-12-01

We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the ‘gradient’ component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 × 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).

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

NASA Astrophysics Data System (ADS)

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

2012-03-01

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

Matrix completion-based reconstruction for undersampled magnetic resonance fingerprinting data.

PubMed

Doneva, Mariya; Amthor, Thomas; Koken, Peter; Sommer, Karsten; Börnert, Peter

2017-09-01

An iterative reconstruction method for undersampled magnetic resonance fingerprinting data is presented. The method performs the reconstruction entirely in k-space and is related to low rank matrix completion methods. A low dimensional data subspace is estimated from a small number of k-space locations fully sampled in the temporal direction and used to reconstruct the missing k-space samples before MRF dictionary matching. Performing the iterations in k-space eliminates the need for applying a forward and an inverse Fourier transform in each iteration required in previously proposed iterative reconstruction methods for undersampled MRF data. A projection onto the low dimensional data subspace is performed as a matrix multiplication instead of a singular value thresholding typically used in low rank matrix completion, further reducing the computational complexity of the reconstruction. The method is theoretically described and validated in phantom and in-vivo experiments. The quality of the parameter maps can be significantly improved compared to direct matching on undersampled data. Copyright © 2017 Elsevier Inc. All rights reserved.

Looking for the Signal: A guide to iterative noise and artefact removal in X-ray tomographic reconstructions of porous geomaterials

NASA Astrophysics Data System (ADS)

Bruns, S.; Stipp, S. L. S.; Sørensen, H. O.

2017-07-01

X-ray micro- and nanotomography has evolved into a quantitative analysis tool rather than a mere qualitative visualization technique for the study of porous natural materials. Tomographic reconstructions are subject to noise that has to be handled by image filters prior to quantitative analysis. Typically, denoising filters are designed to handle random noise, such as Gaussian or Poisson noise. In tomographic reconstructions, noise has been projected from Radon space to Euclidean space, i.e. post reconstruction noise cannot be expected to be random but to be correlated. Reconstruction artefacts, such as streak or ring artefacts, aggravate the filtering process so algorithms performing well with random noise are not guaranteed to provide satisfactory results for X-ray tomography reconstructions. With sufficient image resolution, the crystalline origin of most geomaterials results in tomography images of objects that are untextured. We developed a denoising framework for these kinds of samples that combines a noise level estimate with iterative nonlocal means denoising. This allows splitting the denoising task into several weak denoising subtasks where the later filtering steps provide a controlled level of texture removal. We describe a hands-on explanation for the use of this iterative denoising approach and the validity and quality of the image enhancement filter was evaluated in a benchmarking experiment with noise footprints of a varying level of correlation and residual artefacts. They were extracted from real tomography reconstructions. We found that our denoising solutions were superior to other denoising algorithms, over a broad range of contrast-to-noise ratios on artificial piecewise constant signals.

A fast multi-resolution approach to tomographic PIV

NASA Astrophysics Data System (ADS)

Discetti, Stefano; Astarita, Tommaso

2012-03-01

Tomographic particle image velocimetry (Tomo-PIV) is a recently developed three-component, three-dimensional anemometric non-intrusive measurement technique, based on an optical tomographic reconstruction applied to simultaneously recorded images of the distribution of light intensity scattered by seeding particles immersed into the flow. Nowadays, the reconstruction process is carried out mainly by iterative algebraic reconstruction techniques, well suited to handle the problem of limited number of views, but computationally intensive and memory demanding. The adoption of the multiplicative algebraic reconstruction technique (MART) has become more and more accepted. In the present work, a novel multi-resolution approach is proposed, relying on the adoption of a coarser grid in the first step of the reconstruction to obtain a fast estimation of a reliable and accurate first guess. A performance assessment, carried out on three-dimensional computer-generated distributions of particles, shows a substantial acceleration of the reconstruction process for all the tested seeding densities with respect to the standard method based on 5 MART iterations; a relevant reduction in the memory storage is also achieved. Furthermore, a slight accuracy improvement is noticed. A modified version, improved by a multiplicative line of sight estimation of the first guess on the compressed configuration, is also tested, exhibiting a further remarkable decrease in both memory storage and computational effort, mostly at the lowest tested seeding densities, while retaining the same performances in terms of accuracy.

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.

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

NASA Astrophysics Data System (ADS)

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

1998-04-01

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

Tomography by iterative convolution - Empirical study and application to interferometry

NASA Technical Reports Server (NTRS)

Vest, C. M.; Prikryl, I.

1984-01-01

An algorithm for computer tomography has been developed that is applicable to reconstruction from data having incomplete projections because an opaque object blocks some of the probing radiation as it passes through the object field. The algorithm is based on iteration between the object domain and the projection (Radon transform) domain. Reconstructions are computed during each iteration by the well-known convolution method. Although it is demonstrated that this algorithm does not converge, an empirically justified criterion for terminating the iteration when the most accurate estimate has been computed is presented. The algorithm has been studied by using it to reconstruct several different object fields with several different opaque regions. It also has been used to reconstruct aerodynamic density fields from interferometric data recorded in wind tunnel tests.

Sparse magnetic resonance imaging reconstruction using the bregman iteration

NASA Astrophysics Data System (ADS)

Lee, Dong-Hoon; Hong, Cheol-Pyo; Lee, Man-Woo

2013-01-01

Magnetic resonance imaging (MRI) reconstruction needs many samples that are sequentially sampled by using phase encoding gradients in a MRI system. It is directly connected to the scan time for the MRI system and takes a long time. Therefore, many researchers have studied ways to reduce the scan time, especially, compressed sensing (CS), which is used for sparse images and reconstruction for fewer sampling datasets when the k-space is not fully sampled. Recently, an iterative technique based on the bregman method was developed for denoising. The bregman iteration method improves on total variation (TV) regularization by gradually recovering the fine-scale structures that are usually lost in TV regularization. In this study, we studied sparse sampling image reconstruction using the bregman iteration for a low-field MRI system to improve its temporal resolution and to validate its usefulness. The image was obtained with a 0.32 T MRI scanner (Magfinder II, SCIMEDIX, Korea) with a phantom and an in-vivo human brain in a head coil. We applied random k-space sampling, and we determined the sampling ratios by using half the fully sampled k-space. The bregman iteration was used to generate the final images based on the reduced data. We also calculated the root-mean-square-error (RMSE) values from error images that were obtained using various numbers of bregman iterations. Our reconstructed images using the bregman iteration for sparse sampling images showed good results compared with the original images. Moreover, the RMSE values showed that the sparse reconstructed phantom and the human images converged to the original images. We confirmed the feasibility of sparse sampling image reconstruction methods using the bregman iteration with a low-field MRI system and obtained good results. Although our results used half the sampling ratio, this method will be helpful in increasing the temporal resolution at low-field MRI systems.

Evaluating the effect of increased pitch, iterative reconstruction and dual source CT on dose reduction and image quality.

PubMed

Gariani, Joanna; Martin, Steve P; Botsikas, Diomidis; Becker, Christoph D; Montet, Xavier

2018-06-14

To compare radiation dose and image quality of thoracoabdominal scans obtained with a high-pitch protocol (pitch 3.2) and iterative reconstruction (Sinogram Affirmed Iterative Reconstruction) in comparison to standard pitch reconstructed with filtered back projection (FBP) using dual source CT. 114 CT scans (Somatom Definition Flash, Siemens Healthineers, Erlangen, Germany), 39 thoracic scans, 54 thoracoabdominal scans and 21 abdominal scans were performed. Analysis of three protocols was undertaken; pitch of 1 reconstructed with FBP, pitch of 3.2 reconstructed with SAFIRE, pitch of 3.2 with stellar detectors reconstructed with SAFIRE. Objective and subjective image analysis were performed. Dose differences of the protocols used were compared. Dose was reduced when comparing scans with a pitch of 1 reconstructed with FBP to high-pitch scans with a pitch of 3.2 reconstructed with SAFIRE with a reduction of volume CT dose index of 75% for thoracic scans, 64% for thoracoabdominal scans and 67% for abdominal scans. There was a further reduction after the implementation of stellar detectors reflected in a reduction of 36% of the dose-length product for thoracic scans. This was not at the detriment of image quality, contrast-to-noise ratio, signal-to-noise ratio and the qualitative image analysis revealed a superior image quality in the high-pitch protocols. The combination of a high pitch protocol with iterative reconstruction allows significant dose reduction in routine chest and abdominal scans whilst maintaining or improving diagnostic image quality, with a further reduction in thoracic scans with stellar detectors. Advances in knowledge: High pitch imaging with iterative reconstruction is a tool that can be used to reduce dose without sacrificing image quality.

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

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Iterative methods for tomography problems: implementation to a cross-well tomography problem

NASA Astrophysics Data System (ADS)

Karadeniz, M. F.; Weber, G. W.

2018-01-01

The velocity distribution between two boreholes is reconstructed by cross-well tomography, which is commonly used in geology. In this paper, iterative methods, Kaczmarz’s algorithm, algebraic reconstruction technique (ART), and simultaneous iterative reconstruction technique (SIRT), are implemented to a specific cross-well tomography problem. Convergence to the solution of these methods and their CPU time for the cross-well tomography problem are compared. Furthermore, these three methods for this problem are compared for different tolerance values.

Continuous analog of multiplicative algebraic reconstruction technique for computed tomography

NASA Astrophysics Data System (ADS)

Tateishi, Kiyoko; Yamaguchi, Yusaku; Abou Al-Ola, Omar M.; Kojima, Takeshi; Yoshinaga, Tetsuya

2016-03-01

We propose a hybrid dynamical system as a continuous analog to the block-iterative multiplicative algebraic reconstruction technique (BI-MART), which is a well-known iterative image reconstruction algorithm for computed tomography. The hybrid system is described by a switched nonlinear system with a piecewise smooth vector field or differential equation and, for consistent inverse problems, the convergence of non-negatively constrained solutions to a globally stable equilibrium is guaranteed by the Lyapunov theorem. Namely, we can prove theoretically that a weighted Kullback-Leibler divergence measure can be a common Lyapunov function for the switched system. We show that discretizing the differential equation by using the first-order approximation (Euler's method) based on the geometric multiplicative calculus leads to the same iterative formula of the BI-MART with the scaling parameter as a time-step of numerical discretization. The present paper is the first to reveal that a kind of iterative image reconstruction algorithm is constructed by the discretization of a continuous-time dynamical system for solving tomographic inverse problems. Iterative algorithms with not only the Euler method but also the Runge-Kutta methods of lower-orders applied for discretizing the continuous-time system can be used for image reconstruction. A numerical example showing the characteristics of the discretized iterative methods is presented.

Regularization Parameter Selection for Nonlinear Iterative Image Restoration and MRI Reconstruction Using GCV and SURE-Based Methods

PubMed Central

Ramani, Sathish; Liu, Zhihao; Rosen, Jeffrey; Nielsen, Jon-Fredrik; Fessler, Jeffrey A.

2012-01-01

Regularized iterative reconstruction algorithms for imaging inverse problems require selection of appropriate regularization parameter values. We focus on the challenging problem of tuning regularization parameters for nonlinear algorithms for the case of additive (possibly complex) Gaussian noise. Generalized cross-validation (GCV) and (weighted) mean-squared error (MSE) approaches (based on Stein's Unbiased Risk Estimate— SURE) need the Jacobian matrix of the nonlinear reconstruction operator (representative of the iterative algorithm) with respect to the data. We derive the desired Jacobian matrix for two types of nonlinear iterative algorithms: a fast variant of the standard iterative reweighted least-squares method and the contemporary split-Bregman algorithm, both of which can accommodate a wide variety of analysis- and synthesis-type regularizers. The proposed approach iteratively computes two weighted SURE-type measures: Predicted-SURE and Projected-SURE (that require knowledge of noise variance σ2), and GCV (that does not need σ2) for these algorithms. We apply the methods to image restoration and to magnetic resonance image (MRI) reconstruction using total variation (TV) and an analysis-type ℓ1-regularization. We demonstrate through simulations and experiments with real data that minimizing Predicted-SURE and Projected-SURE consistently lead to near-MSE-optimal reconstructions. We also observed that minimizing GCV yields reconstruction results that are near-MSE-optimal for image restoration and slightly sub-optimal for MRI. Theoretical derivations in this work related to Jacobian matrix evaluations can be extended, in principle, to other types of regularizers and reconstruction algorithms. PMID:22531764

Fast projection/backprojection and incremental methods applied to synchrotron light tomographic reconstruction.

PubMed

de Lima, Camila; Salomão Helou, Elias

2018-01-01

Iterative methods for tomographic image reconstruction have the computational cost of each iteration dominated by the computation of the (back)projection operator, which take roughly O(N 3 ) floating point operations (flops) for N × N pixels images. Furthermore, classical iterative algorithms may take too many iterations in order to achieve acceptable images, thereby making the use of these techniques unpractical for high-resolution images. Techniques have been developed in the literature in order to reduce the computational cost of the (back)projection operator to O(N 2 logN) flops. Also, incremental algorithms have been devised that reduce by an order of magnitude the number of iterations required to achieve acceptable images. The present paper introduces an incremental algorithm with a cost of O(N 2 logN) flops per iteration and applies it to the reconstruction of very large tomographic images obtained from synchrotron light illuminated data.

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

PubMed

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

2013-08-07

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

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

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

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

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

Investigation of iterative image reconstruction in low-dose breast CT

NASA Astrophysics Data System (ADS)

Bian, Junguo; Yang, Kai; Boone, John M.; Han, Xiao; Sidky, Emil Y.; Pan, Xiaochuan

2014-06-01

There is interest in developing computed tomography (CT) dedicated to breast-cancer imaging. Because breast tissues are radiation-sensitive, the total radiation exposure in a breast-CT scan is kept low, often comparable to a typical two-view mammography exam, thus resulting in a challenging low-dose-data-reconstruction problem. In recent years, evidence has been found that suggests that iterative reconstruction may yield images of improved quality from low-dose data. In this work, based upon the constrained image total-variation minimization program and its numerical solver, i.e., the adaptive steepest descent-projection onto the convex set (ASD-POCS), we investigate and evaluate iterative image reconstructions from low-dose breast-CT data of patients, with a focus on identifying and determining key reconstruction parameters, devising surrogate utility metrics for characterizing reconstruction quality, and tailoring the program and ASD-POCS to the specific reconstruction task under consideration. The ASD-POCS reconstructions appear to outperform the corresponding clinical FDK reconstructions, in terms of subjective visualization and surrogate utility metrics.

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

PubMed

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

2017-11-01

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

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

DOE PAGES

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

2017-07-03

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

Optimal spiral phase modulation in Gerchberg-Saxton algorithm for wavefront reconstruction and correction

NASA Astrophysics Data System (ADS)

Baránek, M.; Běhal, J.; Bouchal, Z.

2018-01-01

In the phase retrieval applications, the Gerchberg-Saxton (GS) algorithm is widely used for the simplicity of implementation. This iterative process can advantageously be deployed in the combination with a spatial light modulator (SLM) enabling simultaneous correction of optical aberrations. As recently demonstrated, the accuracy and efficiency of the aberration correction using the GS algorithm can be significantly enhanced by a vortex image spot used as the target intensity pattern in the iterative process. Here we present an optimization of the spiral phase modulation incorporated into the GS algorithm.

Prostate Brachytherapy Seed Reconstruction with Gaussian Blurring and Optimal Coverage Cost

PubMed Central

Lee, Junghoon; Liu, Xiaofeng; Jain, Ameet K.; Song, Danny Y.; Burdette, E. Clif; Prince, Jerry L.; Fichtinger, Gabor

2009-01-01

Intraoperative dosimetry in prostate brachytherapy requires localization of the implanted radioactive seeds. A tomosynthesis-based seed reconstruction method is proposed. A three-dimensional volume is reconstructed from Gaussian-blurred projection images and candidate seed locations are computed from the reconstructed volume. A false positive seed removal process, formulated as an optimal coverage problem, iteratively removes “ghost” seeds that are created by tomosynthesis reconstruction. In an effort to minimize pose errors that are common in conventional C-arms, initial pose parameter estimates are iteratively corrected by using the detected candidate seeds as fiducials, which automatically “focuses” the collected images and improves successive reconstructed volumes. Simulation results imply that the implanted seed locations can be estimated with a detection rate of ≥ 97.9% and ≥ 99.3% from three and four images, respectively, when the C-arm is calibrated and the pose of the C-arm is known. The algorithm was also validated on phantom data sets successfully localizing the implanted seeds from four or five images. In a Phase-1 clinical trial, we were able to localize the implanted seeds from five intraoperative fluoroscopy images with 98.8% (STD=1.6) overall detection rate. PMID:19605321

Iterative Reconstruction Techniques in Abdominopelvic CT: Technical Concepts and Clinical Implementation.

PubMed

Patino, Manuel; Fuentes, Jorge M; Singh, Sarabjeet; Hahn, Peter F; Sahani, Dushyant V

2015-07-01

This article discusses the clinical challenge of low-radiation-dose examinations, the commonly used approaches for dose optimization, and their effect on image quality. We emphasize practical aspects of the different iterative reconstruction techniques, along with their benefits, pitfalls, and clinical implementation. The widespread use of CT has raised concerns about potential radiation risks, motivating diverse strategies to reduce the radiation dose associated with CT. CT manufacturers have developed alternative reconstruction algorithms intended to improve image quality on dose-optimized CT studies, mainly through noise and artifact reduction. Iterative reconstruction techniques take unique approaches to noise reduction and provide distinct strength levels or settings.

An algebraic iterative reconstruction technique for differential X-ray phase-contrast computed tomography.

PubMed

Fu, Jian; Schleede, Simone; Tan, Renbo; Chen, Liyuan; Bech, Martin; Achterhold, Klaus; Gifford, Martin; Loewen, Rod; Ruth, Ronald; Pfeiffer, Franz

2013-09-01

Iterative reconstruction has a wide spectrum of proven advantages in the field of conventional X-ray absorption-based computed tomography (CT). In this paper, we report on an algebraic iterative reconstruction technique for grating-based differential phase-contrast CT (DPC-CT). Due to the differential nature of DPC-CT projections, a differential operator and a smoothing operator are added to the iterative reconstruction, compared to the one commonly used for absorption-based CT data. This work comprises a numerical study of the algorithm and its experimental verification using a dataset measured at a two-grating interferometer setup. Since the algorithm is easy to implement and allows for the extension to various regularization possibilities, we expect a significant impact of the method for improving future medical and industrial DPC-CT applications. Copyright © 2012. Published by Elsevier GmbH.

Image enhancement in positron emission mammography

NASA Astrophysics Data System (ADS)

Slavine, Nikolai V.; Seiler, Stephen; McColl, Roderick W.; Lenkinski, Robert E.

2017-02-01

Purpose: To evaluate an efficient iterative deconvolution method (RSEMD) for improving the quantitative accuracy of previously reconstructed breast images by commercial positron emission mammography (PEM) scanner. Materials and Methods: The RSEMD method was tested on breast phantom data and clinical PEM imaging data. Data acquisition was performed on a commercial Naviscan Flex Solo II PEM camera. This method was applied to patient breast images previously reconstructed with Naviscan software (MLEM) to determine improvements in resolution, signal to noise ratio (SNR) and contrast to noise ratio (CNR.) Results: In all of the patients' breast studies the post-processed images proved to have higher resolution and lower noise as compared with images reconstructed by conventional methods. In general, the values of SNR reached a plateau at around 6 iterations with an improvement factor of about 2 for post-processed Flex Solo II PEM images. Improvements in image resolution after the application of RSEMD have also been demonstrated. Conclusions: A rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach RSEMD that operates on patient DICOM images has been used for quantitative improvement in breast imaging. The RSEMD method can be applied to clinical PEM images to improve image quality to diagnostically acceptable levels and will be crucial in order to facilitate diagnosis of tumor progression at the earliest stages. The RSEMD method can be considered as an extended Richardson-Lucy algorithm with multiple resolution levels (resolution subsets).

SU-D-17A-02: Four-Dimensional CBCT Using Conventional CBCT Dataset and Iterative Subtraction Algorithm of a Lung Patient

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

Hu, E; Lasio, G; Yi, B

2014-06-01

Purpose: The Iterative Subtraction Algorithm (ISA) method generates retrospectively a pre-selected motion phase cone-beam CT image from the full motion cone-beam CT acquired at standard rotation speed. This work evaluates ISA method with real lung patient data. Methods: The goal of the ISA algorithm is to extract motion and no- motion components form the full reconstruction CBCT. The workflow consists of subtracting from the full CBCT all of the undesired motion phases and obtain a motion de-blurred single-phase CBCT image, followed by iteration of this subtraction process. ISA is realized as follows: 1) The projections are sorted to various phases,more » and from all phases, a full reconstruction is performed to generate an image CTM. 2) Generate forward projections of CTM at the desired phase projection angles, the subtraction of projection and the forward projection will reconstruct a CTSub1, which diminishes the desired phase component. 3) By adding back the CTSub1 to CTm, no motion CBCT, CTS1, can be computed. 4) CTS1 still contains residual motion component. 5) This residual motion component can be further reduced by iteration.The ISA 4DCBCT technique was implemented using Varian Trilogy accelerator OBI system. To evaluate the method, a lung patient CBCT dataset was used. The reconstruction algorithm is FDK. Results: The single phase CBCT reconstruction generated via ISA successfully isolates the desired motion phase from the full motion CBCT, effectively reducing motion blur. It also shows improved image quality, with reduced streak artifacts with respect to the reconstructions from unprocessed phase-sorted projections only. Conclusion: A CBCT motion de-blurring algorithm, ISA, has been developed and evaluated with lung patient data. The algorithm allows improved visualization of a single phase motion extracted from a standard CBCT dataset. This study has been supported by National Institute of Health through R01CA133539.« less

Impact of iterative metal artifact reduction on diagnostic image quality in patients with dental hardware.

PubMed

Weiß, Jakob; Schabel, Christoph; Bongers, Malte; Raupach, Rainer; Clasen, Stephan; Notohamiprodjo, Mike; Nikolaou, Konstantin; Bamberg, Fabian

2017-03-01

Background Metal artifacts often impair diagnostic accuracy in computed tomography (CT) imaging. Therefore, effective and workflow implemented metal artifact reduction algorithms are crucial to gain higher diagnostic image quality in patients with metallic hardware. Purpose To assess the clinical performance of a novel iterative metal artifact reduction (iMAR) algorithm for CT in patients with dental fillings. Material and Methods Thirty consecutive patients scheduled for CT imaging and dental fillings were included in the analysis. All patients underwent CT imaging using a second generation dual-source CT scanner (120 kV single-energy; 100/Sn140 kV in dual-energy, 219 mAs, gantry rotation time 0.28-1/s, collimation 0.6 mm) as part of their clinical work-up. Post-processing included standard kernel (B49) and an iterative MAR algorithm. Image quality and diagnostic value were assessed qualitatively (Likert scale) and quantitatively (HU ± SD) by two reviewers independently. Results All 30 patients were included in the analysis, with equal reconstruction times for iMAR and standard reconstruction (17 s ± 0.5 vs. 19 s ± 0.5; P > 0.05). Visual image quality was significantly higher for iMAR as compared with standard reconstruction (3.8 ± 0.5 vs. 2.6 ± 0.5; P < 0.0001, respectively) and showed improved evaluation of adjacent anatomical structures. Similarly, HU-based measurements of degree of artifacts were significantly lower in the iMAR reconstructions as compared with the standard reconstruction (0.9 ± 1.6 vs. -20 ± 47; P < 0.05, respectively). Conclusion The tested iterative, raw-data based reconstruction MAR algorithm allows for a significant reduction of metal artifacts and improved evaluation of adjacent anatomical structures in the head and neck area in patients with dental hardware.

Increasing reconstruction quality of diffractive optical elements displayed with LC SLM

NASA Astrophysics Data System (ADS)

Cheremkhin, Pavel A.; Evtikhiev, Nikolay N.; Krasnov, Vitaly V.; Rodin, Vladislav G.; Starikov, Sergey N.

2015-03-01

Phase liquid crystal (LC) spatial light modulators (SLM) are actively used in various applications. However, majority of scientific applications require stable phase modulation which might be hard to achieve with commercially available SLM due to its consumer origin. The use of digital voltage addressing scheme leads to phase temporal fluctuations, which results in lower diffraction efficiency and reconstruction quality of displayed diffractive optical elements (DOE). Due to high periodicity of fluctuations it should be possible to use knowledge of these fluctuations during DOE synthesis to minimize negative effect. We synthesized DOE using accurately measured phase fluctuations of phase LC SLM "HoloEye PLUTO VIS" to minimize its negative impact on displayed DOE reconstruction. Synthesis was conducted with versatile direct search with random trajectory (DSRT) method in the following way. Before DOE synthesis begun, two-dimensional dependency of SLM phase shift on addressed signal level and time from frame start was obtained. Then synthesis begins. First, initial phase distribution is created. Second, random trajectory of consecutive processing of all DOE elements is generated. Then iterative process begins. Each DOE element sequentially has its value changed to one that provides better value of objective criterion, e.g. lower deviation of reconstructed image from original one. If current element value provides best objective criterion value then it left unchanged. After all elements are processed, iteration repeats until stagnation is reached. It is demonstrated that application of SLM phase fluctuations knowledge in DOE synthesis with DSRT method leads to noticeable increase of DOE reconstruction quality.

Iterative metal artifact reduction for x-ray computed tomography using unmatched projector/backprojector pairs

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

Zhang, Hanming; Wang, Linyuan; Li, Lei

2016-06-15

Purpose: Metal artifact reduction (MAR) is a major problem and a challenging issue in x-ray computed tomography (CT) examinations. Iterative reconstruction from sinograms unaffected by metals shows promising potential in detail recovery. This reconstruction has been the subject of much research in recent years. However, conventional iterative reconstruction methods easily introduce new artifacts around metal implants because of incomplete data reconstruction and inconsistencies in practical data acquisition. Hence, this work aims at developing a method to suppress newly introduced artifacts and improve the image quality around metal implants for the iterative MAR scheme. Methods: The proposed method consists of twomore » steps based on the general iterative MAR framework. An uncorrected image is initially reconstructed, and the corresponding metal trace is obtained. The iterative reconstruction method is then used to reconstruct images from the unaffected sinogram. In the reconstruction step of this work, an iterative strategy utilizing unmatched projector/backprojector pairs is used. A ramp filter is introduced into the back-projection procedure to restrain the inconsistency components in low frequencies and generate more reliable images of the regions around metals. Furthermore, a constrained total variation (TV) minimization model is also incorporated to enhance efficiency. The proposed strategy is implemented based on an iterative FBP and an alternating direction minimization (ADM) scheme, respectively. The developed algorithms are referred to as “iFBP-TV” and “TV-FADM,” respectively. Two projection-completion-based MAR methods and three iterative MAR methods are performed simultaneously for comparison. Results: The proposed method performs reasonably on both simulation and real CT-scanned datasets. This approach could reduce streak metal artifacts effectively and avoid the mentioned effects in the vicinity of the metals. The improvements are evaluated by inspecting regions of interest and by comparing the root-mean-square errors, normalized mean absolute distance, and universal quality index metrics of the images. Both iFBP-TV and TV-FADM methods outperform other counterparts in all cases. Unlike the conventional iterative methods, the proposed strategy utilizing unmatched projector/backprojector pairs shows excellent performance in detail preservation and prevention of the introduction of new artifacts. Conclusions: Qualitative and quantitative evaluations of experimental results indicate that the developed method outperforms classical MAR algorithms in suppressing streak artifacts and preserving the edge structural information of the object. In particular, structures lying close to metals can be gradually recovered because of the reduction of artifacts caused by inconsistency effects.« less

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

PubMed

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

2018-05-15

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

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

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

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

2015-04-15

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

Objective performance assessment of five computed tomography iterative reconstruction algorithms.

PubMed

Omotayo, Azeez; Elbakri, Idris

2016-11-22

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

An iterative reconstruction method for high-pitch helical luggage CT

NASA Astrophysics Data System (ADS)

Xue, Hui; Zhang, Li; Chen, Zhiqiang; Jin, Xin

2012-10-01

X-ray luggage CT is widely used in airports and railway stations for the purpose of detecting contrabands and dangerous goods that may be potential threaten to public safety, playing an important role in homeland security. An X-ray luggage CT is usually in a helical trajectory with a high pitch for achieving a high passing speed of the luggage. The disadvantage of high pitch is that conventional filtered back-projection (FBP) requires a very large slice thickness, leading to bad axial resolution and helical artifacts. Especially when severe data inconsistencies are present in the z-direction, like the ends of a scanning object, the partial volume effect leads to inaccuracy value and may cause a wrong identification. In this paper, an iterative reconstruction method is developed to improve the image quality and accuracy for a large-spacing multi-detector high-pitch helical luggage CT system. In this method, the slice thickness is set to be much smaller than the pitch. Each slice involves projection data collected in a rather small angular range, being an ill-conditioned limited-angle problem. Firstly a low-resolution reconstruction is employed to obtain images, which are used as prior images in the following process. Then iterative reconstruction is performed to obtain high-resolution images. This method enables a high volume coverage speed and a thin reconstruction slice for the helical luggage CT. We validate this method with data collected in a commercial X-ray luggage CT.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Influence of Ultra-Low-Dose and Iterative Reconstructions on the Visualization of Orbital Soft Tissues on Maxillofacial CT.

PubMed

Widmann, G; Juranek, D; Waldenberger, F; Schullian, P; Dennhardt, A; Hoermann, R; Steurer, M; Gassner, E-M; Puelacher, W

2017-08-01

Dose reduction on CT scans for surgical planning and postoperative evaluation of midface and orbital fractures is an important concern. The purpose of this study was to evaluate the variability of various low-dose and iterative reconstruction techniques on the visualization of orbital soft tissues. Contrast-to-noise ratios of the optic nerve and inferior rectus muscle and subjective scores of a human cadaver were calculated from CT with a reference dose protocol (CT dose index volume = 36.69 mGy) and a subsequent series of low-dose protocols (LDPs I-4: CT dose index volume = 4.18, 2.64, 0.99, and 0.53 mGy) with filtered back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR)-50, ASIR-100, and model-based iterative reconstruction. The Dunn Multiple Comparison Test was used to compare each combination of protocols (α = .05). Compared with the reference dose protocol with FBP, the following statistically significant differences in contrast-to-noise ratios were shown (all, P ≤ .012) for the following: 1) optic nerve: LDP-I with FBP; LDP-II with FBP and ASIR-50; LDP-III with FBP, ASIR-50, and ASIR-100; and LDP-IV with FBP, ASIR-50, and ASIR-100; and 2) inferior rectus muscle: LDP-II with FBP, LDP-III with FBP and ASIR-50, and LDP-IV with FBP, ASIR-50, and ASIR-100. Model-based iterative reconstruction showed the best contrast-to-noise ratio in all images and provided similar subjective scores for LDP-II. ASIR-50 had no remarkable effect, and ASIR-100, a small effect on subjective scores. Compared with a reference dose protocol with FBP, model-based iterative reconstruction may show similar diagnostic visibility of orbital soft tissues at a CT dose index volume of 2.64 mGy. Low-dose technology and iterative reconstruction technology may redefine current reference dose levels in maxillofacial CT. © 2017 by American Journal of Neuroradiology.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery.

PubMed

Hashemi, SayedMasoud; Song, William Y; Sahgal, Arjun; Lee, Young; Huynh, Christopher; Grouza, Vladimir; Nordström, Håkan; Eriksson, Markus; Dorenlot, Antoine; Régis, Jean Marie; Mainprize, James G; Ruschin, Mark

2017-04-07

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm -1 which was increased to 1.2 mm -1 by SDIR, at half maximum.

Simultaneous deblurring and iterative reconstruction of CBCT for image guided brain radiosurgery

NASA Astrophysics Data System (ADS)

Hashemi, SayedMasoud; Song, William Y.; Sahgal, Arjun; Lee, Young; Huynh, Christopher; Grouza, Vladimir; Nordström, Håkan; Eriksson, Markus; Dorenlot, Antoine; Régis, Jean Marie; Mainprize, James G.; Ruschin, Mark

2017-04-01

One of the limiting factors in cone-beam CT (CBCT) image quality is system blur, caused by detector response, x-ray source focal spot size, azimuthal blurring, and reconstruction algorithm. In this work, we develop a novel iterative reconstruction algorithm that improves spatial resolution by explicitly accounting for image unsharpness caused by different factors in the reconstruction formulation. While the model-based iterative reconstruction techniques use prior information about the detector response and x-ray source, our proposed technique uses a simple measurable blurring model. In our reconstruction algorithm, denoted as simultaneous deblurring and iterative reconstruction (SDIR), the blur kernel can be estimated using the modulation transfer function (MTF) slice of the CatPhan phantom or any other MTF phantom, such as wire phantoms. The proposed image reconstruction formulation includes two regularization terms: (1) total variation (TV) and (2) nonlocal regularization, solved with a split Bregman augmented Lagrangian iterative method. The SDIR formulation preserves edges, eases the parameter adjustments to achieve both high spatial resolution and low noise variances, and reduces the staircase effect caused by regular TV-penalized iterative algorithms. The proposed algorithm is optimized for a point-of-care head CBCT unit for image-guided radiosurgery and is tested with CatPhan phantom, an anthropomorphic head phantom, and 6 clinical brain stereotactic radiosurgery cases. Our experiments indicate that SDIR outperforms the conventional filtered back projection and TV penalized simultaneous algebraic reconstruction technique methods (represented by adaptive steepest-descent POCS algorithm, ASD-POCS) in terms of MTF and line pair resolution, and retains the favorable properties of the standard TV-based iterative reconstruction algorithms in improving the contrast and reducing the reconstruction artifacts. It improves the visibility of the high contrast details in bony areas and the brain soft-tissue. For example, the results show the ventricles and some brain folds become visible in SDIR reconstructed images and the contrast of the visible lesions is effectively improved. The line-pair resolution was improved from 12 line-pair/cm in FBP to 14 line-pair/cm in SDIR. Adjusting the parameters of the ASD-POCS to achieve 14 line-pair/cm caused the noise variance to be higher than the SDIR. Using these parameters for ASD-POCS, the MTF of FBP and ASD-POCS were very close and equal to 0.7 mm-1 which was increased to 1.2 mm-1 by SDIR, at half maximum.

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

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

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

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

Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

PubMed Central

Tian, Zhen; Jia, Xun; Yuan, Kehong; Pan, Tinsu; Jiang, Steve B.

2014-01-01

High radiation dose in CT scans increases a lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with Total Variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, the low contrast structures tend to be smoothed out by the TV regularization, posing a great challenge for the TV method. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing an energy consisting of an edge-preserving TV norm and a data fidelity term posed by the x-ray projections. The edge-preserving TV term is proposed to preferentially perform smoothing only on non-edge part of the image in order to better preserve the edges, which is realized by introducing a penalty weight to the original total variation norm. During the reconstruction process, the pixels at edges would be gradually identified and given small penalty weight. Our iterative algorithm is implemented on GPU to improve its speed. We test our reconstruction algorithm on a digital NCAT phantom, a physical chest phantom, and a Catphan phantom. Reconstruction results from a conventional FBP algorithm and a TV regularization method without edge preserving penalty are also presented for comparison purpose. The experimental results illustrate that both TV-based algorithm and our edge-preserving TV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under the low dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of low contrast structures and therefore maintain acceptable spatial resolution. PMID:21860076

Highly undersampled contrast-enhanced MRA with iterative reconstruction: Integration in a clinical setting.

PubMed

Stalder, Aurelien F; Schmidt, Michaela; Quick, Harald H; Schlamann, Marc; Maderwald, Stefan; Schmitt, Peter; Wang, Qiu; Nadar, Mariappan S; Zenge, Michael O

2015-12-01

To integrate, optimize, and evaluate a three-dimensional (3D) contrast-enhanced sparse MRA technique with iterative reconstruction on a standard clinical MR system. Data were acquired using a highly undersampled Cartesian spiral phyllotaxis sampling pattern and reconstructed directly on the MR system with an iterative SENSE technique. Undersampling, regularization, and number of iterations of the reconstruction were optimized and validated based on phantom experiments and patient data. Sparse MRA of the whole head (field of view: 265 × 232 × 179 mm(3) ) was investigated in 10 patient examinations. High-quality images with 30-fold undersampling, resulting in 0.7 mm isotropic resolution within 10 s acquisition, were obtained. After optimization of the regularization factor and of the number of iterations of the reconstruction, it was possible to reconstruct images with excellent quality within six minutes per 3D volume. Initial results of sparse contrast-enhanced MRA (CEMRA) in 10 patients demonstrated high-quality whole-head first-pass MRA for both the arterial and venous contrast phases. While sparse MRI techniques have not yet reached clinical routine, this study demonstrates the technical feasibility of high-quality sparse CEMRA of the whole head in a clinical setting. Sparse CEMRA has the potential to become a viable alternative where conventional CEMRA is too slow or does not provide sufficient spatial resolution. © 2014 Wiley Periodicals, Inc.

A methodology for image quality evaluation of advanced CT systems.

PubMed

Wilson, Joshua M; Christianson, Olav I; Richard, Samuel; Samei, Ehsan

2013-03-01

This work involved the development of a phantom-based method to quantify the performance of tube current modulation and iterative reconstruction in modern computed tomography (CT) systems. The quantification included resolution, HU accuracy, noise, and noise texture accounting for the impact of contrast, prescribed dose, reconstruction algorithm, and body size. A 42-cm-long, 22.5-kg polyethylene phantom was designed to model four body sizes. Each size was represented by a uniform section, for the measurement of the noise-power spectrum (NPS), and a feature section containing various rods, for the measurement of HU and the task-based modulation transfer function (TTF). The phantom was scanned on a clinical CT system (GE, 750HD) using a range of tube current modulation settings (NI levels) and reconstruction methods (FBP and ASIR30). An image quality analysis program was developed to process the phantom data to calculate the targeted image quality metrics as a function of contrast, prescribed dose, and body size. The phantom fabrication closely followed the design specifications. In terms of tube current modulation, the tube current and resulting image noise varied as a function of phantom size as expected based on the manufacturer specification: From the 16- to 37-cm section, the HU contrast for each rod was inversely related to phantom size, and noise was relatively constant (<5% change). With iterative reconstruction, the TTF exhibited a contrast dependency with better performance for higher contrast objects. At low noise levels, TTFs of iterative reconstruction were better than those of FBP, but at higher noise, that superiority was not maintained at all contrast levels. Relative to FBP, the NPS of iterative reconstruction exhibited an ~30% decrease in magnitude and a 0.1 mm(-1) shift in the peak frequency. Phantom and image quality analysis software were created for assessing CT image quality over a range of contrasts, doses, and body sizes. The testing platform enabled robust NPS, TTF, HU, and pixel noise measurements as a function of body size capable of characterizing the performance of reconstruction algorithms and tube current modulation techniques.

Fast non-interferometric iterative phase retrieval for holographic data storage.

PubMed

Lin, Xiao; Huang, Yong; Shimura, Tsutomu; Fujimura, Ryushi; Tanaka, Yoshito; Endo, Masao; Nishimoto, Hajimu; Liu, Jinpeng; Li, Yang; Liu, Ying; Tan, Xiaodi

2017-12-11

Fast non-interferometric phase retrieval is a very important technique for phase-encoded holographic data storage and other phase based applications due to its advantage of easy implementation, simple system setup, and robust noise tolerance. Here we present an iterative non-interferometric phase retrieval for 4-level phase encoded holographic data storage based on an iterative Fourier transform algorithm and known portion of the encoded data, which increases the storage code rate to two-times that of an amplitude based method. Only a single image at the Fourier plane of the beam is captured for the iterative reconstruction. Since beam intensity at the Fourier plane of the reconstructed beam is more concentrated than the reconstructed beam itself, the requirement of diffractive efficiency of the recording media is reduced, which will improve the dynamic range of recording media significantly. The phase retrieval only requires 10 iterations to achieve a less than 5% phase data error rate, which is successfully demonstrated by recording and reconstructing a test image data experimentally. We believe our method will further advance the holographic data storage technique in the era of big data.

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

PubMed

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

2014-01-01

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

Imaging complex objects using learning tomography

NASA Astrophysics Data System (ADS)

Lim, JooWon; Goy, Alexandre; Shoreh, Morteza Hasani; Unser, Michael; Psaltis, Demetri

2018-02-01

Optical diffraction tomography (ODT) can be described using the scattering process through an inhomogeneous media. An inherent nonlinearity exists relating the scattering medium and the scattered field due to multiple scattering. Multiple scattering is often assumed to be negligible in weakly scattering media. This assumption becomes invalid as the sample gets more complex resulting in distorted image reconstructions. This issue becomes very critical when we image a complex sample. Multiple scattering can be simulated using the beam propagation method (BPM) as the forward model of ODT combined with an iterative reconstruction scheme. The iterative error reduction scheme and the multi-layer structure of BPM are similar to neural networks. Therefore we refer to our imaging method as learning tomography (LT). To fairly assess the performance of LT in imaging complex samples, we compared LT with the conventional iterative linear scheme using Mie theory which provides the ground truth. We also demonstrate the capacity of LT to image complex samples using experimental data of a biological cell.

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

NASA Astrophysics Data System (ADS)

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

2014-07-01

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

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

PubMed Central

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

2016-01-01

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

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

PubMed Central

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-01-01

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L0 gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements. PMID:29414893

Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement.

PubMed

Zhu, Hong; Tang, Xinming; Xie, Junfeng; Song, Weidong; Mo, Fan; Gao, Xiaoming

2018-02-07

There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L ₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Improvements to image quality using hybrid and model-based iterative reconstructions: a phantom study.

PubMed

Aurumskjöld, Marie-Louise; Ydström, Kristina; Tingberg, Anders; Söderberg, Marcus

2017-01-01

The number of computed tomography (CT) examinations is increasing and leading to an increase in total patient exposure. It is therefore important to optimize CT scan imaging conditions in order to reduce the radiation dose. The introduction of iterative reconstruction methods has enabled an improvement in image quality and a reduction in radiation dose. To investigate how image quality depends on reconstruction method and to discuss patient dose reduction resulting from the use of hybrid and model-based iterative reconstruction. An image quality phantom (Catphan® 600) and an anthropomorphic torso phantom were examined on a Philips Brilliance iCT. The image quality was evaluated in terms of CT numbers, noise, noise power spectra (NPS), contrast-to-noise ratio (CNR), low-contrast resolution, and spatial resolution for different scan parameters and dose levels. The images were reconstructed using filtered back projection (FBP) and different settings of hybrid (iDose 4 ) and model-based (IMR) iterative reconstruction methods. iDose 4 decreased the noise by 15-45% compared with FBP depending on the level of iDose 4 . The IMR reduced the noise even further, by 60-75% compared to FBP. The results are independent of dose. The NPS showed changes in the noise distribution for different reconstruction methods. The low-contrast resolution and CNR were improved with iDose 4 , and the improvement was even greater with IMR. There is great potential to reduce noise and thereby improve image quality by using hybrid or, in particular, model-based iterative reconstruction methods, or to lower radiation dose and maintain image quality. © The Foundation Acta Radiologica 2016.

Regularization iteration imaging algorithm for electrical capacitance tomography

NASA Astrophysics Data System (ADS)

Tong, Guowei; Liu, Shi; Chen, Hongyan; Wang, Xueyao

2018-03-01

The image reconstruction method plays a crucial role in real-world applications of the electrical capacitance tomography technique. In this study, a new cost function that simultaneously considers the sparsity and low-rank properties of the imaging targets is proposed to improve the quality of the reconstruction images, in which the image reconstruction task is converted into an optimization problem. Within the framework of the split Bregman algorithm, an iterative scheme that splits a complicated optimization problem into several simpler sub-tasks is developed to solve the proposed cost function efficiently, in which the fast-iterative shrinkage thresholding algorithm is introduced to accelerate the convergence. Numerical experiment results verify the effectiveness of the proposed algorithm in improving the reconstruction precision and robustness.

COMPARISON OF ADAPTIVE STATISTICAL ITERATIVE RECONSTRUCTION (ASIR™) AND MODEL-BASED ITERATIVE RECONSTRUCTION (VEO™) FOR PAEDIATRIC ABDOMINAL CT EXAMINATIONS: AN OBSERVER PERFORMANCE STUDY OF DIAGNOSTIC IMAGE QUALITY.

PubMed

Hultenmo, Maria; Caisander, Håkan; Mack, Karsten; Thilander-Klang, Anne

2016-06-01

The diagnostic image quality of 75 paediatric abdominal computed tomography (CT) examinations reconstructed with two different iterative reconstruction (IR) algorithms-adaptive statistical IR (ASiR™) and model-based IR (Veo™)-was compared. Axial and coronal images were reconstructed with 70 % ASiR with the Soft™ convolution kernel and with the Veo algorithm. The thickness of the reconstructed images was 2.5 or 5 mm depending on the scanning protocol used. Four radiologists graded the delineation of six abdominal structures and the diagnostic usefulness of the image quality. The Veo reconstruction significantly improved the visibility of most of the structures compared with ASiR in all subgroups of images. For coronal images, the Veo reconstruction resulted in significantly improved ratings of the diagnostic use of the image quality compared with the ASiR reconstruction. This was not seen for the axial images. The greatest improvement using Veo reconstruction was observed for the 2.5 mm coronal slices. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

DOE PAGES

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De; ...

2017-01-28

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

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

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

Local motion-compensated method for high-quality 3D coronary artery reconstruction

PubMed Central

Liu, Bo; Bai, Xiangzhi; Zhou, Fugen

2016-01-01

The 3D reconstruction of coronary artery from X-ray angiograms rotationally acquired on C-arm has great clinical value. While cardiac-gated reconstruction has shown promising results, it suffers from the problem of residual motion. This work proposed a new local motion-compensated reconstruction method to handle this issue. An initial image was firstly reconstructed using a regularized iterative reconstruction method. Then a 3D/2D registration method was proposed to estimate the residual vessel motion. Finally, the residual motion was compensated in the final reconstruction using the extended iterative reconstruction method. Through quantitative evaluation, it was found that high-quality 3D reconstruction could be obtained and the result was comparable to state-of-the-art method. PMID:28018741

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

NASA Astrophysics Data System (ADS)

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

2007-02-01

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

Iterative Nonlinear Tikhonov Algorithm with Constraints for Electromagnetic Tomography

NASA Technical Reports Server (NTRS)

Xu, Feng; Deshpande, Manohar

2012-01-01

Low frequency electromagnetic tomography such as the capacitance tomography (ECT) has been proposed for monitoring and mass-gauging of gas-liquid two-phase system under microgravity condition in NASA's future long-term space missions. Due to the ill-posed inverse problem of ECT, images reconstructed using conventional linear algorithms often suffer from limitations such as low resolution and blurred edges. Hence, new efficient high resolution nonlinear imaging algorithms are needed for accurate two-phase imaging. The proposed Iterative Nonlinear Tikhonov Regularized Algorithm with Constraints (INTAC) is based on an efficient finite element method (FEM) forward model of quasi-static electromagnetic problem. It iteratively minimizes the discrepancy between FEM simulated and actual measured capacitances by adjusting the reconstructed image using the Tikhonov regularized method. More importantly, it enforces the known permittivity of two phases to the unknown pixels which exceed the reasonable range of permittivity in each iteration. This strategy does not only stabilize the converging process, but also produces sharper images. Simulations show that resolution improvement of over 2 times can be achieved by INTAC with respect to conventional approaches. Strategies to further improve spatial imaging resolution are suggested, as well as techniques to accelerate nonlinear forward model and thus increase the temporal resolution.

Fast iterative image reconstruction using sparse matrix factorization with GPU acceleration

NASA Astrophysics Data System (ADS)

Zhou, Jian; Qi, Jinyi

2011-03-01

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

Fisher's method of scoring in statistical image reconstruction: comparison of Jacobi and Gauss-Seidel iterative schemes.

PubMed

Hudson, H M; Ma, J; Green, P

1994-01-01

Many algorithms for medical image reconstruction adopt versions of the expectation-maximization (EM) algorithm. In this approach, parameter estimates are obtained which maximize a complete data likelihood or penalized likelihood, in each iteration. Implicitly (and sometimes explicitly) penalized algorithms require smoothing of the current reconstruction in the image domain as part of their iteration scheme. In this paper, we discuss alternatives to EM which adapt Fisher's method of scoring (FS) and other methods for direct maximization of the incomplete data likelihood. Jacobi and Gauss-Seidel methods for non-linear optimization provide efficient algorithms applying FS in tomography. One approach uses smoothed projection data in its iterations. We investigate the convergence of Jacobi and Gauss-Seidel algorithms with clinical tomographic projection data.

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

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

Nakano, M; Haga, A; Hanaoka, S

2016-06-15

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

HeinzelCluster: accelerated reconstruction for FORE and OSEM3D.

PubMed

Vollmar, S; Michel, C; Treffert, J T; Newport, D F; Casey, M; Knöss, C; Wienhard, K; Liu, X; Defrise, M; Heiss, W D

2002-08-07

Using iterative three-dimensional (3D) reconstruction techniques for reconstruction of positron emission tomography (PET) is not feasible on most single-processor machines due to the excessive computing time needed, especially so for the large sinogram sizes of our high-resolution research tomograph (HRRT). In our first approach to speed up reconstruction time we transform the 3D scan into the format of a two-dimensional (2D) scan with sinograms that can be reconstructed independently using Fourier rebinning (FORE) and a fast 2D reconstruction method. On our dedicated reconstruction cluster (seven four-processor systems, Intel PIII@700 MHz, switched fast ethernet and Myrinet, Windows NT Server), we process these 2D sinograms in parallel. We have achieved a speedup > 23 using 26 processors and also compared results for different communication methods (RPC, Syngo, Myrinet GM). The other approach is to parallelize OSEM3D (implementation of C Michel), which has produced the best results for HRRT data so far and is more suitable for an adequate treatment of the sinogram gaps that result from the detector geometry of the HRRT. We have implemented two levels of parallelization for four dedicated cluster (a shared memory fine-grain level on each node utilizing all four processors and a coarse-grain level allowing for 15 nodes) reducing the time for one core iteration from over 7 h to about 35 min.

String-averaging incremental subgradients for constrained convex optimization with applications to reconstruction of tomographic images

NASA Astrophysics Data System (ADS)

Massambone de Oliveira, Rafael; Salomão Helou, Elias; Fontoura Costa, Eduardo

2016-11-01

We present a method for non-smooth convex minimization which is based on subgradient directions and string-averaging techniques. In this approach, the set of available data is split into sequences (strings) and a given iterate is processed independently along each string, possibly in parallel, by an incremental subgradient method (ISM). The end-points of all strings are averaged to form the next iterate. The method is useful to solve sparse and large-scale non-smooth convex optimization problems, such as those arising in tomographic imaging. A convergence analysis is provided under realistic, standard conditions. Numerical tests are performed in a tomographic image reconstruction application, showing good performance for the convergence speed when measured as the decrease ratio of the objective function, in comparison to classical ISM.

Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views

DOEpatents

Lee, Heung-Rae

1997-01-01

A three-dimensional image reconstruction method comprises treating the object of interest as a group of elements with a size that is determined by the resolution of the projection data, e.g., as determined by the size of each pixel. One of the projections is used as a reference projection. A fictitious object is arbitrarily defined that is constrained by such reference projection. The method modifies the known structure of the fictitious object by comparing and optimizing its four projections to those of the unknown structure of the real object and continues to iterate until the optimization is limited by the residual sum of background noise. The method is composed of several sub-processes that acquire four projections from the real data and the fictitious object: generate an arbitrary distribution to define the fictitious object, optimize the four projections, generate a new distribution for the fictitious object, and enhance the reconstructed image. The sub-process for the acquisition of the four projections from the input real data is simply the function of acquiring the four projections from the data of the transmitted intensity. The transmitted intensity represents the density distribution, that is, the distribution of absorption coefficients through the object.

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

PubMed

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

2013-12-01

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

Development of a prototype chest digital tomosynthesis (CDT) R/F system with fast image reconstruction using graphics processing unit (GPU) programming

NASA Astrophysics Data System (ADS)

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

2017-03-01

Digital tomosynthesis offers the advantage of low radiation doses compared to conventional computed tomography (CT) by utilizing small numbers of projections ( 80) acquired over a limited angular range. It produces 3D volumetric data, although there are artifacts due to incomplete sampling. Based upon these characteristics, we developed a prototype digital tomosynthesis R/F system for applications in chest imaging. Our prototype chest digital tomosynthesis (CDT) R/F system contains an X-ray tube with high power R/F pulse generator, flat-panel detector, R/F table, electromechanical radiographic subsystems including a precise motor controller, and a reconstruction server. For image reconstruction, users select between analytic and iterative reconstruction methods. Our reconstructed images of Catphan700 and LUNGMAN phantoms clearly and rapidly described the internal structures of phantoms using graphics processing unit (GPU) programming. Contrast-to-noise ratio (CNR) values of the CTP682 module of Catphan700 were higher in images using a simultaneous algebraic reconstruction technique (SART) than in those using filtered back-projection (FBP) for all materials by factors of 2.60, 3.78, 5.50, 2.30, 3.70, and 2.52 for air, lung foam, low density polyethylene (LDPE), Delrin® (acetal homopolymer resin), bone 50% (hydroxyapatite), and Teflon, respectively. Total elapsed times for producing 3D volume were 2.92 s and 86.29 s on average for FBP and SART (20 iterations), respectively. The times required for reconstruction were clinically feasible. Moreover, the total radiation dose from our system (5.68 mGy) was lower than that of conventional chest CT scan. Consequently, our prototype tomosynthesis R/F system represents an important advance in digital tomosynthesis applications.

Image reconstruction

NASA Astrophysics Data System (ADS)

Vasilenko, Georgii Ivanovich; Taratorin, Aleksandr Markovich

Linear, nonlinear, and iterative image-reconstruction (IR) algorithms are reviewed. Theoretical results are presented concerning controllable linear filters, the solution of ill-posed functional minimization problems, and the regularization of iterative IR algorithms. Attention is also given to the problem of superresolution and analytical spectrum continuation, the solution of the phase problem, and the reconstruction of images distorted by turbulence. IR in optical and optical-digital systems is discussed with emphasis on holographic techniques.

[Impact to Z-score Mapping of Hyperacute Stroke Images by Computed Tomography in Adaptive Statistical Iterative Reconstruction].

PubMed

Watanabe, Shota; Sakaguchi, Kenta; Hosono, Makoto; Ishii, Kazunari; Murakami, Takamichi; Ichikawa, Katsuhiro

The purpose of this study was to evaluate the effect of a hybrid-type iterative reconstruction method on Z-score mapping of hyperacute stroke in unenhanced computed tomography (CT) images. We used a hybrid-type iterative reconstruction [adaptive statistical iterative reconstruction (ASiR)] implemented in a CT system (Optima CT660 Pro advance, GE Healthcare). With 15 normal brain cases, we reconstructed CT images with a filtered back projection (FBP) and ASiR with a blending factor of 100% (ASiR100%). Two standardized normal brain data were created from normal databases of FBP images (FBP-NDB) and ASiR100% images (ASiR-NDB), and standard deviation (SD) values in basal ganglia were measured. The Z-score mapping was performed for 12 hyperacute stroke cases by using FBP-NDB and ASiR-NDB, and compared Z-score value on hyperacute stroke area and normal area between FBP-NDB and ASiR-NDB. By using ASiR-NDB, the SD value of standardized brain was decreased by 16%. The Z-score value of ASiR-NDB on hyperacute stroke area was significantly higher than FBP-NDB (p<0.05). Therefore, the use of images reconstructed with ASiR100% for Z-score mapping had potential to improve the accuracy of Z-score mapping.

Reduction of Metal Artifact in Single Photon-Counting Computed Tomography by Spectral-Driven Iterative Reconstruction Technique

PubMed Central

Nasirudin, Radin A.; Mei, Kai; Panchev, Petar; Fehringer, Andreas; Pfeiffer, Franz; Rummeny, Ernst J.; Fiebich, Martin; Noël, Peter B.

2015-01-01

Purpose The exciting prospect of Spectral CT (SCT) using photon-counting detectors (PCD) will lead to new techniques in computed tomography (CT) that take advantage of the additional spectral information provided. We introduce a method to reduce metal artifact in X-ray tomography by incorporating knowledge obtained from SCT into a statistical iterative reconstruction scheme. We call our method Spectral-driven Iterative Reconstruction (SPIR). Method The proposed algorithm consists of two main components: material decomposition and penalized maximum likelihood iterative reconstruction. In this study, the spectral data acquisitions with an energy-resolving PCD were simulated using a Monte-Carlo simulator based on EGSnrc C++ class library. A jaw phantom with a dental implant made of gold was used as an object in this study. A total of three dental implant shapes were simulated separately to test the influence of prior knowledge on the overall performance of the algorithm. The generated projection data was first decomposed into three basis functions: photoelectric absorption, Compton scattering and attenuation of gold. A pseudo-monochromatic sinogram was calculated and used as input in the reconstruction, while the spatial information of the gold implant was used as a prior. The results from the algorithm were assessed and benchmarked with state-of-the-art reconstruction methods. Results Decomposition results illustrate that gold implant of any shape can be distinguished from other components of the phantom. Additionally, the result from the penalized maximum likelihood iterative reconstruction shows that artifacts are significantly reduced in SPIR reconstructed slices in comparison to other known techniques, while at the same time details around the implant are preserved. Quantitatively, the SPIR algorithm best reflects the true attenuation value in comparison to other algorithms. Conclusion It is demonstrated that the combination of the additional information from Spectral CT and statistical reconstruction can significantly improve image quality, especially streaking artifacts caused by the presence of materials with high atomic numbers. PMID:25955019

Multishot cartesian turbo spin-echo diffusion imaging using iterative POCSMUSE Reconstruction.

PubMed

Zhang, Zhe; Zhang, Bing; Li, Ming; Liang, Xue; Chen, Xiaodong; Liu, Renyuan; Zhang, Xin; Guo, Hua

2017-07-01

To report a diffusion imaging technique insensitive to off-resonance artifacts and motion-induced ghost artifacts using multishot Cartesian turbo spin-echo (TSE) acquisition and iterative POCS-based reconstruction of multiplexed sensitivity encoded magnetic resonance imaging (MRI) (POCSMUSE) for phase correction. Phase insensitive diffusion preparation was used to deal with the violation of the Carr-Purcell-Meiboom-Gill (CPMG) conditions of TSE diffusion-weighted imaging (DWI), followed by a multishot Cartesian TSE readout for data acquisition. An iterative diffusion phase correction method, iterative POCSMUSE, was developed and implemented to eliminate the ghost artifacts in multishot TSE DWI. The in vivo human brain diffusion images (from one healthy volunteer and 10 patients) using multishot Cartesian TSE were acquired at 3T and reconstructed using iterative POCSMUSE, and compared with single-shot and multishot echo-planar imaging (EPI) results. These images were evaluated by two radiologists using visual scores (considering both image quality and distortion levels) from 1 to 5. The proposed iterative POCSMUSE reconstruction was able to correct the ghost artifacts in multishot DWI. The ghost-to-signal ratio of TSE DWI using iterative POCSMUSE (0.0174 ± 0.0024) was significantly (P < 0.0005) smaller than using POCSMUSE (0.0253 ± 0.0040). The image scores of multishot TSE DWI were significantly higher than single-shot (P = 0.004 and 0.006 from two reviewers) and multishot (P = 0.008 and 0.004 from two reviewers) EPI-based methods. The proposed multishot Cartesian TSE DWI using iterative POCSMUSE reconstruction can provide high-quality diffusion images insensitive to motion-induced ghost artifacts and off-resonance related artifacts such as chemical shifts and susceptibility-induced image distortions. 1 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:167-174. © 2016 International Society for Magnetic Resonance in Medicine.

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

PubMed

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

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

Iterative initial condition reconstruction

NASA Astrophysics Data System (ADS)

Schmittfull, Marcel; Baldauf, Tobias; Zaldarriaga, Matias

2017-07-01

Motivated by recent developments in perturbative calculations of the nonlinear evolution of large-scale structure, we present an iterative algorithm to reconstruct the initial conditions in a given volume starting from the dark matter distribution in real space. In our algorithm, objects are first moved back iteratively along estimated potential gradients, with a progressively reduced smoothing scale, until a nearly uniform catalog is obtained. The linear initial density is then estimated as the divergence of the cumulative displacement, with an optional second-order correction. This algorithm should undo nonlinear effects up to one-loop order, including the higher-order infrared resummation piece. We test the method using dark matter simulations in real space. At redshift z =0 , we find that after eight iterations the reconstructed density is more than 95% correlated with the initial density at k ≤0.35 h Mpc-1 . The reconstruction also reduces the power in the difference between reconstructed and initial fields by more than 2 orders of magnitude at k ≤0.2 h Mpc-1 , and it extends the range of scales where the full broadband shape of the power spectrum matches linear theory by a factor of 2-3. As a specific application, we consider measurements of the baryonic acoustic oscillation (BAO) scale that can be improved by reducing the degradation effects of large-scale flows. In our idealized dark matter simulations, the method improves the BAO signal-to-noise ratio by a factor of 2.7 at z =0 and by a factor of 2.5 at z =0.6 , improving standard BAO reconstruction by 70% at z =0 and 30% at z =0.6 , and matching the optimal BAO signal and signal-to-noise ratio of the linear density in the same volume. For BAO, the iterative nature of the reconstruction is the most important aspect.

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

PubMed

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

2012-06-01

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

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

PubMed

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

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

SU-G-IeP2-12: The Effect of Iterative Reconstruction and CT Tube Voltage On Hounsfield Unit Values of Iodinated Contrast

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

Ogden, K; Greene-Donnelly, K; Vallabhaneni, D

Purpose: To investigate the effects of changing iterative reconstruction strength and tube voltage on Hounsfield Unit (HU) values of varying concentrations of Iodinated contrast medium in a phantom. Method: Iodinated contrast (Omnipaque 300, GE Healthcare, Princeton NJ) was diluted with distilled water to concentrations of 0.6, 0.9, 1.8, 3.6, 7.2, and 10.8 mg/mL of Iodine. The solutions were scanned in a patient equivalent water phantom on two MDCT scanners: VCT 64 slice (GE Medical Systems, Waukesha, WI) and an Aquilion One 320 slice scanner (Toshiba America Medical Systems, Tustin CA). The phantom was scanned at 80, 100, 120, 140 kVmore » using 400, 255, 180, and 130 mAs, respectively, for the VCT scanner, and 80, 100, 120, and 135 kV using 400, 250, 200, and 150 mAs, respectively, on the Aquilion One. Images were reconstructed at 2.5 mm (VCT) and 0.5 mm (Aquilion One). The VCT images were reconstructed using Advanced Statistical Iterative Reconstruction (ASIR) at 6 different strengths: 0%, 20%, 40%, 60%, 80%, and 100%. Aquilion One images were reconstructed using Adaptive Iterative Dose Reduction (AIDR) at 4 strengths: no AIDR, Weak AIDR, Standard AIDR, and Strong AIDR. Regions of interest (ROIs) were drawn on the images to measure the HU values and standard deviations of the diluted contrast. Second order polynomials were used to fit the HU values as a function of Iodine concentration. Results: For both scanners, there was no significant effect of changing the iterative reconstruction strength. The polynomial fits yielded goodness-of-fit (R2) values averaging 0.997. Conclusion: Changing the strength of the iterative reconstruction has no significant effect on the HU values of Iodinated contrast in a tissue-equivalent phantom. Fit values of HU vs Iodine concentration are useful in quantitative imaging protocols such as the determination of cardiac output from time-density curves in the main pulmonary artery.« less

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction

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

Fahimian, Benjamin P.; Zhao Yunzhe; Huang Zhifeng

Purpose: A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. Methods: EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). Inmore » each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Results: Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 mAs produce comparable image quality, resolution, and contrast relative to FBP with the 140 mAs flux setting. Compared to the algebraic reconstruction technique and the expectation maximization statistical reconstruction algorithm, a significant reduction in computation time is achieved with EST. Finally, numerical experiments on helical cone-beam CT data suggest that the combination of EST and ASSR produces reconstructions with higher image quality and lower noise than the Feldkamp Davis and Kress (FDK) method and the conventional ASSR approach. Conclusions: A Fourier-based iterative method has been applied to the reconstruction of fan-bean CT data with reduced x-ray fluence. This method incorporates advantageous features in both real and Fourier space iterative schemes: using a fast and algebraically exact method to calculate forward projection, enforcing the measured data in Fourier space, and applying physical constraints and flexible regularization in real space. Our results suggest that EST can be utilized for radiation dose reduction in x-ray CT via the readily implementable technique of lowering mAs settings. Numerical experiments further indicate that EST requires less computation time than several other iterative algorithms and can, in principle, be extended to helical cone-beam geometry in combination with the ASSR method.« less

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction.

PubMed

Fahimian, Benjamin P; Zhao, Yunzhe; Huang, Zhifeng; Fung, Russell; Mao, Yu; Zhu, Chun; Khatonabadi, Maryam; DeMarco, John J; Osher, Stanley J; McNitt-Gray, Michael F; Miao, Jianwei

2013-03-01

A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 mAs produce comparable image quality, resolution, and contrast relative to FBP with the 140 mAs flux setting. Compared to the algebraic reconstruction technique and the expectation maximization statistical reconstruction algorithm, a significant reduction in computation time is achieved with EST. Finally, numerical experiments on helical cone-beam CT data suggest that the combination of EST and ASSR produces reconstructions with higher image quality and lower noise than the Feldkamp Davis and Kress (FDK) method and the conventional ASSR approach. A Fourier-based iterative method has been applied to the reconstruction of fan-bean CT data with reduced x-ray fluence. This method incorporates advantageous features in both real and Fourier space iterative schemes: using a fast and algebraically exact method to calculate forward projection, enforcing the measured data in Fourier space, and applying physical constraints and flexible regularization in real space. Our results suggest that EST can be utilized for radiation dose reduction in x-ray CT via the readily implementable technique of lowering mAs settings. Numerical experiments further indicate that EST requires less computation time than several other iterative algorithms and can, in principle, be extended to helical cone-beam geometry in combination with the ASSR method.

Radiation dose reduction in medical x-ray CT via Fourier-based iterative reconstruction

PubMed Central

Fahimian, Benjamin P.; Zhao, Yunzhe; Huang, Zhifeng; Fung, Russell; Mao, Yu; Zhu, Chun; Khatonabadi, Maryam; DeMarco, John J.; Osher, Stanley J.; McNitt-Gray, Michael F.; Miao, Jianwei

2013-01-01

Purpose: A Fourier-based iterative reconstruction technique, termed Equally Sloped Tomography (EST), is developed in conjunction with advanced mathematical regularization to investigate radiation dose reduction in x-ray CT. The method is experimentally implemented on fan-beam CT and evaluated as a function of imaging dose on a series of image quality phantoms and anonymous pediatric patient data sets. Numerical simulation experiments are also performed to explore the extension of EST to helical cone-beam geometry. Methods: EST is a Fourier based iterative algorithm, which iterates back and forth between real and Fourier space utilizing the algebraically exact pseudopolar fast Fourier transform (PPFFT). In each iteration, physical constraints and mathematical regularization are applied in real space, while the measured data are enforced in Fourier space. The algorithm is automatically terminated when a proposed termination criterion is met. Experimentally, fan-beam projections were acquired by the Siemens z-flying focal spot technology, and subsequently interleaved and rebinned to a pseudopolar grid. Image quality phantoms were scanned at systematically varied mAs settings, reconstructed by EST and conventional reconstruction methods such as filtered back projection (FBP), and quantified using metrics including resolution, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs). Pediatric data sets were reconstructed at their original acquisition settings and additionally simulated to lower dose settings for comparison and evaluation of the potential for radiation dose reduction. Numerical experiments were conducted to quantify EST and other iterative methods in terms of image quality and computation time. The extension of EST to helical cone-beam CT was implemented by using the advanced single-slice rebinning (ASSR) method. Results: Based on the phantom and pediatric patient fan-beam CT data, it is demonstrated that EST reconstructions with the lowest scanner flux setting of 39 mAs produce comparable image quality, resolution, and contrast relative to FBP with the 140 mAs flux setting. Compared to the algebraic reconstruction technique and the expectation maximization statistical reconstruction algorithm, a significant reduction in computation time is achieved with EST. Finally, numerical experiments on helical cone-beam CT data suggest that the combination of EST and ASSR produces reconstructions with higher image quality and lower noise than the Feldkamp Davis and Kress (FDK) method and the conventional ASSR approach. Conclusions: A Fourier-based iterative method has been applied to the reconstruction of fan-bean CT data with reduced x-ray fluence. This method incorporates advantageous features in both real and Fourier space iterative schemes: using a fast and algebraically exact method to calculate forward projection, enforcing the measured data in Fourier space, and applying physical constraints and flexible regularization in real space. Our results suggest that EST can be utilized for radiation dose reduction in x-ray CT via the readily implementable technique of lowering mAs settings. Numerical experiments further indicate that EST requires less computation time than several other iterative algorithms and can, in principle, be extended to helical cone-beam geometry in combination with the ASSR method. PMID:23464329

Low dose reconstruction algorithm for differential phase contrast imaging.

PubMed

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

2011-01-01

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

Parametric boundary reconstruction algorithm for industrial CT metrology application.

PubMed

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

2009-01-01

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

Evaluation of Bias and Variance in Low-count OSEM List Mode Reconstruction

PubMed Central

Jian, Y; Planeta, B; Carson, R E

2016-01-01

Statistical algorithms have been widely used in PET image reconstruction. The maximum likelihood expectation maximization (MLEM) reconstruction has been shown to produce bias in applications where images are reconstructed from a relatively small number of counts. In this study, image bias and variability in low-count OSEM reconstruction are investigated on images reconstructed with MOLAR (motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction) platform. A human brain ([11C]AFM) and a NEMA phantom are used in the simulation and real experiments respectively, for the HRRT and Biograph mCT. Image reconstructions were repeated with different combination of subsets and iterations. Regions of interest (ROIs) were defined on low-activity and high-activity regions to evaluate the bias and noise at matched effective iteration numbers (iterations x subsets). Minimal negative biases and no positive biases were found at moderate count levels and less than 5% negative bias was found using extremely low levels of counts (0.2 M NEC). At any given count level, other factors, such as subset numbers and frame-based scatter correction may introduce small biases (1–5%) in the reconstructed images. The observed bias was substantially lower than that reported in the literature, perhaps due to the use of point spread function and/or other implementation methods in MOLAR. PMID:25479254

Evaluation of bias and variance in low-count OSEM list mode reconstruction

NASA Astrophysics Data System (ADS)

Jian, Y.; Planeta, B.; Carson, R. E.

2015-01-01

Statistical algorithms have been widely used in PET image reconstruction. The maximum likelihood expectation maximization reconstruction has been shown to produce bias in applications where images are reconstructed from a relatively small number of counts. In this study, image bias and variability in low-count OSEM reconstruction are investigated on images reconstructed with MOLAR (motion-compensation OSEM list-mode algorithm for resolution-recovery reconstruction) platform. A human brain ([11C]AFM) and a NEMA phantom are used in the simulation and real experiments respectively, for the HRRT and Biograph mCT. Image reconstructions were repeated with different combinations of subsets and iterations. Regions of interest were defined on low-activity and high-activity regions to evaluate the bias and noise at matched effective iteration numbers (iterations × subsets). Minimal negative biases and no positive biases were found at moderate count levels and less than 5% negative bias was found using extremely low levels of counts (0.2 M NEC). At any given count level, other factors, such as subset numbers and frame-based scatter correction may introduce small biases (1-5%) in the reconstructed images. The observed bias was substantially lower than that reported in the literature, perhaps due to the use of point spread function and/or other implementation methods in MOLAR.

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

PubMed

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

2017-10-01

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

Self-prior strategy for organ reconstruction in fluorescence molecular tomography

PubMed Central

Zhou, Yuan; Chen, Maomao; Su, Han; Luo, Jianwen

2017-01-01

The purpose of this study is to propose a strategy for organ reconstruction in fluorescence molecular tomography (FMT) without prior information from other imaging modalities, and to overcome the high cost and ionizing radiation caused by the traditional structural prior strategy. The proposed strategy is designed as an iterative architecture to solve the inverse problem of FMT. In each iteration, a short time Fourier transform (STFT) based algorithm is used to extract the self-prior information in the space-frequency energy spectrum with the assumption that the regions with higher fluorescence concentration have larger energy intensity, then the cost function of the inverse problem is modified by the self-prior information, and lastly an iterative Laplacian regularization algorithm is conducted to solve the updated inverse problem and obtains the reconstruction results. Simulations and in vivo experiments on liver reconstruction are carried out to test the performance of the self-prior strategy on organ reconstruction. The organ reconstruction results obtained by the proposed self-prior strategy are closer to the ground truth than those obtained by the iterative Tikhonov regularization (ITKR) method (traditional non-prior strategy). Significant improvements are shown in the evaluation indexes of relative locational error (RLE), relative error (RE) and contrast-to-noise ratio (CNR). The self-prior strategy improves the organ reconstruction results compared with the non-prior strategy and also overcomes the shortcomings of the traditional structural prior strategy. Various applications such as metabolic imaging and pharmacokinetic study can be aided by this strategy. PMID:29082094

Self-prior strategy for organ reconstruction in fluorescence molecular tomography.

PubMed

Zhou, Yuan; Chen, Maomao; Su, Han; Luo, Jianwen

2017-10-01

The purpose of this study is to propose a strategy for organ reconstruction in fluorescence molecular tomography (FMT) without prior information from other imaging modalities, and to overcome the high cost and ionizing radiation caused by the traditional structural prior strategy. The proposed strategy is designed as an iterative architecture to solve the inverse problem of FMT. In each iteration, a short time Fourier transform (STFT) based algorithm is used to extract the self-prior information in the space-frequency energy spectrum with the assumption that the regions with higher fluorescence concentration have larger energy intensity, then the cost function of the inverse problem is modified by the self-prior information, and lastly an iterative Laplacian regularization algorithm is conducted to solve the updated inverse problem and obtains the reconstruction results. Simulations and in vivo experiments on liver reconstruction are carried out to test the performance of the self-prior strategy on organ reconstruction. The organ reconstruction results obtained by the proposed self-prior strategy are closer to the ground truth than those obtained by the iterative Tikhonov regularization (ITKR) method (traditional non-prior strategy). Significant improvements are shown in the evaluation indexes of relative locational error (RLE), relative error (RE) and contrast-to-noise ratio (CNR). The self-prior strategy improves the organ reconstruction results compared with the non-prior strategy and also overcomes the shortcomings of the traditional structural prior strategy. Various applications such as metabolic imaging and pharmacokinetic study can be aided by this strategy.

Spatial and contrast resolution of ultralow dose dentomaxillofacial CT imaging using iterative reconstruction technology

PubMed Central

Bischel, Alexander; Stratis, Andreas; Bosmans, Hilde; Jacobs, Reinhilde; Gassner, Eva-Maria; Puelacher, Wolfgang; Pauwels, Ruben

2017-01-01

Objectives: The objective of this study was to determine how iterative reconstruction technology (IRT) influences contrast and spatial resolution in ultralow-dose dentomaxillofacial CT imaging. Methods: A polymethyl methacrylate phantom with various inserts was scanned using a reference protocol (RP) at CT dose index volume 36.56 mGy, a sinus protocol at 18.28 mGy and ultralow-dose protocols (LD) at 4.17 mGy, 2.36 mGy, 0.99 mGy and 0.53 mGy. All data sets were reconstructed using filtered back projection (FBP) and the following IRTs: adaptive statistical iterative reconstructions (ASIRs) (ASIR-50, ASIR-100) and model-based iterative reconstruction (MBIR). Inserts containing line-pair patterns and contrast detail patterns for three different materials were scored by three observers. Observer agreement was analyzed using Cohen's kappa and difference in performance between the protocols and reconstruction was analyzed with Dunn's test at α = 0.05. Results: Interobserver agreement was acceptable with a mean kappa value of 0.59. Compared with the RP using FBP, similar scores were achieved at 2.36 mGy using MBIR. MIBR reconstructions showed the highest noise suppression as well as good contrast even at the lowest doses. Overall, ASIR reconstructions did not outperform FBP. Conclusions: LD and MBIR at a dose reduction of >90% may show no significant differences in spatial and contrast resolution compared with an RP and FBP. Ultralow-dose CT and IRT should be further explored in clinical studies. PMID:28059562

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

PubMed

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

2018-05-01

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

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

PubMed

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

2003-01-01

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

Computed tomography coronary stent imaging with iterative reconstruction: a trade-off study between medium kernel and sharp kernel.

PubMed

Zhou, Qijing; Jiang, Biao; Dong, Fei; Huang, Peiyu; Liu, Hongtao; Zhang, Minming

2014-01-01

To evaluate the improvement of iterative reconstruction in image space (IRIS) technique in computed tomographic (CT) coronary stent imaging with sharp kernel, and to make a trade-off analysis. Fifty-six patients with 105 stents were examined by 128-slice dual-source CT coronary angiography (CTCA). Images were reconstructed using standard filtered back projection (FBP) and IRIS with both medium kernel and sharp kernel applied. Image noise and the stent diameter were investigated. Image noise was measured both in background vessel and in-stent lumen as objective image evaluation. Image noise score and stent score were performed as subjective image evaluation. The CTCA images reconstructed with IRIS were associated with significant noise reduction compared to that of CTCA images reconstructed using FBP technique in both of background vessel and in-stent lumen (the background noise decreased by approximately 25.4% ± 8.2% in medium kernel (P

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

PubMed

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

2018-06-01

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

A pseudo-discrete algebraic reconstruction technique (PDART) prior image-based suppression of high density artifacts in computed tomography

NASA Astrophysics Data System (ADS)

Pua, Rizza; Park, Miran; Wi, Sunhee; Cho, Seungryong

2016-12-01

We propose a hybrid metal artifact reduction (MAR) approach for computed tomography (CT) that is computationally more efficient than a fully iterative reconstruction method, but at the same time achieves superior image quality to the interpolation-based in-painting techniques. Our proposed MAR method, an image-based artifact subtraction approach, utilizes an intermediate prior image reconstructed via PDART to recover the background information underlying the high density objects. For comparison, prior images generated by total-variation minimization (TVM) algorithm, as a realization of fully iterative approach, were also utilized as intermediate images. From the simulation and real experimental results, it has been shown that PDART drastically accelerates the reconstruction to an acceptable quality of prior images. Incorporating PDART-reconstructed prior images in the proposed MAR scheme achieved higher quality images than those by a conventional in-painting method. Furthermore, the results were comparable to the fully iterative MAR that uses high-quality TVM prior images.

Reconstruction of coded aperture images

NASA Technical Reports Server (NTRS)

Bielefeld, Michael J.; Yin, Lo I.

1987-01-01

Balanced correlation method and the Maximum Entropy Method (MEM) were implemented to reconstruct a laboratory X-ray source as imaged by a Uniformly Redundant Array (URA) system. Although the MEM method has advantages over the balanced correlation method, it is computationally time consuming because of the iterative nature of its solution. Massively Parallel Processing, with its parallel array structure is ideally suited for such computations. These preliminary results indicate that it is possible to use the MEM method in future coded-aperture experiments with the help of the MPP.

3D shape reconstruction of specular surfaces by using phase measuring deflectometry

NASA Astrophysics Data System (ADS)

Zhou, Tian; Chen, Kun; Wei, Haoyun; Li, Yan

2016-10-01

The existing estimation methods for recovering height information from surface gradient are mainly divided into Modal and Zonal techniques. Since specular surfaces used in the industry always have complex and large areas, considerations must be given to both the improvement of measurement accuracy and the acceleration of on-line processing speed, which beyond the capacity of existing estimations. Incorporating the Modal and Zonal approaches into a unifying scheme, we introduce an improved 3D shape reconstruction version of specular surfaces based on Phase Measuring Deflectometry in this paper. The Modal estimation is firstly implemented to derive the coarse height information of the measured surface as initial iteration values. Then the real shape can be recovered utilizing a modified Zonal wave-front reconstruction algorithm. By combining the advantages of Modal and Zonal estimations, the proposed method simultaneously achieves consistently high accuracy and dramatically rapid convergence. Moreover, the iterative process based on an advanced successive overrelaxation technique shows a consistent rejection of measurement errors, guaranteeing the stability and robustness in practical applications. Both simulation and experimentally measurement demonstrate the validity and efficiency of the proposed improved method. According to the experimental result, the computation time decreases approximately 74.92% in contrast to the Zonal estimation and the surface error is about 6.68 μm with reconstruction points of 391×529 pixels of an experimentally measured sphere mirror. In general, this method can be conducted with fast convergence speed and high accuracy, providing an efficient, stable and real-time approach for the shape reconstruction of specular surfaces in practical situations.

Model-based iterative reconstruction and adaptive statistical iterative reconstruction: dose-reduced CT for detecting pancreatic calcification.

PubMed

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

2016-01-01

Iterative reconstruction methods have attracted attention for reducing radiation doses in computed tomography (CT). To investigate the detectability of pancreatic calcification using dose-reduced CT reconstructed with model-based iterative construction (MBIR) and adaptive statistical iterative reconstruction (ASIR). This prospective study approved by Institutional Review Board included 85 patients (57 men, 28 women; mean age, 69.9 years; mean body weight, 61.2 kg). Unenhanced CT was performed three times with different radiation doses (reference-dose CT [RDCT], low-dose CT [LDCT], ultralow-dose CT [ULDCT]). From RDCT, LDCT, and ULDCT, images were reconstructed with filtered-back projection (R-FBP, used for establishing reference standard), ASIR (L-ASIR), and MBIR and ASIR (UL-MBIR and UL-ASIR), respectively. A lesion (pancreatic calcification) detection test was performed by two blinded radiologists with a five-point certainty level scale. Dose-length products of RDCT, LDCT, and ULDCT were 410, 97, and 36 mGy-cm, respectively. Nine patients had pancreatic calcification. The sensitivity for detecting pancreatic calcification with UL-MBIR was high (0.67-0.89) compared to L-ASIR or UL-ASIR (0.11-0.44), and a significant difference was seen between UL-MBIR and UL-ASIR for one reader (P = 0.014). The area under the receiver-operating characteristic curve for UL-MBIR (0.818-0.860) was comparable to that for L-ASIR (0.696-0.844). The specificity was lower with UL-MBIR (0.79-0.92) than with L-ASIR or UL-ASIR (0.96-0.99), and a significant difference was seen for one reader (P < 0.01). In UL-MBIR, pancreatic calcification can be detected with high sensitivity, however, we should pay attention to the slightly lower specificity.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Optimization of tomographic reconstruction workflows on geographically distributed resources

DOE PAGES

Bicer, Tekin; Gursoy, Doga; Kettimuthu, Rajkumar; ...

2016-01-01

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modelingmore » of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Furthermore, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.« less

Optimization of tomographic reconstruction workflows on geographically distributed resources

PubMed Central

Bicer, Tekin; Gürsoy, Doǧa; Kettimuthu, Rajkumar; De Carlo, Francesco; Foster, Ian T.

2016-01-01

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modeling of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Moreover, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks. PMID:27359149

WE-G-207-04: Non-Local Total-Variation (NLTV) Combined with Reweighted L1-Norm for Compressed Sensing Based CT Reconstruction

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

Kim, H; Chen, J; Pouliot, J

2015-06-15

Purpose: Compressed sensing (CS) has been used for CT (4DCT/CBCT) reconstruction with few projections to reduce dose of radiation. Total-variation (TV) in L1-minimization (min.) with local information is the prevalent technique in CS, while it can be prone to noise. To address the problem, this work proposes to apply a new image processing technique, called non-local TV (NLTV), to CS based CT reconstruction, and incorporate reweighted L1-norm into it for more precise reconstruction. Methods: TV minimizes intensity variations by considering two local neighboring voxels, which can be prone to noise, possibly damaging the reconstructed CT image. NLTV, contrarily, utilizes moremore » global information by computing a weight function of current voxel relative to surrounding search area. In fact, it might be challenging to obtain an optimal solution due to difficulty in defining the weight function with appropriate parameters. Introducing reweighted L1-min., designed for approximation to ideal L0-min., can reduce the dependence on defining the weight function, therefore improving accuracy of the solution. This work implemented the NLTV combined with reweighted L1-min. by Split Bregman Iterative method. For evaluation, a noisy digital phantom and a pelvic CT images are employed to compare the quality of images reconstructed by TV, NLTV and reweighted NLTV. Results: In both cases, conventional and reweighted NLTV outperform TV min. in signal-to-noise ratio (SNR) and root-mean squared errors of the reconstructed images. Relative to conventional NLTV, NLTV with reweighted L1-norm was able to slightly improve SNR, while greatly increasing the contrast between tissues due to additional iterative reweighting process. Conclusion: NLTV min. can provide more precise compressed sensing based CT image reconstruction by incorporating the reweighted L1-norm, while maintaining greater robustness to the noise effect than TV min.« less

Optimization of tomographic reconstruction workflows on geographically distributed resources

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

Bicer, Tekin; Gursoy, Doga; Kettimuthu, Rajkumar

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modelingmore » of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Furthermore, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.« less

CT image reconstruction with half precision floating-point values.

PubMed

Maaß, Clemens; Baer, Matthias; Kachelrieß, Marc

2011-07-01

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

PET Image Reconstruction Incorporating 3D Mean-Median Sinogram Filtering

NASA Astrophysics Data System (ADS)

Mokri, S. S.; Saripan, M. I.; Rahni, A. A. Abd; Nordin, A. J.; Hashim, S.; Marhaban, M. H.

2016-02-01

Positron Emission Tomography (PET) projection data or sinogram contained poor statistics and randomness that produced noisy PET images. In order to improve the PET image, we proposed an implementation of pre-reconstruction sinogram filtering based on 3D mean-median filter. The proposed filter is designed based on three aims; to minimise angular blurring artifacts, to smooth flat region and to preserve the edges in the reconstructed PET image. The performance of the pre-reconstruction sinogram filter prior to three established reconstruction methods namely filtered-backprojection (FBP), Maximum likelihood expectation maximization-Ordered Subset (OSEM) and OSEM with median root prior (OSEM-MRP) is investigated using simulated NCAT phantom PET sinogram as generated by the PET Analytical Simulator (ASIM). The improvement on the quality of the reconstructed images with and without sinogram filtering is assessed according to visual as well as quantitative evaluation based on global signal to noise ratio (SNR), local SNR, contrast to noise ratio (CNR) and edge preservation capability. Further analysis on the achieved improvement is also carried out specific to iterative OSEM and OSEM-MRP reconstruction methods with and without pre-reconstruction filtering in terms of contrast recovery curve (CRC) versus noise trade off, normalised mean square error versus iteration, local CNR versus iteration and lesion detectability. Overall, satisfactory results are obtained from both visual and quantitative evaluations.

Model Based Iterative Reconstruction for Bright Field Electron Tomography (Postprint)

DTIC Science & Technology

2013-02-01

which is based on the iterative coordinate descent (ICD), works by constructing a substitute to the original cost4 at every point, and minimizing this...using Beer’s law. Thus the projection integral corresponding to the ith measurement is given by log ( λD λi ) . There can be cases in which the dosage λD...Inputs: Measurements g, Initial reconstruction f ′, Initial dosage d′, Fraction of entries to reject R %Outputs: Reconstruction f̂ and dosage parameter d̂

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

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

Wang, T; Zhu, L

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

Iterative optimizing quantization method for reconstructing three-dimensional images from a limited number of views

DOEpatents

Lee, H.R.

1997-11-18

A three-dimensional image reconstruction method comprises treating the object of interest as a group of elements with a size that is determined by the resolution of the projection data, e.g., as determined by the size of each pixel. One of the projections is used as a reference projection. A fictitious object is arbitrarily defined that is constrained by such reference projection. The method modifies the known structure of the fictitious object by comparing and optimizing its four projections to those of the unknown structure of the real object and continues to iterate until the optimization is limited by the residual sum of background noise. The method is composed of several sub-processes that acquire four projections from the real data and the fictitious object: generate an arbitrary distribution to define the fictitious object, optimize the four projections, generate a new distribution for the fictitious object, and enhance the reconstructed image. The sub-process for the acquisition of the four projections from the input real data is simply the function of acquiring the four projections from the data of the transmitted intensity. The transmitted intensity represents the density distribution, that is, the distribution of absorption coefficients through the object. 5 figs.

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

PubMed

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

2018-06-01

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

Novel Fourier-domain constraint for fast phase retrieval in coherent diffraction imaging.

PubMed

Latychevskaia, Tatiana; Longchamp, Jean-Nicolas; Fink, Hans-Werner

2011-09-26

Coherent diffraction imaging (CDI) for visualizing objects at atomic resolution has been realized as a promising tool for imaging single molecules. Drawbacks of CDI are associated with the difficulty of the numerical phase retrieval from experimental diffraction patterns; a fact which stimulated search for better numerical methods and alternative experimental techniques. Common phase retrieval methods are based on iterative procedures which propagate the complex-valued wave between object and detector plane. Constraints in both, the object and the detector plane are applied. While the constraint in the detector plane employed in most phase retrieval methods requires the amplitude of the complex wave to be equal to the squared root of the measured intensity, we propose a novel Fourier-domain constraint, based on an analogy to holography. Our method allows achieving a low-resolution reconstruction already in the first step followed by a high-resolution reconstruction after further steps. In comparison to conventional schemes this Fourier-domain constraint results in a fast and reliable convergence of the iterative reconstruction process. © 2011 Optical Society of America

Full dose reduction potential of statistical iterative reconstruction for head CT protocols in a predominantly pediatric population

PubMed Central

Mirro, Amy E.; Brady, Samuel L.; Kaufman, Robert. A.

2016-01-01

Purpose To implement the maximum level of statistical iterative reconstruction that can be used to establish dose-reduced head CT protocols in a primarily pediatric population. Methods Select head examinations (brain, orbits, sinus, maxilla and temporal bones) were investigated. Dose-reduced head protocols using an adaptive statistical iterative reconstruction (ASiR) were compared for image quality with the original filtered back projection (FBP) reconstructed protocols in phantom using the following metrics: image noise frequency (change in perceived appearance of noise texture), image noise magnitude, contrast-to-noise ratio (CNR), and spatial resolution. Dose reduction estimates were based on computed tomography dose index (CTDIvol) values. Patient CTDIvol and image noise magnitude were assessed in 737 pre and post dose reduced examinations. Results Image noise texture was acceptable up to 60% ASiR for Soft reconstruction kernel (at both 100 and 120 kVp), and up to 40% ASiR for Standard reconstruction kernel. Implementation of 40% and 60% ASiR led to an average reduction in CTDIvol of 43% for brain, 41% for orbits, 30% maxilla, 43% for sinus, and 42% for temporal bone protocols for patients between 1 month and 26 years, while maintaining an average noise magnitude difference of 0.1% (range: −3% to 5%), improving CNR of low contrast soft tissue targets, and improving spatial resolution of high contrast bony anatomy, as compared to FBP. Conclusion The methodology in this study demonstrates a methodology for maximizing patient dose reduction and maintaining image quality using statistical iterative reconstruction for a primarily pediatric population undergoing head CT examination. PMID:27056425

System Matrix Analysis for Computed Tomography Imaging

PubMed Central

Flores, Liubov; Vidal, Vicent; Verdú, Gumersindo

2015-01-01

In practical applications of computed tomography imaging (CT), it is often the case that the set of projection data is incomplete owing to the physical conditions of the data acquisition process. On the other hand, the high radiation dose imposed on patients is also undesired. These issues demand that high quality CT images can be reconstructed from limited projection data. For this reason, iterative methods of image reconstruction have become a topic of increased research interest. Several algorithms have been proposed for few-view CT. We consider that the accurate solution of the reconstruction problem also depends on the system matrix that simulates the scanning process. In this work, we analyze the application of the Siddon method to generate elements of the matrix and we present results based on real projection data. PMID:26575482

SPIRiT: Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space

PubMed Central

Lustig, Michael; Pauly, John M.

2010-01-01

A new approach to autocalibrating, coil-by-coil parallel imaging reconstruction is presented. It is a generalized reconstruction framework based on self consistency. The reconstruction problem is formulated as an optimization that yields the most consistent solution with the calibration and acquisition data. The approach is general and can accurately reconstruct images from arbitrary k-space sampling patterns. The formulation can flexibly incorporate additional image priors such as off-resonance correction and regularization terms that appear in compressed sensing. Several iterative strategies to solve the posed reconstruction problem in both image and k-space domain are presented. These are based on a projection over convex sets (POCS) and a conjugate gradient (CG) algorithms. Phantom and in-vivo studies demonstrate efficient reconstructions from undersampled Cartesian and spiral trajectories. Reconstructions that include off-resonance correction and nonlinear ℓ1-wavelet regularization are also demonstrated. PMID:20665790

Improved spatial resolution and lower-dose pediatric CT imaging: a feasibility study to evaluate narrowing the X-ray photon energy spectrum.

PubMed

Benz, Mark G; Benz, Matthew W; Birnbaum, Steven B; Chason, Eric; Sheldon, Brian W; McGuire, Dale

2014-08-01

This feasibility study has shown that improved spatial resolution and reduced radiation dose can be achieved in pediatric CT by narrowing the X-ray photon energy spectrum. This is done by placing a hafnium filter between the X-ray generator and a pediatric abdominal phantom. A CT system manufactured in 1999 that was in the process of being remanufactured was used as the platform for this study. This system had the advantage of easy access to the X-ray generator for modifications to change the X-ray photon energy spectrum; it also had the disadvantage of not employing the latest post-imaging noise reduction iterative reconstruction technology. Because we observed improvements after changing the X-ray photon energy spectrum, we recommend a future study combining this change with an optimized iterative reconstruction noise reduction technique.

Penalized maximum likelihood reconstruction for x-ray differential phase-contrast tomography

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

Brendel, Bernhard, E-mail: bernhard.brendel@philips.com; Teuffenbach, Maximilian von; Noël, Peter B.

2016-01-15

Purpose: The purpose of this work is to propose a cost function with regularization to iteratively reconstruct attenuation, phase, and scatter images simultaneously from differential phase contrast (DPC) acquisitions, without the need of phase retrieval, and examine its properties. Furthermore this reconstruction method is applied to an acquisition pattern that is suitable for a DPC tomographic system with continuously rotating gantry (sliding window acquisition), overcoming the severe smearing in noniterative reconstruction. Methods: We derive a penalized maximum likelihood reconstruction algorithm to directly reconstruct attenuation, phase, and scatter image from the measured detector values of a DPC acquisition. The proposed penaltymore » comprises, for each of the three images, an independent smoothing prior. Image quality of the proposed reconstruction is compared to images generated with FBP and iterative reconstruction after phase retrieval. Furthermore, the influence between the priors is analyzed. Finally, the proposed reconstruction algorithm is applied to experimental sliding window data acquired at a synchrotron and results are compared to reconstructions based on phase retrieval. Results: The results show that the proposed algorithm significantly increases image quality in comparison to reconstructions based on phase retrieval. No significant mutual influence between the proposed independent priors could be observed. Further it could be illustrated that the iterative reconstruction of a sliding window acquisition results in images with substantially reduced smearing artifacts. Conclusions: Although the proposed cost function is inherently nonconvex, it can be used to reconstruct images with less aliasing artifacts and less streak artifacts than reconstruction methods based on phase retrieval. Furthermore, the proposed method can be used to reconstruct images of sliding window acquisitions with negligible smearing artifacts.« less

Effect of time-of-flight and point spread function modeling on detectability of myocardial defects in PET

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

Schaefferkoetter, Joshua, E-mail: dnrjds@nus.edu.sg; Ouyang, Jinsong; Rakvongthai, Yothin

2014-06-15

Purpose: A study was designed to investigate the impact of time-of-flight (TOF) and point spread function (PSF) modeling on the detectability of myocardial defects. Methods: Clinical FDG-PET data were used to generate populations of defect-present and defect-absent images. Defects were incorporated at three contrast levels, and images were reconstructed by ordered subset expectation maximization (OSEM) iterative methods including ordinary Poisson, alone and with PSF, TOF, and PSF+TOF. Channelized Hotelling observer signal-to-noise ratio (SNR) was the surrogate for human observer performance. Results: For three iterations, 12 subsets, and no postreconstruction smoothing, TOF improved overall defect detection SNR by 8.6% as comparedmore » to its non-TOF counterpart for all the defect contrasts. Due to the slow convergence of PSF reconstruction, PSF yielded 4.4% less SNR than non-PSF. For reconstruction parameters (iteration number and postreconstruction smoothing kernel size) optimizing observer SNR, PSF showed larger improvement for faint defects. The combination of TOF and PSF improved mean detection SNR as compared to non-TOF and non-PSF counterparts by 3.0% and 3.2%, respectively. Conclusions: For typical reconstruction protocol used in clinical practice, i.e., less than five iterations, TOF improved defect detectability. In contrast, PSF generally yielded less detectability. For large number of iterations, TOF+PSF yields the best observer performance.« less

High-resolution reconstruction for terahertz imaging.

PubMed

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

2014-11-20

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

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI.

PubMed

Hu, Changwei; Qu, Xiaobo; Guo, Di; Bao, Lijun; Chen, Zhong

2011-09-01

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ℓ(2) data fidelity term and ℓ(1) sparsity regularization term. Rather than manually setting the regularization parameter for the ℓ(1) term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression. Copyright © 2011 Elsevier Inc. All rights reserved.

Flexibility and utility of pre-processing methods in converting STXM setups for ptychography - Final Paper

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

Fromm, Catherine

2015-08-20

Ptychography is an advanced diffraction based imaging technique that can achieve resolution of 5nm and below. It is done by scanning a sample through a beam of focused x-rays using discrete yet overlapping scan steps. Scattering data is collected on a CCD camera, and the phase of the scattered light is reconstructed with sophisticated iterative algorithms. Because the experimental setup is similar, ptychography setups can be created by retrofitting existing STXM beam lines with new hardware. The other challenge comes in the reconstruction of the collected scattering images. Scattering data must be adjusted and packaged with experimental parameters to calibratemore » the reconstruction software. The necessary pre-processing of data prior to reconstruction is unique to each beamline setup, and even the optical alignments used on that particular day. Pre-processing software must be developed to be flexible and efficient in order to allow experiments appropriate control and freedom in the analysis of their hard-won data. This paper will describe the implementation of pre-processing software which successfully connects data collection steps to reconstruction steps, letting the user accomplish accurate and reliable ptychography.« less

Ares I-X Best Estimated Trajectory Analysis and Results

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; Beck, Roger E.; Starr, Brett R.; Derry, Stephen D.; Brandon, Jay; Olds, Aaron D.

2011-01-01

The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. The Ares I-X vehicle had a number of on-board and ground based sensors that were available, including inertial measurement units, radar, air-data, and weather balloons. However, due to problems with calibrations and/or data, not all of the sensor data were used. The trajectory estimate was generated using an Iterative Extended Kalman Filter algorithm, which is an industry standard processing algorithm for filtering and estimation applications. This paper describes the methodology and results of the trajectory reconstruction process, including flight data preprocessing and input uncertainties, trajectory estimation algorithms, output transformations, and comparisons with preflight predictions.

Ares I-X Best Estimated Trajectory and Comparison with Pre-Flight Predictions

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; Beck, Roger E.; Derry, Stephen D.; Brandon, Jay M.; Starr, Brett R.; Tartabini, Paul V.; Olds, Aaron D.

2011-01-01

The Ares I-X trajectory reconstruction produced best estimated trajectories of the flight test vehicle ascent through stage separation, and of the first and upper stage entries after separation. The trajectory reconstruction process combines on-board, ground-based, and atmospheric measurements to produce the trajectory estimates. The Ares I-X vehicle had a number of on-board and ground based sensors that were available, including inertial measurement units, radar, air- data, and weather balloons. However, due to problems with calibrations and/or data, not all of the sensor data were used. The trajectory estimate was generated using an Iterative Extended Kalman Filter algorithm, which is an industry standard processing algorithm for filtering and estimation applications. This paper describes the methodology and results of the trajectory reconstruction process, including flight data preprocessing and input uncertainties, trajectory estimation algorithms, output transformations, and comparisons with preflight predictions.

Anisotropic modeling and joint-MAP stitching for improved ultrasound model-based iterative reconstruction of large and thick specimens

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

Almansouri, Hani; Venkatakrishnan, Singanallur V.; Clayton, Dwight A.

One-sided non-destructive evaluation (NDE) is widely used to inspect materials, such as concrete structures in nuclear power plants (NPP). A widely used method for one-sided NDE is the synthetic aperture focusing technique (SAFT). The SAFT algorithm produces reasonable results when inspecting simple structures. However, for complex structures, such as heavily reinforced thick concrete structures, SAFT results in artifacts and hence there is a need for a more sophisticated inversion technique. Model-based iterative reconstruction (MBIR) algorithms, which are typically equivalent to regularized inversion techniques, offer a powerful framework to incorporate complex models for the physics, detector miscalibrations and the materials beingmore » imaged to obtain high quality reconstructions. Previously, we have proposed an ultrasonic MBIR method that signifcantly improves reconstruction quality compared to SAFT. However, the method made some simplifying assumptions on the propagation model and did not disucss ways to handle data that is obtained by raster scanning a system over a surface to inspect large regions. In this paper, we propose a novel MBIR algorithm that incorporates an anisotropic forward model and allows for the joint processing of data obtained from a system that raster scans a large surface. We demonstrate that the new MBIR method can produce dramatic improvements in reconstruction quality compared to SAFT and suppresses articfacts compared to the perviously presented MBIR approach.« less

Anisotropic modeling and joint-MAP stitching for improved ultrasound model-based iterative reconstruction of large and thick specimens

NASA Astrophysics Data System (ADS)

Almansouri, Hani; Venkatakrishnan, Singanallur; Clayton, Dwight; Polsky, Yarom; Bouman, Charles; Santos-Villalobos, Hector

2018-04-01

One-sided non-destructive evaluation (NDE) is widely used to inspect materials, such as concrete structures in nuclear power plants (NPP). A widely used method for one-sided NDE is the synthetic aperture focusing technique (SAFT). The SAFT algorithm produces reasonable results when inspecting simple structures. However, for complex structures, such as heavily reinforced thick concrete structures, SAFT results in artifacts and hence there is a need for a more sophisticated inversion technique. Model-based iterative reconstruction (MBIR) algorithms, which are typically equivalent to regularized inversion techniques, offer a powerful framework to incorporate complex models for the physics, detector miscalibrations and the materials being imaged to obtain high quality reconstructions. Previously, we have proposed an ultrasonic MBIR method that signifcantly improves reconstruction quality compared to SAFT. However, the method made some simplifying assumptions on the propagation model and did not disucss ways to handle data that is obtained by raster scanning a system over a surface to inspect large regions. In this paper, we propose a novel MBIR algorithm that incorporates an anisotropic forward model and allows for the joint processing of data obtained from a system that raster scans a large surface. We demonstrate that the new MBIR method can produce dramatic improvements in reconstruction quality compared to SAFT and suppresses articfacts compared to the perviously presented MBIR approach.

Iterative reconstruction of volumetric particle distribution

NASA Astrophysics Data System (ADS)

Wieneke, Bernhard

2013-02-01

For tracking the motion of illuminated particles in space and time several volumetric flow measurement techniques are available like 3D-particle tracking velocimetry (3D-PTV) recording images from typically three to four viewing directions. For higher seeding densities and the same experimental setup, tomographic PIV (Tomo-PIV) reconstructs voxel intensities using an iterative tomographic reconstruction algorithm (e.g. multiplicative algebraic reconstruction technique, MART) followed by cross-correlation of sub-volumes computing instantaneous 3D flow fields on a regular grid. A novel hybrid algorithm is proposed here that similar to MART iteratively reconstructs 3D-particle locations by comparing the recorded images with the projections calculated from the particle distribution in the volume. But like 3D-PTV, particles are represented by 3D-positions instead of voxel-based intensity blobs as in MART. Detailed knowledge of the optical transfer function and the particle image shape is mandatory, which may differ for different positions in the volume and for each camera. Using synthetic data it is shown that this method is capable of reconstructing densely seeded flows up to about 0.05 ppp with similar accuracy as Tomo-PIV. Finally the method is validated with experimental data.

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.

Novel Fourier-based iterative reconstruction for sparse fan projection using alternating direction total variation minimization

NASA Astrophysics Data System (ADS)

Zhao, Jin; Han-Ming, Zhang; Bin, Yan; Lei, Li; Lin-Yuan, Wang; Ai-Long, Cai

2016-03-01

Sparse-view x-ray computed tomography (CT) imaging is an interesting topic in CT field and can efficiently decrease radiation dose. Compared with spatial reconstruction, a Fourier-based algorithm has advantages in reconstruction speed and memory usage. A novel Fourier-based iterative reconstruction technique that utilizes non-uniform fast Fourier transform (NUFFT) is presented in this work along with advanced total variation (TV) regularization for a fan sparse-view CT. The proposition of a selective matrix contributes to improve reconstruction quality. The new method employs the NUFFT and its adjoin to iterate back and forth between the Fourier and image space. The performance of the proposed algorithm is demonstrated through a series of digital simulations and experimental phantom studies. Results of the proposed algorithm are compared with those of existing TV-regularized techniques based on compressed sensing method, as well as basic algebraic reconstruction technique. Compared with the existing TV-regularized techniques, the proposed Fourier-based technique significantly improves convergence rate and reduces memory allocation, respectively. Projected supported by the National High Technology Research and Development Program of China (Grant No. 2012AA011603) and the National Natural Science Foundation of China (Grant No. 61372172).

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

NASA Astrophysics Data System (ADS)

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

2014-11-01

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

Quantitative proton imaging from multiple physics processes: a proof of concept

NASA Astrophysics Data System (ADS)

Bopp, C.; Rescigno, R.; Rousseau, M.; Brasse, D.

2015-07-01

Proton imaging is developed in order to improve the accuracy of charged particle therapy treatment planning. It makes it possible to directly map the relative stopping powers of the materials using the information on the energy loss of the protons. In order to reach a satisfactory spatial resolution in the reconstructed images, the position and direction of each particle is recorded upstream and downstream from the patient. As a consequence of individual proton detection, information on the transmission rate and scattering of the protons is available. Image reconstruction processes are proposed to make use of this information. A proton tomographic acquisition of an anthropomorphic head phantom was simulated. The transmission rate of the particles was used to reconstruct a map of the macroscopic cross section for nuclear interactions of the materials. A two-step iterative reconstruction process was implemented to reconstruct a map of the inverse scattering length of the materials using the scattering of the protons. Results indicate that, while the reconstruction processes should be optimized, it is possible to extract quantitative information from the transmission rate and scattering of the protons. This suggests that proton imaging could provide additional knowledge on the materials that may be of use to further improve treatment planning.

Quantitative proton imaging from multiple physics processes: a proof of concept.

PubMed

Bopp, C; Rescigno, R; Rousseau, M; Brasse, D

2015-07-07

Proton imaging is developed in order to improve the accuracy of charged particle therapy treatment planning. It makes it possible to directly map the relative stopping powers of the materials using the information on the energy loss of the protons. In order to reach a satisfactory spatial resolution in the reconstructed images, the position and direction of each particle is recorded upstream and downstream from the patient. As a consequence of individual proton detection, information on the transmission rate and scattering of the protons is available. Image reconstruction processes are proposed to make use of this information. A proton tomographic acquisition of an anthropomorphic head phantom was simulated. The transmission rate of the particles was used to reconstruct a map of the macroscopic cross section for nuclear interactions of the materials. A two-step iterative reconstruction process was implemented to reconstruct a map of the inverse scattering length of the materials using the scattering of the protons. Results indicate that, while the reconstruction processes should be optimized, it is possible to extract quantitative information from the transmission rate and scattering of the protons. This suggests that proton imaging could provide additional knowledge on the materials that may be of use to further improve treatment planning.

Influence of Sinogram-Affirmed Iterative Reconstruction on Computed Tomography-Based Lung Volumetry and Quantification of Pulmonary Emphysema.

PubMed

Baumueller, Stephan; Hilty, Regina; Nguyen, Thi Dan Linh; Weder, Walter; Alkadhi, Hatem; Frauenfelder, Thomas

2016-01-01

The purpose of this study was to evaluate the influence of sinogram-affirmed iterative reconstruction (SAFIRE) on quantification of lung volume and pulmonary emphysema in low-dose chest computed tomography compared with filtered back projection (FBP). Enhanced or nonenhanced low-dose chest computed tomography was performed in 20 patients with chronic obstructive pulmonary disease (group A) and in 20 patients without lung disease (group B). Data sets were reconstructed with FBP and SAFIRE strength levels 3 to 5. Two readers semiautomatically evaluated lung volumes and automatically quantified pulmonary emphysema, and another assessed image quality. Radiation dose parameters were recorded. Lung volume between FBP and SAFIRE 3 to 5 was not significantly different among both groups (all P > 0.05). When compared with those of FBP, total emphysema volume was significantly lower among reconstructions with SAFIRE 4 and 5 (mean difference, 0.56 and 0.79 L; all P < 0.001). There was no nondiagnostic image quality. Sinogram-affirmed iterative reconstruction does not alter lung volume measurements, although quantification of lung emphysema is affected at higher strength levels.

PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA.

PubMed

Örtenberg, A; Magnusson, M; Sandborg, M; Alm Carlsson, G; Malusek, A

2016-06-01

New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units (GPU). Despite their obvious benefits, the parallelisation of existing computer programs is not an easy task. In this work, the use of the Open Multiprocessing (OpenMP) and Open Computing Language (OpenCL) frameworks is considered for the parallelisation of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code's execution time. Selected routines were parallelised using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelisation of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelisation with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures. The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Improved bioluminescence and fluorescence reconstruction algorithms using diffuse optical tomography, normalized data, and optimized selection of the permissible source region

PubMed Central

Naser, Mohamed A.; Patterson, Michael S.

2011-01-01

Reconstruction algorithms are presented for two-step solutions of the bioluminescence tomography (BLT) and the fluorescence tomography (FT) problems. In the first step, a continuous wave (cw) diffuse optical tomography (DOT) algorithm is used to reconstruct the tissue optical properties assuming known anatomical information provided by x-ray computed tomography or other methods. Minimization problems are formed based on L1 norm objective functions, where normalized values for the light fluence rates and the corresponding Green’s functions are used. Then an iterative minimization solution shrinks the permissible regions where the sources are allowed by selecting points with higher probability to contribute to the source distribution. Throughout this process the permissible region shrinks from the entire object to just a few points. The optimum reconstructed bioluminescence and fluorescence distributions are chosen to be the results of the iteration corresponding to the permissible region where the objective function has its global minimum This provides efficient BLT and FT reconstruction algorithms without the need for a priori information about the bioluminescence sources or the fluorophore concentration. Multiple small sources and large distributed sources can be reconstructed with good accuracy for the location and the total source power for BLT and the total number of fluorophore molecules for the FT. For non-uniform distributed sources, the size and magnitude become degenerate due to the degrees of freedom available for possible solutions. However, increasing the number of data points by increasing the number of excitation sources can improve the accuracy of reconstruction for non-uniform fluorophore distributions. PMID:21326647

Image reconstruction for PET/CT scanners: past achievements and future challenges

PubMed Central

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

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

PubMed

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

2015-09-01

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

Iterative reconstruction for CT perfusion with a prior-image induced hybrid nonlocal means regularization: Phantom studies

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

Li, Bin; Lyu, Qingwen; Ma, Jianhua

2016-04-15

Purpose: In computed tomography perfusion (CTP) imaging, an initial phase CT acquired with a high-dose protocol can be used to improve the image quality of later phase CT acquired with a low-dose protocol. For dynamic regions, signals in the later low-dose CT may not be completely recovered if the initial CT heavily regularizes the iterative reconstruction process. The authors propose a hybrid nonlocal means (hNLM) regularization model for iterative reconstruction of low-dose CTP to overcome the limitation of the conventional prior-image induced penalty. Methods: The hybrid penalty was constructed by combining the NLM of the initial phase high-dose CT inmore » the stationary region and later phase low-dose CT in the dynamic region. The stationary and dynamic regions were determined by the similarity between the initial high-dose scan and later low-dose scan. The similarity was defined as a Gaussian kernel-based distance between the patch-window of the same pixel in the two scans, and its measurement was then used to weigh the influence of the initial high-dose CT. For regions with high similarity (e.g., stationary region), initial high-dose CT played a dominant role for regularizing the solution. For regions with low similarity (e.g., dynamic region), the regularization relied on a low-dose scan itself. This new hNLM penalty was incorporated into the penalized weighted least-squares (PWLS) for CTP reconstruction. Digital and physical phantom studies were performed to evaluate the PWLS-hNLM algorithm. Results: Both phantom studies showed that the PWLS-hNLM algorithm is superior to the conventional prior-image induced penalty term without considering the signal changes within the dynamic region. In the dynamic region of the Catphan phantom, the reconstruction error measured by root mean square error was reduced by 42.9% in PWLS-hNLM reconstructed image. Conclusions: The PWLS-hNLM algorithm can effectively use the initial high-dose CT to reconstruct low-dose CTP in the stationary region while reducing its influence in the dynamic region.« less

Initial evaluation of discrete orthogonal basis reconstruction of ECT images

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

Moody, E.B.; Donohue, K.D.

1996-12-31

Discrete orthogonal basis restoration (DOBR) is a linear, non-iterative, and robust method for solving inverse problems for systems characterized by shift-variant transfer functions. This simulation study evaluates the feasibility of using DOBR for reconstructing emission computed tomographic (ECT) images. The imaging system model uses typical SPECT parameters and incorporates the effects of attenuation, spatially-variant PSF, and Poisson noise in the projection process. Sample reconstructions and statistical error analyses for a class of digital phantoms compare the DOBR performance for Hartley and Walsh basis functions. Test results confirm that DOBR with either basis set produces images with good statistical properties. Nomore » problems were encountered with reconstruction instability. The flexibility of the DOBR method and its consistent performance warrants further investigation of DOBR as a means of ECT image reconstruction.« less

Temporal resolution and motion artifacts in single-source and dual-source cardiac CT.

PubMed

Schöndube, Harald; Allmendinger, Thomas; Stierstorfer, Karl; Bruder, Herbert; Flohr, Thomas

2013-03-01

The temporal resolution of a given image in cardiac computed tomography (CT) has so far mostly been determined from the amount of CT data employed for the reconstruction of that image. The purpose of this paper is to examine the applicability of such measures to the newly introduced modality of dual-source CT as well as to methods aiming to provide improved temporal resolution by means of an advanced image reconstruction algorithm. To provide a solid base for the examinations described in this paper, an extensive review of temporal resolution in conventional single-source CT is given first. Two different measures for assessing temporal resolution with respect to the amount of data involved are introduced, namely, either taking the full width at half maximum of the respective data weighting function (FWHM-TR) or the total width of the weighting function (total TR) as a base of the assessment. Image reconstruction using both a direct fan-beam filtered backprojection with Parker weighting as well as using a parallel-beam rebinning step are considered. The theory of assessing temporal resolution by means of the data involved is then extended to dual-source CT. Finally, three different advanced iterative reconstruction methods that all use the same input data are compared with respect to the resulting motion artifact level. For brevity and simplicity, the examinations are limited to two-dimensional data acquisition and reconstruction. However, all results and conclusions presented in this paper are also directly applicable to both circular and helical cone-beam CT. While the concept of total TR can directly be applied to dual-source CT, the definition of the FWHM of a weighting function needs to be slightly extended to be applicable to this modality. The three different advanced iterative reconstruction methods examined in this paper result in significantly different images with respect to their motion artifact level, despite exactly the same amount of data being used in the reconstruction process. The concept of assessing temporal resolution by means of the data employed for reconstruction can nicely be extended from single-source to dual-source CT. However, for advanced (possibly nonlinear iterative) reconstruction algorithms the examined approach fails to deliver accurate results. New methods and measures to assess the temporal resolution of CT images need to be developed to be able to accurately compare the performance of such algorithms.

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

PubMed

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

2018-06-01

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

Parallel Reconstruction Using Null Operations (PRUNO)

PubMed Central

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

2011-01-01

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

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.

Digital Model of Fourier and Fresnel Quantized Holograms

NASA Astrophysics Data System (ADS)

Boriskevich, Anatoly A.; Erokhovets, Valery K.; Tkachenko, Vadim V.

Some models schemes of Fourier and Fresnel quantized protective holograms with visual effects are suggested. The condition to arrive at optimum relationship between the quality of reconstructed images, and the coefficient of data reduction about a hologram, and quantity of iterations in the reconstructing hologram process has been estimated through computer model. Higher protection level is achieved by means of greater number both bi-dimensional secret keys (more than 2128) in form of pseudorandom amplitude and phase encoding matrixes, and one-dimensional encoding key parameters for every image of single-layer or superimposed holograms.

Demonstration of a forward iterative method to reconstruct brachytherapy seed configurations from x-ray projections

NASA Astrophysics Data System (ADS)

Murphy, Martin J.; Todor, Dorin A.

2005-06-01

By monitoring brachytherapy seed placement and determining the actual configuration of the seeds in vivo, one can optimize the treatment plan during the process of implantation. Two or more radiographic images from different viewpoints can in principle allow one to reconstruct the configuration of implanted seeds uniquely. However, the reconstruction problem is complicated by several factors: (1) the seeds can overlap and cluster in the images; (2) the images can have distortion that varies with viewpoint when a C-arm fluoroscope is used; (3) there can be uncertainty in the imaging viewpoints; (4) the angular separation of the imaging viewpoints can be small owing to physical space constraints; (5) there can be inconsistency in the number of seeds detected in the images; and (6) the patient can move while being imaged. We propose and conceptually demonstrate a novel reconstruction method that handles all of these complications and uncertainties in a unified process. The method represents the three-dimensional seed and camera configurations as parametrized models that are adjusted iteratively to conform to the observed radiographic images. The morphed model seed configuration that best reproduces the appearance of the seeds in the radiographs is the best estimate of the actual seed configuration. All of the information needed to establish both the seed configuration and the camera model is derived from the seed images without resort to external calibration fixtures. Furthermore, by comparing overall image content rather than individual seed coordinates, the process avoids the need to establish correspondence between seed identities in the several images. The method has been shown to work robustly in simulation tests that simultaneously allow for unknown individual seed positions, uncertainties in the imaging viewpoints and variable image distortion.

A diffusion-based truncated projection artifact reduction method for iterative digital breast tomosynthesis reconstruction

PubMed Central

Lu, Yao; Chan, Heang-Ping; Wei, Jun; Hadjiiski, Lubomir M

2014-01-01

Digital breast tomosynthesis (DBT) has strong promise to improve sensitivity for detecting breast cancer. DBT reconstruction estimates the breast tissue attenuation using projection views (PVs) acquired in a limited angular range. Because of the limited field of view (FOV) of the detector, the PVs may not completely cover the breast in the x-ray source motion direction at large projection angles. The voxels in the imaged volume cannot be updated when they are outside the FOV, thus causing a discontinuity in intensity across the FOV boundaries in the reconstructed slices, which we refer to as the truncated projection artifact (TPA). Most existing TPA reduction methods were developed for the filtered backprojection method in the context of computed tomography. In this study, we developed a new diffusion-based method to reduce TPAs during DBT reconstruction using the simultaneous algebraic reconstruction technique (SART). Our TPA reduction method compensates for the discontinuity in background intensity outside the FOV of the current PV after each PV updating in SART. The difference in voxel values across the FOV boundary is smoothly diffused to the region beyond the FOV of the current PV. Diffusion-based background intensity estimation is performed iteratively to avoid structured artifacts. The method is applicable to TPA in both the forward and backward directions of the PVs and for any number of iterations during reconstruction. The effectiveness of the new method was evaluated by comparing the visual quality of the reconstructed slices and the measured discontinuities across the TPA with and without artifact correction at various iterations. The results demonstrated that the diffusion-based intensity compensation method reduced the TPA while preserving the detailed tissue structures. The visibility of breast lesions obscured by the TPA was improved after artifact reduction. PMID:23318346

An iterative reconstruction of cosmological initial density fields

NASA Astrophysics Data System (ADS)

Hada, Ryuichiro; Eisenstein, Daniel J.

2018-05-01

We present an iterative method to reconstruct the linear-theory initial conditions from the late-time cosmological matter density field, with the intent of improving the recovery of the cosmic distance scale from the baryon acoustic oscillations (BAOs). We present tests using the dark matter density field in both real and redshift space generated from an N-body simulation. In redshift space at z = 0.5, we find that the reconstructed displacement field using our iterative method are more than 80% correlated with the true displacement field of the dark matter particles on scales k < 0.10h Mpc-1. Furthermore, we show that the two-point correlation function of our reconstructed density field matches that of the initial density field substantially better, especially on small scales (<40h-1 Mpc). Our redshift-space results are improved if we use an anisotropic smoothing so as to account for the reduced small-scale information along the line of sight in redshift space.

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

PubMed

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

2009-10-07

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

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

PubMed

Bindu, G; Semenov, S

2013-01-01

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

A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction

PubMed Central

Daum, Volker; Steidl, Stefan; Maier, Andreas; Köstler, Harald; Hornegger, Joachim

2015-01-01

Three-dimensional (3-D) reconstruction of histological slice sequences offers great benefits in the investigation of different morphologies. It features very high-resolution which is still unmatched by in-vivo 3-D imaging modalities, and tissue staining further enhances visibility and contrast. One important step during reconstruction is the reversal of slice deformations introduced during histological slice preparation, a process also called image unwarping. Most methods use an external reference, or rely on conservative stopping criteria during the unwarping optimization to prevent straightening of naturally curved morphology. Our approach shows that the problem of unwarping is based on the superposition of low-frequency anatomy and high-frequency errors. We present an iterative scheme that transfers the ideas of the Gauss-Seidel method to image stacks to separate the anatomy from the deformation. In particular, the scheme is universally applicable without restriction to a specific unwarping method, and uses no external reference. The deformation artifacts are effectively reduced in the resulting histology volumes, while the natural curvature of the anatomy is preserved. The validity of our method is shown on synthetic data, simulated histology data using a CT data set and real histology data. In the case of the simulated histology where the ground truth was known, the mean Target Registration Error (TRE) between the unwarped and original volume could be reduced to less than 1 pixel on average after 6 iterations of our proposed method. PMID:25312918

Model-based iterative reconstruction and adaptive statistical iterative reconstruction: dose-reduced CT for detecting pancreatic calcification

PubMed Central

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

2016-01-01

Background Iterative reconstruction methods have attracted attention for reducing radiation doses in computed tomography (CT). Purpose To investigate the detectability of pancreatic calcification using dose-reduced CT reconstructed with model-based iterative construction (MBIR) and adaptive statistical iterative reconstruction (ASIR). Material and Methods This prospective study approved by Institutional Review Board included 85 patients (57 men, 28 women; mean age, 69.9 years; mean body weight, 61.2 kg). Unenhanced CT was performed three times with different radiation doses (reference-dose CT [RDCT], low-dose CT [LDCT], ultralow-dose CT [ULDCT]). From RDCT, LDCT, and ULDCT, images were reconstructed with filtered-back projection (R-FBP, used for establishing reference standard), ASIR (L-ASIR), and MBIR and ASIR (UL-MBIR and UL-ASIR), respectively. A lesion (pancreatic calcification) detection test was performed by two blinded radiologists with a five-point certainty level scale. Results Dose-length products of RDCT, LDCT, and ULDCT were 410, 97, and 36 mGy-cm, respectively. Nine patients had pancreatic calcification. The sensitivity for detecting pancreatic calcification with UL-MBIR was high (0.67–0.89) compared to L-ASIR or UL-ASIR (0.11–0.44), and a significant difference was seen between UL-MBIR and UL-ASIR for one reader (P = 0.014). The area under the receiver-operating characteristic curve for UL-MBIR (0.818–0.860) was comparable to that for L-ASIR (0.696–0.844). The specificity was lower with UL-MBIR (0.79–0.92) than with L-ASIR or UL-ASIR (0.96–0.99), and a significant difference was seen for one reader (P < 0.01). Conclusion In UL-MBIR, pancreatic calcification can be detected with high sensitivity, however, we should pay attention to the slightly lower specificity. PMID:27110389

Application of low-tube current with iterative model reconstruction on Philips Brilliance iCT Elite FHD in the accuracy of spinal QCT using a European spine phantom.

PubMed

Wu, Yan; Jiang, Yaojun; Han, Xueli; Wang, Mingyue; Gao, Jianbo

2018-02-01

To investigate the repeatability and accuracy of quantitative CT (QCT) measurement of bone mineral density (BMD) by low-mAs using iterative model reconstruction (IMR) technique based on phantom model. European spine phantom (ESP) was selected and measured on the Philips Brilliance iCT Elite FHD machine for 10 times. Data were transmitted to the QCT PRO workstation to measure BMD (mg/cm 3 ) of the ESP (L1, L2, L3). Scanning method: the voltage of X-ray tube is 120 kV, the electric current of X-ray tube output in five respective groups A-E were: 20, 30, 40, 50 and 60 mAs. Reconstruction: all data were reconstructed using filtered back projection (FBP), IR levels of hybrid iterative reconstruction (iDose 4 , levels 1, 2, 3, 4, 5, 6 were used) and IMR (levels 1, 2, 3 were used). ROIs were placed in the middle of L1, L2 and L3 spine phantom in each group. CT values, noise and contrast-to-noise ratio (CNR) were measured and calculated. One-way analysis of variance (ANOVA) was used to compare BMD values of different mAs and different IMR. Radiation dose [volume CT dose index (CTDI vol ) and dose length product (DLP)] was positively correlated with tube current. In L1 with low BMD, different mAs in FBP showed P<0.05, indicating statistically significant BMD in ESP. In other iterative algorithms, different mAs under same iterative algorithms showed P>0.05, indicating no difference in BMD. And P>0.05 was observed among BMD of spine phantom in L1, L2 and L3 under same mAs joined with varied iterative reconstruction. The BMD in L1 varied greatly during FBP reconstruction, and less variation was observed in reconstruction of IMR [1] and IMR [2]. The BMD of L2 changed more during FBP reconstruction, where less was observed in IMR [2]. The BMD of L3 varied greatly during FBP reconstruction, and was less varied in all levels of iDose 4 and reconstruction of IMR [2]. In addition, along with continuous mAs incensement, the CNRs in various algorithms continued to increase. Among them, CNR with the FBP algorithm is the lowest, and CNR of the IMR [3] algorithm is the highest. Repeated measurements of BMD with QCT in the ESP multicenter showed that BMD changes in L1-L3 are the least varied at IMR [2] algorithm. It is recommended to scan at 120 kV with 20 mAs combined with IMR [2] algorithm. In this way, the BMD of spine by QCT could be accurately measured, while radiation dosage significantly reduced and imaging quality improved at the same time.

Motion and positional error correction for cone beam 3D-reconstruction with mobile C-arms.

PubMed

Bodensteiner, C; Darolti, C; Schumacher, H; Matthäus, L; Schweikard, A

2007-01-01

CT-images acquired by mobile C-arm devices can contain artefacts caused by positioning errors. We propose a data driven method based on iterative 3D-reconstruction and 2D/3D-registration to correct projection data inconsistencies. With a 2D/3D-registration algorithm, transformations are computed to align the acquired projection images to a previously reconstructed volume. In an iterative procedure, the reconstruction algorithm uses the results of the registration step. This algorithm also reduces small motion artefacts within 3D-reconstructions. Experiments with simulated projections from real patient data show the feasibility of the proposed method. In addition, experiments with real projection data acquired with an experimental robotised C-arm device have been performed with promising results.

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

PubMed

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

2016-09-08

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

Reconstruction of multiple-pinhole micro-SPECT data using origin ensembles.

PubMed

Lyon, Morgan C; Sitek, Arkadiusz; Metzler, Scott D; Moore, Stephen C

2016-10-01

The authors are currently developing a dual-resolution multiple-pinhole microSPECT imaging system based on three large NaI(Tl) gamma cameras. Two multiple-pinhole tungsten collimator tubes will be used sequentially for whole-body "scout" imaging of a mouse, followed by high-resolution (hi-res) imaging of an organ of interest, such as the heart or brain. Ideally, the whole-body image will be reconstructed in real time such that data need only be acquired until the area of interest can be visualized well-enough to determine positioning for the hi-res scan. The authors investigated the utility of the origin ensemble (OE) algorithm for online and offline reconstructions of the scout data. This algorithm operates directly in image space, and can provide estimates of image uncertainty, along with reconstructed images. Techniques for accelerating the OE reconstruction were also introduced and evaluated. System matrices were calculated for our 39-pinhole scout collimator design. SPECT projections were simulated for a range of count levels using the MOBY digital mouse phantom. Simulated data were used for a comparison of OE and maximum-likelihood expectation maximization (MLEM) reconstructions. The OE algorithm convergence was evaluated by calculating the total-image entropy and by measuring the counts in a volume-of-interest (VOI) containing the heart. Total-image entropy was also calculated for simulated MOBY data reconstructed using OE with various levels of parallelization. For VOI measurements in the heart, liver, bladder, and soft-tissue, MLEM and OE reconstructed images agreed within 6%. Image entropy converged after ∼2000 iterations of OE, while the counts in the heart converged earlier at ∼200 iterations of OE. An accelerated version of OE completed 1000 iterations in <9 min for a 6.8M count data set, with some loss of image entropy performance, whereas the same dataset required ∼79 min to complete 1000 iterations of conventional OE. A combination of the two methods showed decreased reconstruction time and no loss of performance when compared to conventional OE alone. OE-reconstructed images were found to be quantitatively and qualitatively similar to MLEM, yet OE also provided estimates of image uncertainty. Some acceleration of the reconstruction can be gained through the use of parallel computing. The OE algorithm is useful for reconstructing multiple-pinhole SPECT data and can be easily modified for real-time reconstruction.

Assessment of chest CT at CTDIvol less than 1 mGy with iterative reconstruction techniques.

PubMed

Padole, Atul; Digumarthy, Subba; Flores, Efren; Madan, Rachna; Mishra, Shelly; Sharma, Amita; Kalra, Mannudeep K

2017-03-01

To assess the image quality of chest CT reconstructed with image-based iterative reconstruction (SafeCT; MedicVision ® , Tirat Carmel, Israel), adaptive statistical iterative reconstruction (ASIR; GE Healthcare, Waukesha, WI) and model-based iterative reconstruction (MBIR; GE Healthcare, Waukesha, WI) techniques at CT dose index volume (CTDI vol ) <1 mGy. In an institutional review board-approved study, 25 patients gave written informed consent for acquisition of three reduced dose (0.25-, 0.4- and 0.8-mGy) chest CT after standard of care CT (8 mGy) on a 64-channel multidetector CT (MDCT) and reconstructed with SafeCT, ASIR and MBIR. Two board-certified thoracic radiologists evaluated images from the lowest to the highest dose of the reduced dose CT series and subsequently for standard of care CT. Out of the 182 detected lesions, the missed lesions were 35 at 0.25, 24 at 0.4 and 9 at 0.8 mGy with SafeCT, ASIR and MBIR, respectively. The most missed lesions were non-calcified lung nodules (NCLNs) 25/112 (<5 mm) at 0.25, 18/112 (<5 mm) at 0.4 and 3/112 (<4 mm) at 0.8 mGy. There were 78%, 84% and 97% lung nodules detected at 0.25, 0.4 and 0.8 mGy, respectively regardless of iterative reconstruction techniques (IRTs), Most mediastinum structures were not sufficiently seen at 0.25-0.8 mGy. NCLNs can be missed in chest CT at CTDI vol of <1 mGy (0.25, 0.4 and 0.8 mGy) regardless of IRTs. The most lung nodules (97%) were detected at CTDI vol of 0.8 mGy. The most mediastinum structures were not sufficiently seen at 0.25-0.8 mGy. Advances in knowledge: NCLNs can be missed regardless of IRTs in chest CT at CTDI vol of <1 mGy. The performance of ASIR, SafeCT and MBIR was similar for lung nodule detection at 0.25, 0.4 and 0.8 mGy.

SU-E-P-49: Evaluation of Image Quality and Radiation Dose of Various Unenhanced Head CT Protocols

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

Chen, L; Khan, M; Alapati, K

2015-06-15

Purpose: To evaluate the diagnostic value of various unenhanced head CT protocols and predicate acceptable radiation dose level for head CT exam. Methods: Our retrospective analysis included 3 groups, 20 patients per group, who underwent clinical routine unenhanced adult head CT examination. All exams were performed axially with 120 kVp. Three protocols, 380 mAs without iterative reconstruction and automAs, 340 mAs with iterative reconstruction without automAs, 340 mAs with iterative reconstruction and automAs, were applied on each group patients respectively. The images were reconstructed with H30, J30 for brain window and H60, J70 for bone window. Images acquired with threemore » protocols were randomized and blindly reviewed by three radiologists. A 5 point scale was used to rate each exam The percentage of exam score above 3 and average scores of each protocol were calculated for each reviewer and tissue types. Results: For protocols without automAs, the average scores of bone window with iterative reconstruction were higher than those without iterative reconstruction for each reviewer although the radiation dose was 10 percentage lower. 100 percentage exams were scored 3 or higher and the average scores were above 4 for both brain and bone reconstructions. The CTDIvols are 64.4 and 57.8 mGy of 380 and 340 mAs, respectively. With automAs, the radiation dose varied with head size, resulting in 47.5 mGy average CTDIvol between 39.5 and 56.5 mGy. 93 and 98 percentage exams were scored great than 3 for brain and bone windows, respectively. The diagnostic confidence level and image quality of exams with AutomAs were less than those without AutomAs for each reviewer. Conclusion: According to these results, the mAs was reduced to 300 with automAs OFF for head CT exam. The radiation dose was 20 percentage lower than the original protocol and the CTDIvol was reduced to 51.2 mGy.« less

WE-AB-303-09: Rapid Projection Computations for On-Board Digital Tomosynthesis in Radiation Therapy

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

Iliopoulos, AS; Sun, X; Pitsianis, N

2015-06-15

Purpose: To facilitate fast and accurate iterative volumetric image reconstruction from limited-angle on-board projections. Methods: Intrafraction motion hinders the clinical applicability of modern radiotherapy techniques, such as lung stereotactic body radiation therapy (SBRT). The LIVE system may impact clinical practice by recovering volumetric information via Digital Tomosynthesis (DTS), thus entailing low time and radiation dose for image acquisition during treatment. The DTS is estimated as a deformation of prior CT via iterative registration with on-board images; this shifts the challenge to the computational domain, owing largely to repeated projection computations across iterations. We address this issue by composing efficient digitalmore » projection operators from their constituent parts. This allows us to separate the static (projection geometry) and dynamic (volume/image data) parts of projection operations by means of pre-computations, enabling fast on-board processing, while also relaxing constraints on underlying numerical models (e.g. regridding interpolation kernels). Further decoupling the projectors into simpler ones ensures the incurred memory overhead remains low, within the capacity of a single GPU. These operators depend only on the treatment plan and may be reused across iterations and patients. The dynamic processing load is kept to a minimum and maps well to the GPU computational model. Results: We have integrated efficient, pre-computable modules for volumetric ray-casting and FDK-based back-projection with the LIVE processing pipeline. Our results show a 60x acceleration of the DTS computations, compared to the previous version, using a single GPU; presently, reconstruction is attained within a couple of minutes. The present implementation allows for significant flexibility in terms of the numerical and operational projection model; we are investigating the benefit of further optimizations and accurate digital projection sub-kernels. Conclusion: Composable projection operators constitute a versatile research tool which can greatly accelerate iterative registration algorithms and may be conducive to the clinical applicability of LIVE. National Institutes of Health Grant No. R01-CA184173; GPU donation by NVIDIA Corporation.« less

Accuracy of iodine quantification in dual-layer spectral CT: Influence of iterative reconstruction, patient habitus and tube parameters.

PubMed

Sauter, Andreas P; Kopp, Felix K; Münzel, Daniela; Dangelmaier, Julia; Renz, Martin; Renger, Bernhard; Braren, Rickmer; Fingerle, Alexander A; Rummeny, Ernst J; Noël, Peter B

2018-05-01

Evaluation of the influence of iterative reconstruction, tube settings and patient habitus on the accuracy of iodine quantification with dual-layer spectral CT (DL-CT). A CT abdomen phantom with different extension rings and four iodine inserts (1, 2, 5 and 10 mg/ml) was scanned on a DL-CT. The phantom was scanned with tube-voltages of 120 and 140 kVp and CTDI vol of 2.5, 5, 10 and 20 mGy. Reconstructions were performed for eight levels of iterative reconstruction (i0-i7). Diagnostic dose levels are classified depending on patient-size and radiation dose. Measurements of iodine concentration showed accurate and reliable results. Taking all CTDI vol -levels into account, the mean absolute percentage difference (MAPD) showed less accuracy for low CTDI vol -levels (2.5 mGy: 34.72%) than for high CTDI vol -levels (20 mGy: 5.89%). At diagnostic dose levels, accurate quantification of iodine was possible (MAPD 3.38%). Level of iterative reconstruction did not significantly influence iodine measurements. Iodine quantification worked more accurately at a tube voltage of 140 kVp. Phantom size had a considerable effect only at low-dose-levels; at diagnostic dose levels the effect of phantom size decreased (MAPD <5% for all phantom sizes). With DL-CT, even low iodine concentrations can be accurately quantified. Accuracies are higher when diagnostic radiation doses are employed. Copyright © 2018 Elsevier B.V. All rights reserved.

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

PubMed Central

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

2017-01-01

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

SU-D-201-05: Phantom Study to Determine Optimal PET Reconstruction Parameters for PET/MR Imaging of Y-90 Microspheres Following Radioembolization

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

Maughan, N; Conti, M; Parikh, P

2015-06-15

Purpose: Imaging Y-90 microspheres with PET/MRI following hepatic radioembolization has the potential for predicting treatment outcome and, in turn, improving patient care. The positron decay branching ratio, however, is very small (32 ppm), yielding images with poor statistics even when therapy doses are used. Our purpose is to find PET reconstruction parameters that maximize the PET recovery coefficients and minimize noise. Methods: An initial 7.5 GBq of Y-90 chloride solution was used to fill an ACR phantom for measurements with a PET/MRI scanner (Siemens Biograph mMR). Four hot cylinders and a warm background activity volume of the phantom were filledmore » with a 10:1 ratio. Phantom attenuation maps were derived from scaled CT images of the phantom and included the MR phased array coil. The phantom was imaged at six time points between 7.5–1.0 GBq total activity over a period of eight days. PET images were reconstructed via OP-OSEM with 21 subsets and varying iteration number (1–5), post-reconstruction filter size (5–10 mm), and either absolute or relative scatter correction. Recovery coefficients, SNR, and noise were measured as well as total activity in the phantom. Results: For the 120 different reconstructions, recovery coefficients ranged from 0.1–0.6 and improved with increasing iteration number and reduced post-reconstruction filter size. SNR, however, improved substantially with lower iteration numbers and larger post-reconstruction filters. From the phantom data, we found that performing 2 iterations, 21 subsets, and applying a 5 mm Gaussian post-reconstruction filter provided optimal recovery coefficients at a moderate noise level for a wide range of activity levels. Conclusion: The choice of reconstruction parameters for Y-90 PET images greatly influences both the accuracy of measurements and image quality. We have found reconstruction parameters that provide optimal recovery coefficients with minimized noise. Future work will include the effects of the body matrix coil and off-center measurements.« less

Implementation of the pyramid wavefront sensor as a direct phase detector for large amplitude aberrations

NASA Astrophysics Data System (ADS)

Kupke, Renate; Gavel, Don; Johnson, Jess; Reinig, Marc

2008-07-01

We investigate the non-modulating pyramid wave-front sensor's (P-WFS) implementation in the context of Lick Observatory's Villages visible light AO system on the Nickel 1-meter telescope. A complete adaptive optics correction, using a non-modulated P-WFS in slope sensing mode as a boot-strap to a regime in which the P-WFS can act as a direct phase sensor is explored. An iterative approach to reconstructing the wave-front phase, given the pyramid wave-front sensor's non-linear signal, is developed. Using Monte Carlo simulations, the iterative reconstruction method's photon noise propagation behavior is compared to both the pyramid sensor used in slope-sensing mode, and the traditional Shack Hartmann sensor's theoretical performance limits. We determine that bootstrapping using the P-WFS as a slope sensor does not offer enough correction to bring the phase residuals into a regime in which the iterative algorithm can provide much improvement in phase measurement. It is found that both the iterative phase reconstructor and the slope reconstruction methods offer an advantage in noise propagation over Shack Hartmann sensors.

A frequency dependent preconditioned wavelet method for atmospheric tomography

NASA Astrophysics Data System (ADS)

Yudytskiy, Mykhaylo; Helin, Tapio; Ramlau, Ronny

2013-12-01

Atmospheric tomography, i.e. the reconstruction of the turbulence in the atmosphere, is a main task for the adaptive optics systems of the next generation telescopes. For extremely large telescopes, such as the European Extremely Large Telescope, this problem becomes overly complex and an efficient algorithm is needed to reduce numerical costs. Recently, a conjugate gradient method based on wavelet parametrization of turbulence layers was introduced [5]. An iterative algorithm can only be numerically efficient when the number of iterations required for a sufficient reconstruction is low. A way to achieve this is to design an efficient preconditioner. In this paper we propose a new frequency-dependent preconditioner for the wavelet method. In the context of a multi conjugate adaptive optics (MCAO) system simulated on the official end-to-end simulation tool OCTOPUS of the European Southern Observatory we demonstrate robustness and speed of the preconditioned algorithm. We show that three iterations are sufficient for a good reconstruction.

On iterative algorithms for quantitative photoacoustic tomography in the radiative transport regime

NASA Astrophysics Data System (ADS)

Wang, Chao; Zhou, Tie

2017-11-01

In this paper, we present a numerical reconstruction method for quantitative photoacoustic tomography (QPAT), based on the radiative transfer equation (RTE), which models light propagation more accurately than diffusion approximation (DA). We investigate the reconstruction of absorption coefficient and scattering coefficient of biological tissues. An improved fixed-point iterative method to retrieve the absorption coefficient, given the scattering coefficient, is proposed for its cheap computational cost; the convergence of this method is also proved. The Barzilai-Borwein (BB) method is applied to retrieve two coefficients simultaneously. Since the reconstruction of optical coefficients involves the solutions of original and adjoint RTEs in the framework of optimization, an efficient solver with high accuracy is developed from Gao and Zhao (2009 Transp. Theory Stat. Phys. 38 149-92). Simulation experiments illustrate that the improved fixed-point iterative method and the BB method are competitive methods for QPAT in the relevant cases.

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.

Cardiac-gated parametric images from 82 Rb PET from dynamic frames and direct 4D reconstruction.

PubMed

Germino, Mary; Carson, Richard E

2018-02-01

Cardiac perfusion PET data can be reconstructed as a dynamic sequence and kinetic modeling performed to quantify myocardial blood flow, or reconstructed as static gated images to quantify function. Parametric images from dynamic PET are conventionally not gated, to allow use of all events with lower noise. An alternative method for dynamic PET is to incorporate the kinetic model into the reconstruction algorithm itself, bypassing the generation of a time series of emission images and directly producing parametric images. So-called "direct reconstruction" can produce parametric images with lower noise than the conventional method because the noise distribution is more easily modeled in projection space than in image space. In this work, we develop direct reconstruction of cardiac-gated parametric images for 82 Rb PET with an extension of the Parametric Motion compensation OSEM List mode Algorithm for Resolution-recovery reconstruction for the one tissue model (PMOLAR-1T). PMOLAR-1T was extended to accommodate model terms to account for spillover from the left and right ventricles into the myocardium. The algorithm was evaluated on a 4D simulated 82 Rb dataset, including a perfusion defect, as well as a human 82 Rb list mode acquisition. The simulated list mode was subsampled into replicates, each with counts comparable to one gate of a gated acquisition. Parametric images were produced by the indirect (separate reconstructions and modeling) and direct methods for each of eight low-count and eight normal-count replicates of the simulated data, and each of eight cardiac gates for the human data. For the direct method, two initialization schemes were tested: uniform initialization, and initialization with the filtered iteration 1 result of the indirect method. For the human dataset, event-by-event respiratory motion compensation was included. The indirect and direct methods were compared for the simulated dataset in terms of bias and coefficient of variation as a function of iteration. Convergence of direct reconstruction was slow with uniform initialization; lower bias was achieved in fewer iterations by initializing with the filtered indirect iteration 1 images. For most parameters and regions evaluated, the direct method achieved the same or lower absolute bias at matched iteration as the indirect method, with 23%-65% lower noise. Additionally, the direct method gave better contrast between the perfusion defect and surrounding normal tissue than the indirect method. Gated parametric images from the human dataset had comparable relative performance of indirect and direct, in terms of mean parameter values per iteration. Changes in myocardial wall thickness and blood pool size across gates were readily visible in the gated parametric images, with higher contrast between myocardium and left ventricle blood pool in parametric images than gated SUV images. Direct reconstruction can produce parametric images with less noise than the indirect method, opening the potential utility of gated parametric imaging for perfusion PET. © 2017 American Association of Physicists in Medicine.

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

PubMed Central

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

2017-01-01

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

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

PubMed Central

Bindu, G.; Semenov, S.

2013-01-01

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

View-interpolation of sparsely sampled sinogram using convolutional neural network

NASA Astrophysics Data System (ADS)

Lee, Hoyeon; Lee, Jongha; Cho, Suengryong

2017-02-01

Spare-view sampling and its associated iterative image reconstruction in computed tomography have actively investigated. Sparse-view CT technique is a viable option to low-dose CT, particularly in cone-beam CT (CBCT) applications, with advanced iterative image reconstructions with varying degrees of image artifacts. One of the artifacts that may occur in sparse-view CT is the streak artifact in the reconstructed images. Another approach has been investigated for sparse-view CT imaging by use of the interpolation methods to fill in the missing view data and that reconstructs the image by an analytic reconstruction algorithm. In this study, we developed an interpolation method using convolutional neural network (CNN), which is one of the widely used deep-learning methods, to find missing projection data and compared its performances with the other interpolation techniques.

Image quality of CT angiography in young children with congenital heart disease: a comparison between the sinogram-affirmed iterative reconstruction (SAFIRE) and advanced modelled iterative reconstruction (ADMIRE) algorithms.

PubMed

Nam, S B; Jeong, D W; Choo, K S; Nam, K J; Hwang, J-Y; Lee, J W; Kim, J Y; Lim, S J

2017-12-01

To compare the image quality of computed tomography angiography (CTA) reconstructed by sinogram-affirmed iterative reconstruction (SAFIRE) with that of advanced modelled iterative reconstruction (ADMIRE) in children with congenital heart disease (CHD). Thirty-one children (8.23±13.92 months) with CHD who underwent CTA were enrolled. Images were reconstructed using SAFIRE (strength 5) and ADMIRE (strength 5). Objective image qualities (attenuation, noise) were measured in the great vessels and heart chambers. Two radiologists independently calculated the contrast-to-noise ratio (CNR) by measuring the intensity and noise of the myocardial walls. Subjective noise, diagnostic confidence, and sharpness at the level prior to the first branch of the main pulmonary artery were also graded by the two radiologists independently. The objective image noise of ADMIRE was significantly lower than that of SAFIRE in the right atrium, right ventricle, and myocardial wall (p<0.05); however, there were no significant differences observed in the attenuations among the four chambers and great vessels, except in the pulmonary arteries (p>0.05). The mean CNR values were 21.56±10.80 for ADMIRE and 18.21±6.98 for SAFIRE, which were significantly different (p<0.05). In addition, the diagnostic confidence of ADMIRE was significantly lower than that of SAFIRE (p<0.05), while the subjective image noise and sharpness of ADMIRE were not significantly different (p>0.05). CTA using ADMIRE was superior to SAFIRE when comparing the objective and subjective image quality in children with CHD. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

The Detection of Focal Liver Lesions Using Abdominal CT: A Comparison of Image Quality Between Adaptive Statistical Iterative Reconstruction V and Adaptive Statistical Iterative Reconstruction.

PubMed

Lee, Sangyun; Kwon, Heejin; Cho, Jihan

2016-12-01

To investigate image quality characteristics of abdominal computed tomography (CT) scans reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) vs currently using applied adaptive statistical iterative reconstruction (ASIR). This institutional review board-approved study included 35 consecutive patients who underwent CT of the abdomen. Among these 35 patients, 27 with focal liver lesions underwent abdomen CT with a 128-slice multidetector unit using the following parameters: fixed noise index of 30, 1.25 mm slice thickness, 120 kVp, and a gantry rotation time of 0.5 seconds. CT images were analyzed depending on the method of reconstruction: ASIR (30%, 50%, and 70%) vs ASIR-V (30%, 50%, and 70%). Three radiologists independently assessed randomized images in a blinded manner. Imaging sets were compared to focal lesion detection numbers, overall image quality, and objective noise with a paired sample t test. Interobserver agreement was assessed with the intraclass correlation coefficient. The detection of small focal liver lesions (<10 mm) was significantly higher when ASIR-V was used when compared to ASIR (P <0.001). Subjective image noise, artifact, and objective image noise in liver were generally significantly better for ASIR-V compared to ASIR, especially in 50% ASIR-V. Image sharpness and diagnostic acceptability were significantly worse in 70% ASIR-V compared to various levels of ASIR. Images analyzed using 50% ASIR-V were significantly better than three different series of ASIR or other ASIR-V conditions at providing diagnostically acceptable CT scans without compromising image quality and in the detection of focal liver lesions. Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Method for position emission mammography image reconstruction

DOEpatents

Smith, Mark Frederick

2004-10-12

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

Improved dose-volume histogram estimates for radiopharmaceutical therapy by optimizing quantitative SPECT reconstruction parameters

NASA Astrophysics Data System (ADS)

Cheng, Lishui; Hobbs, Robert F.; Segars, Paul W.; Sgouros, George; Frey, Eric C.

2013-06-01

In radiopharmaceutical therapy, an understanding of the dose distribution in normal and target tissues is important for optimizing treatment. Three-dimensional (3D) dosimetry takes into account patient anatomy and the nonuniform uptake of radiopharmaceuticals in tissues. Dose-volume histograms (DVHs) provide a useful summary representation of the 3D dose distribution and have been widely used for external beam treatment planning. Reliable 3D dosimetry requires an accurate 3D radioactivity distribution as the input. However, activity distribution estimates from SPECT are corrupted by noise and partial volume effects (PVEs). In this work, we systematically investigated OS-EM based quantitative SPECT (QSPECT) image reconstruction in terms of its effect on DVHs estimates. A modified 3D NURBS-based Cardiac-Torso (NCAT) phantom that incorporated a non-uniform kidney model and clinically realistic organ activities and biokinetics was used. Projections were generated using a Monte Carlo (MC) simulation; noise effects were studied using 50 noise realizations with clinical count levels. Activity images were reconstructed using QSPECT with compensation for attenuation, scatter and collimator-detector response (CDR). Dose rate distributions were estimated by convolution of the activity image with a voxel S kernel. Cumulative DVHs were calculated from the phantom and QSPECT images and compared both qualitatively and quantitatively. We found that noise, PVEs, and ringing artifacts due to CDR compensation all degraded histogram estimates. Low-pass filtering and early termination of the iterative process were needed to reduce the effects of noise and ringing artifacts on DVHs, but resulted in increased degradations due to PVEs. Large objects with few features, such as the liver, had more accurate histogram estimates and required fewer iterations and more smoothing for optimal results. Smaller objects with fine details, such as the kidneys, required more iterations and less smoothing at early time points post-radiopharmaceutical administration but more smoothing and fewer iterations at later time points when the total organ activity was lower. The results of this study demonstrate the importance of using optimal reconstruction and regularization parameters. Optimal results were obtained with different parameters at each time point, but using a single set of parameters for all time points produced near-optimal dose-volume histograms.

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

NASA Astrophysics Data System (ADS)

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

1998-06-01

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

Adaptive Statistical Iterative Reconstruction-V: Impact on Image Quality in Ultralow-Dose Coronary Computed Tomography Angiography.

PubMed

Benz, Dominik C; Gräni, Christoph; Mikulicic, Fran; Vontobel, Jan; Fuchs, Tobias A; Possner, Mathias; Clerc, Olivier F; Stehli, Julia; Gaemperli, Oliver; Pazhenkottil, Aju P; Buechel, Ronny R; Kaufmann, Philipp A

The clinical utility of a latest generation iterative reconstruction algorithm (adaptive statistical iterative reconstruction [ASiR-V]) has yet to be elucidated for coronary computed tomography angiography (CCTA). This study evaluates the impact of ASiR-V on signal, noise and image quality in CCTA. Sixty-five patients underwent clinically indicated CCTA on a 256-slice CT scanner using an ultralow-dose protocol. Data sets from each patient were reconstructed at 6 different levels of ASiR-V. Signal intensity was measured by placing a region of interest in the aortic root, LMA, and RCA. Similarly, noise was measured in the aortic root. Image quality was visually assessed by 2 readers. Median radiation dose was 0.49 mSv. Image noise decreased with increasing levels of ASiR-V resulting in a significant increase in signal-to-noise ratio in the RCA and LMA (P < 0.001). Correspondingly, image quality significantly increased with higher levels of ASiR-V (P < 0.001). ASiR-V yields substantial noise reduction and improved image quality enabling introduction of ultralow-dose CCTA.

Parameter selection with the Hotelling observer in linear iterative image reconstruction for breast tomosynthesis

NASA Astrophysics Data System (ADS)

Rose, Sean D.; Roth, Jacob; Zimmerman, Cole; Reiser, Ingrid; Sidky, Emil Y.; Pan, Xiaochuan

2018-03-01

In this work we investigate an efficient implementation of a region-of-interest (ROI) based Hotelling observer (HO) in the context of parameter optimization for detection of a rod signal at two orientations in linear iterative image reconstruction for DBT. Our preliminary results suggest that ROI-HO performance trends may be efficiently estimated by modeling only the 2D plane perpendicular to the detector and containing the X-ray source trajectory. In addition, the ROI-HO is seen to exhibit orientation dependent trends in detectability as a function of the regularization strength employed in reconstruction. To further investigate the ROI-HO performance in larger 3D system models, we present and validate an iterative methodology for calculating the ROI-HO. Lastly, we present a real data study investigating the correspondence between ROI-HO performance trends and signal conspicuity. Conspicuity of signals in real data reconstructions is seen to track well with trends in ROI-HO detectability. In particular, we observe orientation dependent conspicuity matching the orientation dependent detectability of the ROI-HO.

Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in quantifying coronary calcium.

PubMed

Takahashi, Masahiro; Kimura, Fumiko; Umezawa, Tatsuya; Watanabe, Yusuke; Ogawa, Harumi

2016-01-01

Adaptive statistical iterative reconstruction (ASIR) has been used to reduce radiation dose in cardiac computed tomography. However, change of image parameters by ASIR as compared to filtered back projection (FBP) may influence quantification of coronary calcium. To investigate the influence of ASIR on calcium quantification in comparison to FBP. In 352 patients, CT images were reconstructed using FBP alone, FBP combined with ASIR 30%, 50%, 70%, and ASIR 100% based on the same raw data. Image noise, plaque density, Agatston scores and calcium volumes were compared among the techniques. Image noise, Agatston score, and calcium volume decreased significantly with ASIR compared to FBP (each P < 0.001). Use of ASIR reduced Agatston score by 10.5% to 31.0%. In calcified plaques both of patients and a phantom, ASIR decreased maximum CT values and calcified plaque size. In comparison to FBP, adaptive statistical iterative reconstruction (ASIR) may significantly decrease Agatston scores and calcium volumes. Copyright © 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Iterative image reconstruction that includes a total variation regularization for radial MRI.

PubMed

Kojima, Shinya; Shinohara, Hiroyuki; Hashimoto, Takeyuki; Hirata, Masami; Ueno, Eiko

2015-07-01

This paper presents an iterative image reconstruction method for radial encodings in MRI based on a total variation (TV) regularization. The algebraic reconstruction method combined with total variation regularization (ART_TV) is implemented with a regularization parameter specifying the weight of the TV term in the optimization process. We used numerical simulations of a Shepp-Logan phantom, as well as experimental imaging of a phantom that included a rectangular-wave chart, to evaluate the performance of ART_TV, and to compare it with that of the Fourier transform (FT) method. The trade-off between spatial resolution and signal-to-noise ratio (SNR) was investigated for different values of the regularization parameter by experiments on a phantom and a commercially available MRI system. ART_TV was inferior to the FT with respect to the evaluation of the modulation transfer function (MTF), especially at high frequencies; however, it outperformed the FT with regard to the SNR. In accordance with the results of SNR measurement, visual impression suggested that the image quality of ART_TV was better than that of the FT for reconstruction of a noisy image of a kiwi fruit. In conclusion, ART_TV provides radial MRI with improved image quality for low-SNR data; however, the regularization parameter in ART_TV is a critical factor for obtaining improvement over the FT.

Progressive compressive imager

NASA Astrophysics Data System (ADS)

Evladov, Sergei; Levi, Ofer; Stern, Adrian

2012-06-01

We have designed and built a working automatic progressive sampling imaging system based on the vector sensor concept, which utilizes a unique sampling scheme of Radon projections. This sampling scheme makes it possible to progressively add information resulting in tradeoff between compression and the quality of reconstruction. The uniqueness of our sampling is that in any moment of the acquisition process the reconstruction can produce a reasonable version of the image. The advantage of the gradual addition of the samples is seen when the sparsity rate of the object is unknown, and thus the number of needed measurements. We have developed the iterative algorithm OSO (Ordered Sets Optimization) which employs our sampling scheme for creation of nearly uniform distributed sets of samples, which allows the reconstruction of Mega-Pixel images. We present the good quality reconstruction from compressed data ratios of 1:20.

Accelerating electron tomography reconstruction algorithm ICON with GPU.

PubMed

Chen, Yu; Wang, Zihao; Zhang, Jingrong; Li, Lun; Wan, Xiaohua; Sun, Fei; Zhang, Fa

2017-01-01

Electron tomography (ET) plays an important role in studying in situ cell ultrastructure in three-dimensional space. Due to limited tilt angles, ET reconstruction always suffers from the "missing wedge" problem. With a validation procedure, iterative compressed-sensing optimized NUFFT reconstruction (ICON) demonstrates its power in the restoration of validated missing information for low SNR biological ET dataset. However, the huge computational demand has become a major problem for the application of ICON. In this work, we analyzed the framework of ICON and classified the operations of major steps of ICON reconstruction into three types. Accordingly, we designed parallel strategies and implemented them on graphics processing units (GPU) to generate a parallel program ICON-GPU. With high accuracy, ICON-GPU has a great acceleration compared to its CPU version, up to 83.7×, greatly relieving ICON's dependence on computing resource.

A multiplicative regularization for force reconstruction

NASA Astrophysics Data System (ADS)

Aucejo, M.; De Smet, O.

2017-02-01

Additive regularizations, such as Tikhonov-like approaches, are certainly the most popular methods for reconstructing forces acting on a structure. These approaches require, however, the knowledge of a regularization parameter, that can be numerically computed using specific procedures. Unfortunately, these procedures are generally computationally intensive. For this particular reason, it could be of primary interest to propose a method able to proceed without defining any regularization parameter beforehand. In this paper, a multiplicative regularization is introduced for this purpose. By construction, the regularized solution has to be calculated in an iterative manner. In doing so, the amount of regularization is automatically adjusted throughout the resolution process. Validations using synthetic and experimental data highlight the ability of the proposed approach in providing consistent reconstructions.

Holographic particle size extraction by using Wigner-Ville distribution

NASA Astrophysics Data System (ADS)

Chuamchaitrakool, Porntip; Widjaja, Joewono; Yoshimura, Hiroyuki

2014-06-01

A new method for measuring object size from in-line holograms by using Wigner-Ville distribution (WVD) is proposed. The proposed method has advantages over conventional numerical reconstruction in that it is free from iterative process and it can extract the object size and position with only single computation of the WVD. Experimental verification of the proposed method is presented.

Reconciling the Knowledge of Scholars & Practitioners: An Extended Case Analysis of the Role of Theory in Student Affairs

ERIC Educational Resources Information Center

Kimball, Ezekiel

2016-01-01

This paper utilizes a critical post-pragmatist epistemological lens in tandem with an extended case analysis to explore how student affairs professionals process truth claims related to student experience. Findings from the study, which include the limited usage of formal theory and the iterative reconstruction of informal theory, are used to…

Impact of view reduction in CT on radiation dose for patients

NASA Astrophysics Data System (ADS)

Parcero, E.; Flores, L.; Sánchez, M. G.; Vidal, V.; Verdú, G.

2017-08-01

Iterative methods have become a hot topic of research in computed tomography (CT) imaging because of their capacity to resolve the reconstruction problem from a limited number of projections. This allows the reduction of radiation exposure on patients during the data acquisition. The reconstruction time and the high radiation dose imposed on patients are the two major drawbacks in CT. To solve them effectively we adapted the method for sparse linear equations and sparse least squares (LSQR) with soft threshold filtering (STF) and the fast iterative shrinkage-thresholding algorithm (FISTA) to computed tomography reconstruction. The feasibility of the proposed methods is demonstrated numerically.

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

PubMed

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

2014-05-01

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

Penalized weighted least-squares approach for low-dose x-ray computed tomography

NASA Astrophysics Data System (ADS)

Wang, Jing; Li, Tianfang; Lu, Hongbing; Liang, Zhengrong

2006-03-01

The noise of low-dose computed tomography (CT) sinogram follows approximately a Gaussian distribution with nonlinear dependence between the sample mean and variance. The noise is statistically uncorrelated among detector bins at any view angle. However the correlation coefficient matrix of data signal indicates a strong signal correlation among neighboring views. Based on above observations, Karhunen-Loeve (KL) transform can be used to de-correlate the signal among the neighboring views. In each KL component, a penalized weighted least-squares (PWLS) objective function can be constructed and optimal sinogram can be estimated by minimizing the objective function, followed by filtered backprojection (FBP) for CT image reconstruction. In this work, we compared the KL-PWLS method with an iterative image reconstruction algorithm, which uses the Gauss-Seidel iterative calculation to minimize the PWLS objective function in image domain. We also compared the KL-PWLS with an iterative sinogram smoothing algorithm, which uses the iterated conditional mode calculation to minimize the PWLS objective function in sinogram space, followed by FBP for image reconstruction. Phantom experiments show a comparable performance of these three PWLS methods in suppressing the noise-induced artifacts and preserving resolution in reconstructed images. Computer simulation concurs with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS noise reduction may have the advantage in computation for low-dose CT imaging, especially for dynamic high-resolution studies.

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

PubMed

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

2015-05-01

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

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

PubMed

Debatin, Maurice; Hesser, Jürgen

2015-01-01

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

Accurate tissue characterization in low-dose CT imaging with pure iterative reconstruction.

PubMed

Murphy, Kevin P; McLaughlin, Patrick D; Twomey, Maria; Chan, Vincent E; Moloney, Fiachra; Fung, Adrian J; Chan, Faimee E; Kao, Tafline; O'Neill, Siobhan B; Watson, Benjamin; O'Connor, Owen J; Maher, Michael M

2017-04-01

We assess the ability of low-dose hybrid iterative reconstruction (IR) and 'pure' model-based IR (MBIR) images to maintain accurate Hounsfield unit (HU)-determined tissue characterization. Standard-protocol (SP) and low-dose modified-protocol (MP) CTs were contemporaneously acquired in 34 Crohn's disease patients referred for CT. SP image reconstruction was via the manufacturer's recommendations (60% FBP, filtered back projection; 40% ASiR, Adaptive Statistical iterative Reconstruction; SP-ASiR40). MP data sets underwent four reconstructions (100% FBP; 40% ASiR; 70% ASiR; MBIR). Three observers measured tissue volumes using HU thresholds for fat, soft tissue and bone/contrast on each data set. Analysis was via SPSS. Inter-observer agreement was strong for 1530 datapoints (rs > 0.9). MP-MBIR tissue volume measurement was superior to other MP reconstructions and closely correlated with the reference SP-ASiR40 images for all tissue types. MP-MBIR superiority was most marked for fat volume calculation - close SP-ASiR40 and MP-MBIR Bland-Altman plot correlation was seen with the lowest average difference (336 cm 3 ) when compared with other MP reconstructions. Hounsfield unit-determined tissue volume calculations from MP-MBIR images resulted in values comparable to SP-ASiR40 calculations and values that are superior to MP-ASiR images. Accuracy of estimation of volume of tissues (e.g. fat) using segmentation software on low-dose CT images appears optimal when reconstructed with pure IR. © 2016 The Royal Australian and New Zealand College of Radiologists.

Low-dose 4D cardiac imaging in small animals using dual source micro-CT

NASA Astrophysics Data System (ADS)

Holbrook, M.; Clark, D. P.; Badea, C. T.

2018-01-01

Micro-CT is widely used in preclinical studies, generating substantial interest in extending its capabilities in functional imaging applications such as blood perfusion and cardiac function. However, imaging cardiac structure and function in mice is challenging due to their small size and rapid heart rate. To overcome these challenges, we propose and compare improvements on two strategies for cardiac gating in dual-source, preclinical micro-CT: fast prospective gating (PG) and uncorrelated retrospective gating (RG). These sampling strategies combined with a sophisticated iterative image reconstruction algorithm provide faster acquisitions and high image quality in low-dose 4D (i.e. 3D  +  Time) cardiac micro-CT. Fast PG is performed under continuous subject rotation which results in interleaved projection angles between cardiac phases. Thus, fast PG provides a well-sampled temporal average image for use as a prior in iterative reconstruction. Uncorrelated RG incorporates random delays during sampling to prevent correlations between heart rate and sampling rate. We have performed both simulations and animal studies to validate these new sampling protocols. Sampling times for 1000 projections using fast PG and RG were 2 and 3 min, respectively, and the total dose was 170 mGy each. Reconstructions were performed using a 4D iterative reconstruction technique based on the split Bregman method. To examine undersampling robustness, subsets of 500 and 250 projections were also used for reconstruction. Both sampling strategies in conjunction with our iterative reconstruction method are capable of resolving cardiac phases and provide high image quality. In general, for equal numbers of projections, fast PG shows fewer errors than RG and is more robust to undersampling. Our results indicate that only 1000-projection based reconstruction with fast PG satisfies a 5% error criterion in left ventricular volume estimation. These methods promise low-dose imaging with a wide range of preclinical applications in cardiac imaging.

Quantitative X-ray fluorescence computed tomography for low-Z samples using an iterative absorption correction algorithm

NASA Astrophysics Data System (ADS)

Huang, Rong; Limburg, Karin; Rohtla, Mehis

2017-05-01

X-ray fluorescence computed tomography is often used to measure trace element distributions within low-Z samples, using algorithms capable of X-ray absorption correction when sample self-absorption is not negligible. Its reconstruction is more complicated compared to transmission tomography, and therefore not widely used. We describe in this paper a very practical iterative method that uses widely available transmission tomography reconstruction software for fluorescence tomography. With this method, sample self-absorption can be corrected not only for the absorption within the measured layer but also for the absorption by material beyond that layer. By combining tomography with analysis for scanning X-ray fluorescence microscopy, absolute concentrations of trace elements can be obtained. By using widely shared software, we not only minimized the coding, took advantage of computing efficiency of fast Fourier transform in transmission tomography software, but also thereby accessed well-developed data processing tools coming with well-known and reliable software packages. The convergence of the iterations was also carefully studied for fluorescence of different attenuation lengths. As an example, fish eye lenses could provide valuable information about fish life-history and endured environmental conditions. Given the lens's spherical shape and sometimes the short distance from sample to detector for detecting low concentration trace elements, its tomography data are affected by absorption related to material beyond the measured layer but can be reconstructed well with our method. Fish eye lens tomography results are compared with sliced lens 2D fluorescence mapping with good agreement, and with tomography providing better spatial resolution.

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

Liu, L; Han, Y; Jin, M

Purpose: To develop an iterative reconstruction method for X-ray CT, in which the reconstruction can quickly converge to the desired solution with much reduced projection views. Methods: The reconstruction is formulated as a convex feasibility problem, i.e. the solution is an intersection of three convex sets: 1) data fidelity (DF) set – the L2 norm of the difference of observed projections and those from the reconstructed image is no greater than an error bound; 2) non-negativity of image voxels (NN) set; and 3) piecewise constant (PC) set - the total variation (TV) of the reconstructed image is no greater thanmore » an upper bound. The solution can be found by applying projection onto convex sets (POCS) sequentially for these three convex sets. Specifically, the algebraic reconstruction technique and setting negative voxels as zero are used for projection onto the DF and NN sets, respectively, while the projection onto the PC set is achieved by solving a standard Rudin, Osher, and Fatemi (ROF) model. The proposed method is named as full sequential POCS (FS-POCS), which is tested using the Shepp-Logan phantom and the Catphan600 phantom and compared with two similar algorithms, TV-POCS and CP-TV. Results: Using the Shepp-Logan phantom, the root mean square error (RMSE) of reconstructed images changing along with the number of iterations is used as the convergence measurement. In general, FS- POCS converges faster than TV-POCS and CP-TV, especially with fewer projection views. FS-POCS can also achieve accurate reconstruction of cone-beam CT of the Catphan600 phantom using only 54 views, comparable to that of FDK using 364 views. Conclusion: We developed an efficient iterative reconstruction for sparse-view CT using full sequential POCS. The simulation and physical phantom data demonstrated the computational efficiency and effectiveness of FS-POCS.« less

An iterative hyperelastic parameters reconstruction for breast cancer assessment

NASA Astrophysics Data System (ADS)

Mehrabian, Hatef; Samani, Abbas

2008-03-01

In breast elastography, breast tissues usually undergo large compressions resulting in significant geometric and structural changes, and consequently nonlinear mechanical behavior. In this study, an elastography technique is presented where parameters characterizing tissue nonlinear behavior is reconstructed. Such parameters can be used for tumor tissue classification. To model the nonlinear behavior, tissues are treated as hyperelastic materials. The proposed technique uses a constrained iterative inversion method to reconstruct the tissue hyperelastic parameters. The reconstruction technique uses a nonlinear finite element (FE) model for solving the forward problem. In this research, we applied Yeoh and Polynomial models to model the tissue hyperelasticity. To mimic the breast geometry, we used a computational phantom, which comprises of a hemisphere connected to a cylinder. This phantom consists of two types of soft tissue to mimic adipose and fibroglandular tissues and a tumor. Simulation results show the feasibility of the proposed method in reconstructing the hyperelastic parameters of the tumor tissue.

Sub-aperture switching based ptychographic iterative engine (sasPIE) method for quantitative imaging

NASA Astrophysics Data System (ADS)

Sun, Aihui; Kong, Yan; Jiang, Zhilong; Yu, Wei; Liu, Fei; Xue, Liang; Wang, Shouyu; Liu, Cheng

2018-03-01

Though ptychographic iterative engine (PIE) has been widely adopted in the quantitative micro-imaging with various illuminations as visible light, X-ray and electron beam, the mechanical inaccuracy in the raster scanning of the sample relative to the illumination always degrades the reconstruction quality seriously and makes the resolution reached much lower than that determined by the numerical aperture of the optical system. To overcome this disadvantage, the sub-aperture switching based PIE method is proposed: the mechanical scanning in the common PIE is replaced by the sub-aperture switching, and the reconstruction error related to the positioning inaccuracy is completely avoided. The proposed technique remarkably improves the reconstruction quality, reduces the complexity of the experimental setup and fundamentally accelerates the data acquisition and reconstruction.

Prospective ECG-Triggered Coronary CT Angiography: Clinical Value of Noise-Based Tube Current Reduction Method with Iterative Reconstruction

PubMed Central

Shen, Junlin; Du, Xiangying; Guo, Daode; Cao, Lizhen; Gao, Yan; Yang, Qi; Li, Pengyu; Liu, Jiabin; Li, Kuncheng

2013-01-01

Objectives To evaluate the clinical value of noise-based tube current reduction method with iterative reconstruction for obtaining consistent image quality with dose optimization in prospective electrocardiogram (ECG)-triggered coronary CT angiography (CCTA). Materials and Methods We performed a prospective randomized study evaluating 338 patients undergoing CCTA with prospective ECG-triggering. Patients were randomly assigned to fixed tube current with filtered back projection (Group 1, n = 113), noise-based tube current with filtered back projection (Group 2, n = 109) or with iterative reconstruction (Group 3, n = 116). Tube voltage was fixed at 120 kV. Qualitative image quality was rated on a 5-point scale (1 = impaired, to 5 = excellent, with 3–5 defined as diagnostic). Image noise and signal intensity were measured; signal-to-noise ratio was calculated; radiation dose parameters were recorded. Statistical analyses included one-way analysis of variance, chi-square test, Kruskal-Wallis test and multivariable linear regression. Results Image noise was maintained at the target value of 35HU with small interquartile range for Group 2 (35.00–35.03HU) and Group 3 (34.99–35.02HU), while from 28.73 to 37.87HU for Group 1. All images in the three groups were acceptable for diagnosis. A relative 20% and 51% reduction in effective dose for Group 2 (2.9 mSv) and Group 3 (1.8 mSv) were achieved compared with Group 1 (3.7 mSv). After adjustment for scan characteristics, iterative reconstruction was associated with 26% reduction in effective dose. Conclusion Noise-based tube current reduction method with iterative reconstruction maintains image noise precisely at the desired level and achieves consistent image quality. Meanwhile, effective dose can be reduced by more than 50%. PMID:23741444

Clinical application and validation of an iterative forward projection matching algorithm for permanent brachytherapy seed localization from conebeam-CT x-ray projections.

PubMed

Pokhrel, Damodar; Murphy, Martin J; Todor, Dorin A; Weiss, Elisabeth; Williamson, Jeffrey F

2010-09-01

To experimentally validate a new algorithm for reconstructing the 3D positions of implanted brachytherapy seeds from postoperatively acquired 2D conebeam-CT (CBCT) projection images. The iterative forward projection matching (IFPM) algorithm finds the 3D seed geometry that minimizes the sum of the squared intensity differences between computed projections of an initial estimate of the seed configuration and radiographic projections of the implant. In-house machined phantoms, containing arrays of 12 and 72 seeds, respectively, are used to validate this method. Also, four 103Pd postimplant patients are scanned using an ACUITY digital simulator. Three to ten x-ray images are selected from the CBCT projection set and processed to create binary seed-only images. To quantify IFPM accuracy, the reconstructed seed positions are forward projected and overlaid on the measured seed images to find the nearest-neighbor distance between measured and computed seed positions for each image pair. Also, the estimated 3D seed coordinates are compared to known seed positions in the phantom and clinically obtained VariSeed planning coordinates for the patient data. For the phantom study, seed localization error is (0.58 +/- 0.33) mm. For all four patient cases, the mean registration error is better than 1 mm while compared against the measured seed projections. IFPM converges in 20-28 iterations, with a computation time of about 1.9-2.8 min/ iteration on a 1 GHz processor. The IFPM algorithm avoids the need to match corresponding seeds in each projection as required by standard back-projection methods. The authors' results demonstrate approximately 1 mm accuracy in reconstructing the 3D positions of brachytherapy seeds from the measured 2D projections. This algorithm also successfully localizes overlapping clustered and highly migrated seeds in the implant.

Clinical application and validation of an iterative forward projection matching algorithm for permanent brachytherapy seed localization from conebeam-CT x-ray projections

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

Pokhrel, Damodar; Murphy, Martin J.; Todor, Dorin A.

2010-09-15

Purpose: To experimentally validate a new algorithm for reconstructing the 3D positions of implanted brachytherapy seeds from postoperatively acquired 2D conebeam-CT (CBCT) projection images. Methods: The iterative forward projection matching (IFPM) algorithm finds the 3D seed geometry that minimizes the sum of the squared intensity differences between computed projections of an initial estimate of the seed configuration and radiographic projections of the implant. In-house machined phantoms, containing arrays of 12 and 72 seeds, respectively, are used to validate this method. Also, four {sup 103}Pd postimplant patients are scanned using an ACUITY digital simulator. Three to ten x-ray images are selectedmore » from the CBCT projection set and processed to create binary seed-only images. To quantify IFPM accuracy, the reconstructed seed positions are forward projected and overlaid on the measured seed images to find the nearest-neighbor distance between measured and computed seed positions for each image pair. Also, the estimated 3D seed coordinates are compared to known seed positions in the phantom and clinically obtained VariSeed planning coordinates for the patient data. Results: For the phantom study, seed localization error is (0.58{+-}0.33) mm. For all four patient cases, the mean registration error is better than 1 mm while compared against the measured seed projections. IFPM converges in 20-28 iterations, with a computation time of about 1.9-2.8 min/iteration on a 1 GHz processor. Conclusions: The IFPM algorithm avoids the need to match corresponding seeds in each projection as required by standard back-projection methods. The authors' results demonstrate {approx}1 mm accuracy in reconstructing the 3D positions of brachytherapy seeds from the measured 2D projections. This algorithm also successfully localizes overlapping clustered and highly migrated seeds in the implant.« less

Reducing the latency of the Fractal Iterative Method to half an iteration

NASA Astrophysics Data System (ADS)

Béchet, Clémentine; Tallon, Michel

2013-12-01

The fractal iterative method for atmospheric tomography (FRiM-3D) has been introduced to solve the wavefront reconstruction at the dimensions of an ELT with a low-computational cost. Previous studies reported the requirement of only 3 iterations of the algorithm in order to provide the best adaptive optics (AO) performance. Nevertheless, any iterative method in adaptive optics suffer from the intrinsic latency induced by the fact that one iteration can start only once the previous one is completed. Iterations hardly match the low-latency requirement of the AO real-time computer. We present here a new approach to avoid iterations in the computation of the commands with FRiM-3D, thus allowing low-latency AO response even at the scale of the European ELT (E-ELT). The method highlights the importance of "warm-start" strategy in adaptive optics. To our knowledge, this particular way to use the "warm-start" has not been reported before. Futhermore, removing the requirement of iterating to compute the commands, the computational cost of the reconstruction with FRiM-3D can be simplified and at least reduced to half the computational cost of a classical iteration. Thanks to simulations of both single-conjugate and multi-conjugate AO for the E-ELT,with FRiM-3D on Octopus ESO simulator, we demonstrate the benefit of this approach. We finally enhance the robustness of this new implementation with respect to increasing measurement noise, wind speed and even modeling errors.

Filtered gradient reconstruction algorithm for compressive spectral imaging

NASA Astrophysics Data System (ADS)

Mejia, Yuri; Arguello, Henry

2017-04-01

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

Quantitative neuroanatomy for connectomics in Drosophila

PubMed Central

Schneider-Mizell, Casey M; Gerhard, Stephan; Longair, Mark; Kazimiers, Tom; Li, Feng; Zwart, Maarten F; Champion, Andrew; Midgley, Frank M; Fetter, Richard D; Saalfeld, Stephan; Cardona, Albert

2016-01-01

Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of Drosophila sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free 'twigs' which branch off a single microtubule-containing 'backbone'. Omission of individual twigs accounted for 96% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity. DOI: http://dx.doi.org/10.7554/eLife.12059.001 PMID:26990779

A combined reconstruction-classification method for diffuse optical tomography.

PubMed

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

2009-11-07

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

A Unified Approach to Diffusion Direction Sensitive Slice Registration and 3-D DTI Reconstruction From Moving Fetal Brain Anatomy

PubMed Central

Fogtmann, Mads; Seshamani, Sharmishtaa; Kroenke, Christopher; Cheng, Xi; Chapman, Teresa; Wilm, Jakob; Rousseau, François

2014-01-01

This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain. Motion scatters the slice measurements in the spatial and spherical diffusion domain with respect to the underlying anatomy. Previous image registration techniques have been described to estimate the between slice fetal head motion, allowing the reconstruction of 3-D a diffusion estimate on a regular grid using interpolation. We propose Approach to Unified Diffusion Sensitive Slice Alignment and Reconstruction (AUDiSSAR) that explicitly formulates a process for diffusion direction sensitive DW-slice-to-DTI-volume alignment. This also incorporates image resolution modeling to iteratively deconvolve the effects of the imaging point spread function using the multiple views provided by thick slices acquired in different anatomical planes. The algorithm is implemented using a multi-resolution iterative scheme and multiple real and synthetic data are used to evaluate the performance of the technique. An accuracy experiment using synthetically created motion data of an adult head and a experiment using synthetic motion added to sedated fetal monkey dataset show a significant improvement in motion-trajectory estimation compared to a state-of-the-art approaches. The performance of the method is then evaluated on challenging but clinically typical in utero fetal scans of four different human cases, showing improved rendition of cortical anatomy and extraction of white matter tracts. While the experimental work focuses on DTI reconstruction (second-order tensor model), the proposed reconstruction framework can employ any 5-D diffusion volume model that can be represented by the spatial parameterizations of an orientation distribution function. PMID:24108711

Development of a prototype chest digital tomosynthesis R/F system

NASA Astrophysics Data System (ADS)

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

2017-03-01

Digital tomosynthesis has an advantage of low radiation dose compared to conventional computed tomography (CT) by utilizing small number of projections ( 80) acquired over a limited angular range. It can produce 3D volumetric data although they may have some artifacts due to incomplete sampling. Based upon these attractive merits, we developed a prototype digital tomosynthesis R/F system especially for the purpose of applications in chest imaging. Prototype chest digital tomosynthesis (CDT) R/F system contains an X-ray tube with high power R/F pulse generator, flat-panel detector, R/F table, electromechanical radiographic subsystems including precise motor controller, and a reconstruction server. For image reconstruction, users could select the reconstruction option between analytic and iterative methods. Reconstructed images of Catphan700 and LUNGMAN phantoms clearly and rapidly described the internal structures of the phantoms using graphics processing unit (GPU) programming. Contrast-to-noise ratio (CNR) values of the CTP682 module was higher in images using the simultaneous algebraic reconstruction technique (SART) than those using filtered backprojection (FBP) for all materials by factors of 2.60, 3.78, 5.50, 2.30, 3.70, and 2.52 for air, lung foam, low density polyethylene (LDPE), Delrin (acetal homopolymer resin), bone 50% (hydroxyapatite), and Teflon, respectively. Total elapsed times for producing 3D volume were 2.92 sec and 86.29 sec on average for FBP and SART (20 iterations), respectively. The times required for reconstruction were clinically feasible. Moreover, the total radiation dose from the system (5.68 mGy) could demonstrate a significant lowered radiation dose compared to conventional chest CT scan. Consequently, our prototype tomosynthesis R/F system represents an important advance in digital tomosynthesis applications.

Sequentially reweighted TV minimization for CT metal artifact reduction.

PubMed

Zhang, Xiaomeng; Xing, Lei

2013-07-01

Metal artifact reduction has long been an important topic in x-ray CT image reconstruction. In this work, the authors propose an iterative method that sequentially minimizes a reweighted total variation (TV) of the image and produces substantially artifact-reduced reconstructions. A sequentially reweighted TV minimization algorithm is proposed to fully exploit the sparseness of image gradients (IG). The authors first formulate a constrained optimization model that minimizes a weighted TV of the image, subject to the constraint that the estimated projection data are within a specified tolerance of the available projection measurements, with image non-negativity enforced. The authors then solve a sequence of weighted TV minimization problems where weights used for the next iteration are computed from the current solution. Using the complete projection data, the algorithm first reconstructs an image from which a binary metal image can be extracted. Forward projection of the binary image identifies metal traces in the projection space. The metal-free background image is then reconstructed from the metal-trace-excluded projection data by employing a different set of weights. Each minimization problem is solved using a gradient method that alternates projection-onto-convex-sets and steepest descent. A series of simulation and experimental studies are performed to evaluate the proposed approach. Our study shows that the sequentially reweighted scheme, by altering a single parameter in the weighting function, flexibly controls the sparsity of the IG and reconstructs artifacts-free images in a two-stage process. It successfully produces images with significantly reduced streak artifacts, suppressed noise and well-preserved contrast and edge properties. The sequentially reweighed TV minimization provides a systematic approach for suppressing CT metal artifacts. The technique can also be generalized to other "missing data" problems in CT image reconstruction.

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

PubMed

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-07

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

Time-of-flight PET image reconstruction using origin ensembles

NASA Astrophysics Data System (ADS)

Wülker, Christian; Sitek, Arkadiusz; Prevrhal, Sven

2015-03-01

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

The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique.

PubMed

Kwon, Heejin; Cho, Jinhan; Oh, Jongyeong; Kim, Dongwon; Cho, Junghyun; Kim, Sanghyun; Lee, Sangyun; Lee, Jihyun

2015-10-01

To investigate whether reduced radiation dose abdominal CT images reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) compromise the depiction of clinically competent features when compared with the currently used routine radiation dose CT images reconstructed with ASIR. 27 consecutive patients (mean body mass index: 23.55 kg m(-2) underwent CT of the abdomen at two time points. At the first time point, abdominal CT was scanned at 21.45 noise index levels of automatic current modulation at 120 kV. Images were reconstructed with 40% ASIR, the routine protocol of Dong-A University Hospital. At the second time point, follow-up scans were performed at 30 noise index levels. Images were reconstructed with filtered back projection (FBP), 40% ASIR, 30% ASIR-V, 50% ASIR-V and 70% ASIR-V for the reduced radiation dose. Both quantitative and qualitative analyses of image quality were conducted. The CT dose index was also recorded. At the follow-up study, the mean dose reduction relative to the currently used common radiation dose was 35.37% (range: 19-49%). The overall subjective image quality and diagnostic acceptability of the 50% ASIR-V scores at the reduced radiation dose were nearly identical to those recorded when using the initial routine-dose CT with 40% ASIR. Subjective ratings of the qualitative analysis revealed that of all reduced radiation dose CT series reconstructed, 30% ASIR-V and 50% ASIR-V were associated with higher image quality with lower noise and artefacts as well as good sharpness when compared with 40% ASIR and FBP. However, the sharpness score at 70% ASIR-V was considered to be worse than that at 40% ASIR. Objective image noise for 50% ASIR-V was 34.24% and 46.34% which was lower than 40% ASIR and FBP. Abdominal CT images reconstructed with ASIR-V facilitate radiation dose reductions of to 35% when compared with the ASIR. This study represents the first clinical research experiment to use ASIR-V, the newest version of iterative reconstruction. Use of the ASIR-V algorithm decreased image noise and increased image quality when compared with the ASIR and FBP methods. These results suggest that high-quality low-dose CT may represent a new clinical option.

The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique

PubMed Central

Cho, Jinhan; Oh, Jongyeong; Kim, Dongwon; Cho, Junghyun; Kim, Sanghyun; Lee, Sangyun; Lee, Jihyun

2015-01-01

Objective: To investigate whether reduced radiation dose abdominal CT images reconstructed with adaptive statistical iterative reconstruction V (ASIR-V) compromise the depiction of clinically competent features when compared with the currently used routine radiation dose CT images reconstructed with ASIR. Methods: 27 consecutive patients (mean body mass index: 23.55 kg m−2 underwent CT of the abdomen at two time points. At the first time point, abdominal CT was scanned at 21.45 noise index levels of automatic current modulation at 120 kV. Images were reconstructed with 40% ASIR, the routine protocol of Dong-A University Hospital. At the second time point, follow-up scans were performed at 30 noise index levels. Images were reconstructed with filtered back projection (FBP), 40% ASIR, 30% ASIR-V, 50% ASIR-V and 70% ASIR-V for the reduced radiation dose. Both quantitative and qualitative analyses of image quality were conducted. The CT dose index was also recorded. Results: At the follow-up study, the mean dose reduction relative to the currently used common radiation dose was 35.37% (range: 19–49%). The overall subjective image quality and diagnostic acceptability of the 50% ASIR-V scores at the reduced radiation dose were nearly identical to those recorded when using the initial routine-dose CT with 40% ASIR. Subjective ratings of the qualitative analysis revealed that of all reduced radiation dose CT series reconstructed, 30% ASIR-V and 50% ASIR-V were associated with higher image quality with lower noise and artefacts as well as good sharpness when compared with 40% ASIR and FBP. However, the sharpness score at 70% ASIR-V was considered to be worse than that at 40% ASIR. Objective image noise for 50% ASIR-V was 34.24% and 46.34% which was lower than 40% ASIR and FBP. Conclusion: Abdominal CT images reconstructed with ASIR-V facilitate radiation dose reductions of to 35% when compared with the ASIR. Advances in knowledge: This study represents the first clinical research experiment to use ASIR-V, the newest version of iterative reconstruction. Use of the ASIR-V algorithm decreased image noise and increased image quality when compared with the ASIR and FBP methods. These results suggest that high-quality low-dose CT may represent a new clinical option. PMID:26234823

Charm: Cosmic history agnostic reconstruction method

NASA Astrophysics Data System (ADS)

Porqueres, Natalia; Ensslin, Torsten A.

2017-03-01

Charm (cosmic history agnostic reconstruction method) reconstructs the cosmic expansion history in the framework of Information Field Theory. The reconstruction is performed via the iterative Wiener filter from an agnostic or from an informative prior. The charm code allows one to test the compatibility of several different data sets with the LambdaCDM model in a non-parametric way.

A new non-iterative reconstruction method for the electrical impedance tomography problem

NASA Astrophysics Data System (ADS)

Ferreira, A. D.; Novotny, A. A.

2017-03-01

The electrical impedance tomography (EIT) problem consists in determining the distribution of the electrical conductivity of a medium subject to a set of current fluxes, from measurements of the corresponding electrical potentials on its boundary. EIT is probably the most studied inverse problem since the fundamental works by Calderón from the 1980s. It has many relevant applications in medicine (detection of tumors), geophysics (localization of mineral deposits) and engineering (detection of corrosion in structures). In this work, we are interested in reconstructing a number of anomalies with different electrical conductivity from the background. Since the EIT problem is written in the form of an overdetermined boundary value problem, the idea is to rewrite it as a topology optimization problem. In particular, a shape functional measuring the misfit between the boundary measurements and the electrical potentials obtained from the model is minimized with respect to a set of ball-shaped anomalies by using the concept of topological derivatives. It means that the objective functional is expanded and then truncated up to the second order term, leading to a quadratic and strictly convex form with respect to the parameters under consideration. Thus, a trivial optimization step leads to a non-iterative second order reconstruction algorithm. As a result, the reconstruction process becomes very robust with respect to noisy data and independent of any initial guess. Finally, in order to show the effectiveness of the devised reconstruction algorithm, some numerical experiments into two spatial dimensions are presented, taking into account total and partial boundary measurements.

Fluorescence molecular tomography reconstruction via discrete cosine transform-based regularization

NASA Astrophysics Data System (ADS)

Shi, Junwei; Liu, Fei; Zhang, Jiulou; Luo, Jianwen; Bai, Jing

2015-05-01

Fluorescence molecular tomography (FMT) as a noninvasive imaging modality has been widely used for biomedical preclinical applications. However, FMT reconstruction suffers from severe ill-posedness, especially when a limited number of projections are used. In order to improve the quality of FMT reconstruction results, a discrete cosine transform (DCT) based reweighted L1-norm regularization algorithm is proposed. In each iteration of the reconstruction process, different reweighted regularization parameters are adaptively assigned according to the values of DCT coefficients to suppress the reconstruction noise. In addition, the permission region of the reconstructed fluorophores is adaptively constructed to increase the convergence speed. In order to evaluate the performance of the proposed algorithm, physical phantom and in vivo mouse experiments with a limited number of projections are carried out. For comparison, different L1-norm regularization strategies are employed. By quantifying the signal-to-noise ratio (SNR) of the reconstruction results in the phantom and in vivo mouse experiments with four projections, the proposed DCT-based reweighted L1-norm regularization shows higher SNR than other L1-norm regularizations employed in this work.

WE-AB-207A-08: BEST IN PHYSICS (IMAGING): Advanced Scatter Correction and Iterative Reconstruction for Improved Cone-Beam CT Imaging On the TrueBeam Radiotherapy Machine

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

Wang, A; Paysan, P; Brehm, M

2016-06-15

Purpose: To improve CBCT image quality for image-guided radiotherapy by applying advanced reconstruction algorithms to overcome scatter, noise, and artifact limitations Methods: CBCT is used extensively for patient setup in radiotherapy. However, image quality generally falls short of diagnostic CT, limiting soft-tissue based positioning and potential applications such as adaptive radiotherapy. The conventional TrueBeam CBCT reconstructor uses a basic scatter correction and FDK reconstruction, resulting in residual scatter artifacts, suboptimal image noise characteristics, and other artifacts like cone-beam artifacts. We have developed an advanced scatter correction that uses a finite-element solver (AcurosCTS) to model the behavior of photons as theymore » pass (and scatter) through the object. Furthermore, iterative reconstruction is applied to the scatter-corrected projections, enforcing data consistency with statistical weighting and applying an edge-preserving image regularizer to reduce image noise. The combined algorithms have been implemented on a GPU. CBCT projections from clinically operating TrueBeam systems have been used to compare image quality between the conventional and improved reconstruction methods. Planning CT images of the same patients have also been compared. Results: The advanced scatter correction removes shading and inhomogeneity artifacts, reducing the scatter artifact from 99.5 HU to 13.7 HU in a typical pelvis case. Iterative reconstruction provides further benefit by reducing image noise and eliminating streak artifacts, thereby improving soft-tissue visualization. In a clinical head and pelvis CBCT, the noise was reduced by 43% and 48%, respectively, with no change in spatial resolution (assessed visually). Additional benefits include reduction of cone-beam artifacts and reduction of metal artifacts due to intrinsic downweighting of corrupted rays. Conclusion: The combination of an advanced scatter correction with iterative reconstruction substantially improves CBCT image quality. It is anticipated that clinically acceptable reconstruction times will result from a multi-GPU implementation (the algorithms are under active development and not yet commercially available). All authors are employees of and (may) own stock of Varian Medical Systems.« less

WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

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

Bai, T; UT Southwestern Medical Center, Dallas, TX; Yan, H

2014-06-15

Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm inmore » a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application. A high zresolution is preferred to stabilize statistical iterative reconstruction. This work was supported in part by NIH(1R01CA154747-01), NSFC((No. 61172163), Research Fund for the Doctoral Program of Higher Education of China (No. 20110201110011), China Scholarship Council.« less

Iterative simulated quenching for designing irregular-spot-array generators.

PubMed

Gillet, J N; Sheng, Y

2000-07-10

We propose a novel, to our knowledge, algorithm of iterative simulated quenching with temperature rescaling for designing diffractive optical elements, based on an analogy between simulated annealing and statistical thermodynamics. The temperature is iteratively rescaled at the end of each quenching process according to ensemble statistics to bring the system back from a frozen imperfect state with a local minimum of energy to a dynamic state in a Boltzmann heat bath in thermal equilibrium at the rescaled temperature. The new algorithm achieves much lower cost function and reconstruction error and higher diffraction efficiency than conventional simulated annealing with a fast exponential cooling schedule and is easy to program. The algorithm is used to design binary-phase generators of large irregular spot arrays. The diffractive phase elements have trapezoidal apertures of varying heights, which fit ideal arbitrary-shaped apertures better than do trapezoidal apertures of fixed heights.

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

PubMed

Wang, Guobao; Qi, Jinyi

2010-03-07

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

A novel iterative scheme and its application to differential equations.

PubMed

Khan, Yasir; Naeem, F; Šmarda, Zdeněk

2014-01-01

The purpose of this paper is to employ an alternative approach to reconstruct the standard variational iteration algorithm II proposed by He, including Lagrange multiplier, and to give a simpler formulation of Adomian decomposition and modified Adomian decomposition method in terms of newly proposed variational iteration method-II (VIM). Through careful investigation of the earlier variational iteration algorithm and Adomian decomposition method, we find unnecessary calculations for Lagrange multiplier and also repeated calculations involved in each iteration, respectively. Several examples are given to verify the reliability and efficiency of the method.

Assessment of acquisition protocols for routine imaging of Y-90 using PET/CT

PubMed Central

2013-01-01

Background Despite the early theoretical prediction of the 0+-0+ transition of 90Zr, 90Y-PET underwent only recently a growing interest for the development of imaging radioembolization of liver tumors. The aim of this work was to determine the minimum detectable activity (MDA) of 90Y by PET imaging and the impact of time-of-flight (TOF) reconstruction on detectability and quantitative accuracy according to the lesion size. Methods The study was conducted using a Siemens Biograph® mCT with a 22 cm large axial field of view. An IEC torso-shaped phantom containing five coplanar spheres was uniformly filled to achieve sphere-to-background ratios of 40:1. The phantom was imaged nine times in 14 days over 30 min. Sinograms were reconstructed with and without TOF information. A contrast-to-noise ratio (CNR) index was calculated using the Rose criterion, taking partial volume effects into account. The impact of reconstruction parameters on quantification accuracy, detectability, and spatial localization of the signal was investigated. Finally, six patients with hepatocellular carcinoma and four patients included in different 90Y-based radioimmunotherapy protocols were enrolled for the evaluation of the imaging parameters in a clinical situation. Results The highest CNR was achieved with one iteration for both TOF and non-TOF reconstructions. The MDA, however, was found to be lower with TOF than with non-TOF reconstruction. There was no gain by adding TOF information in terms of CNR for concentrations higher than 2 to 3 MBq mL−1, except for infra-centimetric lesions. Recovered activity was highly underestimated when a single iteration or non-TOF reconstruction was used (10% to 150% less depending on the lesion size). The MDA was estimated at 1 MBq mL−1 for a TOF reconstruction and infra-centimetric lesions. Images from patients treated with microspheres were clinically relevant, unlike those of patients who received systemic injections of 90Y. Conclusions Only one iteration and TOF were necessary to achieve an MDA around 1 MBq mL−1 and the most accurate localization of lesions. For precise quantification, at least three iterations gave the best performance, using TOF reconstruction and keeping an MDA of roughly 1 MBq mL−1. One and three iterations were mandatory to prevent false positive results for quantitative analysis of clinical data. Trial registration http://IDRCB 2011-A00043-38 P101103 PMID:23414629

Sparse-view proton computed tomography using modulated proton beams.

PubMed

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

2015-02-01

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

Optimal control of a coupled partial and ordinary differential equations system for the assimilation of polarimetry Stokes vector measurements in tokamak free-boundary equilibrium reconstruction with application to ITER

NASA Astrophysics Data System (ADS)

Faugeras, Blaise; Blum, Jacques; Heumann, Holger; Boulbe, Cédric

2017-08-01

The modelization of polarimetry Faraday rotation measurements commonly used in tokamak plasma equilibrium reconstruction codes is an approximation to the Stokes model. This approximation is not valid for the foreseen ITER scenarios where high current and electron density plasma regimes are expected. In this work a method enabling the consistent resolution of the inverse equilibrium reconstruction problem in the framework of non-linear free-boundary equilibrium coupled to the Stokes model equation for polarimetry is provided. Using optimal control theory we derive the optimality system for this inverse problem. A sequential quadratic programming (SQP) method is proposed for its numerical resolution. Numerical experiments with noisy synthetic measurements in the ITER tokamak configuration for two test cases, the second of which is an H-mode plasma, show that the method is efficient and that the accuracy of the identification of the unknown profile functions is improved compared to the use of classical Faraday measurements.

Compressed sensing with gradient total variation for low-dose CBCT reconstruction

NASA Astrophysics Data System (ADS)

Seo, Chang-Woo; Cha, Bo Kyung; Jeon, Seongchae; Huh, Young; Park, Justin C.; Lee, Byeonghun; Baek, Junghee; Kim, Eunyoung

2015-06-01

This paper describes the improvement of convergence speed with gradient total variation (GTV) in compressed sensing (CS) for low-dose cone-beam computed tomography (CBCT) reconstruction. We derive a fast algorithm for the constrained total variation (TV)-based a minimum number of noisy projections. To achieve this task we combine the GTV with a TV-norm regularization term to promote an accelerated sparsity in the X-ray attenuation characteristics of the human body. The GTV is derived from a TV and enforces more efficient computationally and faster in convergence until a desired solution is achieved. The numerical algorithm is simple and derives relatively fast convergence. We apply a gradient projection algorithm that seeks a solution iteratively in the direction of the projected gradient while enforcing a non-negatively of the found solution. In comparison with the Feldkamp, Davis, and Kress (FDK) and conventional TV algorithms, the proposed GTV algorithm showed convergence in ≤18 iterations, whereas the original TV algorithm needs at least 34 iterations in reducing 50% of the projections compared with the FDK algorithm in order to reconstruct the chest phantom images. Future investigation includes improving imaging quality, particularly regarding X-ray cone-beam scatter, and motion artifacts of CBCT reconstruction.

Direct reconstruction of cardiac PET kinetic parametric images using a preconditioned conjugate gradient approach

PubMed Central

Rakvongthai, Yothin; Ouyang, Jinsong; Guerin, Bastien; Li, Quanzheng; Alpert, Nathaniel M.; El Fakhri, Georges

2013-01-01

Purpose: Our research goal is to develop an algorithm to reconstruct cardiac positron emission tomography (PET) kinetic parametric images directly from sinograms and compare its performance with the conventional indirect approach. Methods: Time activity curves of a NCAT phantom were computed according to a one-tissue compartmental kinetic model with realistic kinetic parameters. The sinograms at each time frame were simulated using the activity distribution for the time frame. The authors reconstructed the parametric images directly from the sinograms by optimizing a cost function, which included the Poisson log-likelihood and a spatial regularization terms, using the preconditioned conjugate gradient (PCG) algorithm with the proposed preconditioner. The proposed preconditioner is a diagonal matrix whose diagonal entries are the ratio of the parameter and the sensitivity of the radioactivity associated with parameter. The authors compared the reconstructed parametric images using the direct approach with those reconstructed using the conventional indirect approach. Results: At the same bias, the direct approach yielded significant relative reduction in standard deviation by 12%–29% and 32%–70% for 50 × 106 and 10 × 106 detected coincidences counts, respectively. Also, the PCG method effectively reached a constant value after only 10 iterations (with numerical convergence achieved after 40–50 iterations), while more than 500 iterations were needed for CG. Conclusions: The authors have developed a novel approach based on the PCG algorithm to directly reconstruct cardiac PET parametric images from sinograms, and yield better estimation of kinetic parameters than the conventional indirect approach, i.e., curve fitting of reconstructed images. The PCG method increases the convergence rate of reconstruction significantly as compared to the conventional CG method. PMID:24089922

Can sinogram-affirmed iterative (SAFIRE) reconstruction improve imaging quality on low-dose lung CT screening compared with traditional filtered back projection (FBP) reconstruction?

PubMed

Yang, Wen Jie; Yan, Fu Hua; Liu, Bo; Pang, Li Fang; Hou, Liang; Zhang, Huan; Pan, Zi Lai; Chen, Ke Min

2013-01-01

To evaluate the performance of sinogram-affirmed iterative (SAFIRE) reconstruction on image quality of low-dose lung computed tomographic (CT) screening compared with filtered back projection (FBP). Three hundred four patients for annual low-dose lung CT screening were examined by a dual-source CT system at 120 kilovolt (peak) with reference tube current of 40 mA·s. Six image serials were reconstructed, including one data set of FBP and 5 data sets of SAFIRE with different reconstruction strengths from 1 to 5. Image noise was recorded; and subjective scores of image noise, images artifacts, and the overall image quality were also assessed by 2 radiologists. The mean ± SD weight for all patients was 66.3 ± 12.8 kg, and the body mass index was 23.4 ± 3.2. The mean ± SD dose-length product was 95.2 ± 30.6 mGy cm, and the mean ± SD effective dose was 1.6 ± 0.5 mSv. The observation agreements for image noise grade, artifact grade, and the overall image quality were 0.785, 0.595 and 0.512, respectively. Among the overall 6 data sets, both the measured mean objective image noise and the subjective image noise of FBP was the highest, and the image noise decreased with the increasing of SAFIRE reconstruction strength. The data sets of S3 obtained the best image quality scores. Sinogram-affirmed iterative reconstruction can significantly improve image quality of low-dose lung CT screening compared with FBP, and SAFIRE with reconstruction strength 3 was a pertinent choice for low-dose lung CT.

Direct reconstruction of cardiac PET kinetic parametric images using a preconditioned conjugate gradient approach.

PubMed

Rakvongthai, Yothin; Ouyang, Jinsong; Guerin, Bastien; Li, Quanzheng; Alpert, Nathaniel M; El Fakhri, Georges

2013-10-01

Our research goal is to develop an algorithm to reconstruct cardiac positron emission tomography (PET) kinetic parametric images directly from sinograms and compare its performance with the conventional indirect approach. Time activity curves of a NCAT phantom were computed according to a one-tissue compartmental kinetic model with realistic kinetic parameters. The sinograms at each time frame were simulated using the activity distribution for the time frame. The authors reconstructed the parametric images directly from the sinograms by optimizing a cost function, which included the Poisson log-likelihood and a spatial regularization terms, using the preconditioned conjugate gradient (PCG) algorithm with the proposed preconditioner. The proposed preconditioner is a diagonal matrix whose diagonal entries are the ratio of the parameter and the sensitivity of the radioactivity associated with parameter. The authors compared the reconstructed parametric images using the direct approach with those reconstructed using the conventional indirect approach. At the same bias, the direct approach yielded significant relative reduction in standard deviation by 12%-29% and 32%-70% for 50 × 10(6) and 10 × 10(6) detected coincidences counts, respectively. Also, the PCG method effectively reached a constant value after only 10 iterations (with numerical convergence achieved after 40-50 iterations), while more than 500 iterations were needed for CG. The authors have developed a novel approach based on the PCG algorithm to directly reconstruct cardiac PET parametric images from sinograms, and yield better estimation of kinetic parameters than the conventional indirect approach, i.e., curve fitting of reconstructed images. The PCG method increases the convergence rate of reconstruction significantly as compared to the conventional CG method.

WE-FG-207B-05: Iterative Reconstruction Via Prior Image Constrained Total Generalized Variation for Spectral CT

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

Niu, S; Zhang, Y; Ma, J

Purpose: To investigate iterative reconstruction via prior image constrained total generalized variation (PICTGV) for spectral computed tomography (CT) using fewer projections while achieving greater image quality. Methods: The proposed PICTGV method is formulated as an optimization problem, which balances the data fidelity and prior image constrained total generalized variation of reconstructed images in one framework. The PICTGV method is based on structure correlations among images in the energy domain and high-quality images to guide the reconstruction of energy-specific images. In PICTGV method, the high-quality image is reconstructed from all detector-collected X-ray signals and is referred as the broad-spectrum image. Distinctmore » from the existing reconstruction methods applied on the images with first order derivative, the higher order derivative of the images is incorporated into the PICTGV method. An alternating optimization algorithm is used to minimize the PICTGV objective function. We evaluate the performance of PICTGV on noise and artifacts suppressing using phantom studies and compare the method with the conventional filtered back-projection method as well as TGV based method without prior image. Results: On the digital phantom, the proposed method outperforms the existing TGV method in terms of the noise reduction, artifacts suppression, and edge detail preservation. Compared to that obtained by the TGV based method without prior image, the relative root mean square error in the images reconstructed by the proposed method is reduced by over 20%. Conclusion: The authors propose an iterative reconstruction via prior image constrained total generalize variation for spectral CT. Also, we have developed an alternating optimization algorithm and numerically demonstrated the merits of our approach. Results show that the proposed PICTGV method outperforms the TGV method for spectral CT.« less

Direct Reconstruction of CT-Based Attenuation Correction Images for PET With Cluster-Based Penalties

NASA Astrophysics Data System (ADS)

Kim, Soo Mee; Alessio, Adam M.; De Man, Bruno; Kinahan, Paul E.

2017-03-01

Extremely low-dose (LD) CT acquisitions used for PET attenuation correction have high levels of noise and potential bias artifacts due to photon starvation. This paper explores the use of a priori knowledge for iterative image reconstruction of the CT-based attenuation map. We investigate a maximum a posteriori framework with cluster-based multinomial penalty for direct iterative coordinate decent (dICD) reconstruction of the PET attenuation map. The objective function for direct iterative attenuation map reconstruction used a Poisson log-likelihood data fit term and evaluated two image penalty terms of spatial and mixture distributions. The spatial regularization is based on a quadratic penalty. For the mixture penalty, we assumed that the attenuation map may consist of four material clusters: air + background, lung, soft tissue, and bone. Using simulated noisy sinogram data, dICD reconstruction was performed with different strengths of the spatial and mixture penalties. The combined spatial and mixture penalties reduced the root mean squared error (RMSE) by roughly two times compared with a weighted least square and filtered backprojection reconstruction of CT images. The combined spatial and mixture penalties resulted in only slightly lower RMSE compared with a spatial quadratic penalty alone. For direct PET attenuation map reconstruction from ultra-LD CT acquisitions, the combination of spatial and mixture penalties offers regularization of both variance and bias and is a potential method to reconstruct attenuation maps with negligible patient dose. The presented results, using a best-case histogram suggest that the mixture penalty does not offer a substantive benefit over conventional quadratic regularization and diminishes enthusiasm for exploring future application of the mixture penalty.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Ultralow dose dentomaxillofacial CT imaging and iterative reconstruction techniques: variability of Hounsfield units and contrast-to-noise ratio

PubMed Central

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

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

PubMed Central

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

2016-01-01

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

Real-time photo-magnetic imaging.

PubMed

Nouizi, Farouk; Erkol, Hakan; Luk, Alex; Unlu, Mehmet B; Gulsen, Gultekin

2016-10-01

We previously introduced a new high resolution diffuse optical imaging modality termed, photo-magnetic imaging (PMI). PMI irradiates the object under investigation with near-infrared light and monitors the variations of temperature using magnetic resonance thermometry (MRT). In this paper, we present a real-time PMI image reconstruction algorithm that uses analytic methods to solve the forward problem and assemble the Jacobian matrix much faster. The new algorithm is validated using real MRT measured temperature maps. In fact, it accelerates the reconstruction process by more than 250 times compared to a single iteration of the FEM-based algorithm, which opens the possibility for the real-time PMI.

A constrained modulus reconstruction technique for breast cancer assessment.

PubMed

Samani, A; Bishop, J; Plewes, D B

2001-09-01

A reconstruction technique for breast tissue elasticity modulus is described. This technique assumes that the geometry of normal and suspicious tissues is available from a contrast-enhanced magnetic resonance image. Furthermore, it is assumed that the modulus is constant throughout each tissue volume. The technique, which uses quasi-static strain data, is iterative where each iteration involves modulus updating followed by stress calculation. Breast mechanical stimulation is assumed to be done by two compressional rigid plates. As a result, stress is calculated using the finite element method based on the well-controlled boundary conditions of the compression plates. Using the calculated stress and the measured strain, modulus updating is done element-by-element based on Hooke's law. Breast tissue modulus reconstruction using simulated data and phantom modulus reconstruction using experimental data indicate that the technique is robust.

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

PubMed

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

2017-04-01

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

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

PubMed Central

Li, Guang; Liu, Xinming; Dodge, Cristina T.; Jensen, Corey T.

2016-01-01

The purpose of this study was to evaluate performance of the third generation of model‐based iterative reconstruction (MBIR) system, Veo 3.0, based on noise power spectrum (NPS) analysis with various clinical presets over a wide range of clinically applicable dose levels. A CatPhan 600 surrounded by an oval, fat‐equivalent ring to mimic patient size/shape was scanned 10 times at each of six dose levels on a GE HD 750 scanner. NPS analysis was performed on images reconstructed with various Veo 3.0 preset combinations for comparisons of those images reconstructed using Veo 2.0, filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASiR). The new Target Thickness setting resulted in higher noise in thicker axial images. The new Texture Enhancement function achieved a more isotropic noise behavior with less image artifacts. Veo 3.0 provides additional reconstruction options designed to allow the user choice of balance between spatial resolution and image noise, relative to Veo 2.0. Veo 3.0 provides more user selectable options and in general improved isotropic noise behavior in comparison to Veo 2.0. The overall noise reduction performance of both versions of MBIR was improved in comparison to FBP and ASiR, especially at low‐dose levels. PACS number(s): 87.57.‐s, 87.57.Q‐, 87.57.C‐, 87.57.nf, 87.57.C‐, 87.57.cm PMID:27685118

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

An object-oriented simulator for 3D digital breast tomosynthesis imaging system.

PubMed

Seyyedi, Saeed; Cengiz, Kubra; Kamasak, Mustafa; Yildirim, Isa

2013-01-01

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging. Filtered back projection algorithm has traditionally been used to reconstruct images from projections. Iterative reconstruction algorithms such as algebraic reconstruction technique (ART) were later developed. Recently, compressed sensing based methods have been proposed in tomosynthesis imaging problem. We have developed an object-oriented simulator for 3D digital breast tomosynthesis (DBT) imaging system using C++ programming language. The simulator is capable of implementing different iterative and compressed sensing based reconstruction methods on 3D digital tomosynthesis data sets and phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom models or real data sets. The simulator has been tested on a phantom study that simulates breast tomosynthesis imaging problem. Results obtained with various methods including algebraic reconstruction technique (ART) and total variation regularized reconstruction techniques (ART+TV) are presented. Reconstruction results of the methods are compared both visually and quantitatively by evaluating performances of the methods using mean structural similarity (MSSIM) values.

An Object-Oriented Simulator for 3D Digital Breast Tomosynthesis Imaging System

PubMed Central

Cengiz, Kubra

2013-01-01

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging. Filtered back projection algorithm has traditionally been used to reconstruct images from projections. Iterative reconstruction algorithms such as algebraic reconstruction technique (ART) were later developed. Recently, compressed sensing based methods have been proposed in tomosynthesis imaging problem. We have developed an object-oriented simulator for 3D digital breast tomosynthesis (DBT) imaging system using C++ programming language. The simulator is capable of implementing different iterative and compressed sensing based reconstruction methods on 3D digital tomosynthesis data sets and phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom models or real data sets. The simulator has been tested on a phantom study that simulates breast tomosynthesis imaging problem. Results obtained with various methods including algebraic reconstruction technique (ART) and total variation regularized reconstruction techniques (ART+TV) are presented. Reconstruction results of the methods are compared both visually and quantitatively by evaluating performances of the methods using mean structural similarity (MSSIM) values. PMID:24371468

GPU implementation of prior image constrained compressed sensing (PICCS)

NASA Astrophysics Data System (ADS)

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

2010-04-01

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

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

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

Hofmann, Christian; Sawall, Stefan; Knaup, Michael

2014-06-15

Purpose: Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise (and thereby patient dose), to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporatea priori knowledge into iterative image reconstruction, for example, by adding additional constraints to the cost function, which penalize variations between neighboring voxels. However, this approach to regularization in general poses a resolution noise trade-off because the stronger the regularization, and thus the noise reduction, the stronger themore » loss of spatial resolution and thus loss of anatomical detail. The authors propose a method which tries to improve this trade-off. The proposed reconstruction algorithm is called alpha image reconstruction (AIR). One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The AIR algorithm reconstructs voxel-specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image, one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade-off. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and low contrast disks is simulated. A filtered backprojection (FBP) reconstruction with a Ram-Lak kernel is used as a reference reconstruction. The results of AIR are compared against the FBP results and against a penalized weighted least squares reconstruction which uses total variation as regularization. The simulations are based on the geometry of the Siemens Somatom Definition Flash scanner. To quantitatively assess image quality, the authors analyze line profiles through resolution patterns to define a contrast factor for contrast-resolution plots. Furthermore, the authors calculate the contrast-to-noise ratio with the low contrast disks and the authors compare the agreement of the reconstructions with the ground truth by calculating the normalized cross-correlation and the root-mean-square deviation. To evaluate the clinical performance of the proposed method, the authors reconstruct patient data acquired with a Somatom Definition Flash dual source CT scanner (Siemens Healthcare, Forchheim, Germany). Results: The results of the simulation study show that among the compared algorithms AIR achieves the highest resolution and the highest agreement with the ground truth. Compared to the reference FBP reconstruction AIR is able to reduce the relative pixel noise by up to 50% and at the same time achieve a higher resolution by maintaining the edge information from the basis images. These results can be confirmed with the patient data. Conclusions: To evaluate the AIR algorithm simulated and measured patient data of a state-of-the-art clinical CT system were processed. It is shown, that generating CT images through the reconstruction of weighting coefficients has the potential to improve the resolution noise trade-off and thus to improve the dose usage in clinical CT.« less

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

Andreyev, A.

Purpose: Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. Methods: To validate the proposed algorithm we used Monte Carlomore » simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Results: Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2–3 orders of magnitude per iteration. Conclusions: The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.« less

Resolution recovery for Compton camera using origin ensemble algorithm.

PubMed

Andreyev, A; Celler, A; Ozsahin, I; Sitek, A

2016-08-01

Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2-3 orders of magnitude per iteration. The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.

Scientific data processing for Hipparcos

NASA Astrophysics Data System (ADS)

van der Marel, H.

1988-04-01

The scientific aims of the ESA Hipparcos astrometric satellite are reviewed, and the fundamental principles and practical implementation of the data-analysis and data-reduction procedures are discussed in detail. Hipparcos is to determine the positions and proper motions of a catalog of 110,000 stars to a limit of 12 mag with accuracy a few marcsec and obtain photometric observations of 400,000 stars (the Tycho mission). Consideration is given to the organization of the data-processing consortia FAST, NDAC, and TDAC; the basic problems of astrometry; the measurement principle; the large amounts of data to be generated during the 2.5-year mission; and the three-step iterative method to be applied (positional reconstruction and reduction to a reference great circle, spherical reconstruction, and extraction of the astrometric parameters). Diagrams and a flow chart are provided.

Optical asymmetric cryptography based on amplitude reconstruction of elliptically polarized light

NASA Astrophysics Data System (ADS)

Cai, Jianjun; Shen, Xueju; Lei, Ming

2017-11-01

We propose a novel optical asymmetric image encryption method based on amplitude reconstruction of elliptically polarized light, which is free from silhouette problem. The original image is analytically separated into two phase-only masks firstly, and then the two masks are encoded into amplitudes of the orthogonal polarization components of an elliptically polarized light. Finally, the elliptically polarized light propagates through a linear polarizer, and the output intensity distribution is recorded by a CCD camera to obtain the ciphertext. The whole encryption procedure could be implemented by using commonly used optical elements, and it combines diffusion process and confusion process. As a result, the proposed method achieves high robustness against iterative-algorithm-based attacks. Simulation results are presented to prove the validity of the proposed cryptography.

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

PubMed Central

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

2015-01-01

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

Improved compressed sensing-based cone-beam CT reconstruction using adaptive prior image constraints

NASA Astrophysics Data System (ADS)

Lee, Ho; Xing, Lei; Davidi, Ran; Li, Ruijiang; Qian, Jianguo; Lee, Rena

2012-04-01

Volumetric cone-beam CT (CBCT) images are acquired repeatedly during a course of radiation therapy and a natural question to ask is whether CBCT images obtained earlier in the process can be utilized as prior knowledge to reduce patient imaging dose in subsequent scans. The purpose of this work is to develop an adaptive prior image constrained compressed sensing (APICCS) method to solve this problem. Reconstructed images using full projections are taken on the first day of radiation therapy treatment and are used as prior images. The subsequent scans are acquired using a protocol of sparse projections. In the proposed APICCS algorithm, the prior images are utilized as an initial guess and are incorporated into the objective function in the compressed sensing (CS)-based iterative reconstruction process. Furthermore, the prior information is employed to detect any possible mismatched regions between the prior and current images for improved reconstruction. For this purpose, the prior images and the reconstructed images are classified into three anatomical regions: air, soft tissue and bone. Mismatched regions are identified by local differences of the corresponding groups in the two classified sets of images. A distance transformation is then introduced to convert the information into an adaptive voxel-dependent relaxation map. In constructing the relaxation map, the matched regions (unchanged anatomy) between the prior and current images are assigned with smaller weight values, which are translated into less influence on the CS iterative reconstruction process. On the other hand, the mismatched regions (changed anatomy) are associated with larger values and the regions are updated more by the new projection data, thus avoiding any possible adverse effects of prior images. The APICCS approach was systematically assessed by using patient data acquired under standard and low-dose protocols for qualitative and quantitative comparisons. The APICCS method provides an effective way for us to enhance the image quality at the matched regions between the prior and current images compared to the existing PICCS algorithm. Compared to the current CBCT imaging protocols, the APICCS algorithm allows an imaging dose reduction of 10-40 times due to the greatly reduced number of projections and lower x-ray tube current level coming from the low-dose protocol.

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

PubMed

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

2018-01-04

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

A modified discrete algebraic reconstruction technique for multiple grey image reconstruction for limited angle range tomography.

PubMed

Liang, Zhiting; Guan, Yong; Liu, Gang; Chen, Xiangyu; Li, Fahu; Guo, Pengfei; Tian, Yangchao

2016-03-01

The `missing wedge', which is due to a restricted rotation range, is a major challenge for quantitative analysis of an object using tomography. With prior knowledge of the grey levels, the discrete algebraic reconstruction technique (DART) is able to reconstruct objects accurately with projections in a limited angle range. However, the quality of the reconstructions declines as the number of grey levels increases. In this paper, a modified DART (MDART) was proposed, in which each independent region of homogeneous material was chosen as a research object, instead of the grey values. The grey values of each discrete region were estimated according to the solution of the linear projection equations. The iterative process of boundary pixels updating and correcting the grey values of each region was executed alternately. Simulation experiments of binary phantoms as well as multiple grey phantoms show that MDART is capable of achieving high-quality reconstructions with projections in a limited angle range. The interesting advancement of MDART is that neither prior knowledge of the grey values nor the number of grey levels is necessary.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

3D and 4D magnetic susceptibility tomography based on complex MR images

DOEpatents

Chen, Zikuan; Calhoun, Vince D

2014-11-11

Magnetic susceptibility is the physical property for T2*-weighted magnetic resonance imaging (T2*MRI). The invention relates to methods for reconstructing an internal distribution (3D map) of magnetic susceptibility values, .chi. (x,y,z), of an object, from 3D T2*MRI phase images, by using Computed Inverse Magnetic Resonance Imaging (CIMRI) tomography. The CIMRI technique solves the inverse problem of the 3D convolution by executing a 3D Total Variation (TV) regularized iterative convolution scheme, using a split Bregman iteration algorithm. The reconstruction of .chi. (x,y,z) can be designed for low-pass, band-pass, and high-pass features by using a convolution kernel that is modified from the standard dipole kernel. Multiple reconstructions can be implemented in parallel, and averaging the reconstructions can suppress noise. 4D dynamic magnetic susceptibility tomography can be implemented by reconstructing a 3D susceptibility volume from a 3D phase volume by performing 3D CIMRI magnetic susceptibility tomography at each snapshot time.

Feature-based three-dimensional registration for repetitive geometry in machine vision

PubMed Central

Gong, Yuanzheng; Seibel, Eric J.

2016-01-01

As an important step in three-dimensional (3D) machine vision, 3D registration is a process of aligning two or multiple 3D point clouds that are collected from different perspectives together into a complete one. The most popular approach to register point clouds is to minimize the difference between these point clouds iteratively by Iterative Closest Point (ICP) algorithm. However, ICP does not work well for repetitive geometries. To solve this problem, a feature-based 3D registration algorithm is proposed to align the point clouds that are generated by vision-based 3D reconstruction. By utilizing texture information of the object and the robustness of image features, 3D correspondences can be retrieved so that the 3D registration of two point clouds is to solve a rigid transformation. The comparison of our method and different ICP algorithms demonstrates that our proposed algorithm is more accurate, efficient and robust for repetitive geometry registration. Moreover, this method can also be used to solve high depth uncertainty problem caused by little camera baseline in vision-based 3D reconstruction. PMID:28286703

Sound source identification and sound radiation modeling in a moving medium using the time-domain equivalent source method.

PubMed

Zhang, Xiao-Zheng; Bi, Chuan-Xing; Zhang, Yong-Bin; Xu, Liang

2015-05-01

Planar near-field acoustic holography has been successfully extended to reconstruct the sound field in a moving medium, however, the reconstructed field still contains the convection effect that might lead to the wrong identification of sound sources. In order to accurately identify sound sources in a moving medium, a time-domain equivalent source method is developed. In the method, the real source is replaced by a series of time-domain equivalent sources whose strengths are solved iteratively by utilizing the measured pressure and the known convective time-domain Green's function, and time averaging is used to reduce the instability in the iterative solving process. Since these solved equivalent source strengths are independent of the convection effect, they can be used not only to identify sound sources but also to model sound radiations in both moving and static media. Numerical simulations are performed to investigate the influence of noise on the solved equivalent source strengths and the effect of time averaging on reducing the instability, and to demonstrate the advantages of the proposed method on the source identification and sound radiation modeling.

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

NASA Astrophysics Data System (ADS)

Wang, Tonghe; Zhu, Lei

2016-09-01

Conventional dual-energy CT (DECT) reconstruction requires two full-size projection datasets with two different energy spectra. In this study, we propose an iterative algorithm to enable a new data acquisition scheme which requires one full scan and a second sparse-view scan for potential reduction in imaging dose and engineering cost of DECT. A bilateral filter is calculated as a similarity matrix from the first full-scan CT image to quantify the similarity between any two pixels, which is assumed unchanged on a second CT image since DECT scans are performed on the same object. The second CT image from reduced projections is reconstructed by an iterative algorithm which updates the image by minimizing the total variation of the difference between the image and its filtered image by the similarity matrix under data fidelity constraint. As the redundant structural information of the two CT images is contained in the similarity matrix for CT reconstruction, we refer to the algorithm as structure preserving iterative reconstruction (SPIR). The proposed method is evaluated on both digital and physical phantoms, and is compared with the filtered-backprojection (FBP) method, the conventional total-variation-regularization-based algorithm (TVR) and prior-image-constrained-compressed-sensing (PICCS). SPIR with a second 10-view scan reduces the image noise STD by a factor of one order of magnitude with same spatial resolution as full-view FBP image. SPIR substantially improves over TVR on the reconstruction accuracy of a 10-view scan by decreasing the reconstruction error from 6.18% to 1.33%, and outperforms TVR at 50 and 20-view scans on spatial resolution with a higher frequency at the modulation transfer function value of 10% by an average factor of 4. Compared with the 20-view scan PICCS result, the SPIR image has 7 times lower noise STD with similar spatial resolution. The electron density map obtained from the SPIR-based DECT images with a second 10-view scan has an average error of less than 1%.

Projection matrix acquisition for cone-beam computed tomography iterative reconstruction

NASA Astrophysics Data System (ADS)

Yang, Fuqiang; Zhang, Dinghua; Huang, Kuidong; Shi, Wenlong; Zhang, Caixin; Gao, Zongzhao

2017-02-01

Projection matrix is an essential and time-consuming part in computed tomography (CT) iterative reconstruction. In this article a novel calculation algorithm of three-dimensional (3D) projection matrix is proposed to quickly acquire the matrix for cone-beam CT (CBCT). The CT data needed to be reconstructed is considered as consisting of the three orthogonal sets of equally spaced and parallel planes, rather than the individual voxels. After getting the intersections the rays with the surfaces of the voxels, the coordinate points and vertex is compared to obtain the index value that the ray traversed. Without considering ray-slope to voxel, it just need comparing the position of two points. Finally, the computer simulation is used to verify the effectiveness of the algorithm.

Born iterative reconstruction using perturbed-phase field estimates.

PubMed

Astheimer, Jeffrey P; Waag, Robert C

2008-10-01

A method of image reconstruction from scattering measurements for use in ultrasonic imaging is presented. The method employs distorted-wave Born iteration but does not require using a forward-problem solver or solving large systems of equations. These calculations are avoided by limiting intermediate estimates of medium variations to smooth functions in which the propagated fields can be approximated by phase perturbations derived from variations in a geometric path along rays. The reconstruction itself is formed by a modification of the filtered-backpropagation formula that includes correction terms to account for propagation through an estimated background. Numerical studies that validate the method for parameter ranges of interest in medical applications are presented. The efficiency of this method offers the possibility of real-time imaging from scattering measurements.

Three-dimensional focus of attention for iterative cone-beam micro-CT reconstruction

NASA Astrophysics Data System (ADS)

Benson, T. M.; Gregor, J.

2006-09-01

Three-dimensional iterative reconstruction of high-resolution, circular orbit cone-beam x-ray CT data is often considered impractical due to the demand for vast amounts of computer cycles and associated memory. In this paper, we show that the computational burden can be reduced by limiting the reconstruction to a small, well-defined portion of the image volume. We first discuss using the support region defined by the set of voxels covered by all of the projection views. We then present a data-driven preprocessing technique called focus of attention that heuristically separates both image and projection data into object and background before reconstruction, thereby further reducing the reconstruction region of interest. We present experimental results for both methods based on mouse data and a parallelized implementation of the SIRT algorithm. The computational savings associated with the support region are substantial. However, the results for focus of attention are even more impressive in that only about one quarter of the computer cycles and memory are needed compared with reconstruction of the entire image volume. The image quality is not compromised by either method.

Rapid alignment of nanotomography data using joint iterative reconstruction and reprojection.

PubMed

Gürsoy, Doğa; Hong, Young P; He, Kuan; Hujsak, Karl; Yoo, Seunghwan; Chen, Si; Li, Yue; Ge, Mingyuan; Miller, Lisa M; Chu, Yong S; De Andrade, Vincent; He, Kai; Cossairt, Oliver; Katsaggelos, Aggelos K; Jacobsen, Chris

2017-09-18

As x-ray and electron tomography is pushed further into the nanoscale, the limitations of rotation stages become more apparent, leading to challenges in the alignment of the acquired projection images. Here we present an approach for rapid post-acquisition alignment of these projections to obtain high quality three-dimensional images. Our approach is based on a joint estimation of alignment errors, and the object, using an iterative refinement procedure. With simulated data where we know the alignment error of each projection image, our approach shows a residual alignment error that is a factor of a thousand smaller, and it reaches the same error level in the reconstructed image in less than half the number of iterations. We then show its application to experimental data in x-ray and electron nanotomography.

Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish

PubMed Central

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

2015-01-01

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

Initial phantom study comparing image quality in computed tomography using adaptive statistical iterative reconstruction and new adaptive statistical iterative reconstruction v.

PubMed

Lim, Kyungjae; Kwon, Heejin; Cho, Jinhan; Oh, Jongyoung; Yoon, Seongkuk; Kang, Myungjin; Ha, Dongho; Lee, Jinhwa; Kang, Eunju

2015-01-01

The purpose of this study was to assess the image quality of a novel advanced iterative reconstruction (IR) method called as "adaptive statistical IR V" (ASIR-V) by comparing the image noise, contrast-to-noise ratio (CNR), and spatial resolution from those of filtered back projection (FBP) and adaptive statistical IR (ASIR) on computed tomography (CT) phantom image. We performed CT scans at 5 different tube currents (50, 70, 100, 150, and 200 mA) using 3 types of CT phantoms. Scanned images were subsequently reconstructed in 7 different scan settings, such as FBP, and 3 levels of ASIR and ASIR-V (30%, 50%, and 70%). The image noise was measured in the first study using body phantom. The CNR was measured in the second study using contrast phantom and the spatial resolutions were measured in the third study using a high-resolution phantom. We compared the image noise, CNR, and spatial resolution among the 7 reconstructed image scan settings to determine whether noise reduction, high CNR, and high spatial resolution could be achieved at ASIR-V. At quantitative analysis of the first and second studies, it showed that the images reconstructed using ASIR-V had reduced image noise and improved CNR compared with those of FBP and ASIR (P < 0.001). At qualitative analysis of the third study, it also showed that the images reconstructed using ASIR-V had significantly improved spatial resolution than those of FBP and ASIR (P < 0.001). Our phantom studies showed that ASIR-V provides a significant reduction in image noise and a significant improvement in CNR as well as spatial resolution. Therefore, this technique has the potential to reduce the radiation dose further without compromising image quality.

The motional Stark effect diagnostic for ITER using a line-shift approach.

PubMed

Foley, E L; Levinton, F M; Yuh, H Y; Zakharov, L E

2008-10-01

The United States has been tasked with the development and implementation of a motional Stark effect (MSE) system on ITER. In the harsh ITER environment, MSE is particularly susceptible to degradation, as it depends on polarimetry, and the polarization reflection properties of surfaces are highly sensitive to thin film effects due to plasma deposition and erosion of a first mirror. Here we present the results of a comprehensive study considering a new MSE-based approach to internal plasma magnetic field measurements for ITER. The proposed method uses the line shifts in the MSE spectrum (MSE-LS) to provide a radial profile of the magnetic field magnitude. To determine the utility of MSE-LS for equilibrium reconstruction, studies were performed using the ESC-ERV code system. A near-term opportunity to test the use of MSE-LS for equilibrium reconstruction is being pursued in the implementation of MSE with laser-induced fluorescence on NSTX. Though the field values and beam energies are very different from ITER, the use of a laser allows precision spectroscopy with a similar ratio of linewidth to line spacing on NSTX as would be achievable with a passive system on ITER. Simulation results for ITER and NSTX are presented, and the relative merits of the traditional line polarization approach and the new line-shift approach are discussed.

Accelerating 4D flow MRI by exploiting vector field divergence regularization.

PubMed

Santelli, Claudio; Loecher, Michael; Busch, Julia; Wieben, Oliver; Schaeffter, Tobias; Kozerke, Sebastian

2016-01-01

To improve velocity vector field reconstruction from undersampled four-dimensional (4D) flow MRI by penalizing divergence of the measured flow field. Iterative image reconstruction in which magnitude and phase are regularized separately in alternating iterations was implemented. The approach allows incorporating prior knowledge of the flow field being imaged. In the present work, velocity data were regularized to reduce divergence, using either divergence-free wavelets (DFW) or a finite difference (FD) method using the ℓ1-norm of divergence and curl. The reconstruction methods were tested on a numerical phantom and in vivo data. Results of the DFW and FD approaches were compared with data obtained with standard compressed sensing (CS) reconstruction. Relative to standard CS, directional errors of vector fields and divergence were reduced by 55-60% and 38-48% for three- and six-fold undersampled data with the DFW and FD methods. Velocity vector displays of the numerical phantom and in vivo data were found to be improved upon DFW or FD reconstruction. Regularization of vector field divergence in image reconstruction from undersampled 4D flow data is a valuable approach to improve reconstruction accuracy of velocity vector fields. © 2014 Wiley Periodicals, Inc.

Improved image decompression for reduced transform coding artifacts

NASA Technical Reports Server (NTRS)

Orourke, Thomas P.; Stevenson, Robert L.

1994-01-01

The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image which best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem which can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality.

RMP: Reduced-set matching pursuit approach for efficient compressed sensing signal reconstruction.

PubMed

Abdel-Sayed, Michael M; Khattab, Ahmed; Abu-Elyazeed, Mohamed F

2016-11-01

Compressed sensing enables the acquisition of sparse signals at a rate that is much lower than the Nyquist rate. Compressed sensing initially adopted [Formula: see text] minimization for signal reconstruction which is computationally expensive. Several greedy recovery algorithms have been recently proposed for signal reconstruction at a lower computational complexity compared to the optimal [Formula: see text] minimization, while maintaining a good reconstruction accuracy. In this paper, the Reduced-set Matching Pursuit (RMP) greedy recovery algorithm is proposed for compressed sensing. Unlike existing approaches which either select too many or too few values per iteration, RMP aims at selecting the most sufficient number of correlation values per iteration, which improves both the reconstruction time and error. Furthermore, RMP prunes the estimated signal, and hence, excludes the incorrectly selected values. The RMP algorithm achieves a higher reconstruction accuracy at a significantly low computational complexity compared to existing greedy recovery algorithms. It is even superior to [Formula: see text] minimization in terms of the normalized time-error product, a new metric introduced to measure the trade-off between the reconstruction time and error. RMP superior performance is illustrated with both noiseless and noisy samples.

Development of an efficient multigrid method for the NEM form of the multigroup neutron diffusion equation

NASA Astrophysics Data System (ADS)

Al-Chalabi, Rifat M. Khalil

1997-09-01

Development of an improvement to the computational efficiency of the existing nested iterative solution strategy of the Nodal Exapansion Method (NEM) nodal based neutron diffusion code NESTLE is presented. The improvement in the solution strategy is the result of developing a multilevel acceleration scheme that does not suffer from the numerical stalling associated with a number of iterative solution methods. The acceleration scheme is based on the multigrid method, which is specifically adapted for incorporation into the NEM nonlinear iterative strategy. This scheme optimizes the computational interplay between the spatial discretization and the NEM nonlinear iterative solution process through the use of the multigrid method. The combination of the NEM nodal method, calculation of the homogenized, neutron nodal balance coefficients (i.e. restriction operator), efficient underlying smoothing algorithm (power method of NESTLE), and the finer mesh reconstruction algorithm (i.e. prolongation operator), all operating on a sequence of coarser spatial nodes, constitutes the multilevel acceleration scheme employed in this research. Two implementations of the multigrid method into the NESTLE code were examined; the Imbedded NEM Strategy and the Imbedded CMFD Strategy. The main difference in implementation between the two methods is that in the Imbedded NEM Strategy, the NEM solution is required at every MG level. Numerical tests have shown that the Imbedded NEM Strategy suffers from divergence at coarse- grid levels, hence all the results for the different benchmarks presented here were obtained using the Imbedded CMFD Strategy. The novelties in the developed MG method are as follows: the formulation of the restriction and prolongation operators, and the selection of the relaxation method. The restriction operator utilizes a variation of the reactor physics, consistent homogenization technique. The prolongation operator is based upon a variant of the pin power reconstruction methodology. The relaxation method, which is the power method, utilizes a constant coefficient matrix within the NEM non-linear iterative strategy. The choice of the MG nesting within the nested iterative strategy enables the incorporation of other non-linear effects with no additional coding effort. In addition, if an eigenvalue problem is being solved, it remains an eigenvalue problem at all grid levels, simplifying coding implementation. The merit of the developed MG method was tested by incorporating it into the NESTLE iterative solver, and employing it to solve four different benchmark problems. In addition to the base cases, three different sensitivity studies are performed, examining the effects of number of MG levels, homogenized coupling coefficients correction (i.e. restriction operator), and fine-mesh reconstruction algorithm (i.e. prolongation operator). The multilevel acceleration scheme developed in this research provides the foundation for developing adaptive multilevel acceleration methods for steady-state and transient NEM nodal neutron diffusion equations. (Abstract shortened by UMI.)

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

PubMed

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

2007-09-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging

PubMed Central

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

Iterative wave-front reconstruction in the Fourier domain.

PubMed

Bond, Charlotte Z; Correia, Carlos M; Sauvage, Jean-François; Neichel, Benoit; Fusco, Thierry

2017-05-15

The use of Fourier methods in wave-front reconstruction can significantly reduce the computation time for large telescopes with a high number of degrees of freedom. However, Fourier algorithms for discrete data require a rectangular data set which conform to specific boundary requirements, whereas wave-front sensor data is typically defined over a circular domain (the telescope pupil). Here we present an iterative Gerchberg routine modified for the purposes of discrete wave-front reconstruction which adapts the measurement data (wave-front sensor slopes) for Fourier analysis, fulfilling the requirements of the fast Fourier transform (FFT) and providing accurate reconstruction. The routine is used in the adaptation step only and can be coupled to any other Wiener-like or least-squares method. We compare simulations using this method with previous Fourier methods and show an increase in performance in terms of Strehl ratio and a reduction in noise propagation for a 40×40 SPHERE-like adaptive optics system. For closed loop operation with minimal iterations the Gerchberg method provides an improvement in Strehl, from 95.4% to 96.9% in K-band. This corresponds to ~ 40 nm improvement in rms, and avoids the high spatial frequency errors present in other methods, providing an increase in contrast towards the edge of the correctable band.

Investigation of Image Reconstruction Parameters of the Mediso nanoScan PC Small-Animal PET/CT Scanner for Two Different Positron Emitters Under NEMA NU 4-2008 Standards.

PubMed

Gaitanis, Anastasios; Kastis, George A; Vlastou, Elena; Bouziotis, Penelope; Verginis, Panayotis; Anagnostopoulos, Constantinos D

2017-08-01

The Tera-Tomo 3D image reconstruction algorithm (a version of OSEM), provided with the Mediso nanoScan® PC (PET8/2) small-animal positron emission tomograph (PET)/x-ray computed tomography (CT) scanner, has various parameter options such as total level of regularization, subsets, and iterations. Also, the acquisition time in PET plays an important role. This study aims to assess the performance of this new small-animal PET/CT scanner for different acquisition times and reconstruction parameters, for 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) and Ga-68, under the NEMA NU 4-2008 standards. Various image quality metrics were calculated for different realizations of [ 18 F]FDG and Ga-68 filled image quality (IQ) phantoms. [ 18 F]FDG imaging produced improved images over Ga-68. The best compromise for the optimization of all image quality factors is achieved for at least 30 min acquisition and image reconstruction with 52 iteration updates combined with a high regularization level. A high regularization level at 52 iteration updates and 30 min acquisition time were found to optimize most of the figures of merit investigated.

Iterative Correction Scheme Based on Discrete Cosine Transform and L1 Regularization for Fluorescence Molecular Tomography With Background Fluorescence.

PubMed

Zhang, Jiulou; Shi, Junwei; Guang, Huizhi; Zuo, Simin; Liu, Fei; Bai, Jing; Luo, Jianwen

2016-06-01

High-intensity background fluorescence is generally encountered in fluorescence molecular tomography (FMT), because of the accumulation of fluorescent probes in nontarget tissues or the existence of autofluorescence in biological tissues. The reconstruction results are affected or even distorted by the background fluorescence, especially when the distribution of fluorescent targets is relatively sparse. The purpose of this paper is to reduce the negative effect of background fluorescence on FMT reconstruction. After each iteration of the Tikhonov regularization algorithm, 3-D discrete cosine transform is adopted to filter the intermediate results. And then, a sparsity constraint step based on L1 regularization is applied to restrain the energy of the objective function. Phantom experiments with different fluorescence intensities of homogeneous and heterogeneous background are carried out to validate the performance of the proposed scheme. The results show that the reconstruction quality can be improved with the proposed iterative correction scheme. The influence of background fluorescence in FMT can be reduced effectively because of the filtering of the intermediate results, the detail preservation, and noise suppression of L1 regularization.

Photogrammetric 3d Building Reconstruction from Thermal Images

NASA Astrophysics Data System (ADS)

Maset, E.; Fusiello, A.; Crosilla, F.; Toldo, R.; Zorzetto, D.

2017-08-01

This paper addresses the problem of 3D building reconstruction from thermal infrared (TIR) images. We show that a commercial Computer Vision software can be used to automatically orient sequences of TIR images taken from an Unmanned Aerial Vehicle (UAV) and to generate 3D point clouds, without requiring any GNSS/INS data about position and attitude of the images nor camera calibration parameters. Moreover, we propose a procedure based on Iterative Closest Point (ICP) algorithm to create a model that combines high resolution and geometric accuracy of RGB images with the thermal information deriving from TIR images. The process can be carried out entirely by the aforesaid software in a simple and efficient way.

Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design

PubMed Central

Mata, Edson; Bandeira, Silvio; de Mattos Neto, Paulo; Lopes, Waslon; Madeiro, Francisco

2016-01-01

The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families of fuzzy K-means algorithms for codebook design. The acceleration is obtained by reducing the number of iterations of the algorithms and applying efficient nearest neighbor search techniques. Simulation results concerning image vector quantization have shown that the acceleration obtained so far does not decrease the quality of the reconstructed images. Codebook design time savings up to about 40% are obtained by the accelerated versions with respect to the original versions of the algorithms. PMID:27886061

Accelerating Families of Fuzzy K-Means Algorithms for Vector Quantization Codebook Design.

PubMed

Mata, Edson; Bandeira, Silvio; de Mattos Neto, Paulo; Lopes, Waslon; Madeiro, Francisco

2016-11-23

The performance of signal processing systems based on vector quantization depends on codebook design. In the image compression scenario, the quality of the reconstructed images depends on the codebooks used. In this paper, alternatives are proposed for accelerating families of fuzzy K-means algorithms for codebook design. The acceleration is obtained by reducing the number of iterations of the algorithms and applying efficient nearest neighbor search techniques. Simulation results concerning image vector quantization have shown that the acceleration obtained so far does not decrease the quality of the reconstructed images. Codebook design time savings up to about 40% are obtained by the accelerated versions with respect to the original versions of the algorithms.

Expectation maximization for hard X-ray count modulation profiles

NASA Astrophysics Data System (ADS)

Benvenuto, F.; Schwartz, R.; Piana, M.; Massone, A. M.

2013-07-01

Context. This paper is concerned with the image reconstruction problem when the measured data are solar hard X-ray modulation profiles obtained from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) instrument. Aims: Our goal is to demonstrate that a statistical iterative method classically applied to the image deconvolution problem is very effective when utilized to analyze count modulation profiles in solar hard X-ray imaging based on rotating modulation collimators. Methods: The algorithm described in this paper solves the maximum likelihood problem iteratively and encodes a positivity constraint into the iterative optimization scheme. The result is therefore a classical expectation maximization method this time applied not to an image deconvolution problem but to image reconstruction from count modulation profiles. The technical reason that makes our implementation particularly effective in this application is the use of a very reliable stopping rule which is able to regularize the solution providing, at the same time, a very satisfactory Cash-statistic (C-statistic). Results: The method is applied to both reproduce synthetic flaring configurations and reconstruct images from experimental data corresponding to three real events. In this second case, the performance of expectation maximization, when compared to Pixon image reconstruction, shows a comparable accuracy and a notably reduced computational burden; when compared to CLEAN, shows a better fidelity with respect to the measurements with a comparable computational effectiveness. Conclusions: If optimally stopped, expectation maximization represents a very reliable method for image reconstruction in the RHESSI context when count modulation profiles are used as input data.

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography.

PubMed

Precht, Helle; Thygesen, Jesper; Gerke, Oke; Egstrup, Kenneth; Waaler, Dag; Lambrechtsen, Jess

2016-12-01

Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure. To evaluate whether adaptive statistical iterative reconstruction (ASIR) enhances perceived image quality in CCTA compared to filtered back projection (FBP). Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA) and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR]) was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data. VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR ( P  = 0.004). The objective measures showed significant differences between FBP and 60% ASIR ( P  < 0.0001) for noise, with an estimated ratio of 0.82, and for CNR, with an estimated ratio of 1.26. ASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR.

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

PubMed

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

2014-12-01

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

Recent progress in 3-D imaging of sea freight containers

NASA Astrophysics Data System (ADS)

Fuchs, Theobald; Schön, Tobias; Dittmann, Jonas; Sukowski, Frank; Hanke, Randolf

2015-03-01

The inspection of very large objects like sea freight containers with X-ray Computed Tomography (CT) is an emerging technology. A complete 3-D CT scan of a see-freight container takes several hours. Of course, this is too slow to apply it to a large number of containers. However, the benefits of a 3-D CT for sealed freight are obvious: detection of potential threats or illicit cargo without being confronted with legal complications or high time consumption and risks for the security personnel during a manual inspection. Recently distinct progress was made in the field of reconstruction of projections with only a relatively low number of angular positions. Instead of today's 500 to 1000 rotational steps, as needed for conventional CT reconstruction techniques, this new class of algorithms provides the potential to reduce the number of projection angles approximately by a factor of 10. The main drawback of these advanced iterative methods is the high consumption for numerical processing. But as computational power is getting steadily cheaper, there will be practical applications of these complex algorithms in a foreseeable future. In this paper, we discuss the properties of iterative image reconstruction algorithms and show results of their application to CT of extremely large objects scanning a sea-freight container. A specific test specimen is used to quantitatively evaluate the image quality in terms of spatial and contrast resolution and depending on different number of projections.

Inferring the demographic history from DNA sequences: An importance sampling approach based on non-homogeneous processes.

PubMed

Ait Kaci Azzou, S; Larribe, F; Froda, S

2016-10-01

In Ait Kaci Azzou et al. (2015) we introduced an Importance Sampling (IS) approach for estimating the demographic history of a sample of DNA sequences, the skywis plot. More precisely, we proposed a new nonparametric estimate of a population size that changes over time. We showed on simulated data that the skywis plot can work well in typical situations where the effective population size does not undergo very steep changes. In this paper, we introduce an iterative procedure which extends the previous method and gives good estimates under such rapid variations. In the iterative calibrated skywis plot we approximate the effective population size by a piecewise constant function, whose values are re-estimated at each step. These piecewise constant functions are used to generate the waiting times of non homogeneous Poisson processes related to a coalescent process with mutation under a variable population size model. Moreover, the present IS procedure is based on a modified version of the Stephens and Donnelly (2000) proposal distribution. Finally, we apply the iterative calibrated skywis plot method to a simulated data set from a rapidly expanding exponential model, and we show that the method based on this new IS strategy correctly reconstructs the demographic history. Copyright © 2016. Published by Elsevier Inc.

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

NASA Astrophysics Data System (ADS)

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

2003-02-01

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

Born iterative reconstruction using perturbed-phase field estimates

PubMed Central

Astheimer, Jeffrey P.; Waag, Robert C.

2008-01-01

A method of image reconstruction from scattering measurements for use in ultrasonic imaging is presented. The method employs distorted-wave Born iteration but does not require using a forward-problem solver or solving large systems of equations. These calculations are avoided by limiting intermediate estimates of medium variations to smooth functions in which the propagated fields can be approximated by phase perturbations derived from variations in a geometric path along rays. The reconstruction itself is formed by a modification of the filtered-backpropagation formula that includes correction terms to account for propagation through an estimated background. Numerical studies that validate the method for parameter ranges of interest in medical applications are presented. The efficiency of this method offers the possibility of real-time imaging from scattering measurements. PMID:19062873

Modelling the physics in iterative reconstruction for transmission computed tomography

PubMed Central

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

2013-01-01

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

Road detection in SAR images using a tensor voting algorithm

NASA Astrophysics Data System (ADS)

Shen, Dajiang; Hu, Chun; Yang, Bing; Tian, Jinwen; Liu, Jian

2007-11-01

In this paper, the problem of the detection of road networks in Synthetic Aperture Radar (SAR) images is addressed. Most of the previous methods extract the road by detecting lines and network reconstruction. Traditional algorithms such as MRFs, GA, Level Set, used in the progress of reconstruction are iterative. The tensor voting methodology we proposed is non-iterative, and non-sensitive to initialization. Furthermore, the only free parameter is the size of the neighborhood, related to the scale. The algorithm we present is verified to be effective when it's applied to the road extraction using the real Radarsat Image.

Iterative algorithm for reconstructing rotationally asymmetric surface deviation with pixel-level spatial resolution

NASA Astrophysics Data System (ADS)

Quan, Haiyang; Wu, Fan; Hou, Xi

2015-10-01

New method for reconstructing rotationally asymmetric surface deviation with pixel-level spatial resolution is proposed. It is based on basic iterative scheme and accelerates the Gauss-Seidel method by introducing an acceleration parameter. This modified Successive Over-relaxation (SOR) is effective for solving the rotationally asymmetric components with pixel-level spatial resolution, without the usage of a fitting procedure. Compared to the Jacobi and Gauss-Seidel method, the modified SOR method with an optimal relaxation factor converges much faster and saves more computational costs and memory space without reducing accuracy. It has been proved by real experimental results.

Variable aperture-based ptychographical iterative engine method

NASA Astrophysics Data System (ADS)

Sun, Aihui; Kong, Yan; Meng, Xin; He, Xiaoliang; Du, Ruijun; Jiang, Zhilong; Liu, Fei; Xue, Liang; Wang, Shouyu; Liu, Cheng

2018-02-01

A variable aperture-based ptychographical iterative engine (vaPIE) is demonstrated both numerically and experimentally to reconstruct the sample phase and amplitude rapidly. By adjusting the size of a tiny aperture under the illumination of a parallel light beam to change the illumination on the sample step by step and recording the corresponding diffraction patterns sequentially, both the sample phase and amplitude can be faithfully reconstructed with a modified ptychographical iterative engine (PIE) algorithm. Since many fewer diffraction patterns are required than in common PIE and the shape, the size, and the position of the aperture need not to be known exactly, this proposed vaPIE method remarkably reduces the data acquisition time and makes PIE less dependent on the mechanical accuracy of the translation stage; therefore, the proposed technique can be potentially applied for various scientific researches.

Rapid alignment of nanotomography data using joint iterative reconstruction and reprojection

DOE PAGES

Gürsoy, Doğa; Hong, Young P.; He, Kuan; ...

2017-09-18

As x-ray and electron tomography is pushed further into the nanoscale, the limitations of rotation stages become more apparent, leading to challenges in the alignment of the acquired projection images. Here we present an approach for rapid post-acquisition alignment of these projections to obtain high quality three-dimensional images. Our approach is based on a joint estimation of alignment errors, and the object, using an iterative refinement procedure. With simulated data where we know the alignment error of each projection image, our approach shows a residual alignment error that is a factor of a thousand smaller, and it reaches the samemore » error level in the reconstructed image in less than half the number of iterations. We then show its application to experimental data in x-ray and electron nanotomography.« less

On the retrieval of crystallographic information from atom probe microscopy data via signal mapping from the detector coordinate space.

PubMed

Wallace, Nathan D; Ceguerra, Anna V; Breen, Andrew J; Ringer, Simon P

2018-06-01

Atom probe tomography is a powerful microscopy technique capable of reconstructing the 3D position and chemical identity of millions of atoms within engineering materials, at the atomic level. Crystallographic information contained within the data is particularly valuable for the purposes of reconstruction calibration and grain boundary analysis. Typically, analysing this data is a manual, time-consuming and error prone process. In many cases, the crystallographic signal is so weak that it is difficult to detect at all. In this study, a new automated signal processing methodology is demonstrated. We use the affine properties of the detector coordinate space, or the 'detector stack', as the basis for our calculations. The methodological framework and the visualisation tools are shown to be superior to the standard method of crystallographic pole visualisation directly from field evaporation images and there is no requirement for iterations between a full real-space initial tomographic reconstruction and the detector stack. The mapping approaches are demonstrated for aluminium, tungsten, magnesium and molybdenum. Implications for reconstruction calibration, accuracy of crystallographic measurements, reliability and repeatability are discussed. Copyright © 2018 Elsevier B.V. All rights reserved.

Ultrasound guided electrical impedance tomography for 2D free-interface reconstruction

NASA Astrophysics Data System (ADS)

Liang, Guanghui; Ren, Shangjie; Dong, Feng

2017-07-01

The free-interface detection problem is normally seen in industrial or biological processes. Electrical impedance tomography (EIT) is a non-invasive technique with advantages of high-speed and low cost, and is a promising solution for free-interface detection problems. However, due to the ill-posed and nonlinear characteristics, the spatial resolution of EIT is low. To deal with the issue, an ultrasound guided EIT is proposed to directly reconstruct the geometric configuration of the target free-interface. In the method, the position of the central point of the target interface is measured by a pair of ultrasound transducers mounted at the opposite side of the objective domain, and then the position measurement is used as the prior information for guiding the EIT-based free-interface reconstruction. During the process, a constrained least squares framework is used to fuse the information from different measurement modalities, and the Lagrange multiplier-based Levenberg-Marquardt method is adopted to provide the iterative solution of the constraint optimization problem. The numerical results show that the proposed ultrasound guided EIT method for the free-interface reconstruction is more accurate than the single modality method, especially when the number of valid electrodes is limited.

Optimization of the volume reconstruction for classical Tomo-PIV algorithms (MART, BIMART and SMART): synthetic and experimental studies

NASA Astrophysics Data System (ADS)

Thomas, L.; Tremblais, B.; David, L.

2014-03-01

Optimization of multiplicative algebraic reconstruction technique (MART), simultaneous MART and block iterative MART reconstruction techniques was carried out on synthetic and experimental data. Different criteria were defined to improve the preprocessing of the initial images. Knowledge of how each reconstruction parameter influences the quality of particle volume reconstruction and computing time is the key in Tomo-PIV. These criteria were applied to a real case, a jet in cross flow, and were validated.

Reference-free Shack-Hartmann wavefront sensor.

PubMed

Zhao, Liping; Guo, Wenjiang; Li, Xiang; Chen, I-Ming

2011-08-01

The traditional Shack-Hartmann wavefront sensing (SHWS) system measures the wavefront slope by calculating the centroid shift between the sample and a reference piece, and then the wavefront is reconstructed by a suitable iterative reconstruction method. Because of the necessity of a reference, many issues are brought up, which limit the system in most applications. This Letter proposes a reference-free wavefront sensing (RFWS) methodology, and an RFWS system is built up where wavefront slope changes are measured by introducing a lateral disturbance to the sampling aperture. By using Southwell reconstruction two times to process the measured data, the form of the wavefront at the sampling plane can be well reconstructed. A theoretical simulation platform of RFWS is established, and various surface forms are investigated. Practical measurements with two measurement systems-SHWS and our RFWS-are conducted, analyzed, and compared. All the simulation and measurement results prove and demonstrate the correctness and effectiveness of the method. © 2011 Optical Society of America

Jointly reconstructing ground motion and resistivity for ERT-based slope stability monitoring

NASA Astrophysics Data System (ADS)

Boyle, Alistair; Wilkinson, Paul B.; Chambers, Jonathan E.; Meldrum, Philip I.; Uhlemann, Sebastian; Adler, Andy

2018-02-01

Electrical resistivity tomography (ERT) is increasingly being used to investigate unstable slopes and monitor the hydrogeological processes within. But movement of electrodes or incorrect placement of electrodes with respect to an assumed model can introduce significant resistivity artefacts into the reconstruction. In this work, we demonstrate a joint resistivity and electrode movement reconstruction algorithm within an iterative Gauss-Newton framework. We apply this to ERT monitoring data from an active slow-moving landslide in the UK. Results show fewer resistivity artefacts and suggest that electrode movement and resistivity can be reconstructed at the same time under certain conditions. A new 2.5-D formulation for the electrode position Jacobian is developed and is shown to give accurate numerical solutions when compared to the adjoint method on 3-D models. On large finite element meshes, the calculation time of the newly developed approach was also proven to be orders of magnitude faster than the 3-D adjoint method and addressed modelling errors in the 2-D perturbation and adjoint electrode position Jacobian.

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

NASA Astrophysics Data System (ADS)

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

1981-02-01

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

Reconstruction of electrical impedance tomography (EIT) images based on the expectation maximum (EM) method.

PubMed

Wang, Qi; Wang, Huaxiang; Cui, Ziqiang; Yang, Chengyi

2012-11-01

Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Study of noise propagation and the effects of insufficient numbers of projection angles and detector samplings for iterative reconstruction using planar-integral data

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

Zhang, B.; Zeng, G. L.

2006-09-15

A rotating slat collimator can be used to acquire planar-integral data. It achieves higher geometric efficiency than a parallel-hole collimator by accepting more photons, but the planar-integral data contain less tomographic information that may result in larger noise amplification in the reconstruction. Lodge evaluated the rotating slat system and the parallel-hole system based on noise behavior for an FBP reconstruction. Here, we evaluate the noise propagation properties of the two collimation systems for iterative reconstruction. We extend Huesman's noise propagation analysis of the line-integral system to the planar-integral case, and show that approximately 2.0(D/dp) SPECT angles, 2.5(D/dp) self-spinning angles atmore » each detector position, and a 0.5dp detector sampling interval are required in order for the planar-integral data to be efficiently utilized. Here, D is the diameter of the object and dp is the linear dimension of the voxels that subdivide the object. The noise propagation behaviors of the two systems are then compared based on a least-square reconstruction using the ratio of the SNR in the image reconstructed using a planar-integral system to that reconstructed using a line-integral system. The ratio is found to be proportional to {radical}(F/D), where F is a geometric efficiency factor. This result has been verified by computer simulations. It confirms that for an iterative reconstruction, the noise tradeoff of the two systems is not only dependent on the increase of the geometric efficiency afforded by the planar projection method, but also dependent on the size of the object. The planar-integral system works better for small objects, while the line-integral system performs better for large ones. This result is consistent with Lodge's results based on the FBP method.« less

Optimization of image reconstruction method for SPECT studies performed using [⁹⁹mTc-EDDA/HYNIC] octreotate in patients with neuroendocrine tumors.

PubMed

Sowa-Staszczak, Anna; Lenda-Tracz, Wioletta; Tomaszuk, Monika; Głowa, Bogusław; Hubalewska-Dydejczyk, Alicja

2013-01-01

Somatostatin receptor scintigraphy (SRS) is a useful tool in the assessment of GEP-NET (gastroenteropancreatic neuroendocrine tumor) patients. The choice of appropriate settings of image reconstruction parameters is crucial in interpretation of these images. The aim of the study was to investigate how the GEP NET lesion signal to noise ratio (TCS/TCB) depends on different reconstruction settings for Flash 3D software (Siemens). SRS results of 76 randomly selected patients with confirmed GEP-NET were analyzed. For SPECT studies the data were acquired using standard clinical settings 3-4 h after the injection of 740 MBq 99mTc-[EDDA/HYNIC] octreotate. To obtain final images the OSEM 3D Flash reconstruction with different settings and FBP reconstruction were used. First, the TCS/TCB ratio in voxels was analyzed for different combinations of the number of subsets and the number of iterations of the OSEM 3D Flash reconstruction. Secondly, the same ratio was analyzed for different parameters of the Gaussian filter (with FWHM = 2-4 times greater from the pixel size). Also the influence of scatter correction on the TCS/TCB ratio was investigated. With increasing number of subsets and iterations, the increase of TCS/TCB ratio was observed. With increasing settings of Gauss [FWHM coefficient] filter, the decrease of TCS/TCB ratio was reported. The use of scatter correction slightly decreases the values of this ratio. OSEM algorithm provides a meaningfully better reconstruction of the SRS SPECT study as compared to the FBP technique. A high number of subsets improves image quality (images are smoother). Increasing number of iterations gives a better contrast and the shapes of lesions and organs are sharper. The choice of reconstruction parameters is a compromise between image qualitative appearance and its quantitative accuracy and should not be modified when comparing multiple studies of the same patient.

Radiation dose reduction in soft tissue neck CT using adaptive statistical iterative reconstruction (ASIR).

PubMed

Vachha, Behroze; Brodoefel, Harald; Wilcox, Carol; Hackney, David B; Moonis, Gul

2013-12-01

To compare objective and subjective image quality in neck CT images acquired at different tube current-time products (275 mAs and 340 mAs) and reconstructed with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction (ASIR). HIPAA-compliant study with IRB approval and waiver of informed consent. 66 consecutive patients were randomly assigned to undergo contrast-enhanced neck CT at a standard tube-current-time-product (340 mAs; n = 33) or reduced tube-current-time-product (275 mAs, n = 33). Data sets were reconstructed with FBP and 2 levels (30%, 40%) of ASIR-FBP blending at 340 mAs and 275 mAs. Two neuroradiologists assessed subjective image quality in a blinded and randomized manner. Volume CT dose index (CTDIvol), dose-length-product (DLP), effective dose, and objective image noise were recorded. Signal-to-noise ratio (SNR) was computed as mean attenuation in a region of interest in the sternocleidomastoid muscle divided by image noise. Compared with FBP, ASIR resulted in a reduction of image noise at both 340 mAs and 275 mAs. Reduction of tube current from 340 mAs to 275 mAs resulted in an increase in mean objective image noise (p=0.02) and a decrease in SNR (p = 0.03) when images were reconstructed with FBP. However, when the 275 mAs images were reconstructed using ASIR, the mean objective image noise and SNR were similar to those of the standard 340 mAs CT images reconstructed with FBP (p>0.05). Subjective image noise was ranked by both raters as either average or less-than-average irrespective of the tube current and iterative reconstruction technique. Adapting ASIR into neck CT protocols reduced effective dose by 17% without compromising image quality. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Gaussian mixed model in support of semiglobal matching leveraged by ground control points

NASA Astrophysics Data System (ADS)

Ma, Hao; Zheng, Shunyi; Li, Chang; Li, Yingsong; Gui, Li

2017-04-01

Semiglobal matching (SGM) has been widely applied in large aerial images because of its good tradeoff between complexity and robustness. The concept of ground control points (GCPs) is adopted to make SGM more robust. We model the effect of GCPs as two data terms for stereo matching between high-resolution aerial epipolar images in an iterative scheme. One term based on GCPs is formulated by Gaussian mixture model, which strengths the relation between GCPs and the pixels to be estimated and encodes some degree of consistency between them with respect to disparity values. Another term depends on pixel-wise confidence, and we further design a confidence updating equation based on three rules. With this confidence-based term, the assignment of disparity can be heuristically selected among disparity search ranges during the iteration process. Several iterations are sufficient to bring out satisfactory results according to our experiments. Experimental results validate that the proposed method outperforms surface reconstruction, which is a representative variant of SGM and behaves excellently on aerial images.

Progressive Stochastic Reconstruction Technique (PSRT) for cryo electron tomography.

PubMed

Turoňová, Beata; Marsalek, Lukas; Davidovič, Tomáš; Slusallek, Philipp

2015-03-01

Cryo Electron Tomography (cryoET) plays an essential role in Structural Biology, as it is the only technique that allows to study the structure of large macromolecular complexes in their close to native environment in situ. The reconstruction methods currently in use, such as Weighted Back Projection (WBP) or Simultaneous Iterative Reconstruction Technique (SIRT), deliver noisy and low-contrast reconstructions, which complicates the application of high-resolution protocols, such as Subtomogram Averaging (SA). We propose a Progressive Stochastic Reconstruction Technique (PSRT) - a novel iterative approach to tomographic reconstruction in cryoET based on Monte Carlo random walks guided by Metropolis-Hastings sampling strategy. We design a progressive reconstruction scheme to suit the conditions present in cryoET and apply it successfully to reconstructions of macromolecular complexes from both synthetic and experimental datasets. We show how to integrate PSRT into SA, where it provides an elegant solution to the region-of-interest problem and delivers high-contrast reconstructions that significantly improve template-based localization without any loss of high-resolution structural information. Furthermore, the locality of SA is exploited to design an importance sampling scheme which significantly speeds up the otherwise slow Monte Carlo approach. Finally, we design a new memory efficient solution for the specimen-level interior problem of cryoET, removing all associated artifacts. Copyright © 2015 Elsevier Inc. All rights reserved.

A high-resolution photon-counting breast CT system with tensor-framelet based iterative image reconstruction for radiation dose reduction

NASA Astrophysics Data System (ADS)

Ding, Huanjun; Gao, Hao; Zhao, Bo; Cho, Hyo-Min; Molloi, Sabee

2014-10-01

Both computer simulations and experimental phantom studies were carried out to investigate the radiation dose reduction with tensor framelet based iterative image reconstruction (TFIR) for a dedicated high-resolution spectral breast computed tomography (CT) based on a silicon strip photon-counting detector. The simulation was performed with a 10 cm-diameter water phantom including three contrast materials (polyethylene, 8 mg ml-1 iodine and B-100 bone-equivalent plastic). In the experimental study, the data were acquired with a 1.3 cm-diameter polymethylmethacrylate (PMMA) phantom containing iodine in three concentrations (8, 16 and 32 mg ml-1) at various radiation doses (1.2, 2.4 and 3.6 mGy) and then CT images were reconstructed using the filtered-back-projection (FBP) technique and the TFIR technique, respectively. The image quality between these two techniques was evaluated by the quantitative analysis on contrast-to-noise ratio (CNR) and spatial resolution that was evaluated using the task-based modulation transfer function (MTF). Both the simulation and experimental results indicated that the task-based MTF obtained from TFIR reconstruction with one-third of the radiation dose was comparable to that from the FBP reconstruction for low contrast target. For high contrast target, the TFIR was substantially superior to the FBP reconstruction in terms of spatial resolution. In addition, TFIR was able to achieve a factor of 1.6-1.8 increase in CNR, depending on the target contrast level. This study demonstrates that the TFIR can reduce the required radiation dose by a factor of two-thirds for a CT image reconstruction compared to the FBP technique. It achieves much better CNR and spatial resolution for high contrast target in addition to retaining similar spatial resolution for low contrast target. This TFIR technique has been implemented with a graphic processing unit system and it takes approximately 10 s to reconstruct a single-slice CT image, which can potentially be used in a future multi-slit multi-slice spiral CT system.

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.

SU-E-I-04: Improving CT Quality for Radiation Therapy of Patients with High Body Mass Index Using Iterative Reconstruction Algorithms

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

Noid, G; Tai, A; Li, X

2015-06-15

Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. The CT IQ for patients with a high Body Mass Index (BMI) can suffer from increased noise due to photon starvation. The purpose of this study is to investigate and to quantify the IQ enhancement for high BMI patients through the application of IR algorithms. Methods: CT raw data collected for 6 radiotherapy (RT) patients with BMI, greater than or equal to 30 were retrospectively analyzed. All CT data were acquired using a CT scanner (Somaton Definition ASmore » Open, Siemens) installed in a linac room (CT-on-rails) using standard imaging protocols. The CT data were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared and correlated with patient depth and BMI. The patient depth was defined as the largest distance from anterior to posterior along the bilateral symmetry axis. Results: IR techniques are demonstrated to preserve contrast and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is roughly doubled by adopting the highest SAFIRE strength. A significant correlation was observed between patient depth and IR noise reduction through Pearson’s correlation test (R = 0.9429/P = 0.0167). The mean patient depth was 30.4 cm and the average relative noise reduction for the strongest iterative reconstruction was 55%. Conclusion: The IR techniques produce a measureable enhancement to CT IQ by reducing the noise. Dramatic noise reduction is evident for the high BMI patients. The improved CT IQ enables more accurate delineation of tumors and organs at risk and more accuarte dose calculations for RT planning and delivery guidance. Supported by Siemens.« less

SU-G-BRA-11: Tumor Tracking in An Iterative Volume of Interest Based 4D CBCT Reconstruction

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

Martin, R; Pan, T; Ahmad, M

2016-06-15

Purpose: 4D CBCT can allow evaluation of tumor motion immediately prior to radiation therapy, but suffers from heavy artifacts that limit its ability to track tumors. Various iterative and compressed sensing reconstructions have been proposed to reduce these artifacts, but are costly time-wise and can degrade the image quality of bony anatomy for alignment with regularization. We have previously proposed an iterative volume of interest (I4D VOI) method which minimizes reconstruction time and maintains image quality of bony anatomy by focusing a 4D reconstruction within a VOI. The purpose of this study is to test the tumor tracking accuracy ofmore » this method compared to existing methods. Methods: Long scan (8–10 mins) CBCT data with corresponding RPM data was collected for 12 lung cancer patients. The full data set was sorted into 8 phases and reconstructed using FDK cone beam reconstruction to serve as a gold standard. The data was reduced in way that maintains a normal breathing pattern and used to reconstruct 4D images using FDK, low and high regularization TV minimization (λ=2,10), and the proposed I4D VOI method with PTVs used for the VOI. Tumor trajectories were found using rigid registration within the VOI for each reconstruction and compared to the gold standard. Results: The root mean square error (RMSE) values were 2.70mm for FDK, 2.50mm for low regularization TV, 1.48mm for high regularization TV, and 2.34mm for I4D VOI. Streak artifacts in I4D VOI were reduced compared to FDK and images were less blurred than TV reconstructed images. Conclusion: I4D VOI performed at least as well as existing methods in tumor tracking, with the exception of high regularization TV minimization. These results along with the reconstruction time and outside VOI image quality advantages suggest I4D VOI to be an improvement over existing methods. Funding support provided by CPRIT grant RP110562-P2-01.« less

Improved Image Quality in Head and Neck CT Using a 3D Iterative Approach to Reduce Metal Artifact.

PubMed

Wuest, W; May, M S; Brand, M; Bayerl, N; Krauss, A; Uder, M; Lell, M

2015-10-01

Metal artifacts from dental fillings and other devices degrade image quality and may compromise the detection and evaluation of lesions in the oral cavity and oropharynx by CT. The aim of this study was to evaluate the effect of iterative metal artifact reduction on CT of the oral cavity and oropharynx. Data from 50 consecutive patients with metal artifacts from dental hardware were reconstructed with standard filtered back-projection, linear interpolation metal artifact reduction (LIMAR), and iterative metal artifact reduction. The image quality of sections that contained metal was analyzed for the severity of artifacts and diagnostic value. A total of 455 sections (mean ± standard deviation, 9.1 ± 4.1 sections per patient) contained metal and were evaluated with each reconstruction method. Sections without metal were not affected by the algorithms and demonstrated image quality identical to each other. Of these sections, 38% were considered nondiagnostic with filtered back-projection, 31% with LIMAR, and only 7% with iterative metal artifact reduction. Thirty-three percent of the sections had poor image quality with filtered back-projection, 46% with LIMAR, and 10% with iterative metal artifact reduction. Thirteen percent of the sections with filtered back-projection, 17% with LIMAR, and 22% with iterative metal artifact reduction were of moderate image quality, 16% of the sections with filtered back-projection, 5% with LIMAR, and 30% with iterative metal artifact reduction were of good image quality, and 1% of the sections with LIMAR and 31% with iterative metal artifact reduction were of excellent image quality. Iterative metal artifact reduction yields the highest image quality in comparison with filtered back-projection and linear interpolation metal artifact reduction in patients with metal hardware in the head and neck area. © 2015 by American Journal of Neuroradiology.

A fractional-order accumulative regularization filter for force reconstruction

NASA Astrophysics Data System (ADS)

Wensong, Jiang; Zhongyu, Wang; Jing, Lv

2018-02-01

The ill-posed inverse problem of the force reconstruction comes from the influence of noise to measured responses and results in an inaccurate or non-unique solution. To overcome this ill-posedness, in this paper, the transfer function of the reconstruction model is redefined by a Fractional order Accumulative Regularization Filter (FARF). First, the measured responses with noise are refined by a fractional-order accumulation filter based on a dynamic data refresh strategy. Second, a transfer function, generated by the filtering results of the measured responses, is manipulated by an iterative Tikhonov regularization with a serious of iterative Landweber filter factors. Third, the regularization parameter is optimized by the Generalized Cross-Validation (GCV) to improve the ill-posedness of the force reconstruction model. A Dynamic Force Measurement System (DFMS) for the force reconstruction is designed to illustrate the application advantages of our suggested FARF method. The experimental result shows that the FARF method with r = 0.1 and α = 20, has a PRE of 0.36% and an RE of 2.45%, is superior to other cases of the FARF method and the traditional regularization methods when it comes to the dynamic force reconstruction.

Iterative Reconstruction of Volumetric Particle Distribution for 3D Velocimetry

NASA Astrophysics Data System (ADS)

Wieneke, Bernhard; Neal, Douglas

2011-11-01

A number of different volumetric flow measurement techniques exist for following the motion of illuminated particles. For experiments that have lower seeding densities, 3D-PTV uses recorded images from typically 3-4 cameras and then tracks the individual particles in space and time. This technique is effective in flows that have lower seeding densities. For flows that have a higher seeding density, tomographic PIV uses a tomographic reconstruction algorithm (e.g. MART) to reconstruct voxel intensities of the recorded volume followed by the cross-correlation of subvolumes to provide the instantaneous 3D vector fields on a regular grid. A new hybrid algorithm is presented which iteratively reconstructs the 3D-particle distribution directly using particles with certain imaging properties instead of voxels as base functions. It is shown with synthetic data that this method is capable of reconstructing densely seeded flows up to 0.05 particles per pixel (ppp) with the same or higher accuracy than 3D-PTV and tomographic PIV. Finally, this new method is validated using experimental data on a turbulent jet.

An efficient gridding reconstruction method for multishot non-Cartesian imaging with correction of off-resonance artifacts.

PubMed

Meng, Yuguang; Lei, Hao

2010-06-01

An efficient iterative gridding reconstruction method with correction of off-resonance artifacts was developed, which is especially tailored for multiple-shot non-Cartesian imaging. The novelty of the method lies in that the transformation matrix for gridding (T) was constructed as the convolution of two sparse matrices, among which the former is determined by the sampling interval and the spatial distribution of the off-resonance frequencies and the latter by the sampling trajectory and the target grid in the Cartesian space. The resulting T matrix is also sparse and can be solved efficiently with the iterative conjugate gradient algorithm. It was shown that, with the proposed method, the reconstruction speed in multiple-shot non-Cartesian imaging can be improved significantly while retaining high reconstruction fidelity. More important, the method proposed allows tradeoff between the accuracy and the computation time of reconstruction, making customization of the use of such a method in different applications possible. The performance of the proposed method was demonstrated by numerical simulation and multiple-shot spiral imaging on rat brain at 4.7 T. (c) 2010 Wiley-Liss, Inc.

WE-G-BRF-07: Non-Circular Scanning Trajectories with Varian Developer Mode

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

Davis, A; Pearson, E; Pan, X

2014-06-15

Purpose: Cone-beam CT (CBCT) in image-guide radiation therapy (IGRT) typicallyacquires scan data via the circular trajectory of the linearaccelerator's (linac) gantry rotation. Though this lends itself toanalytic reconstruction algorithms like FDK, iterative reconstructionalgorithms allow for a broader range of scanning trajectories. Weimplemented a non-circular scanning trajectory with Varian's TrueBeamDeveloper Mode and performed some preliminary reconstructions toverify the geometry. Methods: We used TrueBeam Developer Mode to program a new scanning trajectorythat increases the field of view (FOV) along the gantry rotation axiswithout moving the patient. This trajectory consisted of moving thegantry in a circle, then translating the source and detector alongmore » theaxial direction before acquiring another circular scan 19 cm away fromthe first. The linear portion of the trajectory includes an additional4.5 cm above and below the axial planes of the source's circularrotation. We scanned a calibration phantom consisting of a lucite tubewith a spiral pattern of CT spots and used the maximum-likelihoodalgorithm to iteratively reconstruct the CBCT volume. Results: With the TrueBeam trajectory definition, we acquired projection dataof the calibration phantom using the previously described trajectory.We obtained a scan of the treatment couch for log normalization byscanning with the same trajectory but without the phantom present.Using the nominal geometric parameters reported in the projectionheaders with our iterative reconstruction algorithm, we obtained acorrect reconstruction of the calibration phantom. Conclusion: The ability to implement new scanning trajectories with the TrueBeamDeveloper Mode enables us access to a new parameter space for imagingwith CBCT for IGRT. Previous simulations and simple dual circle scanshave shown iterative reconstruction with non-circular trajectories canincrease the axial FOV with CBCT. Use of Developer Mode allowsexperimentally testing these and other new scanning trajectories. Support was provided in part by the University of Chicago Research Computing Center, Varian Medical Systems, and NIH Grants 1RO1CA120540, T32EB002103, S10 RR021039 and P30 CA14599. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the supporting organizations.« less

Reduced Radiation Dose with Model-based Iterative Reconstruction versus Standard Dose with Adaptive Statistical Iterative Reconstruction in Abdominal CT for Diagnosis of Acute Renal Colic.

PubMed

Fontarensky, Mikael; Alfidja, Agaïcha; Perignon, Renan; Schoenig, Arnaud; Perrier, Christophe; Mulliez, Aurélien; Guy, Laurent; Boyer, Louis

2015-07-01

To evaluate the accuracy of reduced-dose abdominal computed tomographic (CT) imaging by using a new generation model-based iterative reconstruction (MBIR) to diagnose acute renal colic compared with a standard-dose abdominal CT with 50% adaptive statistical iterative reconstruction (ASIR). This institutional review board-approved prospective study included 118 patients with symptoms of acute renal colic who underwent the following two successive CT examinations: standard-dose ASIR 50% and reduced-dose MBIR. Two radiologists independently reviewed both CT examinations for presence or absence of renal calculi, differential diagnoses, and associated abnormalities. The imaging findings, radiation dose estimates, and image quality of the two CT reconstruction methods were compared. Concordance was evaluated by κ coefficient, and descriptive statistics and t test were used for statistical analysis. Intraobserver correlation was 100% for the diagnosis of renal calculi (κ = 1). Renal calculus (τ = 98.7%; κ = 0.97) and obstructive upper urinary tract disease (τ = 98.16%; κ = 0.95) were detected, and differential or alternative diagnosis was performed (τ = 98.87% κ = 0.95). MBIR allowed a dose reduction of 84% versus standard-dose ASIR 50% (mean volume CT dose index, 1.7 mGy ± 0.8 [standard deviation] vs 10.9 mGy ± 4.6; mean size-specific dose estimate, 2.2 mGy ± 0.7 vs 13.7 mGy ± 3.9; P < .001) without a conspicuous deterioration in image quality (reduced-dose MBIR vs ASIR 50% mean scores, 3.83 ± 0.49 vs 3.92 ± 0.27, respectively; P = .32) or increase in noise (reduced-dose MBIR vs ASIR 50% mean, respectively, 18.36 HU ± 2.53 vs 17.40 HU ± 3.42). Its main drawback remains the long time required for reconstruction (mean, 40 minutes). A reduced-dose protocol with MBIR allowed a dose reduction of 84% without increasing noise and without an conspicuous deterioration in image quality in patients suspected of having renal colic.

Ultralow-dose CT of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction and model-based iterative reconstruction: 2D and 3D image quality.

PubMed

Widmann, Gerlig; Schullian, Peter; Gassner, Eva-Maria; Hoermann, Romed; Bale, Reto; Puelacher, Wolfgang

2015-03-01

OBJECTIVE. The purpose of this article is to evaluate 2D and 3D image quality of high-resolution ultralow-dose CT images of the craniofacial bone for navigated surgery using adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) in comparison with standard filtered backprojection (FBP). MATERIALS AND METHODS. A formalin-fixed human cadaver head was scanned using a clinical reference protocol at a CT dose index volume of 30.48 mGy and a series of five ultralow-dose protocols at 3.48, 2.19, 0.82, 0.44, and 0.22 mGy using FBP and ASIR at 50% (ASIR-50), ASIR at 100% (ASIR-100), and MBIR. Blinded 2D axial and 3D volume-rendered images were compared with each other by three readers using top-down scoring. Scores were analyzed per protocol or dose and reconstruction. All images were compared with the FBP reference at 30.48 mGy. A nonparametric Mann-Whitney U test was used. Statistical significance was set at p < 0.05. RESULTS. For 2D images, the FBP reference at 30.48 mGy did not statistically significantly differ from ASIR-100 at 3.48 mGy, ASIR-100 at 2.19 mGy, and MBIR at 0.82 mGy. MBIR at 2.19 and 3.48 mGy scored statistically significantly better than the FBP reference (p = 0.032 and 0.001, respectively). For 3D images, the FBP reference at 30.48 mGy did not statistically significantly differ from all reconstructions at 3.48 mGy; FBP and ASIR-100 at 2.19 mGy; FBP, ASIR-100, and MBIR at 0.82 mGy; MBIR at 0.44 mGy; and MBIR at 0.22 mGy. CONCLUSION. MBIR (2D and 3D) and ASIR-100 (2D) may significantly improve subjective image quality of ultralow-dose images and may allow more than 90% dose reductions.

Effects of ray profile modeling on resolution recovery in clinical CT

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

Hofmann, Christian; Knaup, Michael; Kachelrieß, Marc, E-mail: marc.kachelriess@dkfz-heidelberg.de

2014-02-15

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. However, among vendors and researchers, there is no consensus of how to best achieve these goals. The authors are focusing on the aspect of geometric ray profile modeling, which is realized by some algorithms, while others model the ray as a straight line. The authors incorporate ray-modeling (RM) in nonregularized iterative reconstruction. That means, instead of using one simple single needle beam to represent the x-ray, the authors evaluatemore » the double integral of attenuation path length over the finite source distribution and the finite detector element size in the numerical forward projection. Our investigations aim at analyzing the resolution recovery (RR) effects of RM. Resolution recovery means that frequencies can be recovered beyond the resolution limit of the imaging system. In order to evaluate, whether clinical CT images can benefit from modeling the geometrical properties of each x-ray, the authors performed a 2D simulation study of a clinical CT fan-beam geometry that includes the precise modeling of these geometrical properties. Methods: All simulations and reconstructions are performed in native fan-beam geometry. A water phantom with resolution bar patterns and a Forbild thorax phantom with circular resolution patterns representing calcifications in the heart region are simulated. An FBP reconstruction with a Ram–Lak kernel is used as a reference reconstruction. The FBP is compared to iterative reconstruction techniques with and without RM: An ordered subsets convex (OSC) algorithm without any RM (OSC), an OSC where the forward projection is modeled concerning the finite focal spot and detector size (OSC-RM) and an OSC with RM and with a matched forward and backprojection pair (OSC-T-RM, T for transpose). In all cases, noise was matched to be able to focus on comparing spatial resolution. The authors use two different simulation settings. Both are based on the geometry of a typical clinical CT system (0.7 mm detector element size at isocenter, 1024 projections per rotation). Setting one has an exaggerated source width of 5.0 mm. Setting two has a realistically small source width of 0.5 mm. The authors also investigate the transition from setting one to two. To quantify image quality, the authors analyze line profiles through the resolution patterns to define a contrast factor (CF) for contrast-resolution plots, and the authors compare the normalized cross-correlation (NCC) with respect to the ground truth of the circular resolution patterns. To independently analyze whether RM is of advantage, the authors implemented several iterative reconstruction algorithms: The statistical iterative reconstruction algorithm OSC, the ordered subsets simultaneous algebraic reconstruction technique (OSSART) and another statistical iterative reconstruction algorithm, denoted with ordered subsets maximum likelihood (OSML) algorithm. All algorithms were implemented both without RM (denoted as OSC, OSSART, and OSML) and with RM (denoted as OSC-RM, OSSART-RM, and OSML-RM). Results: For the unrealistic case of a 5.0 mm focal spot the CF can be improved by a factor of two due to RM: the 4.2 LP/cm bar pattern, which is the first bar pattern that cannot be resolved without RM, can be easily resolved with RM. For the realistic case of a 0.5 mm focus, all results show approximately the same CF. The NCC shows no significant dependency on RM when the source width is smaller than 2.0 mm (as in clinical CT). From 2.0 mm to 5.0 mm focal spot size increasing improvements can be observed with RM. Conclusions: Geometric RM in iterative reconstruction helps improving spatial resolution, if the ray cross-section is significantly larger than the ray sampling distance. In clinical CT, however, the ray is not much thicker than the distance between neighboring ray centers, as the focal spot size is small and detector crosstalk is negligible, due to reflective coatings between detector elements. Therefore,RM appears not to be necessary in clinical CT to achieve resolution recovery.« less

Combined Use of Automatic Tube Voltage Selection and Current Modulation with Iterative Reconstruction for CT Evaluation of Small Hypervascular Hepatocellular Carcinomas: Effect on Lesion Conspicuity and Image Quality

PubMed Central

Lv, Peijie; Liu, Jie; Zhang, Rui; Jia, Yan

2015-01-01

Objective To assess the lesion conspicuity and image quality in CT evaluation of small (≤ 3 cm) hepatocellular carcinomas (HCCs) using automatic tube voltage selection (ATVS) and automatic tube current modulation (ATCM) with or without iterative reconstruction. Materials and Methods One hundred and five patients with 123 HCC lesions were included. Fifty-seven patients were scanned using both ATVS and ATCM and images were reconstructed using either filtered back-projection (FBP) (group A1) or sinogram-affirmed iterative reconstruction (SAFIRE) (group A2). Forty-eight patients were imaged using only ATCM, with a fixed tube potential of 120 kVp and FBP reconstruction (group B). Quantitative parameters (image noise in Hounsfield unit and contrast-to-noise ratio of the aorta, the liver, and the hepatic tumors) and qualitative visual parameters (image noise, overall image quality, and lesion conspicuity as graded on a 5-point scale) were compared among the groups. Results Group A2 scanned with the automatically chosen 80 kVp and 100 kVp tube voltages ranked the best in lesion conspicuity and subjective and objective image quality (p values ranging from < 0.001 to 0.004) among the three groups, except for overall image quality between group A2 and group B (p = 0.022). Group A1 showed higher image noise (p = 0.005) but similar lesion conspicuity and overall image quality as compared with group B. The radiation dose in group A was 19% lower than that in group B (p = 0.022). Conclusion CT scanning with combined use of ATVS and ATCM and image reconstruction with SAFIRE algorithm provides higher lesion conspicuity and better image quality for evaluating small hepatic HCCs with radiation dose reduction. PMID:25995682

Investigation of cone-beam CT image quality trade-off for image-guided radiation therapy

NASA Astrophysics Data System (ADS)

Bian, Junguo; Sharp, Gregory C.; Park, Yang-Kyun; Ouyang, Jinsong; Bortfeld, Thomas; El Fakhri, Georges

2016-05-01

It is well-known that projections acquired over an angular range slightly over 180° (so-called short scan) are sufficient for fan-beam reconstruction. However, due to practical imaging conditions (projection data and reconstruction image discretization, physical factors, and data noise), the short-scan reconstructions may have different appearances and properties from the full-scan (scans over 360°) reconstructions. Nevertheless, short-scan configurations have been used in applications such as cone-beam CT (CBCT) for head-neck-cancer image-guided radiation therapy (IGRT) that only requires a small field of view due to the potential reduced imaging time and dose. In this work, we studied the image quality trade-off for full, short, and full/short scan configurations with both conventional filtered-backprojection (FBP) reconstruction and iterative reconstruction algorithms based on total-variation (TV) minimization for head-neck-cancer IGRT. Anthropomorphic and Catphan phantoms were scanned at different exposure levels with a clinical scanner used in IGRT. Both visualization- and numerical-metric-based evaluation studies were performed. The results indicate that the optimal exposure level and number of views are in the middle range for both FBP and TV-based iterative algorithms and the optimization is object-dependent and task-dependent. The optimal view numbers decrease with the total exposure levels for both FBP and TV-based algorithms. The results also indicate there are slight differences between FBP and TV-based iterative algorithms for the image quality trade-off: FBP seems to be more in favor of larger number of views while the TV-based algorithm is more robust to different data conditions (number of views and exposure levels) than the FBP algorithm. The studies can provide a general guideline for image-quality optimization for CBCT used in IGRT and other applications.

Statistical iterative reconstruction to improve image quality for digital breast tomosynthesis

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

Xu, Shiyu, E-mail: shiyu.xu@gmail.com; Chen, Ying, E-mail: adachen@siu.edu; Lu, Jianping

2015-09-15

Purpose: Digital breast tomosynthesis (DBT) is a novel modality with the potential to improve early detection of breast cancer by providing three-dimensional (3D) imaging with a low radiation dose. 3D image reconstruction presents some challenges: cone-beam and flat-panel geometry, and highly incomplete sampling. A promising means to overcome these challenges is statistical iterative reconstruction (IR), since it provides the flexibility of accurate physics modeling and a general description of system geometry. The authors’ goal was to develop techniques for applying statistical IR to tomosynthesis imaging data. Methods: These techniques include the following: a physics model with a local voxel-pair basedmore » prior with flexible parameters to fine-tune image quality; a precomputed parameter λ in the prior, to remove data dependence and to achieve a uniform resolution property; an effective ray-driven technique to compute the forward and backprojection; and an oversampled, ray-driven method to perform high resolution reconstruction with a practical region-of-interest technique. To assess the performance of these techniques, the authors acquired phantom data on the stationary DBT prototype system. To solve the estimation problem, the authors proposed an optimization-transfer based algorithm framework that potentially allows fewer iterations to achieve an acceptably converged reconstruction. Results: IR improved the detectability of low-contrast and small microcalcifications, reduced cross-plane artifacts, improved spatial resolution, and lowered noise in reconstructed images. Conclusions: Although the computational load remains a significant challenge for practical development, the superior image quality provided by statistical IR, combined with advancing computational techniques, may bring benefits to screening, diagnostics, and intraoperative imaging in clinical applications.« less

Optimisation of reconstruction--reprojection-based motion correction for cardiac SPECT.

PubMed

Kangasmaa, Tuija S; Sohlberg, Antti O

2014-07-01

Cardiac motion is a challenging cause of image artefacts in myocardial perfusion SPECT. A wide range of motion correction methods have been developed over the years, and so far automatic algorithms based on the reconstruction--reprojection principle have proved to be the most effective. However, these methods have not been fully optimised in terms of their free parameters and implementational details. Two slightly different implementations of reconstruction--reprojection-based motion correction techniques were optimised for effective, good-quality motion correction and then compared with each other. The first of these methods (Method 1) was the traditional reconstruction-reprojection motion correction algorithm, where the motion correction is done in projection space, whereas the second algorithm (Method 2) performed motion correction in reconstruction space. The parameters that were optimised include the type of cost function (squared difference, normalised cross-correlation and mutual information) that was used to compare measured and reprojected projections, and the number of iterations needed. The methods were tested with motion-corrupt projection datasets, which were generated by adding three different types of motion (lateral shift, vertical shift and vertical creep) to motion-free cardiac perfusion SPECT studies. Method 2 performed slightly better overall than Method 1, but the difference between the two implementations was small. The execution time for Method 2 was much longer than for Method 1, which limits its clinical usefulness. The mutual information cost function gave clearly the best results for all three motion sets for both correction methods. Three iterations were sufficient for a good quality correction using Method 1. The traditional reconstruction--reprojection-based method with three update iterations and mutual information cost function is a good option for motion correction in clinical myocardial perfusion SPECT.

Can use of adaptive statistical iterative reconstruction reduce radiation dose in unenhanced head CT? An analysis of qualitative and quantitative image quality

PubMed Central

Heggen, Kristin Livelten; Pedersen, Hans Kristian; Andersen, Hilde Kjernlie; Martinsen, Anne Catrine T

2016-01-01

Background Iterative reconstruction can reduce image noise and thereby facilitate dose reduction. Purpose To evaluate qualitative and quantitative image quality for full dose and dose reduced head computed tomography (CT) protocols reconstructed using filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR). Material and Methods Fourteen patients undergoing follow-up head CT were included. All patients underwent full dose (FD) exam and subsequent 15% dose reduced (DR) exam, reconstructed using FBP and 30% ASIR. Qualitative image quality was assessed using visual grading characteristics. Quantitative image quality was assessed using ROI measurements in cerebrospinal fluid (CSF), white matter, peripheral and central gray matter. Additionally, quantitative image quality was measured in Catphan and vendor’s water phantom. Results There was no significant difference in qualitative image quality between FD FBP and DR ASIR. Comparing same scan FBP versus ASIR, a noise reduction of 28.6% in CSF and between −3.7 and 3.5% in brain parenchyma was observed. Comparing FD FBP versus DR ASIR, a noise reduction of 25.7% in CSF, and −7.5 and 6.3% in brain parenchyma was observed. Image contrast increased in ASIR reconstructions. Contrast-to-noise ratio was improved in DR ASIR compared to FD FBP. In phantoms, noise reduction was in the range of 3 to 28% with image content. Conclusion There was no significant difference in qualitative image quality between full dose FBP and dose reduced ASIR. CNR improved in DR ASIR compared to FD FBP mostly due to increased contrast, not reduced noise. Therefore, we recommend using caution if reducing dose and applying ASIR to maintain image quality. PMID:27583169

Can use of adaptive statistical iterative reconstruction reduce radiation dose in unenhanced head CT? An analysis of qualitative and quantitative image quality.

PubMed

Østerås, Bjørn Helge; Heggen, Kristin Livelten; Pedersen, Hans Kristian; Andersen, Hilde Kjernlie; Martinsen, Anne Catrine T

2016-08-01

Iterative reconstruction can reduce image noise and thereby facilitate dose reduction. To evaluate qualitative and quantitative image quality for full dose and dose reduced head computed tomography (CT) protocols reconstructed using filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR). Fourteen patients undergoing follow-up head CT were included. All patients underwent full dose (FD) exam and subsequent 15% dose reduced (DR) exam, reconstructed using FBP and 30% ASIR. Qualitative image quality was assessed using visual grading characteristics. Quantitative image quality was assessed using ROI measurements in cerebrospinal fluid (CSF), white matter, peripheral and central gray matter. Additionally, quantitative image quality was measured in Catphan and vendor's water phantom. There was no significant difference in qualitative image quality between FD FBP and DR ASIR. Comparing same scan FBP versus ASIR, a noise reduction of 28.6% in CSF and between -3.7 and 3.5% in brain parenchyma was observed. Comparing FD FBP versus DR ASIR, a noise reduction of 25.7% in CSF, and -7.5 and 6.3% in brain parenchyma was observed. Image contrast increased in ASIR reconstructions. Contrast-to-noise ratio was improved in DR ASIR compared to FD FBP. In phantoms, noise reduction was in the range of 3 to 28% with image content. There was no significant difference in qualitative image quality between full dose FBP and dose reduced ASIR. CNR improved in DR ASIR compared to FD FBP mostly due to increased contrast, not reduced noise. Therefore, we recommend using caution if reducing dose and applying ASIR to maintain image quality.

Reduction of metal artifacts due to dental hardware in computed tomography angiography: assessment of the utility of model-based iterative reconstruction.

PubMed

Kuya, Keita; Shinohara, Yuki; Kato, Ayumi; Sakamoto, Makoto; Kurosaki, Masamichi; Ogawa, Toshihide

2017-03-01

The aim of this study is to assess the value of adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR) for reduction of metal artifacts due to dental hardware in carotid CT angiography (CTA). Thirty-seven patients with dental hardware who underwent carotid CTA were included. CTA was performed with a GE Discovery CT750 HD scanner and reconstructed with filtered back projection (FBP), ASIR, and MBIR. We measured the standard deviation at the cervical segment of the internal carotid artery that was affected most by dental metal artifacts (SD 1 ) and the standard deviation at the common carotid artery that was not affected by the artifact (SD 2 ). We calculated the artifact index (AI) as follows: AI = [(SD 1 )2 - (SD 2 )2]1/2 and compared each AI for FBP, ASIR, and MBIR. Visual assessment of the internal carotid artery was also performed by two neuroradiologists using a five-point scale for each axial and reconstructed sagittal image. The inter-observer agreement was analyzed using weighted kappa analysis. MBIR significantly improved AI compared with FBP and ASIR (p < 0.001, each). We found no significant difference in AI between FBP and ASIR (p = 0.502). The visual score of MBIR was significantly better than those of FBP and ASIR (p < 0.001, each), whereas the scores of ASIR were the same as those of FBP. Kappa values indicated good inter-observer agreements in all reconstructed images (0.747-0.778). MBIR resulted in a significant reduction in artifact from dental hardware in carotid CTA.

Ultralow dose computed tomography attenuation correction for pediatric PET CT using adaptive statistical iterative reconstruction

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

Brady, Samuel L., E-mail: samuel.brady@stjude.org; Shulkin, Barry L.

2015-02-15

Purpose: To develop ultralow dose computed tomography (CT) attenuation correction (CTAC) acquisition protocols for pediatric positron emission tomography CT (PET CT). Methods: A GE Discovery 690 PET CT hybrid scanner was used to investigate the change to quantitative PET and CT measurements when operated at ultralow doses (10–35 mA s). CT quantitation: noise, low-contrast resolution, and CT numbers for 11 tissue substitutes were analyzed in-phantom. CT quantitation was analyzed to a reduction of 90% volume computed tomography dose index (0.39/3.64; mGy) from baseline. To minimize noise infiltration, 100% adaptive statistical iterative reconstruction (ASiR) was used for CT reconstruction. PET imagesmore » were reconstructed with the lower-dose CTAC iterations and analyzed for: maximum body weight standardized uptake value (SUV{sub bw}) of various diameter targets (range 8–37 mm), background uniformity, and spatial resolution. Radiation dose and CTAC noise magnitude were compared for 140 patient examinations (76 post-ASiR implementation) to determine relative dose reduction and noise control. Results: CT numbers were constant to within 10% from the nondose reduced CTAC image for 90% dose reduction. No change in SUV{sub bw}, background percent uniformity, or spatial resolution for PET images reconstructed with CTAC protocols was found down to 90% dose reduction. Patient population effective dose analysis demonstrated relative CTAC dose reductions between 62% and 86% (3.2/8.3–0.9/6.2). Noise magnitude in dose-reduced patient images increased but was not statistically different from predose-reduced patient images. Conclusions: Using ASiR allowed for aggressive reduction in CT dose with no change in PET reconstructed images while maintaining sufficient image quality for colocalization of hybrid CT anatomy and PET radioisotope uptake.« less

Investigation of cone-beam CT image quality trade-off for image-guided radiation therapy.

PubMed

Bian, Junguo; Sharp, Gregory C; Park, Yang-Kyun; Ouyang, Jinsong; Bortfeld, Thomas; El Fakhri, Georges

2016-05-07

It is well-known that projections acquired over an angular range slightly over 180° (so-called short scan) are sufficient for fan-beam reconstruction. However, due to practical imaging conditions (projection data and reconstruction image discretization, physical factors, and data noise), the short-scan reconstructions may have different appearances and properties from the full-scan (scans over 360°) reconstructions. Nevertheless, short-scan configurations have been used in applications such as cone-beam CT (CBCT) for head-neck-cancer image-guided radiation therapy (IGRT) that only requires a small field of view due to the potential reduced imaging time and dose. In this work, we studied the image quality trade-off for full, short, and full/short scan configurations with both conventional filtered-backprojection (FBP) reconstruction and iterative reconstruction algorithms based on total-variation (TV) minimization for head-neck-cancer IGRT. Anthropomorphic and Catphan phantoms were scanned at different exposure levels with a clinical scanner used in IGRT. Both visualization- and numerical-metric-based evaluation studies were performed. The results indicate that the optimal exposure level and number of views are in the middle range for both FBP and TV-based iterative algorithms and the optimization is object-dependent and task-dependent. The optimal view numbers decrease with the total exposure levels for both FBP and TV-based algorithms. The results also indicate there are slight differences between FBP and TV-based iterative algorithms for the image quality trade-off: FBP seems to be more in favor of larger number of views while the TV-based algorithm is more robust to different data conditions (number of views and exposure levels) than the FBP algorithm. The studies can provide a general guideline for image-quality optimization for CBCT used in IGRT and other applications.

Feasibility of dual-low scheme combined with iterative reconstruction technique in acute cerebral infarction volume CT whole brain perfusion imaging.

PubMed

Wang, Tao; Gong, Yi; Shi, Yibing; Hua, Rong; Zhang, Qingshan

2017-07-01

The feasibility of application of low-concentration contrast agent and low tube voltage combined with iterative reconstruction in whole brain computed tomography perfusion (CTP) imaging of patients with acute cerebral infarction was investigated. Fifty-nine patients who underwent whole brain CTP examination and diagnosed with acute cerebral infarction from September 2014 to March 2016 were selected. Patients were randomly divided into groups A and B. There were 28 cases in group A [tube voltage, 100 kV; contrast agent, iohexol (350 mg I/ml), reconstructed by filtered back projection] and 31 cases in group B [tube voltage, 80 kV; contrast agent, iodixanol (270 mg I/ml), reconstructed by algebraic reconstruction technique]. The artery CT value, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), dose length product, effective dose (ED) of radiation and brain iodine intake of both groups were measured and statistically analyzed. Two physicians carried out kappa (κ) analysis on the consistency of image quality evaluation. The difference in subjective image quality evaluation between the groups was tested by χ 2 . The differences in CT value, SNR, CNR, CTP and CT angiography subjective image quality evaluation between both groups were not statistically significant (P>0.05); the diagnosis rate of the acute infarcts between the two groups was not significantly different; while the ED and iodine intake in group B (dual low-dose group) were lower than group A. In conclusion, combination of low tube voltage and iterative reconstruction technique, and application of low-concentration contrast agent (270 mg I/ml) in whole brain CTP examination reduced ED and iodine intake without compromising image quality, thereby reducing the risk of contrast-induced nephropathy.

Effect of automated tube voltage selection, integrated circuit detector and advanced iterative reconstruction on radiation dose and image quality of 3rd generation dual-source aortic CT angiography: An intra-individual comparison.

PubMed

Mangold, Stefanie; De Cecco, Carlo N; Wichmann, Julian L; Canstein, Christian; Varga-Szemes, Akos; Caruso, Damiano; Fuller, Stephen R; Bamberg, Fabian; Nikolaou, Konstantin; Schoepf, U Joseph

2016-05-01

To compare, on an intra-individual basis, the effect of automated tube voltage selection (ATVS), integrated circuit detector and advanced iterative reconstruction on radiation dose and image quality of aortic CTA studies using 2nd and 3rd generation dual-source CT (DSCT). We retrospectively evaluated 32 patients who had undergone CTA of the entire aorta with both 2nd generation DSCT at 120kV using filtered back projection (FBP) (protocol 1) and 3rd generation DSCT using ATVS, an integrated circuit detector and advanced iterative reconstruction (protocol 2). Contrast-to-noise ratio (CNR) was calculated. Image quality was subjectively evaluated using a five-point scale. Radiation dose parameters were recorded. All studies were considered of diagnostic image quality. CNR was significantly higher with protocol 2 (15.0±5.2 vs 11.0±4.2; p<.0001). Subjective image quality analysis revealed no significant differences for evaluation of attenuation (p=0.08501) but image noise was rated significantly lower with protocol 2 (p=0.0005). Mean tube voltage and effective dose were 94.7±14.1kV and 6.7±3.9mSv with protocol 2; 120±0kV and 11.5±5.2mSv with protocol 1 (p<0.0001, respectively). Aortic CTA performed with 3rd generation DSCT, ATVS, integrated circuit detector, and advanced iterative reconstruction allow a substantial reduction of radiation exposure while improving image quality in comparison to 120kV imaging with FBP. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation.

PubMed

Xiaodong Zhuge; Palenstijn, Willem Jan; Batenburg, Kees Joost

2016-01-01

In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.

The Ettention software package.

PubMed

Dahmen, Tim; Marsalek, Lukas; Marniok, Nico; Turoňová, Beata; Bogachev, Sviatoslav; Trampert, Patrick; Nickels, Stefan; Slusallek, Philipp

2016-02-01

We present a novel software package for the problem "reconstruction from projections" in electron microscopy. The Ettention framework consists of a set of modular building-blocks for tomographic reconstruction algorithms. The well-known block iterative reconstruction method based on Kaczmarz algorithm is implemented using these building-blocks, including adaptations specific to electron tomography. Ettention simultaneously features (1) a modular, object-oriented software design, (2) optimized access to high-performance computing (HPC) platforms such as graphic processing units (GPU) or many-core architectures like Xeon Phi, and (3) accessibility to microscopy end-users via integration in the IMOD package and eTomo user interface. We also provide developers with a clean and well-structured application programming interface (API) that allows for extending the software easily and thus makes it an ideal platform for algorithmic research while hiding most of the technical details of high-performance computing. Copyright © 2015 Elsevier B.V. All rights reserved.

Super resolution for astronomical observations

NASA Astrophysics Data System (ADS)

Li, Zhan; Peng, Qingyu; Bhanu, Bir; Zhang, Qingfeng; He, Haifeng

2018-05-01

In order to obtain detailed information from multiple telescope observations a general blind super-resolution (SR) reconstruction approach for astronomical images is proposed in this paper. A pixel-reliability-based SR reconstruction algorithm is described and implemented, where the developed process incorporates flat field correction, automatic star searching and centering, iterative star matching, and sub-pixel image registration. Images captured by the 1-m telescope at Yunnan Observatory are used to test the proposed technique. The results of these experiments indicate that, following SR reconstruction, faint stars are more distinct, bright stars have sharper profiles, and the backgrounds have higher details; thus these results benefit from the high-precision star centering and image registration provided by the developed method. Application of the proposed approach not only provides more opportunities for new discoveries from astronomical image sequences, but will also contribute to enhancing the capabilities of most spatial or ground-based telescopes.

Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery.

PubMed

Penza, Veronica; Ortiz, Jesús; Mattos, Leonardo S; Forgione, Antonello; De Momi, Elena

2016-02-01

Single-incision laparoscopic surgery decreases postoperative infections, but introduces limitations in the surgeon's maneuverability and in the surgical field of view. This work aims at enhancing intra-operative surgical visualization by exploiting the 3D information about the surgical site. An interactive guidance system is proposed wherein the pose of preoperative tissue models is updated online. A critical process involves the intra-operative acquisition of tissue surfaces. It can be achieved using stereoscopic imaging and 3D reconstruction techniques. This work contributes to this process by proposing new methods for improved dense 3D reconstruction of soft tissues, which allows a more accurate deformation identification and facilitates the registration process. Two methods for soft tissue 3D reconstruction are proposed: Method 1 follows the traditional approach of the block matching algorithm. Method 2 performs a nonparametric modified census transform to be more robust to illumination variation. The simple linear iterative clustering (SLIC) super-pixel algorithm is exploited for disparity refinement by filling holes in the disparity images. The methods were validated using two video datasets from the Hamlyn Centre, achieving an accuracy of 2.95 and 1.66 mm, respectively. A comparison with ground-truth data demonstrated the disparity refinement procedure: (1) increases the number of reconstructed points by up to 43 % and (2) does not affect the accuracy of the 3D reconstructions significantly. Both methods give results that compare favorably with the state-of-the-art methods. The computational time constraints their applicability in real time, but can be greatly improved by using a GPU implementation.

Accelerating an Ordered-Subset Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction with a Power Factor and Total Variation Minimization.

PubMed

Huang, Hsuan-Ming; Hsiao, Ing-Tsung

2016-01-01

In recent years, there has been increased interest in low-dose X-ray cone beam computed tomography (CBCT) in many fields, including dentistry, guided radiotherapy and small animal imaging. Despite reducing the radiation dose, low-dose CBCT has not gained widespread acceptance in routine clinical practice. In addition to performing more evaluation studies, developing a fast and high-quality reconstruction algorithm is required. In this work, we propose an iterative reconstruction method that accelerates ordered-subsets (OS) reconstruction using a power factor. Furthermore, we combine it with the total-variation (TV) minimization method. Both simulation and phantom studies were conducted to evaluate the performance of the proposed method. Results show that the proposed method can accelerate conventional OS methods, greatly increase the convergence speed in early iterations. Moreover, applying the TV minimization to the power acceleration scheme can further improve the image quality while preserving the fast convergence rate.

Acoustical source reconstruction from non-synchronous sequential measurements by Fast Iterative Shrinkage Thresholding Algorithm

NASA Astrophysics Data System (ADS)

Yu, Liang; Antoni, Jerome; Leclere, Quentin; Jiang, Weikang

2017-11-01

Acoustical source reconstruction is a typical inverse problem, whose minimum frequency of reconstruction hinges on the size of the array and maximum frequency depends on the spacing distance between the microphones. For the sake of enlarging the frequency of reconstruction and reducing the cost of an acquisition system, Cyclic Projection (CP), a method of sequential measurements without reference, was recently investigated (JSV,2016,372:31-49). In this paper, the Propagation based Fast Iterative Shrinkage Thresholding Algorithm (Propagation-FISTA) is introduced, which improves CP in two aspects: (1) the number of acoustic sources is no longer needed and the only making assumption is that of a "weakly sparse" eigenvalue spectrum; (2) the construction of the spatial basis is much easier and adaptive to practical scenarios of acoustical measurements benefiting from the introduction of propagation based spatial basis. The proposed Propagation-FISTA is first investigated with different simulations and experimental setups and is next illustrated with an industrial case.

Bayesian image reconstruction for improving detection performance of muon tomography.

PubMed

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.

Accelerating an Ordered-Subset Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction with a Power Factor and Total Variation Minimization

PubMed Central

Huang, Hsuan-Ming; Hsiao, Ing-Tsung

2016-01-01

In recent years, there has been increased interest in low-dose X-ray cone beam computed tomography (CBCT) in many fields, including dentistry, guided radiotherapy and small animal imaging. Despite reducing the radiation dose, low-dose CBCT has not gained widespread acceptance in routine clinical practice. In addition to performing more evaluation studies, developing a fast and high-quality reconstruction algorithm is required. In this work, we propose an iterative reconstruction method that accelerates ordered-subsets (OS) reconstruction using a power factor. Furthermore, we combine it with the total-variation (TV) minimization method. Both simulation and phantom studies were conducted to evaluate the performance of the proposed method. Results show that the proposed method can accelerate conventional OS methods, greatly increase the convergence speed in early iterations. Moreover, applying the TV minimization to the power acceleration scheme can further improve the image quality while preserving the fast convergence rate. PMID:27073853

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Split Bregman's optimization method for image construction in compressive sensing

NASA Astrophysics Data System (ADS)

Skinner, D.; Foo, S.; Meyer-Bäse, A.

2014-05-01

The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.

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.

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

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Semi-automated Image Processing for Preclinical Bioluminescent Imaging.

PubMed

Slavine, Nikolai V; McColl, Roderick W

Bioluminescent imaging is a valuable noninvasive technique for investigating tumor dynamics and specific biological molecular events in living animals to better understand the effects of human disease in animal models. The purpose of this study was to develop and test a strategy behind automated methods for bioluminescence image processing from the data acquisition to obtaining 3D images. In order to optimize this procedure a semi-automated image processing approach with multi-modality image handling environment was developed. To identify a bioluminescent source location and strength we used the light flux detected on the surface of the imaged object by CCD cameras. For phantom calibration tests and object surface reconstruction we used MLEM algorithm. For internal bioluminescent sources we used the diffusion approximation with balancing the internal and external intensities on the boundary of the media and then determined an initial order approximation for the photon fluence we subsequently applied a novel iterative deconvolution method to obtain the final reconstruction result. We find that the reconstruction techniques successfully used the depth-dependent light transport approach and semi-automated image processing to provide a realistic 3D model of the lung tumor. Our image processing software can optimize and decrease the time of the volumetric imaging and quantitative assessment. The data obtained from light phantom and lung mouse tumor images demonstrate the utility of the image reconstruction algorithms and semi-automated approach for bioluminescent image processing procedure. We suggest that the developed image processing approach can be applied to preclinical imaging studies: characteristics of tumor growth, identify metastases, and potentially determine the effectiveness of cancer treatment.

Effects of sparse sampling in combination with iterative reconstruction on quantitative bone microstructure assessment

NASA Astrophysics Data System (ADS)

Mei, Kai; Kopp, Felix K.; Fehringer, Andreas; Pfeiffer, Franz; Rummeny, Ernst J.; Kirschke, Jan S.; Noël, Peter B.; Baum, Thomas

2017-03-01

The trabecular bone microstructure is a key to the early diagnosis and advanced therapy monitoring of osteoporosis. Regularly measuring bone microstructure with conventional multi-detector computer tomography (MDCT) would expose patients with a relatively high radiation dose. One possible solution to reduce exposure to patients is sampling fewer projection angles. This approach can be supported by advanced reconstruction algorithms, with their ability to achieve better image quality under reduced projection angles or high levels of noise. In this work, we investigated the performance of iterative reconstruction from sparse sampled projection data on trabecular bone microstructure in in-vivo MDCT scans of human spines. The computed MDCT images were evaluated by calculating bone microstructure parameters. We demonstrated that bone microstructure parameters were still computationally distinguishable when half or less of the radiation dose was employed.

Variable aperture-based ptychographical iterative engine method.

PubMed

Sun, Aihui; Kong, Yan; Meng, Xin; He, Xiaoliang; Du, Ruijun; Jiang, Zhilong; Liu, Fei; Xue, Liang; Wang, Shouyu; Liu, Cheng

2018-02-01

A variable aperture-based ptychographical iterative engine (vaPIE) is demonstrated both numerically and experimentally to reconstruct the sample phase and amplitude rapidly. By adjusting the size of a tiny aperture under the illumination of a parallel light beam to change the illumination on the sample step by step and recording the corresponding diffraction patterns sequentially, both the sample phase and amplitude can be faithfully reconstructed with a modified ptychographical iterative engine (PIE) algorithm. Since many fewer diffraction patterns are required than in common PIE and the shape, the size, and the position of the aperture need not to be known exactly, this proposed vaPIE method remarkably reduces the data acquisition time and makes PIE less dependent on the mechanical accuracy of the translation stage; therefore, the proposed technique can be potentially applied for various scientific researches. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Fully iterative scatter corrected digital breast tomosynthesis using GPU-based fast Monte Carlo simulation and composition ratio update

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

Kim, Kyungsang; Ye, Jong Chul, E-mail: jong.ye@kaist.ac.kr; Lee, Taewon

2015-09-15

Purpose: In digital breast tomosynthesis (DBT), scatter correction is highly desirable, as it improves image quality at low doses. Because the DBT detector panel is typically stationary during the source rotation, antiscatter grids are not generally compatible with DBT; thus, a software-based scatter correction is required. This work proposes a fully iterative scatter correction method that uses a novel fast Monte Carlo simulation (MCS) with a tissue-composition ratio estimation technique for DBT imaging. Methods: To apply MCS to scatter estimation, the material composition in each voxel should be known. To overcome the lack of prior accurate knowledge of tissue compositionmore » for DBT, a tissue-composition ratio is estimated based on the observation that the breast tissues are principally composed of adipose and glandular tissues. Using this approximation, the composition ratio can be estimated from the reconstructed attenuation coefficients, and the scatter distribution can then be estimated by MCS using the composition ratio. The scatter estimation and image reconstruction procedures can be performed iteratively until an acceptable accuracy is achieved. For practical use, (i) the authors have implemented a fast MCS using a graphics processing unit (GPU), (ii) the MCS is simplified to transport only x-rays in the energy range of 10–50 keV, modeling Rayleigh and Compton scattering and the photoelectric effect using the tissue-composition ratio of adipose and glandular tissues, and (iii) downsampling is used because the scatter distribution varies rather smoothly. Results: The authors have demonstrated that the proposed method can accurately estimate the scatter distribution, and that the contrast-to-noise ratio of the final reconstructed image is significantly improved. The authors validated the performance of the MCS by changing the tissue thickness, composition ratio, and x-ray energy. The authors confirmed that the tissue-composition ratio estimation was quite accurate under a variety of conditions. Our GPU-based fast MCS implementation took approximately 3 s to generate each angular projection for a 6 cm thick breast, which is believed to make this process acceptable for clinical applications. In addition, the clinical preferences of three radiologists were evaluated; the preference for the proposed method compared to the preference for the convolution-based method was statistically meaningful (p < 0.05, McNemar test). Conclusions: The proposed fully iterative scatter correction method and the GPU-based fast MCS using tissue-composition ratio estimation successfully improved the image quality within a reasonable computational time, which may potentially increase the clinical utility of DBT.« less

Optical image encryption via high-quality computational ghost imaging using iterative phase retrieval

NASA Astrophysics Data System (ADS)

Liansheng, Sui; Yin, Cheng; Bing, Li; Ailing, Tian; Krishna Asundi, Anand

2018-07-01

A novel computational ghost imaging scheme based on specially designed phase-only masks, which can be efficiently applied to encrypt an original image into a series of measured intensities, is proposed in this paper. First, a Hadamard matrix with a certain order is generated, where the number of elements in each row is equal to the size of the original image to be encrypted. Each row of the matrix is rearranged into the corresponding 2D pattern. Then, each pattern is encoded into the phase-only masks by making use of an iterative phase retrieval algorithm. These specially designed masks can be wholly or partially used in the process of computational ghost imaging to reconstruct the original information with high quality. When a significantly small number of phase-only masks are used to record the measured intensities in a single-pixel bucket detector, the information can be authenticated without clear visualization by calculating the nonlinear correlation map between the original image and its reconstruction. The results illustrate the feasibility and effectiveness of the proposed computational ghost imaging mechanism, which will provide an effective alternative for enriching the related research on the computational ghost imaging technique.

Secret shared multiple-image encryption based on row scanning compressive ghost imaging and phase retrieval in the Fresnel domain

NASA Astrophysics Data System (ADS)

Li, Xianye; Meng, Xiangfeng; Wang, Yurong; Yang, Xiulun; Yin, Yongkai; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

2017-09-01

A multiple-image encryption method is proposed that is based on row scanning compressive ghost imaging, (t, n) threshold secret sharing, and phase retrieval in the Fresnel domain. In the encryption process, after wavelet transform and Arnold transform of the target image, the ciphertext matrix can be first detected using a bucket detector. Based on a (t, n) threshold secret sharing algorithm, the measurement key used in the row scanning compressive ghost imaging can be decomposed and shared into two pairs of sub-keys, which are then reconstructed using two phase-only mask (POM) keys with fixed pixel values, placed in the input plane and transform plane 2 of the phase retrieval scheme, respectively; and the other POM key in the transform plane 1 can be generated and updated by the iterative encoding of each plaintext image. In each iteration, the target image acts as the input amplitude constraint in the input plane. During decryption, each plaintext image possessing all the correct keys can be successfully decrypted by measurement key regeneration, compression algorithm reconstruction, inverse wavelet transformation, and Fresnel transformation. Theoretical analysis and numerical simulations both verify the feasibility of the proposed method.

Preprocessing of region of interest localization based on local surface curvature analysis for three-dimensional reconstruction with multiresolution

NASA Astrophysics Data System (ADS)

Li, Wanjing; Schütze, Rainer; Böhler, Martin; Boochs, Frank; Marzani, Franck S.; Voisin, Yvon

2009-06-01

We present an approach to integrate a preprocessing step of the region of interest (ROI) localization into 3-D scanners (laser or stereoscopic). The definite objective is to make the 3-D scanner intelligent enough to localize rapidly in the scene, during the preprocessing phase, the regions with high surface curvature, so that precise scanning will be done only in these regions instead of in the whole scene. In this way, the scanning time can be largely reduced, and the results contain only pertinent data. To test its feasibility and efficiency, we simulated the preprocessing process under an active stereoscopic system composed of two cameras and a video projector. The ROI localization is done in an iterative way. First, the video projector projects a regular point pattern in the scene, and then the pattern is modified iteratively according to the local surface curvature of each reconstructed 3-D point. Finally, the last pattern is used to determine the ROI. Our experiments showed that with this approach, the system is capable to localize all types of objects, including small objects with small depth.

Influence of adaptive statistical iterative reconstruction algorithm on image quality in coronary computed tomography angiography

PubMed Central

Thygesen, Jesper; Gerke, Oke; Egstrup, Kenneth; Waaler, Dag; Lambrechtsen, Jess

2016-01-01

Background Coronary computed tomography angiography (CCTA) requires high spatial and temporal resolution, increased low contrast resolution for the assessment of coronary artery stenosis, plaque detection, and/or non-coronary pathology. Therefore, new reconstruction algorithms, particularly iterative reconstruction (IR) techniques, have been developed in an attempt to improve image quality with no cost in radiation exposure. Purpose To evaluate whether adaptive statistical iterative reconstruction (ASIR) enhances perceived image quality in CCTA compared to filtered back projection (FBP). Material and Methods Thirty patients underwent CCTA due to suspected coronary artery disease. Images were reconstructed using FBP, 30% ASIR, and 60% ASIR. Ninety image sets were evaluated by five observers using the subjective visual grading analysis (VGA) and assessed by proportional odds modeling. Objective quality assessment (contrast, noise, and the contrast-to-noise ratio [CNR]) was analyzed with linear mixed effects modeling on log-transformed data. The need for ethical approval was waived by the local ethics committee as the study only involved anonymously collected clinical data. Results VGA showed significant improvements in sharpness by comparing FBP with ASIR, resulting in odds ratios of 1.54 for 30% ASIR and 1.89 for 60% ASIR (P = 0.004). The objective measures showed significant differences between FBP and 60% ASIR (P < 0.0001) for noise, with an estimated ratio of 0.82, and for CNR, with an estimated ratio of 1.26. Conclusion ASIR improved the subjective image quality of parameter sharpness and, objectively, reduced noise and increased CNR. PMID:28405477

Effects of pure and hybrid iterative reconstruction algorithms on high-resolution computed tomography in the evaluation of interstitial lung disease.

PubMed

Katsura, Masaki; Sato, Jiro; Akahane, Masaaki; Mise, Yoko; Sumida, Kaoru; Abe, Osamu

2017-08-01

To compare image quality characteristics of high-resolution computed tomography (HRCT) in the evaluation of interstitial lung disease using three different reconstruction methods: model-based iterative reconstruction (MBIR), adaptive statistical iterative reconstruction (ASIR), and filtered back projection (FBP). Eighty-nine consecutive patients with interstitial lung disease underwent standard-of-care chest CT with 64-row multi-detector CT. HRCT images were reconstructed in 0.625-mm contiguous axial slices using FBP, ASIR, and MBIR. Two radiologists independently assessed the images in a blinded manner for subjective image noise, streak artifacts, and visualization of normal and pathologic structures. Objective image noise was measured in the lung parenchyma. Spatial resolution was assessed by measuring the modulation transfer function (MTF). MBIR offered significantly lower objective image noise (22.24±4.53, P<0.01 among all pairs, Student's t-test) compared with ASIR (39.76±7.41) and FBP (51.91±9.71). MTF (spatial resolution) was increased using MBIR compared with ASIR and FBP. MBIR showed improvements in visualization of normal and pathologic structures over ASIR and FBP, while ASIR was rated quite similarly to FBP. MBIR significantly improved subjective image noise (P<0.01 among all pairs, the sign test), and streak artifacts (P<0.01 each for MBIR vs. the other 2 image data sets). MBIR provides high-quality HRCT images for interstitial lung disease by reducing image noise and streak artifacts and improving spatial resolution compared with ASIR and FBP. Copyright © 2017 Elsevier B.V. All rights reserved.

Enhanced spatial resolution in fluorescence molecular tomography using restarted L1-regularized nonlinear conjugate gradient algorithm.

PubMed

Shi, Junwei; Liu, Fei; Zhang, Guanglei; Luo, Jianwen; Bai, Jing

2014-04-01

Owing to the high degree of scattering of light through tissues, the ill-posedness of fluorescence molecular tomography (FMT) inverse problem causes relatively low spatial resolution in the reconstruction results. Unlike L2 regularization, L1 regularization can preserve the details and reduce the noise effectively. Reconstruction is obtained through a restarted L1 regularization-based nonlinear conjugate gradient (re-L1-NCG) algorithm, which has been proven to be able to increase the computational speed with low memory consumption. The algorithm consists of inner and outer iterations. In the inner iteration, L1-NCG is used to obtain the L1-regularized results. In the outer iteration, the restarted strategy is used to increase the convergence speed of L1-NCG. To demonstrate the performance of re-L1-NCG in terms of spatial resolution, simulation and physical phantom studies with fluorescent targets located with different edge-to-edge distances were carried out. The reconstruction results show that the re-L1-NCG algorithm has the ability to resolve targets with an edge-to-edge distance of 0.1 cm at a depth of 1.5 cm, which is a significant improvement for FMT.

Optimization of OSEM parameters in myocardial perfusion imaging reconstruction as a function of body mass index: a clinical approach*

PubMed Central

de Barros, Pietro Paolo; Metello, Luis F.; Camozzato, Tatiane Sabriela Cagol; Vieira, Domingos Manuel da Silva

2015-01-01

Objective The present study is aimed at contributing to identify the most appropriate OSEM parameters to generate myocardial perfusion imaging reconstructions with the best diagnostic quality, correlating them with patients’ body mass index. Materials and Methods The present study included 28 adult patients submitted to myocardial perfusion imaging in a public hospital. The OSEM method was utilized in the images reconstruction with six different combinations of iterations and subsets numbers. The images were analyzed by nuclear cardiology specialists taking their diagnostic value into consideration and indicating the most appropriate images in terms of diagnostic quality. Results An overall scoring analysis demonstrated that the combination of four iterations and four subsets has generated the most appropriate images in terms of diagnostic quality for all the classes of body mass index; however, the role played by the combination of six iterations and four subsets is highlighted in relation to the higher body mass index classes. Conclusion The use of optimized parameters seems to play a relevant role in the generation of images with better diagnostic quality, ensuring the diagnosis and consequential appropriate and effective treatment for the patient. PMID:26543282

Dynamic re-weighted total variation technique and statistic Iterative reconstruction method for x-ray CT metal artifact reduction

NASA Astrophysics Data System (ADS)

Peng, Chengtao; Qiu, Bensheng; Zhang, Cheng; Ma, Changyu; Yuan, Gang; Li, Ming

2017-07-01

Over the years, the X-ray computed tomography (CT) has been successfully used in clinical diagnosis. However, when the body of the patient to be examined contains metal objects, the image reconstructed would be polluted by severe metal artifacts, which affect the doctor's diagnosis of disease. In this work, we proposed a dynamic re-weighted total variation (DRWTV) technique combined with the statistic iterative reconstruction (SIR) method to reduce the artifacts. The DRWTV method is based on the total variation (TV) and re-weighted total variation (RWTV) techniques, but it provides a sparser representation than TV and protects the tissue details better than RWTV. Besides, the DRWTV can suppress the artifacts and noise, and the SIR convergence speed is also accelerated. The performance of the algorithm is tested on both simulated phantom dataset and clinical dataset, which are the teeth phantom with two metal implants and the skull with three metal implants, respectively. The proposed algorithm (SIR-DRWTV) is compared with two traditional iterative algorithms, which are SIR and SIR constrained by RWTV regulation (SIR-RWTV). The results show that the proposed algorithm has the best performance in reducing metal artifacts and protecting tissue details.

Optimization of the spatial resolution for the GE discovery PET/CT 710 by using NEMA NU 2-2007 standards

NASA Astrophysics Data System (ADS)

Yoon, Hyun Jin; Jeong, Young Jin; Son, Hye Joo; Kang, Do-Young; Hyun, Kyung-Yae; Lee, Min-Kyung

2015-01-01

The spatial resolution in positron emission tomography (PET) is fundamentally limited by the geometry of the detector element, the positron's recombination range with electrons, the acollinearity of the positron, the crystal decoding error, the penetration into the detector ring, and the reconstruction algorithms. In this paper, optimized parameters are suggested to produce high-resolution PET images by using an iterative reconstruction algorithm. A phantom with three point sources structured with three capillary tubes was prepared with an axial extension of less than 1 mm and was filled with 18F-fluorodeoxyglucose (18F-FDG) with concentrations above 200 MBq/cc. The performance measures of all the PET images were acquired according to the National Electrical Manufacturers Association (NEMA) NU 2-2007 standards procedures. The parameters for the iterative reconstruction were adjusted around the values recommended by General Electric GE, and the optimized values of the spatial resolution and the full width at half maximum (FWHM) or the full width at tenth of maximum (FWTM) values were found for the best PET resolution. The axial and the transverse spatial resolutions, according to the filtered back-projection (FBP) at 1 cm off-axis, were 4.81 and 4.48 mm, respectively. The axial and the transaxial spatial resolutions at 10 cm off-axis were 5.63 mm and 5.08 mm, respectively, and the trans-axial resolution at 10 cm was evaluated as the average of the radial and the tangential measurements. The recommended optimized parameters of the spatial resolution according to the NEMA phantom for the number of subsets, the number of iterations, and the Gaussian post-filter are 12, 3, and 3 mm for the iterative reconstruction VUE Point HD without the SharpIR algorithm (HD), and 12, 12, and 5.2 mm with SharpIR (HD.S), respectively, according to the Advantage Workstation Volume Share 5 (AW4.6). The performance measurements for the GE Discovery PET/CT 710 using the NEMA NU 2-2007 standards from our results will be helpful in the quantitative analysis of PET scanner images. The spatial resolution was modified more by using an improved algorithm such as HD.S, than by using HD and FBP. The use of the optimized parameters for iterative reconstructions is strongly recommended for qualitative images from the GE Discovery PET/CT 710 scanner.

Application of kernel method in fluorescence molecular tomography

NASA Astrophysics Data System (ADS)

Zhao, Yue; Baikejiang, Reheman; Li, Changqing

2017-02-01

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

Multiresolution Iterative Reconstruction in High-Resolution Extremity Cone-Beam CT

PubMed Central

Cao, Qian; Zbijewski, Wojciech; Sisniega, Alejandro; Yorkston, John; Siewerdsen, Jeffrey H; Stayman, J Webster

2016-01-01

Application of model-based iterative reconstruction (MBIR) to high resolution cone-beam CT (CBCT) is computationally challenging because of the very fine discretization (voxel size <100 µm) of the reconstructed volume. Moreover, standard MBIR techniques require that the complete transaxial support for the acquired projections is reconstructed, thus precluding acceleration by restricting the reconstruction to a region-of-interest. To reduce the computational burden of high resolution MBIR, we propose a multiresolution Penalized-Weighted Least Squares (PWLS) algorithm, where the volume is parameterized as a union of fine and coarse voxel grids as well as selective binning of detector pixels. We introduce a penalty function designed to regularize across the boundaries between the two grids. The algorithm was evaluated in simulation studies emulating an extremity CBCT system and in a physical study on a test-bench. Artifacts arising from the mismatched discretization of the fine and coarse sub-volumes were investigated. The fine grid region was parameterized using 0.15 mm voxels and the voxel size in the coarse grid region was varied by changing a downsampling factor. No significant artifacts were found in either of the regions for downsampling factors of up to 4×. For a typical extremities CBCT volume size, this downsampling corresponds to an acceleration of the reconstruction that is more than five times faster than a brute force solution that applies fine voxel parameterization to the entire volume. For certain configurations of the coarse and fine grid regions, in particular when the boundary between the regions does not cross high attenuation gradients, downsampling factors as high as 10× can be used without introducing artifacts, yielding a ~50× speedup in PWLS. The proposed multiresolution algorithm significantly reduces the computational burden of high resolution iterative CBCT reconstruction and can be extended to other applications of MBIR where computationally expensive, high-fidelity forward models are applied only to a sub-region of the field-of-view. PMID:27694701

See-through Detection and 3D Reconstruction Using Terahertz Leaky-Wave Radar Based on Sparse Signal Processing

NASA Astrophysics Data System (ADS)

Murata, Koji; Murano, Kosuke; Watanabe, Issei; Kasamatsu, Akifumi; Tanaka, Toshiyuki; Monnai, Yasuaki

2018-02-01

We experimentally demonstrate see-through detection and 3D reconstruction using terahertz leaky-wave radar based on sparse signal processing. The application of terahertz waves to radar has received increasing attention in recent years for its potential to high-resolution and see-through detection. Among others, the implementation using a leaky-wave antenna is promising for compact system integration with beam steering capability based on frequency sweep. However, the use of a leaky-wave antenna poses a challenge on signal processing. Since a leaky-wave antenna combines the entire signal captured by each part of the aperture into a single output, the conventional array signal processing assuming access to a respective antenna element is not applicable. In this paper, we apply an iterative recovery algorithm "CoSaMP" to signals acquired with terahertz leaky-wave radar for clutter mitigation and aperture synthesis. We firstly demonstrate see-through detection of target location even when the radar is covered with an opaque screen, and therefore, the radar signal is disturbed by clutter. Furthermore, leveraging the robustness of the algorithm against noise, we also demonstrate 3D reconstruction of distributed targets by synthesizing signals collected from different orientations. The proposed approach will contribute to the smart implementation of terahertz leaky-wave radar.

Recent progress in 3-D imaging of sea freight containers

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

Fuchs, Theobald, E-mail: theobold.fuchs@iis.fraunhofer.de; Schön, Tobias, E-mail: theobold.fuchs@iis.fraunhofer.de; Sukowski, Frank

The inspection of very large objects like sea freight containers with X-ray Computed Tomography (CT) is an emerging technology. A complete 3-D CT scan of a see-freight container takes several hours. Of course, this is too slow to apply it to a large number of containers. However, the benefits of a 3-D CT for sealed freight are obvious: detection of potential threats or illicit cargo without being confronted with legal complications or high time consumption and risks for the security personnel during a manual inspection. Recently distinct progress was made in the field of reconstruction of projections with only amore » relatively low number of angular positions. Instead of today’s 500 to 1000 rotational steps, as needed for conventional CT reconstruction techniques, this new class of algorithms provides the potential to reduce the number of projection angles approximately by a factor of 10. The main drawback of these advanced iterative methods is the high consumption for numerical processing. But as computational power is getting steadily cheaper, there will be practical applications of these complex algorithms in a foreseeable future. In this paper, we discuss the properties of iterative image reconstruction algorithms and show results of their application to CT of extremely large objects scanning a sea-freight container. A specific test specimen is used to quantitatively evaluate the image quality in terms of spatial and contrast resolution and depending on different number of projections.« less

High resolution computational on-chip imaging of biological samples using sparsity constraint (Conference Presentation)

NASA Astrophysics Data System (ADS)

Rivenson, Yair; Wu, Chris; Wang, Hongda; Zhang, Yibo; Ozcan, Aydogan

2017-03-01

Microscopic imaging of biological samples such as pathology slides is one of the standard diagnostic methods for screening various diseases, including cancer. These biological samples are usually imaged using traditional optical microscopy tools; however, the high cost, bulkiness and limited imaging throughput of traditional microscopes partially restrict their deployment in resource-limited settings. In order to mitigate this, we previously demonstrated a cost-effective and compact lens-less on-chip microscopy platform with a wide field-of-view of >20-30 mm^2. The lens-less microscopy platform has shown its effectiveness for imaging of highly connected biological samples, such as pathology slides of various tissue samples and smears, among others. This computational holographic microscope requires a set of super-resolved holograms acquired at multiple sample-to-sensor distances, which are used as input to an iterative phase recovery algorithm and holographic reconstruction process, yielding high-resolution images of the samples in phase and amplitude channels. Here we demonstrate that in order to reconstruct clinically relevant images with high resolution and image contrast, we require less than 50% of the previously reported nominal number of holograms acquired at different sample-to-sensor distances. This is achieved by incorporating a loose sparsity constraint as part of the iterative holographic object reconstruction. We demonstrate the success of this sparsity-based computational lens-less microscopy platform by imaging pathology slides of breast cancer tissue and Papanicolaou (Pap) smears.

Split Bregman multicoil accelerated reconstruction technique: A new framework for rapid reconstruction of cardiac perfusion MRI

PubMed Central

Kamesh Iyer, Srikant; Tasdizen, Tolga; Likhite, Devavrat; DiBella, Edward

2016-01-01

Purpose: Rapid reconstruction of undersampled multicoil MRI data with iterative constrained reconstruction method is a challenge. The authors sought to develop a new substitution based variable splitting algorithm for faster reconstruction of multicoil cardiac perfusion MRI data. Methods: The new method, split Bregman multicoil accelerated reconstruction technique (SMART), uses a combination of split Bregman based variable splitting and iterative reweighting techniques to achieve fast convergence. Total variation constraints are used along the spatial and temporal dimensions. The method is tested on nine ECG-gated dog perfusion datasets, acquired with a 30-ray golden ratio radial sampling pattern and ten ungated human perfusion datasets, acquired with a 24-ray golden ratio radial sampling pattern. Image quality and reconstruction speed are evaluated and compared to a gradient descent (GD) implementation and to multicoil k-t SLR, a reconstruction technique that uses a combination of sparsity and low rank constraints. Results: Comparisons based on blur metric and visual inspection showed that SMART images had lower blur and better texture as compared to the GD implementation. On average, the GD based images had an ∼18% higher blur metric as compared to SMART images. Reconstruction of dynamic contrast enhanced (DCE) cardiac perfusion images using the SMART method was ∼6 times faster than standard gradient descent methods. k-t SLR and SMART produced images with comparable image quality, though SMART was ∼6.8 times faster than k-t SLR. Conclusions: The SMART method is a promising approach to reconstruct good quality multicoil images from undersampled DCE cardiac perfusion data rapidly. PMID:27036592

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

DOE PAGES

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

2017-09-05

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

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

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

Pryor, Alan; Yang, Yongsoo; Rana, Arjun

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

CT coronary angiography: impact of adapted statistical iterative reconstruction (ASIR) on coronary stenosis and plaque composition analysis.

PubMed

Fuchs, Tobias A; Fiechter, Michael; Gebhard, Cathérine; Stehli, Julia; Ghadri, Jelena R; Kazakauskaite, Egle; Herzog, Bernhard A; Husmann, Lars; Gaemperli, Oliver; Kaufmann, Philipp A

2013-03-01

To assess the impact of adaptive statistical iterative reconstruction (ASIR) on coronary plaque volume and composition analysis as well as on stenosis quantification in high definition coronary computed tomography angiography (CCTA). We included 50 plaques in 29 consecutive patients who were referred for the assessment of known or suspected coronary artery disease (CAD) with contrast-enhanced CCTA on a 64-slice high definition CT scanner (Discovery HD 750, GE Healthcare). CCTA scans were reconstructed with standard filtered back projection (FBP) with no ASIR (0 %) or with increasing contributions of ASIR, i.e. 20, 40, 60, 80 and 100 % (no FBP). Plaque analysis (volume, components and stenosis degree) was performed using a previously validated automated software. Mean values for minimal diameter and minimal area as well as degree of stenosis did not change significantly using different ASIR reconstructions. There was virtually no impact of reconstruction algorithms on mean plaque volume or plaque composition (e.g. soft, intermediate and calcified component). However, with increasing ASIR contribution, the percentage of plaque volume component between 401 and 500 HU decreased significantly (p < 0.05). Modern image reconstruction algorithms such as ASIR, which has been developed for noise reduction in latest high resolution CCTA scans, can be used reliably without interfering with the plaque analysis and stenosis severity assessment.

LCAMP: Location Constrained Approximate Message Passing for Compressed Sensing MRI

PubMed Central

Sung, Kyunghyun; Daniel, Bruce L; Hargreaves, Brian A

2016-01-01

Iterative thresholding methods have been extensively studied as faster alternatives to convex optimization methods for solving large-sized problems in compressed sensing. A novel iterative thresholding method called LCAMP (Location Constrained Approximate Message Passing) is presented for reducing computational complexity and improving reconstruction accuracy when a nonzero location (or sparse support) constraint can be obtained from view shared images. LCAMP modifies the existing approximate message passing algorithm by replacing the thresholding stage with a location constraint, which avoids adjusting regularization parameters or thresholding levels. This work is first compared with other conventional reconstruction methods using random 1D signals and then applied to dynamic contrast-enhanced breast MRI to demonstrate the excellent reconstruction accuracy (less than 2% absolute difference) and low computation time (5 - 10 seconds using Matlab) with highly undersampled 3D data (244 × 128 × 48; overall reduction factor = 10). PMID:23042658

Comparison of SIRT and SQS for Regularized Weighted Least Squares Image Reconstruction

PubMed Central

Gregor, Jens; Fessler, Jeffrey A.

2015-01-01

Tomographic image reconstruction is often formulated as a regularized weighted least squares (RWLS) problem optimized by iterative algorithms that are either inherently algebraic or derived from a statistical point of view. This paper compares a modified version of SIRT (Simultaneous Iterative Reconstruction Technique), which is of the former type, with a version of SQS (Separable Quadratic Surrogates), which is of the latter type. We show that the two algorithms minimize the same criterion function using similar forms of preconditioned gradient descent. We present near-optimal relaxation for both based on eigenvalue bounds and include a heuristic extension for use with ordered subsets. We provide empirical evidence that SIRT and SQS converge at the same rate for all intents and purposes. For context, we compare their performance with an implementation of preconditioned conjugate gradient. The illustrative application is X-ray CT of luggage for aviation security. PMID:26478906

High-order noise analysis for low dose iterative image reconstruction methods: ASIR, IRIS, and MBAI

NASA Astrophysics Data System (ADS)

Do, Synho; Singh, Sarabjeet; Kalra, Mannudeep K.; Karl, W. Clem; Brady, Thomas J.; Pien, Homer

2011-03-01

Iterative reconstruction techniques (IRTs) has been shown to suppress noise significantly in low dose CT imaging. However, medical doctors hesitate to accept this new technology because visual impression of IRT images are different from full-dose filtered back-projection (FBP) images. Most common noise measurements such as the mean and standard deviation of homogeneous region in the image that do not provide sufficient characterization of noise statistics when probability density function becomes non-Gaussian. In this study, we measure L-moments of intensity values of images acquired at 10% of normal dose and reconstructed by IRT methods of two state-of-art clinical scanners (i.e., GE HDCT and Siemens DSCT flash) by keeping dosage level identical to each other. The high- and low-dose scans (i.e., 10% of high dose) were acquired from each scanner and L-moments of noise patches were calculated for the comparison.

Stokes-Doppler coherence imaging for ITER boundary tomography.

PubMed

Howard, J; Kocan, M; Lisgo, S; Reichle, R

2016-11-01

An optical coherence imaging system is presently being designed for impurity transport studies and other applications on ITER. The wide variation in magnetic field strength and pitch angle (assumed known) across the field of view generates additional Zeeman-polarization-weighting information that can improve the reliability of tomographic reconstructions. Because background reflected light will be somewhat depolarized analysis of only the polarized fraction may be enough to provide a level of background suppression. We present the principles behind these ideas and some simulations that demonstrate how the approach might work on ITER. The views and opinions expressed herein do not necessarily reflect those of the ITER Organization.

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

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

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

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

Muon reconstruction in the Daya Bay water pools

DOE PAGES

Hackenburg, R. W.

2017-08-12

Muon reconstruction in the Daya Bay water pools would serve to verify the simulated muon fluxes and offer the possibility of studying cosmic muons in general. This reconstruction is, however, complicated by many optical obstacles and the small coverage of photomultiplier tubes (PMTs) as compared to other large water Cherenkov detectors. The PMTs’ timing information is useful only in the case of direct, unreflected Cherenkov light. This requires PMTs to be added and removed as an hypothesized muon trajectory is iteratively improved, to account for the changing effects of obstacles and direction of light. Therefore, muon reconstruction in the Dayamore » Bay water pools does not lend itself to a general fitting procedure employing smoothly varying functions with continuous derivatives. Here, we describe an algorithm which overcomes these complications. It employs the method of Least Mean Squares to determine an hypothesized trajectory from the PMTs’ charge-weighted positions. This initially hypothesized trajectory is then iteratively refined using the PMTs’ timing information. Reconstructions with simulated data reproduce the simulated trajectory to within about 5° in direction and about 45 cm in position at the pool surface, with a bias that tends to pull tracks away from the vertical by about 3°.« less

Muon reconstruction in the Daya Bay water pools

NASA Astrophysics Data System (ADS)

Hackenburg, R. W.

2017-11-01

Muon reconstruction in the Daya Bay water pools would serve to verify the simulated muon fluxes and offer the possibility of studying cosmic muons in general. This reconstruction is, however, complicated by many optical obstacles and the small coverage of photomultiplier tubes (PMTs) as compared to other large water Cherenkov detectors. The PMTs' timing information is useful only in the case of direct, unreflected Cherenkov light. This requires PMTs to be added and removed as an hypothesized muon trajectory is iteratively improved, to account for the changing effects of obstacles and direction of light. Therefore, muon reconstruction in the Daya Bay water pools does not lend itself to a general fitting procedure employing smoothly varying functions with continuous derivatives. Here, an algorithm is described which overcomes these complications. It employs the method of Least Mean Squares to determine an hypothesized trajectory from the PMTs' charge-weighted positions. This initially hypothesized trajectory is then iteratively refined using the PMTs' timing information. Reconstructions with simulated data reproduce the simulated trajectory to within about 5°in direction and about 45 cm in position at the pool surface, with a bias that tends to pull tracks away from the vertical by about 3°.

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

PubMed

Liu, Li; Lin, Weikai; Jin, Mingwu

2015-01-01

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

X-ray computed tomography using curvelet sparse regularization.

PubMed

Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

2015-04-01

Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

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.

Quantitative Features of Liver Lesions, Lung Nodules, and Renal Stones at Multi-Detector Row CT Examinations: Dependency on Radiation Dose and Reconstruction Algorithm.

PubMed

Solomon, Justin; Mileto, Achille; Nelson, Rendon C; Roy Choudhury, Kingshuk; Samei, Ehsan

2016-04-01

To determine if radiation dose and reconstruction algorithm affect the computer-based extraction and analysis of quantitative imaging features in lung nodules, liver lesions, and renal stones at multi-detector row computed tomography (CT). Retrospective analysis of data from a prospective, multicenter, HIPAA-compliant, institutional review board-approved clinical trial was performed by extracting 23 quantitative imaging features (size, shape, attenuation, edge sharpness, pixel value distribution, and texture) of lesions on multi-detector row CT images of 20 adult patients (14 men, six women; mean age, 63 years; range, 38-72 years) referred for known or suspected focal liver lesions, lung nodules, or kidney stones. Data were acquired between September 2011 and April 2012. All multi-detector row CT scans were performed at two different radiation dose levels; images were reconstructed with filtered back projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction (MBIR) algorithms. A linear mixed-effects model was used to assess the effect of radiation dose and reconstruction algorithm on extracted features. Among the 23 imaging features assessed, radiation dose had a significant effect on five, three, and four of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Adaptive statistical iterative reconstruction had a significant effect on three, one, and one of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). MBIR reconstruction had a significant effect on nine, 11, and 15 of the features for liver lesions, lung nodules, and renal stones, respectively (P < .002 for all comparisons). Of note, the measured size of lung nodules and renal stones with MBIR was significantly different than those for the other two algorithms (P < .002 for all comparisons). Although lesion texture was significantly affected by the reconstruction algorithm used (average of 3.33 features affected by MBIR throughout lesion types; P < .002, for all comparisons), no significant effect of the radiation dose setting was observed for all but one of the texture features (P = .002-.998). Radiation dose settings and reconstruction algorithms affect the extraction and analysis of quantitative imaging features in lesions at multi-detector row CT.

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

PubMed

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

2017-03-16

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

A Novel Pairwise Comparison-Based Method to Determine Radiation Dose Reduction Potentials of Iterative Reconstruction Algorithms, Exemplified Through Circle of Willis Computed Tomography Angiography.

PubMed

Ellmann, Stephan; Kammerer, Ferdinand; Brand, Michael; Allmendinger, Thomas; May, Matthias S; Uder, Michael; Lell, Michael M; Kramer, Manuel

2016-05-01

The aim of this study was to determine the dose reduction potential of iterative reconstruction (IR) algorithms in computed tomography angiography (CTA) of the circle of Willis using a novel method of evaluating the quality of radiation dose-reduced images. This study relied on ReconCT, a proprietary reconstruction software that allows simulating CT scans acquired with reduced radiation dose based on the raw data of true scans. To evaluate the performance of ReconCT in this regard, a phantom study was performed to compare the image noise of true and simulated scans within simulated vessels of a head phantom. That followed, 10 patients scheduled for CTA of the circle of Willis were scanned according to our institute's standard protocol (100 kV, 145 reference mAs). Subsequently, CTA images of these patients were reconstructed as either a full-dose weighted filtered back projection or with radiation dose reductions down to 10% of the full-dose level and Sinogram-Affirmed Iterative Reconstruction (SAFIRE) with either strength 3 or 5. Images were marked with arrows pointing on vessels of different sizes, and image pairs were presented to observers. Five readers assessed image quality with 2-alternative forced choice comparisons. In the phantom study, no significant differences were observed between the noise levels of simulated and true scans in filtered back projection, SAFIRE 3, and SAFIRE 5 reconstructions.The dose reduction potential for patient scans showed a strong dependence on IR strength as well as on the size of the vessel of interest. Thus, the potential radiation dose reductions ranged from 84.4% for the evaluation of great vessels reconstructed with SAFIRE 5 to 40.9% for the evaluation of small vessels reconstructed with SAFIRE 3. This study provides a novel image quality evaluation method based on 2-alternative forced choice comparisons. In CTA of the circle of Willis, higher IR strengths and greater vessel sizes allowed higher degrees of radiation dose reduction.

Deterministic compressive sampling for high-quality image reconstruction of ultrasound tomography.

PubMed

Huy, Tran Quang; Tue, Huynh Huu; Long, Ton That; Duc-Tan, Tran

2017-05-25

A well-known diagnostic imaging modality, termed ultrasound tomography, was quickly developed for the detection of very small tumors whose sizes are smaller than the wavelength of the incident pressure wave without ionizing radiation, compared to the current gold-standard X-ray mammography. Based on inverse scattering technique, ultrasound tomography uses some material properties such as sound contrast or attenuation to detect small targets. The Distorted Born Iterative Method (DBIM) based on first-order Born approximation is an efficient diffraction tomography approach. One of the challenges for a high quality reconstruction is to obtain many measurements from the number of transmitters and receivers. Given the fact that biomedical images are often sparse, the compressed sensing (CS) technique could be therefore effectively applied to ultrasound tomography by reducing the number of transmitters and receivers, while maintaining a high quality of image reconstruction. There are currently several work on CS that dispose randomly distributed locations for the measurement system. However, this random configuration is relatively difficult to implement in practice. Instead of it, we should adopt a methodology that helps determine the locations of measurement devices in a deterministic way. For this, we develop the novel DCS-DBIM algorithm that is highly applicable in practice. Inspired of the exploitation of the deterministic compressed sensing technique (DCS) introduced by the authors few years ago with the image reconstruction process implemented using l 1 regularization. Simulation results of the proposed approach have demonstrated its high performance, with the normalized error approximately 90% reduced, compared to the conventional approach, this new approach can save half of number of measurements and only uses two iterations. Universal image quality index is also evaluated in order to prove the efficiency of the proposed approach. Numerical simulation results indicate that CS and DCS techniques offer equivalent image reconstruction quality with simpler practical implementation. It would be a very promising approach in practical applications of modern biomedical imaging technology.

Fast GPU-based computation of spatial multigrid multiframe LMEM for PET.

PubMed

Nassiri, Moulay Ali; Carrier, Jean-François; Després, Philippe

2015-09-01

Significant efforts were invested during the last decade to accelerate PET list-mode reconstructions, notably with GPU devices. However, the computation time per event is still relatively long, and the list-mode efficiency on the GPU is well below the histogram-mode efficiency. Since list-mode data are not arranged in any regular pattern, costly accesses to the GPU global memory can hardly be optimized and geometrical symmetries cannot be used. To overcome obstacles that limit the acceleration of reconstruction from list-mode on the GPU, a multigrid and multiframe approach of an expectation-maximization algorithm was developed. The reconstruction process is started during data acquisition, and calculations are executed concurrently on the GPU and the CPU, while the system matrix is computed on-the-fly. A new convergence criterion also was introduced, which is computationally more efficient on the GPU. The implementation was tested on a Tesla C2050 GPU device for a Gemini GXL PET system geometry. The results show that the proposed algorithm (multigrid and multiframe list-mode expectation-maximization, MGMF-LMEM) converges to the same solution as the LMEM algorithm more than three times faster. The execution time of the MGMF-LMEM algorithm was 1.1 s per million of events on the Tesla C2050 hardware used, for a reconstructed space of 188 x 188 x 57 voxels of 2 x 2 x 3.15 mm3. For 17- and 22-mm simulated hot lesions, the MGMF-LMEM algorithm led on the first iteration to contrast recovery coefficients (CRC) of more than 75 % of the maximum CRC while achieving a minimum in the relative mean square error. Therefore, the MGMF-LMEM algorithm can be used as a one-pass method to perform real-time reconstructions for low-count acquisitions, as in list-mode gated studies. The computation time for one iteration and 60 millions of events was approximately 66 s.

Reduction of effective dose and organ dose to the eye lens in head MDCT using iterative image reconstruction and automatic tube current modulation.

PubMed

Ryska, Pavel; Kvasnicka, Tomas; Jandura, Jiri; Klzo, Ludovit; Grepl, Jakub; Zizka, Jan

2014-06-01

To compare the effective and eye lens radiation dose in helical MDCT brain examinations using automatic tube current modulation in conjunction with either standard filtered back projection (FBP) technique or iterative reconstruction in image space (IRIS). Of 400 adult brain MDCT examinations, 200 were performed using FBP and 200 using IRIS with the following parameters: tube voltage 120 kV, rotation period 1 second, pitch factor 0.55, automatic tube current modulation in both transverse and longitudinal planes with reference mAs 300 (FBP) and 200 (IRIS). Doses were calculated from CT dose index and dose length product values utilising ImPACT software; the organ dose to the lens was derived from the actual tube current-time product value applied to the lens. Image quality was assessed by two independent readers blinded to the type of image reconstruction technique. The average effective scan dose was 1.47±0.26 mSv (FBP) and 0.98±0.15 mSv (IRIS), respectively (33.3% decrease). The average organ dose to the eye lens decreased from 40.0±3.3 mGy (FBP) to 26.6±2.0 mGy (IRIS, 33.5% decrease). No significant change in diagnostic image quality was noted between IRIS and FBP scans (P=0.17). Iterative reconstruction of cerebral MDCT examinations enables reduction of both effective and organ eye lens dose by one third without signficant loss of image quality.

An Improved DINEOF Algorithm for Filling Missing Values in Spatio-Temporal Sea Surface Temperature Data.

PubMed

Ping, Bo; Su, Fenzhen; Meng, Yunshan

2016-01-01

In this study, an improved Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm for determination of missing values in a spatio-temporal dataset is presented. Compared with the ordinary DINEOF algorithm, the iterative reconstruction procedure until convergence based on every fixed EOF to determine the optimal EOF mode is not necessary and the convergence criterion is only reached once in the improved DINEOF algorithm. Moreover, in the ordinary DINEOF algorithm, after optimal EOF mode determination, the initial matrix with missing data will be iteratively reconstructed based on the optimal EOF mode until the reconstruction is convergent. However, the optimal EOF mode may be not the best EOF for some reconstructed matrices generated in the intermediate steps. Hence, instead of using asingle EOF to fill in the missing data, in the improved algorithm, the optimal EOFs for reconstruction are variable (because the optimal EOFs are variable, the improved algorithm is called VE-DINEOF algorithm in this study). To validate the accuracy of the VE-DINEOF algorithm, a sea surface temperature (SST) data set is reconstructed by using the DINEOF, I-DINEOF (proposed in 2015) and VE-DINEOF algorithms. Four parameters (Pearson correlation coefficient, signal-to-noise ratio, root-mean-square error, and mean absolute difference) are used as a measure of reconstructed accuracy. Compared with the DINEOF and I-DINEOF algorithms, the VE-DINEOF algorithm can significantly enhance the accuracy of reconstruction and shorten the computational time.

A new art code for tomographic interferometry

NASA Technical Reports Server (NTRS)

Tan, H.; Modarress, D.

1987-01-01

A new algebraic reconstruction technique (ART) code based on the iterative refinement method of least squares solution for tomographic reconstruction is presented. Accuracy and the convergence of the technique is evaluated through the application of numerically generated interferometric data. It was found that, in general, the accuracy of the results was superior to other reported techniques. The iterative method unconditionally converged to a solution for which the residual was minimum. The effects of increased data were studied. The inversion error was found to be a function of the input data error only. The convergence rate, on the other hand, was affected by all three parameters. Finally, the technique was applied to experimental data, and the results are reported.

Theoretical stability in coefficient inverse problems for general hyperbolic equations with numerical reconstruction

NASA Astrophysics Data System (ADS)

Yu, Jie; Liu, Yikan; Yamamoto, Masahiro

2018-04-01

In this article, we investigate the determination of the spatial component in the time-dependent second order coefficient of a hyperbolic equation from both theoretical and numerical aspects. By the Carleman estimates for general hyperbolic operators and an auxiliary Carleman estimate, we establish local Hölder stability with either partial boundary or interior measurements under certain geometrical conditions. For numerical reconstruction, we minimize a Tikhonov functional which penalizes the gradient of the unknown function. Based on the resulting variational equation, we design an iteration method which is updated by solving a Poisson equation at each step. One-dimensional prototype examples illustrate the numerical performance of the proposed iteration.

Tensor-based Dictionary Learning for Spectral CT Reconstruction

PubMed Central

Zhang, Yanbo; Wang, Ge

2016-01-01

Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods. PMID:27541628

Evaluation of an iterative model-based CT reconstruction algorithm by intra-patient comparison of standard and ultra-low-dose examinations.

PubMed

Noël, Peter B; Engels, Stephan; Köhler, Thomas; Muenzel, Daniela; Franz, Daniela; Rasper, Michael; Rummeny, Ernst J; Dobritz, Martin; Fingerle, Alexander A

2018-01-01

Background The explosive growth of computer tomography (CT) has led to a growing public health concern about patient and population radiation dose. A recently introduced technique for dose reduction, which can be combined with tube-current modulation, over-beam reduction, and organ-specific dose reduction, is iterative reconstruction (IR). Purpose To evaluate the quality, at different radiation dose levels, of three reconstruction algorithms for diagnostics of patients with proven liver metastases under tumor follow-up. Material and Methods A total of 40 thorax-abdomen-pelvis CT examinations acquired from 20 patients in a tumor follow-up were included. All patients were imaged using the standard-dose and a specific low-dose CT protocol. Reconstructed slices were generated by using three different reconstruction algorithms: a classical filtered back projection (FBP); a first-generation iterative noise-reduction algorithm (iDose4); and a next generation model-based IR algorithm (IMR). Results The overall detection of liver lesions tended to be higher with the IMR algorithm than with FBP or iDose4. The IMR dataset at standard dose yielded the highest overall detectability, while the low-dose FBP dataset showed the lowest detectability. For the low-dose protocols, a significantly improved detectability of the liver lesion can be reported compared to FBP or iDose 4 ( P = 0.01). The radiation dose decreased by an approximate factor of 5 between the standard-dose and the low-dose protocol. Conclusion The latest generation of IR algorithms significantly improved the diagnostic image quality and provided virtually noise-free images for ultra-low-dose CT imaging.

Low-dose CT reconstruction via L1 dictionary learning regularization using iteratively reweighted least-squares.

PubMed

Zhang, Cheng; Zhang, Tao; Li, Ming; Peng, Chengtao; Liu, Zhaobang; Zheng, Jian

2016-06-18

In order to reduce the radiation dose of CT (computed tomography), compressed sensing theory has been a hot topic since it provides the possibility of a high quality recovery from the sparse sampling data. Recently, the algorithm based on DL (dictionary learning) was developed to deal with the sparse CT reconstruction problem. However, the existing DL algorithm focuses on the minimization problem with the L2-norm regularization term, which leads to reconstruction quality deteriorating while the sampling rate declines further. Therefore, it is essential to improve the DL method to meet the demand of more dose reduction. In this paper, we replaced the L2-norm regularization term with the L1-norm one. It is expected that the proposed L1-DL method could alleviate the over-smoothing effect of the L2-minimization and reserve more image details. The proposed algorithm solves the L1-minimization problem by a weighting strategy, solving the new weighted L2-minimization problem based on IRLS (iteratively reweighted least squares). Through the numerical simulation, the proposed algorithm is compared with the existing DL method (adaptive dictionary based statistical iterative reconstruction, ADSIR) and other two typical compressed sensing algorithms. It is revealed that the proposed algorithm is more accurate than the other algorithms especially when further reducing the sampling rate or increasing the noise. The proposed L1-DL algorithm can utilize more prior information of image sparsity than ADSIR. By transforming the L2-norm regularization term of ADSIR with the L1-norm one and solving the L1-minimization problem by IRLS strategy, L1-DL could reconstruct the image more exactly.

SU-E-I-82: Improving CT Image Quality for Radiation Therapy Using Iterative Reconstruction Algorithms and Slightly Increasing Imaging Doses

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

Noid, G; Chen, G; Tai, A

2014-06-01

Purpose: Iterative reconstruction (IR) algorithms are developed to improve CT image quality (IQ) by reducing noise without diminishing spatial resolution or contrast. For CT in radiation therapy (RT), slightly increasing imaging dose to improve IQ may be justified if it can substantially enhance structure delineation. The purpose of this study is to investigate and to quantify the IQ enhancement as a result of increasing imaging doses and using IR algorithms. Methods: CT images were acquired for phantoms, built to evaluate IQ metrics including spatial resolution, contrast and noise, with a variety of imaging protocols using a CT scanner (Definition ASmore » Open, Siemens) installed inside a Linac room. Representative patients were scanned once the protocols were optimized. Both phantom and patient scans were reconstructed using the Sinogram Affirmed Iterative Reconstruction (SAFIRE) and the Filtered Back Projection (FBP) methods. IQ metrics of the obtained CTs were compared. Results: IR techniques are demonstrated to preserve spatial resolution as measured by the point spread function and reduce noise in comparison to traditional FBP. Driven by the reduction in noise, the contrast to noise ratio is doubled by adopting the highest SAFIRE strength. As expected, increasing imaging dose reduces noise for both SAFIRE and FBP reconstructions. The contrast to noise increases from 3 to 5 by increasing the dose by a factor of 4. Similar IQ improvement was observed on the CTs for selected patients with pancreas and prostrate cancers. Conclusion: The IR techniques produce a measurable enhancement to CT IQ by reducing the noise. Increasing imaging dose further reduces noise independent of the IR techniques. The improved CT enables more accurate delineation of tumors and/or organs at risk during RT planning and delivery guidance.« less

SU-D-206-01: Employing a Novel Consensus Optimization Strategy to Achieve Iterative Cone Beam CT Reconstruction On a Multi-GPU Platform

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

Li, B; Southern Medical University, Guangzhou, Guangdong; Tian, Z

Purpose: While compressed sensing-based cone-beam CT (CBCT) iterative reconstruction techniques have demonstrated tremendous capability of reconstructing high-quality images from undersampled noisy data, its long computation time still hinders wide application in routine clinic. The purpose of this study is to develop a reconstruction framework that employs modern consensus optimization techniques to achieve CBCT reconstruction on a multi-GPU platform for improved computational efficiency. Methods: Total projection data were evenly distributed to multiple GPUs. Each GPU performed reconstruction using its own projection data with a conventional total variation regularization approach to ensure image quality. In addition, the solutions from GPUs were subjectmore » to a consistency constraint that they should be identical. We solved the optimization problem with all the constraints considered rigorously using an alternating direction method of multipliers (ADMM) algorithm. The reconstruction framework was implemented using OpenCL on a platform with two Nvidia GTX590 GPU cards, each with two GPUs. We studied the performance of our method and demonstrated its advantages through a simulation case with a NCAT phantom and an experimental case with a Catphan phantom. Result: Compared with the CBCT images reconstructed using conventional FDK method with full projection datasets, our proposed method achieved comparable image quality with about one third projection numbers. The computation time on the multi-GPU platform was ∼55 s and ∼ 35 s in the two cases respectively, achieving a speedup factor of ∼ 3.0 compared with single GPU reconstruction. Conclusion: We have developed a consensus ADMM-based CBCT reconstruction method which enabled performing reconstruction on a multi-GPU platform. The achieved efficiency made this method clinically attractive.« less

Improving the quality of reconstructed X-ray CT images of polymer gel dosimeters: zero-scan coupled with adaptive mean filtering.

PubMed

Kakakhel, M B; Jirasek, A; Johnston, H; Kairn, T; Trapp, J V

2017-03-01

This study evaluated the feasibility of combining the 'zero-scan' (ZS) X-ray computed tomography (CT) based polymer gel dosimeter (PGD) readout with adaptive mean (AM) filtering for improving the signal to noise ratio (SNR), and to compare these results with available average scan (AS) X-ray CT readout techniques. NIPAM PGD were manufactured, irradiated with 6 MV photons, CT imaged and processed in Matlab. AM filter for two iterations, with 3 × 3 and 5 × 5 pixels (kernel size), was used in two scenarios (a) the CT images were subjected to AM filtering (pre-processing) and these were further employed to generate AS and ZS gel images, and (b) the AS and ZS images were first reconstructed from the CT images and then AM filtering was carried out (post-processing). SNR was computed in an ROI of 30 × 30 for different pre and post processing cases. Results showed that the ZS technique combined with AM filtering resulted in improved SNR. Using the previously-recommended 25 images for reconstruction the ZS pre-processed protocol can give an increase of 44% and 80% in SNR for 3 × 3 and 5 × 5 kernel sizes respectively. However, post processing using both techniques and filter sizes introduced blur and a reduction in the spatial resolution. Based on this work, it is possible to recommend that the ZS method may be combined with pre-processed AM filtering using appropriate kernel size, to produce a large increase in the SNR of the reconstructed PGD images.

CT of the chest with model-based, fully iterative reconstruction: comparison with adaptive statistical iterative reconstruction.

PubMed

Ichikawa, Yasutaka; Kitagawa, Kakuya; Nagasawa, Naoki; Murashima, Shuichi; Sakuma, Hajime

2013-08-09

The recently developed model-based iterative reconstruction (MBIR) enables significant reduction of image noise and artifacts, compared with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP). The purpose of this study was to evaluate lesion detectability of low-dose chest computed tomography (CT) with MBIR in comparison with ASIR and FBP. Chest CT was acquired with 64-slice CT (Discovery CT750HD) with standard-dose (5.7 ± 2.3 mSv) and low-dose (1.6 ± 0.8 mSv) conditions in 55 patients (aged 72 ± 7 years) who were suspected of lung disease on chest radiograms. Low-dose CT images were reconstructed with MBIR, ASIR 50% and FBP, and standard-dose CT images were reconstructed with FBP, using a reconstructed slice thickness of 0.625 mm. Two observers evaluated the image quality of abnormal lung and mediastinal structures on a 5-point scale (Score 5 = excellent and score 1 = non-diagnostic). The objective image noise was also measured as the standard deviation of CT intensity in the descending aorta. The image quality score of enlarged mediastinal lymph nodes on low-dose MBIR CT (4.7 ± 0.5) was significantly improved in comparison with low-dose FBP and ASIR CT (3.0 ± 0.5, p = 0.004; 4.0 ± 0.5, p = 0.02, respectively), and was nearly identical to the score of standard-dose FBP image (4.8 ± 0.4, p = 0.66). Concerning decreased lung attenuation (bulla, emphysema, or cyst), the image quality score on low-dose MBIR CT (4.9 ± 0.2) was slightly better compared to low-dose FBP and ASIR CT (4.5 ± 0.6, p = 0.01; 4.6 ± 0.5, p = 0.01, respectively). There were no significant differences in image quality scores of visualization of consolidation or mass, ground-glass attenuation, or reticular opacity among low- and standard-dose CT series. Image noise with low-dose MBIR CT (11.6 ± 1.0 Hounsfield units (HU)) were significantly lower than with low-dose ASIR (21.1 ± 2.6 HU, p < 0.0005), low-dose FBP CT (30.9 ± 3.9 HU, p < 0.0005), and standard-dose FBP CT (16.6 ± 2.3 HU, p < 0.0005). MBIR shows greater potential than ASIR for providing diagnostically acceptable low-dose CT without compromising image quality. With radiation dose reduction of >70%, MBIR can provide equivalent lesion detectability of standard-dose FBP CT.

Reconstruction of truncated TCT and SPECT data from a right-angle dual-camera system for myocardial SPECT

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

Tsui, B.M.W.; Frey, E.C.; Lalush, D.S.

1996-12-31

We investigated methods to accurately reconstruct 180{degrees} truncated TCT and SPECT projection data obtained from a right-angle dual-camera SPECT system for myocardial SPECT with attenuation compensation. The 180{degrees} data reconstruction methods would permit substantial savings in transmission data acquisition time. Simulation data from the 3D MCAT phantom and clinical data from large patients were used in the evaluation study. Different transmission reconstruction methods including the FBP, transmission ML-EM, transmission ML-SA, and BIT algorithms with and without using the body contour as support, were used in the TCT image reconstructions. The accuracy of both the TCT and attenuation compensated SPECT imagesmore » were evaluated for different degrees of truncation and noise levels. We found that using the FBP reconstructed TCT images resulted in higher count density in the left ventricular (LV) wall of the attenuation compensated SPECT images. The LV wall count density obtained using the iteratively reconstructed TCT images with and without support were similar to each other and were more accurate than that using the FBP. However, the TCT images obtained with support show fewer image artifacts than without support. Among the iterative reconstruction algorithms, the ML-SA algorithm provides the most accurate reconstruction but is the slowest. The BIT algorithm is the fastest but shows the most image artifacts. We conclude that accurate attenuation compensated images can be obtained with truncated 180{degrees} data from large patients using a right-angle dual-camera SPECT system.« less

Multiscale Reconstruction for Magnetic Resonance Fingerprinting

PubMed Central

Pierre, Eric Y.; Ma, Dan; Chen, Yong; Badve, Chaitra; Griswold, Mark A.

2015-01-01

Purpose To reduce acquisition time needed to obtain reliable parametric maps with Magnetic Resonance Fingerprinting. Methods An iterative-denoising algorithm is initialized by reconstructing the MRF image series at low image resolution. For subsequent iterations, the method enforces pixel-wise fidelity to the best-matching dictionary template then enforces fidelity to the acquired data at slightly higher spatial resolution. After convergence, parametric maps with desirable spatial resolution are obtained through template matching of the final image series. The proposed method was evaluated on phantom and in-vivo data using the highly-undersampled, variable-density spiral trajectory and compared with the original MRF method. The benefits of additional sparsity constraints were also evaluated. When available, gold standard parameter maps were used to quantify the performance of each method. Results The proposed approach allowed convergence to accurate parametric maps with as few as 300 time points of acquisition, as compared to 1000 in the original MRF work. Simultaneous quantification of T1, T2, proton density (PD) and B0 field variations in the brain was achieved in vivo for a 256×256 matrix for a total acquisition time of 10.2s, representing a 3-fold reduction in acquisition time. Conclusions The proposed iterative multiscale reconstruction reliably increases MRF acquisition speed and accuracy. PMID:26132462

Dose reduction in abdominal computed tomography: intraindividual comparison of image quality of full-dose standard and half-dose iterative reconstructions with dual-source computed tomography.

PubMed

May, Matthias S; Wüst, Wolfgang; Brand, Michael; Stahl, Christian; Allmendinger, Thomas; Schmidt, Bernhard; Uder, Michael; Lell, Michael M

2011-07-01

We sought to evaluate the image quality of iterative reconstruction in image space (IRIS) in half-dose (HD) datasets compared with full-dose (FD) and HD filtered back projection (FBP) reconstruction in abdominal computed tomography (CT). To acquire data with FD and HD simultaneously, contrast-enhanced abdominal CT was performed with a dual-source CT system, both tubes operating at 120 kV, 100 ref.mAs, and pitch 0.8. Three different image datasets were reconstructed from the raw data: Standard FD images applying FBP which served as reference, HD images applying FBP and HD images applying IRIS. For the HD data sets, only data from 1 tube detector-system was used. Quantitative image quality analysis was performed by measuring image noise in tissue and air. Qualitative image quality was evaluated according to the European Guidelines on Quality criteria for CT. Additional assessment of artifacts, lesion conspicuity, and edge sharpness was performed. : Image noise in soft tissue was substantially decreased in HD-IRIS (-3.4 HU, -22%) and increased in HD-FBP (+6.2 HU, +39%) images when compared with the reference (mean noise, 15.9 HU). No significant differences between the FD-FBP and HD-IRIS images were found for the visually sharp anatomic reproduction, overall diagnostic acceptability (P = 0.923), lesion conspicuity (P = 0.592), and edge sharpness (P = 0.589), while HD-FBP was rated inferior. Streak artifacts and beam hardening was significantly more prominent in HD-FBP while HD-IRIS images exhibited a slightly different noise pattern. Direct intrapatient comparison of standard FD body protocols and HD-IRIS reconstruction suggest that the latest iterative reconstruction algorithms allow for approximately 50% dose reduction without deterioration of the high image quality necessary for confident diagnosis.

TU-F-18A-06: Dual Energy CT Using One Full Scan and a Second Scan with Very Few Projections

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

Wang, T; Zhu, L

Purpose: The conventional dual energy CT (DECT) requires two full CT scans at different energy levels, resulting in dose increase as well as imaging errors from patient motion between the two scans. To shorten the scan time of DECT and thus overcome these drawbacks, we propose a new DECT algorithm using one full scan and a second scan with very few projections by preserving structural information. Methods: We first reconstruct a CT image on the full scan using a standard filtered-backprojection (FBP) algorithm. We then use a compressed sensing (CS) based iterative algorithm on the second scan for reconstruction frommore » very few projections. The edges extracted from the first scan are used as weights in the Objectives: function of the CS-based reconstruction to substantially improve the image quality of CT reconstruction. The basis material images are then obtained by an iterative image-domain decomposition method and an electron density map is finally calculated. The proposed method is evaluated on phantoms. Results: On the Catphan 600 phantom, the CT reconstruction mean error using the proposed method on 20 and 5 projections are 4.76% and 5.02%, respectively. Compared with conventional iterative reconstruction, the proposed edge weighting preserves object structures and achieves a better spatial resolution. With basis materials of Iodine and Teflon, our method on 20 projections obtains similar quality of decomposed material images compared with FBP on a full scan and the mean error of electron density in the selected regions of interest is 0.29%. Conclusion: We propose an effective method for reducing projections and therefore scan time in DECT. We show that a full scan plus a 20-projection scan are sufficient to provide DECT images and electron density with similar quality compared with two full scans. Our future work includes more phantom studies to validate the performance of our method.« less

SU-F-I-08: CT Image Ring Artifact Reduction Based On Prior Image

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

Yuan, C; Qi, H; Chen, Z

Purpose: In computed tomography (CT) system, CT images with ring artifacts will be reconstructed when some adjacent bins of detector don’t work. The ring artifacts severely degrade CT image quality. We present a useful CT ring artifacts reduction based on projection data correction, aiming at estimating the missing data of projection data accurately, thus removing the ring artifacts of CT images. Methods: The method consists of ten steps: 1) Identification of abnormal pixel line in projection sinogram; 2) Linear interpolation within the pixel line of projection sinogram; 3) FBP reconstruction using interpolated projection data; 4) Filtering FBP image using meanmore » filter; 5) Forwarding projection of filtered FBP image; 6) Subtraction forwarded projection from original projection; 7) Linear interpolation of abnormal pixel line area in the subtraction projection; 8) Adding the interpolated subtraction projection on the forwarded projection; 9) FBP reconstruction using corrected projection data; 10) Return to step 4 until the pre-set iteration number is reached. The method is validated on simulated and real data to restore missing projection data and reconstruct ring artifact-free CT images. Results: We have studied impact of amount of dead bins of CT detector on the accuracy of missing data estimation in projection sinogram. For the simulated case with a resolution of 256 by 256 Shepp-Logan phantom, three iterations are sufficient to restore projection data and reconstruct ring artifact-free images when the dead bins rating is under 30%. The dead-bin-induced artifacts are substantially reduced. More iteration number is needed to reconstruct satisfactory images while the rating of dead bins increases. Similar results were found for a real head phantom case. Conclusion: A practical CT image ring artifact correction scheme based on projection data is developed. This method can produce ring artifact-free CT images feasibly and effectively.« less

Upgrade to iterative image reconstruction (IR) in abdominal MDCT imaging: a clinical study for detailed parameter optimization beyond vendor recommendations using the adaptive statistical iterative reconstruction environment (ASIR).

PubMed

Mueck, F G; Körner, M; Scherr, M K; Geyer, L L; Deak, Z; Linsenmaier, U; Reiser, M; Wirth, S

2012-03-01

To compare the image quality of dose-reduced 64-row abdominal CT reconstructed at different levels of adaptive statistical iterative reconstruction (ASIR) to full-dose baseline examinations reconstructed with filtered back-projection (FBP) in a clinical setting and upgrade situation. Abdominal baseline examinations (noise index NI = 29; LightSpeed VCT XT, GE) were intra-individually compared to follow-up studies on a CT with an ASIR option (NI = 43; Discovery HD750, GE), n = 42. Standard-kernel images were calculated with ASIR blendings of 0 - 100 % in slice and volume mode, respectively. Three experienced radiologists compared the image quality of these 567 sets to their corresponding full-dose baseline examination (- 2: diagnostically inferior, - 1: inferior, 0: equal, + 1: superior, + 2: diagnostically superior). Furthermore, a phantom was scanned. Statistical analysis used the Wilcoxon - the Mann-Whitney U-test and the intra-class correlation (ICC). The mean CTDIvol decreased from 19.7 ± 5.5 to 12.2 ± 4.7 mGy (p < 0.001). The ICC was 0.861. The total image quality of the dose-reduced ASIR studies was comparable to the baseline at ASIR 50 % in slice (p = 0.18) and ASIR 50 - 100 % in volume mode (p > 0.10). Volume mode performed 73 % slower than slice mode (p < 0.01). After the system upgrade, the vendor recommendation of ASIR 50 % in slice mode allowed for a dose reduction of 38 % in abdominal CT with comparable image quality and time expenditure. However, there is still further dose reduction potential for more complex reconstruction settings. © Georg Thieme Verlag KG Stuttgart · New York.

Ultra-Low-Dose Fetal CT With Model-Based Iterative Reconstruction: A Prospective Pilot Study.

PubMed

Imai, Rumi; Miyazaki, Osamu; Horiuchi, Tetsuya; Asano, Keisuke; Nishimura, Gen; Sago, Haruhiko; Nosaka, Shunsuke

2017-06-01

Prenatal diagnosis of skeletal dysplasia by means of 3D skeletal CT examination is highly accurate. However, it carries a risk of fetal exposure to radiation. Model-based iterative reconstruction (MBIR) technology can reduce radiation exposure; however, to our knowledge, the lower limit of an optimal dose is currently unknown. The objectives of this study are to establish ultra-low-dose fetal CT as a method for prenatal diagnosis of skeletal dysplasia and to evaluate the appropriate radiation dose for ultra-low-dose fetal CT. Relationships between tube current and image noise in adaptive statistical iterative reconstruction and MBIR were examined using a 32-cm CT dose index (CTDI) phantom. On the basis of the results of this examination and the recommended methods for the MBIR option and the known relationship between noise and tube current for filtered back projection, as represented by the expression SD = (milliamperes) -0.5 , the lower limit of the optimal dose in ultra-low-dose fetal CT with MBIR was set. The diagnostic power of the CT images obtained using the aforementioned scanning conditions was evaluated, and the radiation exposure associated with ultra-low-dose fetal CT was compared with that noted in previous reports. Noise increased in nearly inverse proportion to the square root of the dose in adaptive statistical iterative reconstruction and in inverse proportion to the fourth root of the dose in MBIR. Ultra-low-dose fetal CT was found to have a volume CTDI of 0.5 mGy. Prenatal diagnosis was accurately performed on the basis of ultra-low-dose fetal CT images that were obtained using this protocol. The level of fetal exposure to radiation was 0.7 mSv. The use of ultra-low-dose fetal CT with MBIR led to a substantial reduction in radiation exposure, compared with the CT imaging method currently used at our institution, but it still enabled diagnosis of skeletal dysplasia without reducing diagnostic power.

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

NASA Astrophysics Data System (ADS)

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

2014-08-01

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

Metal artefact reduction for patients with metallic dental fillings in helical neck computed tomography: comparison of adaptive iterative dose reduction 3D (AIDR 3D), forward-projected model-based iterative reconstruction solution (FIRST) and AIDR 3D with single-energy metal artefact reduction (SEMAR).

PubMed

Yasaka, Koichiro; Kamiya, Kouhei; Irie, Ryusuke; Maeda, Eriko; Sato, Jiro; Ohtomo, Kuni

To compare the differences in metal artefact degree and the depiction of structures in helical neck CT, in patients with metallic dental fillings, among adaptive iterative dose reduction three dimensional (AIDR 3D), forward-projected model-based iterative reconstruction solution (FIRST) and AIDR 3D with single-energy metal artefact reduction (SEMAR-A). In this retrospective clinical study, 22 patients (males, 13; females, 9; mean age, 64.6 ± 12.6 years) with metallic dental fillings who underwent contrast-enhanced helical CT involving the oropharyngeal region were included. Neck axial images were reconstructed with AIDR 3D, FIRST and SEMAR-A. Metal artefact degree and depiction of structures (the apex and root of the tongue, parapharyngeal space, superior portion of the internal jugular chain and parotid gland) were evaluated on a four-point scale by two radiologists. Placing regions of interest, standard deviations of the oral cavity and nuchal muscle (at the slice where no metal exists) were measured and metal artefact indices were calculated (the square root of the difference of the squares of them). In SEMAR-A, metal artefact was significantly reduced and depictions of all structures were significantly improved compared with those in FIRST and AIDR 3D (p ≤ 0.001, sign test). Metal artefact index for the oral cavity in AIDR 3D/FIRST/SEMAR-A was 572.0/477.7/88.4, and significant differences were seen between each reconstruction algorithm (p < 0.0001, Wilcoxon signed-rank test). SEMAR-A could provide images with lesser metal artefact and better depiction of structures than AIDR 3D and FIRST.

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

PubMed

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

2010-04-01

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

Utilizing the Iterative Closest Point (ICP) algorithm for enhanced registration of high resolution surface models - more than a simple black-box application

NASA Astrophysics Data System (ADS)

Stöcker, Claudia; Eltner, Anette

2016-04-01

Advances in computer vision and digital photogrammetry (i.e. structure from motion) allow for fast and flexible high resolution data supply. Within geoscience applications and especially in the field of small surface topography, high resolution digital terrain models and dense 3D point clouds are valuable data sources to capture actual states as well as for multi-temporal studies. However, there are still some limitations regarding robust registration and accuracy demands (e.g. systematic positional errors) which impede the comparison and/or combination of multi-sensor data products. Therefore, post-processing of 3D point clouds can heavily enhance data quality. In this matter the Iterative Closest Point (ICP) algorithm represents an alignment tool which iteratively minimizes distances of corresponding points within two datasets. Even though tool is widely used; it is often applied as a black-box application within 3D data post-processing for surface reconstruction. Aiming for precise and accurate combination of multi-sensor data sets, this study looks closely at different variants of the ICP algorithm including sub-steps of point selection, point matching, weighting, rejection, error metric and minimization. Therefore, an agricultural utilized field was investigated simultaneously by terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) sensors two times (once covered with sparse vegetation and once bare soil). Due to different perspectives both data sets show diverse consistency in terms of shadowed areas and thus gaps so that data merging would provide consistent surface reconstruction. Although photogrammetric processing already included sub-cm accurate ground control surveys, UAV point cloud exhibits an offset towards TLS point cloud. In order to achieve the transformation matrix for fine registration of UAV point clouds, different ICP variants were tested. Statistical analyses of the results show that final success of registration and therefore data quality depends particularly on parameterization and choice of error metric, especially for erroneous data sets as in the case of sparse vegetation cover. At this, the point-to-point metric is more sensitive to data "noise" than the point-to-plane metric which results in considerably higher cloud-to-cloud distances. Concluding, in order to comply with accuracy demands of high resolution surface reconstruction and the aspect that ground control surveys can reach their limits both in time exposure and terrain accessibility ICP algorithm represents a great tool to refine rough initial alignment. Here different variants of registration modules allow for individual application according to the quality of the input data.

Genovo: De Novo Assembly for Metagenomes

NASA Astrophysics Data System (ADS)

Laserson, Jonathan; Jojic, Vladimir; Koller, Daphne

Next-generation sequencing technologies produce a large number of noisy reads from the DNA in a sample. Metagenomics and population sequencing aim to recover the genomic sequences of the species in the sample, which could be of high diversity. Methods geared towards single sequence reconstruction are not sensitive enough when applied in this setting. We introduce a generative probabilistic model of read generation from environmental samples and present Genovo, a novel de novo sequence assembler that discovers likely sequence reconstructions under the model. A Chinese restaurant process prior accounts for the unknown number of genomes in the sample. Inference is made by applying a series of hill-climbing steps iteratively until convergence. We compare the performance of Genovo to three other short read assembly programs across one synthetic dataset and eight metagenomic datasets created using the 454 platform, the largest of which has 311k reads. Genovo's reconstructions cover more bases and recover more genes than the other methods, and yield a higher assembly score.

Parallel ptychographic reconstruction

DOE PAGES

Nashed, Youssef S. G.; Vine, David J.; Peterka, Tom; ...

2014-12-19

Ptychography is an imaging method whereby a coherent beam is scanned across an object, and an image is obtained by iterative phasing of the set of diffraction patterns. It is able to be used to image extended objects at a resolution limited by scattering strength of the object and detector geometry, rather than at an optics-imposed limit. As technical advances allow larger fields to be imaged, computational challenges arise for reconstructing the correspondingly larger data volumes, yet at the same time there is also a need to deliver reconstructed images immediately so that one can evaluate the next steps tomore » take in an experiment. Here we present a parallel method for real-time ptychographic phase retrieval. It uses a hybrid parallel strategy to divide the computation between multiple graphics processing units (GPUs) and then employs novel techniques to merge sub-datasets into a single complex phase and amplitude image. Results are shown on a simulated specimen and a real dataset from an X-ray experiment conducted at a synchrotron light source.« less

Monte Carlo simulations for 20 MV X-ray spectrum reconstruction of a linear induction accelerator

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Qin; Jiang, Xiao-Guo

2012-09-01

To study the spectrum reconstruction of the 20 MV X-ray generated by the Dragon-I linear induction accelerator, the Monte Carlo method is applied to simulate the attenuations of the X-ray in the attenuators of different thicknesses and thus provide the transmission data. As is known, the spectrum estimation from transmission data is an ill-conditioned problem. The method based on iterative perturbations is employed to derive the X-ray spectra, where initial guesses are used to start the process. This algorithm takes into account not only the minimization of the differences between the measured and the calculated transmissions but also the smoothness feature of the spectrum function. In this work, various filter materials are put to use as the attenuator, and the condition for an accurate and robust solution of the X-ray spectrum calculation is demonstrated. The influences of the scattering photons within different intervals of emergence angle on the X-ray spectrum reconstruction are also analyzed.

Superficial vessel reconstruction with a multiview camera system

PubMed Central

Marreiros, Filipe M. M.; Rossitti, Sandro; Karlsson, Per M.; Wang, Chunliang; Gustafsson, Torbjörn; Carleberg, Per; Smedby, Örjan

2016-01-01

Abstract. We aim at reconstructing superficial vessels of the brain. Ultimately, they will serve to guide the deformation methods to compensate for the brain shift. A pipeline for three-dimensional (3-D) vessel reconstruction using three mono-complementary metal-oxide semiconductor cameras has been developed. Vessel centerlines are manually selected in the images. Using the properties of the Hessian matrix, the centerline points are assigned direction information. For correspondence matching, a combination of methods was used. The process starts with epipolar and spatial coherence constraints (geometrical constraints), followed by relaxation labeling and an iterative filtering where the 3-D points are compared to surfaces obtained using the thin-plate spline with decreasing relaxation parameter. Finally, the points are shifted to their local centroid position. Evaluation in virtual, phantom, and experimental images, including intraoperative data from patient experiments, shows that, with appropriate camera positions, the error estimates (root-mean square error and mean error) are ∼1  mm. PMID:26759814

Joint synthetic aperture radar plus ground moving target indicator from single-channel radar using compressive sensing

DOEpatents

Thompson, Douglas; Hallquist, Aaron; Anderson, Hyrum

2017-10-17

The various embodiments presented herein relate to utilizing an operational single-channel radar to collect and process synthetic aperture radar (SAR) and ground moving target indicator (GMTI) imagery from a same set of radar returns. In an embodiment, data is collected by randomly staggering a slow-time pulse repetition interval (PRI) over a SAR aperture such that a number of transmitted pulses in the SAR aperture is preserved with respect to standard SAR, but many of the pulses are spaced very closely enabling movers (e.g., targets) to be resolved, wherein a relative velocity of the movers places them outside of the SAR ground patch. The various embodiments of image reconstruction can be based on compressed sensing inversion from undersampled data, which can be solved efficiently using such techniques as Bregman iteration. The various embodiments enable high-quality SAR reconstruction, and high-quality GMTI reconstruction from the same set of radar returns.

Use of a corrugated beam pipe as a passive deflector for bunch length measurements

NASA Astrophysics Data System (ADS)

Seok, Jimin; Chung, Moses; Kang, Heung-Sik; Min, Chang-Ki; Na, Donghyun

2018-02-01

We report the experimental demonstration of bunch length measurements using a corrugated metallic beam pipe as a passive deflector. The corrugated beam pipe has been adopted for reducing longitudinal chirping after the bunch compressors in several XFEL facilities worldwide. In the meantime, there have been attempts to measure the electron bunch's longitudinal current profile using the dipole wakefields generated in the corrugated pipe. Nevertheless, the bunch shape reconstructed from the nonlinearly deflected beam suffers from significant distortion, particularly near the head of the bunch. In this paper, we introduce an iterative process to improve the resolution of the bunch shape reconstruction. The astra and elegant simulations have been performed for pencil beam and cigar beam cases, in order to verify the effectiveness of the reconstruction process. To overcome the undesirable effects of transverse beam spreads, a measurement scheme involving both the corrugated beam pipe and the spectrometer magnet has been employed, both of which do not require a dedicated (and likely very expensive) rf system. A proof-of-principle experiment was carried out at Pohang Accelerator Laboratory (PAL) Injector Test Facility (ITF), and its results are discussed together with a comparison with the rf deflector measurement.

MR Image Reconstruction Using Block Matching and Adaptive Kernel Methods.

PubMed

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.

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

PubMed

Wang, Jin; Zhang, Chen; Wang, Yuanyuan

2017-05-30

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

Iterative methods for dose reduction and image enhancement in tomography

DOEpatents

Miao, Jianwei; Fahimian, Benjamin Pooya

2012-09-18

A system and method for creating a three dimensional cross sectional image of an object by the reconstruction of its projections that have been iteratively refined through modification in object space and Fourier space is disclosed. The invention provides systems and methods for use with any tomographic imaging system that reconstructs an object from its projections. In one embodiment, the invention presents a method to eliminate interpolations present in conventional tomography. The method has been experimentally shown to provide higher resolution and improved image quality parameters over existing approaches. A primary benefit of the method is radiation dose reduction since the invention can produce an image of a desired quality with a fewer number projections than seen with conventional methods.

Complex amplitude reconstruction by iterative amplitude-phase retrieval algorithm with reference

NASA Astrophysics Data System (ADS)

Shen, Cheng; Guo, Cheng; Tan, Jiubin; Liu, Shutian; Liu, Zhengjun

2018-06-01

Multi-image iterative phase retrieval methods have been successfully applied in plenty of research fields due to their simple but efficient implementation. However, there is a mismatch between the measurement of the first long imaging distance and the sequential interval. In this paper, an amplitude-phase retrieval algorithm with reference is put forward without additional measurements or priori knowledge. It gets rid of measuring the first imaging distance. With a designed update formula, it significantly raises the convergence speed and the reconstruction fidelity, especially in phase retrieval. Its superiority over the original amplitude-phase retrieval (APR) method is validated by numerical analysis and experiments. Furthermore, it provides a conceptual design of a compact holographic image sensor, which can achieve numerical refocusing easily.

Does Iterative Reconstruction Lower CT Radiation Dose: Evaluation of 15,000 Examinations

PubMed Central

Noël, Peter B.; Renger, Bernhard; Fiebich, Martin; Münzel, Daniela; Fingerle, Alexander A.; Rummeny, Ernst J.; Dobritz, Martin

2013-01-01

Purpose Evaluation of 15,000 computed tomography (CT) examinations to investigate if iterative reconstruction (IR) reduces sustainably radiation exposure. Method and Materials Information from 15,000 CT examinations was collected, including all aspects of the exams such as scan parameter, patient information, and reconstruction instructions. The examinations were acquired between January 2010 and December 2012, while after 15 months a first generation IR algorithm was installed. To collect the necessary information from PACS, RIS, MPPS and structured reports a Dose Monitoring System was developed. To harvest all possible information an optical character recognition system was integrated, for example to collect information from the screenshot CT-dose report. The tool transfers all data to a database for further processing such as the calculation of effective dose and organ doses. To evaluate if IR provides a sustainable dose reduction, the effective dose values were statistically analyzed with respect to protocol type, diagnostic indication, and patient population. Results IR has the potential to reduce radiation dose significantly. Before clinical introduction of IR the average effective dose was 10.1±7.8mSv and with IR 8.9±7.1mSv (p*=0.01). Especially in CTA, with the possibility to use kV reduction protocols, such as in aortic CTAs (before IR: average14.2±7.8mSv; median11.4mSv /with IR:average9.9±7.4mSv; median7.4mSv), or pulmonary CTAs (before IR: average9.7±6.2mSV; median7.7mSv /with IR: average6.4±4.7mSv; median4.8mSv) the dose reduction effect is significant(p*=0.01). On the contrary for unenhanced low-dose scans of the cranial (for example sinuses) the reduction is not significant (before IR:average6.6±5.8mSv; median3.9mSv/with IR:average6.0±3.1mSV; median3.2mSv). Conclusion The dose aspect remains a priority in CT research. Iterative reconstruction algorithms reduce sustainably and significantly radiation dose in the clinical routine. Our results illustrate that not only in studies with a limited number of patients but also in the clinical routine, IRs provide long-term dose saving. PMID:24303035

Does iterative reconstruction lower CT radiation dose: evaluation of 15,000 examinations.

PubMed

Noël, Peter B; Renger, Bernhard; Fiebich, Martin; Münzel, Daniela; Fingerle, Alexander A; Rummeny, Ernst J; Dobritz, Martin

2013-01-01

Evaluation of 15,000 computed tomography (CT) examinations to investigate if iterative reconstruction (IR) reduces sustainably radiation exposure. Information from 15,000 CT examinations was collected, including all aspects of the exams such as scan parameter, patient information, and reconstruction instructions. The examinations were acquired between January 2010 and December 2012, while after 15 months a first generation IR algorithm was installed. To collect the necessary information from PACS, RIS, MPPS and structured reports a Dose Monitoring System was developed. To harvest all possible information an optical character recognition system was integrated, for example to collect information from the screenshot CT-dose report. The tool transfers all data to a database for further processing such as the calculation of effective dose and organ doses. To evaluate if IR provides a sustainable dose reduction, the effective dose values were statistically analyzed with respect to protocol type, diagnostic indication, and patient population. IR has the potential to reduce radiation dose significantly. Before clinical introduction of IR the average effective dose was 10.1±7.8mSv and with IR 8.9±7.1mSv (p*=0.01). Especially in CTA, with the possibility to use kV reduction protocols, such as in aortic CTAs (before IR: average14.2±7.8mSv; median11.4mSv /with IR:average9.9±7.4mSv; median7.4mSv), or pulmonary CTAs (before IR: average9.7±6.2mSV; median7.7mSv /with IR: average6.4±4.7mSv; median4.8mSv) the dose reduction effect is significant(p*=0.01). On the contrary for unenhanced low-dose scans of the cranial (for example sinuses) the reduction is not significant (before IR:average6.6±5.8mSv; median3.9mSv/with IR:average6.0±3.1mSV; median3.2mSv). The dose aspect remains a priority in CT research. Iterative reconstruction algorithms reduce sustainably and significantly radiation dose in the clinical routine. Our results illustrate that not only in studies with a limited number of patients but also in the clinical routine, IRs provide long-term dose saving.

Iterative Refinement of Transmission Map for Stereo Image Defogging Using a Dual Camera Sensor.

PubMed

Kim, Heegwang; Park, Jinho; Park, Hasil; Paik, Joonki

2017-12-09

Recently, the stereo imaging-based image enhancement approach has attracted increasing attention in the field of video analysis. This paper presents a dual camera-based stereo image defogging algorithm. Optical flow is first estimated from the stereo foggy image pair, and the initial disparity map is generated from the estimated optical flow. Next, an initial transmission map is generated using the initial disparity map. Atmospheric light is then estimated using the color line theory. The defogged result is finally reconstructed using the estimated transmission map and atmospheric light. The proposed method can refine the transmission map iteratively. Experimental results show that the proposed method can successfully remove fog without color distortion. The proposed method can be used as a pre-processing step for an outdoor video analysis system and a high-end smartphone with a dual camera system.

Iterative Refinement of Transmission Map for Stereo Image Defogging Using a Dual Camera Sensor

PubMed Central

Park, Jinho; Park, Hasil

2017-01-01

Recently, the stereo imaging-based image enhancement approach has attracted increasing attention in the field of video analysis. This paper presents a dual camera-based stereo image defogging algorithm. Optical flow is first estimated from the stereo foggy image pair, and the initial disparity map is generated from the estimated optical flow. Next, an initial transmission map is generated using the initial disparity map. Atmospheric light is then estimated using the color line theory. The defogged result is finally reconstructed using the estimated transmission map and atmospheric light. The proposed method can refine the transmission map iteratively. Experimental results show that the proposed method can successfully remove fog without color distortion. The proposed method can be used as a pre-processing step for an outdoor video analysis system and a high-end smartphone with a dual camera system. PMID:29232826

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

PubMed

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

2016-10-01

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

Performance analysis of model based iterative reconstruction with dictionary learning in transportation security CT

NASA Astrophysics Data System (ADS)

Haneda, Eri; Luo, Jiajia; Can, Ali; Ramani, Sathish; Fu, Lin; De Man, Bruno

2016-05-01

In this study, we implement and compare model based iterative reconstruction (MBIR) with dictionary learning (DL) over MBIR with pairwise pixel-difference regularization, in the context of transportation security. DL is a technique of sparse signal representation using an over complete dictionary which has provided promising results in image processing applications including denoising,1 as well as medical CT reconstruction.2 It has been previously reported that DL produces promising results in terms of noise reduction and preservation of structural details, especially for low dose and few-view CT acquisitions.2 A distinguishing feature of transportation security CT is that scanned baggage may contain items with a wide range of material densities. While medical CT typically scans soft tissues, blood with and without contrast agents, and bones, luggage typically contains more high density materials (i.e. metals and glass), which can produce severe distortions such as metal streaking artifacts. Important factors of security CT are the emphasis on image quality such as resolution, contrast, noise level, and CT number accuracy for target detection. While MBIR has shown exemplary performance in the trade-off of noise reduction and resolution preservation, we demonstrate that DL may further improve this trade-off. In this study, we used the KSVD-based DL3 combined with the MBIR cost-minimization framework and compared results to Filtered Back Projection (FBP) and MBIR with pairwise pixel-difference regularization. We performed a parameter analysis to show the image quality impact of each parameter. We also investigated few-view CT acquisitions where DL can show an additional advantage relative to pairwise pixel difference regularization.

Re-examination of Chinese semantic processing and syntactic processing: evidence from conventional ERPs and reconstructed ERPs by residue iteration decomposition (RIDE).

PubMed

Wang, Fang; Ouyang, Guang; Zhou, Changsong; Wang, Suiping

2015-01-01

A number of studies have explored the time course of Chinese semantic and syntactic processing. However, whether syntactic processing occurs earlier than semantics during Chinese sentence reading is still under debate. To further explore this issue, an event-related potentials (ERPs) experiment was conducted on 21 native Chinese speakers who read individually-presented Chinese simple sentences (NP1+VP+NP2) word-by-word for comprehension and made semantic plausibility judgments. The transitivity of the verbs was manipulated to form three types of stimuli: congruent sentences (CON), sentences with a semantically violated NP2 following a transitive verb (semantic violation, SEM), and sentences with a semantically violated NP2 following an intransitive verb (combined semantic and syntactic violation, SEM+SYN). The ERPs evoked from the target NP2 were analyzed by using the Residue Iteration Decomposition (RIDE) method to reconstruct the ERP waveform blurred by trial-to-trial variability, as well as by using the conventional ERP method based on stimulus-locked averaging. The conventional ERP analysis showed that, compared with the critical words in CON, those in SEM and SEM+SYN elicited an N400-P600 biphasic pattern. The N400 effects in both violation conditions were of similar size and distribution, but the P600 in SEM+SYN was bigger than that in SEM. Compared with the conventional ERP analysis, RIDE analysis revealed a larger N400 effect and an earlier P600 effect (in the time window of 500-800 ms instead of 570-810ms). Overall, the combination of conventional ERP analysis and the RIDE method for compensating for trial-to-trial variability confirmed the non-significant difference between SEM and SEM+SYN in the earlier N400 time window. Converging with previous findings on other Chinese structures, the current study provides further precise evidence that syntactic processing in Chinese does not occur earlier than semantic processing.

Diagnostic accuracy of second-generation dual-source computed tomography coronary angiography with iterative reconstructions: a real-world experience.

PubMed

Maffei, E; Martini, C; Rossi, A; Mollet, N; Lario, C; Castiglione Morelli, M; Clemente, A; Gentile, G; Arcadi, T; Seitun, S; Catalano, O; Aldrovandi, A; Cademartiri, F

2012-08-01

The authors evaluated the diagnostic accuracy of second-generation dual-source (DSCT) computed tomography coronary angiography (CTCA) with iterative reconstructions for detecting obstructive coronary artery disease (CAD). Between June 2010 and February 2011, we enrolled 160 patients (85 men; mean age 61.2±11.6 years) with suspected CAD. All patients underwent CTCA and conventional coronary angiography (CCA). For the CTCA scan (Definition Flash, Siemens), we use prospective tube current modulation and 70-100 ml of iodinated contrast material (Iomeprol 400 mgI/ ml, Bracco). Data sets were reconstructed with iterative reconstruction algorithm (IRIS, Siemens). CTCA and CCA reports were used to evaluate accuracy using the threshold for significant stenosis at ≥50% and ≥70%, respectively. No patient was excluded from the analysis. Heart rate was 64.3±11.9 bpm and radiation dose was 7.2±2.1 mSv. Disease prevalence was 30% (48/160). Sensitivity, specificity and positive and negative predictive values of CTCA in detecting significant stenosis were 90.1%, 93.3%, 53.2% and 99.1% (per segment), 97.5%, 91.2%, 61.4% and 99.6% (per vessel) and 100%, 83%, 71.6% and 100% (per patient), respectively. Positive and negative likelihood ratios at the per-patient level were 5.89 and 0.0, respectively. CTCA with second-generation DSCT in the real clinical world shows a diagnostic performance comparable with previously reported validation studies. The excellent negative predictive value and likelihood ratio make CTCA a first-line noninvasive method for diagnosing obstructive CAD.

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

PubMed

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

2017-01-01

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

Radiation dose reduction for CT lung cancer screening using ASIR and MBIR: a phantom study.

PubMed

Mathieu, Kelsey B; Ai, Hua; Fox, Patricia S; Godoy, Myrna Cobos Barco; Munden, Reginald F; de Groot, Patricia M; Pan, Tinsu

2014-03-06

The purpose of this study was to reduce the radiation dosage associated with computed tomography (CT) lung cancer screening while maintaining overall diagnostic image quality and definition of ground-glass opacities (GGOs). A lung screening phantom and a multipurpose chest phantom were used to quantitatively assess the performance of two iterative image reconstruction algorithms (adaptive statistical iterative reconstruction (ASIR) and model-based iterative reconstruction (MBIR)) used in conjunction with reduced tube currents relative to a standard clinical lung cancer screening protocol (51 effective mAs (3.9 mGy) and filtered back-projection (FBP) reconstruction). To further assess the algorithms' performances, qualitative image analysis was conducted (in the form of a reader study) using the multipurpose chest phantom, which was implanted with GGOs of two densities. Our quantitative image analysis indicated that tube current, and thus radiation dose, could be reduced by 40% or 80% from ASIR or MBIR, respectively, compared with conventional FBP, while maintaining similar image noise magnitude and contrast-to-noise ratio. The qualitative portion of our study, which assessed reader preference, yielded similar results, indicating that dose could be reduced by 60% (to 20 effective mAs (1.6 mGy)) with either ASIR or MBIR, while maintaining GGO definition. Additionally, the readers' preferences (as indicated by their ratings) regarding overall image quality were equal or better (for a given dose) when using ASIR or MBIR, compared with FBP. In conclusion, combining ASIR or MBIR with reduced tube current may allow for lower doses while maintaining overall diagnostic image quality, as well as GGO definition, during CT lung cancer screening.

Influence of reconstruction algorithms on image quality in SPECT myocardial perfusion imaging.

PubMed

Davidsson, Anette; Olsson, Eva; Engvall, Jan; Gustafsson, Agnetha

2017-11-01

We investigated if image- and diagnostic quality in SPECT MPI could be maintained despite a reduced acquisition time adding Depth Dependent Resolution Recovery (DDRR) for image reconstruction. Images were compared with filtered back projection (FBP) and iterative reconstruction using Ordered Subsets Expectation Maximization with (IRAC) and without (IRNC) attenuation correction (AC). Stress- and rest imaging for 15 min was performed on 21 subjects with a dual head gamma camera (Infinia Hawkeye; GE Healthcare), ECG-gating with 8 frames/cardiac cycle and a low-dose CT-scan. A 9 min acquisition was generated using five instead of eight gated frames and was reconstructed with DDRR, with (IRACRR) and without AC (IRNCRR) as well as with FBP. Three experienced nuclear medicine specialists visually assessed anonymized images according to eight criteria on a four point scale, three related to image quality and five to diagnostic confidence. Statistical analysis was performed using Visual Grading Regression (VGR). Observer confidence in statements on image quality was highest for the images that were reconstructed using DDRR (P<0·01 compared to FBP). Iterative reconstruction without DDRR was not superior to FBP. Interobserver variability was significant for statements on image quality (P<0·05) but lower in the diagnostic statements on ischemia and scar. The confidence in assessing ischemia and scar was not different between the reconstruction techniques (P = n.s.). SPECT MPI collected in 9 min, reconstructed with DDRR and AC, produced better image quality than the standard procedure. The observers expressed the highest diagnostic confidence in the DDRR reconstruction. © 2016 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

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

NASA Astrophysics Data System (ADS)

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

1997-08-01

Algebraic Reconstruction Technique (ART) is an age-old method used for solving the problem of three-dimensional (3-D) reconstruction from projections in electron microscopy and radiology. In medical applications, direct 3-D reconstruction is at the forefront of investigation. The simultaneous iterative reconstruction technique (SIRT) is an ART-type algorithm with the potential of generating in a few iterations tomographic images of a quality comparable to that of convolution backprojection (CBP) methods. Pixel-based reconstruction (PBR) is similar to SIRT reconstruction, and it has been shown that PBR algorithms give better quality pictures compared to those produced by SIRT algorithms. In this work, we propose a few modifications to the PBR algorithms. The modified algorithms are shown to give better quality pictures compared to PBR algorithms. The PBR algorithm and the modified PBR algorithms are highly compute intensive, Not many attempts have been made to reconstruct objects in the true 3-D sense because of the high computational overhead. In this study, we have developed parallel two-dimensional (2-D) and 3-D reconstruction algorithms based on modified PBR. We attempt to solve the two problems encountered by the PBR and modified PBR algorithms, i.e., the long computational time and the large memory requirements, by parallelizing the algorithm on a multiprocessor system. We investigate the possible task and data partitioning schemes by exploiting the potential parallelism in the PBR algorithm subject to minimizing the memory requirement. We have implemented an extended hypercube (EH) architecture for the high-speed execution of the 3-D reconstruction algorithm using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PEs) and dual-port random access memories (DPR) as channels between the PEs. We discuss and compare the performances of the PBR algorithm on an IBM 6000 RISC workstation, on a Silicon Graphics Indigo 2 workstation, and on an EH system. The results show that an EH(3,1) using DSP chips as PEs executes the modified PBR algorithm about 100 times faster than an LBM 6000 RISC workstation. We have executed the algorithms on a 4-node IBM SP2 parallel computer. The results show that execution time of the algorithm on an EH(3,1) is better than that of a 4-node IBM SP2 system. The speed-up of an EH(3,1) system with eight PEs and one network controller is approximately 7.85.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Anisotropic elastic moduli reconstruction in transversely isotropic model using MRE

NASA Astrophysics Data System (ADS)

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

2012-11-01

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

A calibrated iterative reconstruction for quantitative photoacoustic tomography using multi-angle light-sheet illuminations

NASA Astrophysics Data System (ADS)

Wang, Yihan; Lu, Tong; Zhang, Songhe; Song, Shaoze; Wang, Bingyuan; Li, Jiao; Zhao, Huijuan; Gao, Feng

2018-02-01

Quantitative photoacoustic tomography (q-PAT) is a nontrivial technique can be used to reconstruct the absorption image with a high spatial resolution. Several attempts have been investigated by setting point sources or fixed-angle illuminations. However, in practical applications, these schemes normally suffer from low signal-to-noise ratio (SNR) or poor quantification especially for large-size domains, due to the limitation of the ANSI-safety incidence and incompleteness in the data acquisition. We herein present a q-PAT implementation that uses multi-angle light-sheet illuminations and a calibrated iterative multi-angle reconstruction. The approach can acquire more complete information on the intrinsic absorption and SNR-boosted photoacoustic signals at selected planes from the multi-angle wide-field excitations of light-sheet. Therefore, the sliced absorption maps over whole body can be recovered in a measurementflexible, noise-robust and computation-economic way. The proposed approach is validated by the phantom experiment, exhibiting promising performances in image fidelity and quantitative accuracy.

Noniterative approach to the missing data problem in coherent diffraction imaging by phase retrieval.

PubMed

Nakajima, Nobuharu

2010-07-20

When a very intense beam is used for illuminating an object in coherent x-ray diffraction imaging, the intensities at the center of the diffraction pattern for the object are cut off by a beam stop that is utilized to block the intense beam. Until now, only iterative phase-retrieval methods have been applied to object reconstruction from a single diffraction pattern with a deficiency of central data due to a beam stop. As an alternative method, I present a noniterative solution in which an interpolation method based on the sampling theorem for the missing data is used for object reconstruction with our previously proposed phase-retrieval method using an aperture-array filter. Computer simulations demonstrate the reconstruction of a complex-amplitude object from a single diffraction pattern with a missing data area, which is generally difficult to treat with the iterative methods because a nonnegativity constraint cannot be used for such an object.

3D near-to-surface conductivity reconstruction by inversion of VETEM data using the distorted Born iterative method

USGS Publications Warehouse

Wang, G.L.; Chew, W.C.; Cui, T.J.; Aydiner, A.A.; Wright, D.L.; Smith, D.V.

2004-01-01

Three-dimensional (3D) subsurface imaging by using inversion of data obtained from the very early time electromagnetic system (VETEM) was discussed. The study was carried out by using the distorted Born iterative method to match the internal nonlinear property of the 3D inversion problem. The forward solver was based on the total-current formulation bi-conjugate gradient-fast Fourier transform (BCCG-FFT). It was found that the selection of regularization parameter follow a heuristic rule as used in the Levenberg-Marquardt algorithm so that the iteration is stable.

High speed imaging of dynamic processes with a switched source x-ray CT system

NASA Astrophysics Data System (ADS)

Thompson, William M.; Lionheart, William R. B.; Morton, Edward J.; Cunningham, Mike; Luggar, Russell D.

2015-05-01

Conventional x-ray computed tomography (CT) scanners are limited in their scanning speed by the mechanical constraints of their rotating gantries and as such do not provide the necessary temporal resolution for imaging of fast-moving dynamic processes, such as moving fluid flows. The Real Time Tomography (RTT) system is a family of fast cone beam CT scanners which instead use multiple fixed discrete sources and complete rings of detectors in an offset geometry. We demonstrate the potential of this system for use in the imaging of such high speed dynamic processes and give results using simulated and real experimental data. The unusual scanning geometry results in some challenges in image reconstruction, which are overcome using algebraic iterative reconstruction techniques and explicit regularisation. Through the use of a simple temporal regularisation term and by optimising the source firing pattern, we show that temporal resolution of the system may be increased at the expense of spatial resolution, which may be advantageous in some situations. Results are given showing temporal resolution of approximately 500 µs with simulated data and 3 ms with real experimental data.

Image transmission system using adaptive joint source and channel decoding

NASA Astrophysics Data System (ADS)

Liu, Weiliang; Daut, David G.

2005-03-01

In this paper, an adaptive joint source and channel decoding method is designed to accelerate the convergence of the iterative log-dimain sum-product decoding procedure of LDPC codes as well as to improve the reconstructed image quality. Error resilience modes are used in the JPEG2000 source codec, which makes it possible to provide useful source decoded information to the channel decoder. After each iteration, a tentative decoding is made and the channel decoded bits are then sent to the JPEG2000 decoder. Due to the error resilience modes, some bits are known to be either correct or in error. The positions of these bits are then fed back to the channel decoder. The log-likelihood ratios (LLR) of these bits are then modified by a weighting factor for the next iteration. By observing the statistics of the decoding procedure, the weighting factor is designed as a function of the channel condition. That is, for lower channel SNR, a larger factor is assigned, and vice versa. Results show that the proposed joint decoding methods can greatly reduce the number of iterations, and thereby reduce the decoding delay considerably. At the same time, this method always outperforms the non-source controlled decoding method up to 5dB in terms of PSNR for various reconstructed images.

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

NASA Astrophysics Data System (ADS)

He, Tongdi; Che, Zongxi

2018-01-01

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

Sensor-Based Vibration Signal Feature Extraction Using an Improved Composite Dictionary Matching Pursuit Algorithm

PubMed Central

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-01-01

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective. PMID:25207870

Sensor-based vibration signal feature extraction using an improved composite dictionary matching pursuit algorithm.

PubMed

Cui, Lingli; Wu, Na; Wang, Wenjing; Kang, Chenhui

2014-09-09

This paper presents a new method for a composite dictionary matching pursuit algorithm, which is applied to vibration sensor signal feature extraction and fault diagnosis of a gearbox. Three advantages are highlighted in the new method. First, the composite dictionary in the algorithm has been changed from multi-atom matching to single-atom matching. Compared to non-composite dictionary single-atom matching, the original composite dictionary multi-atom matching pursuit (CD-MaMP) algorithm can achieve noise reduction in the reconstruction stage, but it cannot dramatically reduce the computational cost and improve the efficiency in the decomposition stage. Therefore, the optimized composite dictionary single-atom matching algorithm (CD-SaMP) is proposed. Second, the termination condition of iteration based on the attenuation coefficient is put forward to improve the sparsity and efficiency of the algorithm, which adjusts the parameters of the termination condition constantly in the process of decomposition to avoid noise. Third, composite dictionaries are enriched with the modulation dictionary, which is one of the important structural characteristics of gear fault signals. Meanwhile, the termination condition of iteration settings, sub-feature dictionary selections and operation efficiency between CD-MaMP and CD-SaMP are discussed, aiming at gear simulation vibration signals with noise. The simulation sensor-based vibration signal results show that the termination condition of iteration based on the attenuation coefficient enhances decomposition sparsity greatly and achieves a good effect of noise reduction. Furthermore, the modulation dictionary achieves a better matching effect compared to the Fourier dictionary, and CD-SaMP has a great advantage of sparsity and efficiency compared with the CD-MaMP. The sensor-based vibration signals measured from practical engineering gearbox analyses have further shown that the CD-SaMP decomposition and reconstruction algorithm is feasible and effective.

Interferometric tomography of continuous fields with incomplete projections

NASA Technical Reports Server (NTRS)

Cha, Soyoung S.; Sun, Hogwei

1988-01-01

Interferometric tomography in the presence of an opaque object is investigated. The developed iterative algorithm does not need to augment the missing information. It is based on the successive reconstruction of the difference field, the difference between the object field to be reconstructed and its estimate, only in the difined region. The application of the algorithm results in stable convergence.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Optimum Image Formation for Spaceborne Microwave Radiometer Products.

PubMed

Long, David G; Brodzik, Mary J

2016-05-01

This paper considers some of the issues of radiometer brightness image formation and reconstruction for use in the NASA-sponsored Calibrated Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature Earth System Data Record project, which generates a multisensor multidecadal time series of high-resolution radiometer products designed to support climate studies. Two primary reconstruction algorithms are considered: the Backus-Gilbert approach and the radiometer form of the scatterometer image reconstruction (SIR) algorithm. These are compared with the conventional drop-in-the-bucket (DIB) gridded image formation approach. Tradeoff study results for the various algorithm options are presented to select optimum values for the grid resolution, the number of SIR iterations, and the BG gamma parameter. We find that although both approaches are effective in improving the spatial resolution of the surface brightness temperature estimates compared to DIB, SIR requires significantly less computation. The sensitivity of the reconstruction to the accuracy of the measurement spatial response function (MRF) is explored. The partial reconstruction of the methods can tolerate errors in the description of the sensor measurement response function, which simplifies the processing of historic sensor data for which the MRF is not known as well as modern sensors. Simulation tradeoff results are confirmed using actual data.

Intraoperative on-the-fly organ-mosaicking for laparoscopic surgery

PubMed Central

Reichard, Daniel; Bodenstedt, Sebastian; Suwelack, Stefan; Mayer, Benjamin; Preukschas, Anas; Wagner, Martin; Kenngott, Hannes; Müller-Stich, Beat; Dillmann, Rüdiger; Speidel, Stefanie

2015-01-01

Abstract. The goal of computer-assisted surgery is to provide the surgeon with guidance during an intervention, e.g., using augmented reality. To display preoperative data, soft tissue deformations that occur during surgery have to be taken into consideration. Laparoscopic sensors, such as stereo endoscopes, can be used to create a three-dimensional reconstruction of stereo frames for registration. Due to the small field of view and the homogeneous structure of tissue, reconstructing just one frame, in general, will not provide enough detail to register preoperative data, since every frame only contains a part of an organ surface. A correct assignment to the preoperative model is possible only if the patch geometry can be unambiguously matched to a part of the preoperative surface. We propose and evaluate a system that combines multiple smaller reconstructions from different viewpoints to segment and reconstruct a large model of an organ. Using graphics processing unit-based methods, we achieved four frames per second. We evaluated the system with in silico, phantom, ex vivo, and in vivo (porcine) data, using different methods for estimating the camera pose (optical tracking, iterative closest point, and a combination). The results indicate that the proposed method is promising for on-the-fly organ reconstruction and registration. PMID:26693166