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

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

doi:10.1118/1.4942812

Cited by 33 publications

(22 citation statements)

References 73 publications

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“…In the future, the proposed reconstruction could be enhanced by incorporating more sophisticated data fidelity models, such as a noise model for statistical ray weighting, or improving convergence by, e. g., pre-conditioned optimization, cf. (Xu et al 2016). Regarding regularization, the image characteristic of TV is helpful for segmentation and in turn volumetry, which was the goal in our study.…”

Section: Discussionmentioning

confidence: 99%

Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling

et al. 2017

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Time-resolved tomographic cardiac imaging using an angiographic C-arm device may support clinicians during minimally invasive therapy by enabling a thorough analysis of the heart function directly in the catheter laboratory. However, clinically feasible acquisition protocols entail a highly challenging reconstruction problem which suffers from sparse angular sampling of the trajectory. Compressed sensing theory promises that useful images can be recovered despite massive undersampling by means of sparsity-based regularization. For a multitude of reasons-most notably the desired reduction of scan time, dose and contrast agent required-it is of great interest to know just how little data is actually sufficient for a certain task. In this work, we apply a convex optimization approach based on primal-dual splitting to 4D cardiac C-arm computed tomography. We examine how the quality of spatially and temporally total-variation-regularized reconstruction degrades when using as few as [Formula: see text] projection views per heart phase. First, feasible regularization weights are determined in a numerical phantom study, demonstrating the individual benefits of both regularizers. Secondly, a task-based evaluation is performed in eight clinical patients. Semi-automatic segmentation-based volume measurements of the left ventricular blood pool performed on strongly undersampled images show a correlation of close to 99% with measurements obtained from less sparsely sampled data.

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Section: Discussionmentioning

confidence: 99%

Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling

et al. 2017

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“…To solve for x , we apply FISTA. In particular, we employ the composite splitting approach in [24] to decompose the above problem into two sub problems. The first problem iŝ…”

Section: Mrf-based Fistamentioning

confidence: 99%

Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

Amin

Zhao

et al. 2020

IEEE Geosci. Remote Sensing Lett.

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Recent progress in compressive sensing states the importance of exploiting intrinsic structures in sparse signal reconstruction. In this letter, we propose a Markov random field (MRF) prior in conjunction with fast iterative shrinkagethresholding algorithm (FISTA) for image reconstruction. The MRF prior is used to represent the support of sparse signals with clustered nonzero coefficients. The proposed approach is applied to the inverse synthetic aperture radar (ISAR) imaging problem. Simulations and experimental results are provided to demonstrate the performance advantages of this approach in comparison with the standard FISTA and existing MRF-based methods.Index Terms-Compressive sensing (CS), Markov random field (MRF), FISTA, inverse synthetic aperture radar (ISAR).

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“…This last category is probably the most studied in image reconstruction. Such methods are often improved by using ordered subsets (Erdoǧan and Fessler, 1999b;Hudson and Larkin, 1994) or Nesterov momentum (Nesterov, 1983;Kim et al, 2014;Xu, Yang, Tan, Sawatzky, and Anastasio, 2016;Choi, Wang, Zhu, Suh, Boyd, and Xing, 2010;Jensen, Jørgensen, Hansen, and Jensen, 2012). Recently, problem-splitting and proximal approaches have been proposed in the context of sparse reconstruction (Sidky, Jørgensen, and Pan, 2013;Nien and Fessler, 2015;Xu et al, 2016).…”

Section: Motivation and Previous Workmentioning

confidence: 99%

Scaled projected-directions methods with application to transmission tomography

2020

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Statistical image reconstruction in X-Ray computed tomography yields large-scale regularized linear least-squares problems with nonnegativity bounds, where the memory footprint of the operator is a concern. Discretizing images in cylindrical coordinates results in significant memory savings, and allows parallel operator-vector products without on-the-fly computation of the operator, without necessarily decreasing image quality. However, it deteriorates the conditioning of the operator. We improve the Hessian conditioning by way of a blockcirculant scaling operator and we propose a strategy to handle nondiagonal scaling in the context of projected-directions methods for bound-constrained problems. We describe our implementation of the scaling strategy using two algorithms: TRON, a trust-region method with exact second derivatives, and L-BFGS-B, a linesearch method with a limited-memory quasi-Newton Hessian approximation. We compare our approach with one where a change of variable is made in the problem. On two reconstruction problems, our approach converges faster than the change of variable approach, and achieves much tighter accuracy in terms of optimality residual than a first-order method.

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

Cited by 33 publications

References 73 publications

Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling

Assessing cardiac function from total-variation-regularized 4D C-arm CT in the presence of angular undersampling

Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

Scaled projected-directions methods with application to transmission tomography

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