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A review of GPU-based medical image reconstruction. Phys Med 2017; 42:76-92. [PMID: 29173924 DOI: 10.1016/j.ejmp.2017.07.024] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 07/06/2017] [Accepted: 07/30/2017] [Indexed: 11/20/2022] Open
Abstract
Tomographic image reconstruction is a computationally demanding task, even more so when advanced models are used to describe a more complete and accurate picture of the image formation process. Such advanced modeling and reconstruction algorithms can lead to better images, often with less dose, but at the price of long calculation times that are hardly compatible with clinical workflows. Fortunately, reconstruction tasks can often be executed advantageously on Graphics Processing Units (GPUs), which are exploited as massively parallel computational engines. This review paper focuses on recent developments made in GPU-based medical image reconstruction, from a CT, PET, SPECT, MRI and US perspective. Strategies and approaches to get the most out of GPUs in image reconstruction are presented as well as innovative applications arising from an increased computing capacity. The future of GPU-based image reconstruction is also envisioned, based on current trends in high-performance computing.
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Liu Y, Castro M, Lederlin M, Shu H, Kaladji A, Haigron P. Edge-preserving denoising for intra-operative cone beam CT in endovascular aneurysm repair. Comput Med Imaging Graph 2017; 56:49-59. [PMID: 28231555 DOI: 10.1016/j.compmedimag.2017.01.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Revised: 11/18/2016] [Accepted: 01/26/2017] [Indexed: 11/17/2022]
Abstract
C-arm cone-beam computed tomography (CBCT) acquisition during endovascular aneurysm repair (EVAR) is an emergent technology with more and more applications. It offers real time imaging with a stationary patient and provides 3-D information to achieve guidance of intervention. However, there is growing concern on the overall radiation doses delivered to patients all along the endovascular management due to pre-, intra-, and post-operative X-ray imaging. Manufactures may have their low dose protocols to realize reduction of radiation dose, but CBCT with a low dose protocol has too many artifacts, particularly streak artifacts, and decreased contrast-to-noise ratio (CNR). To reduce noise and artifacts, a penalized weighted least-squares (PWLS) algorithm with an edge-preserving penalty is proposed. The proposed method is evaluated by quantitative parameters including a defined signal-to-noise ratio (SNR), CNR, and modulation transfer function (MTF) on clinical CBCT. Comparisons with PWLS algorithms with isotropic, TV, Huber, anisotropic penalties demonstrate that the proposed edge-preserving penalty performs well not only on edge preservation, but also on streak artifacts suppression, which may be crucial for observing guidewire and stentgraft in EVAR.
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Affiliation(s)
- Yi Liu
- INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France
| | - Miguel Castro
- INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France
| | - Mathieu Lederlin
- INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France; CHU Rennes, Department of Radiology, F-35000, France
| | - Huazhong Shu
- Ctr Rech Informat Med Sino Francais, CRIBs, Rennes, F-35000, France; Southeast University, Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration of Ministry of Education, Nanjing 210096, Jiangsu, People's Republic of China
| | - Adrien Kaladji
- INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France; CHU Rennes, Department of Cardiothoracic and Vascular Surgery, F-35000, France
| | - Pascal Haigron
- INSERM, U1099, Rennes, F-35000, France; Université de Rennes 1, LTSI, Rennes, F-35000, France; Ctr Rech Informat Med Sino Francais, CRIBs, Rennes, F-35000, France.
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McGaffin MG, Fessler JA. Alternating dual updates algorithm for X-ray CT reconstruction on the GPU. IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2015; 1:186-199. [PMID: 26878031 PMCID: PMC4749040 DOI: 10.1109/tci.2015.2479555] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Model-based image reconstruction (MBIR) for X-ray computed tomography (CT) offers improved image quality and potential low-dose operation, but has yet to reach ubiquity in the clinic. MBIR methods form an image by solving a large statistically motivated optimization problem, and the long time it takes to numerically solve this problem has hampered MBIR's adoption. We present a new optimization algorithm for X-ray CT MBIR based on duality and group coordinate ascent that may converge even with approximate updates and can handle a wide range of regularizers, including total variation (TV). The algorithm iteratively updates groups of dual variables corresponding to terms in the cost function; these updates are highly parallel and map well onto the GPU. Although the algorithm stores a large number of variables, the "working size" for each of the algorithm's steps is small and can be efficiently streamed to the GPU while other calculations are being performed. The proposed algorithm converges rapidly on both real and simulated data and shows promising parallelization over multiple devices.
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McGaffin MG, Fessler JA. Edge-preserving image denoising via group coordinate descent on the GPU. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2015; 24:1273-81. [PMID: 25675454 PMCID: PMC4339499 DOI: 10.1109/tip.2015.2400813] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Image denoising is a fundamental operation in image processing, and its applications range from the direct (photographic enhancement) to the technical (as a subproblem in image reconstruction algorithms). In many applications, the number of pixels has continued to grow, while the serial execution speed of computational hardware has begun to stall. New image processing algorithms must exploit the power offered by massively parallel architectures like graphics processing units (GPUs). This paper describes a family of image denoising algorithms well-suited to the GPU. The algorithms iteratively perform a set of independent, parallel 1D pixel-update subproblems. To match GPU memory limitations, they perform these pixel updates in-place and only store the noisy data, denoised image, and problem parameters. The algorithms can handle a wide range of edge-preserving roughness penalties, including differentiable convex penalties and anisotropic total variation. Both algorithms use the majorize-minimize framework to solve the 1D pixel update subproblem. Results from a large 2D image denoising problem and a 3D medical imaging denoising problem demonstrate that the proposed algorithms converge rapidly in terms of both iteration and run-time.
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Nien H, Fessler JA. Fast X-ray CT image reconstruction using a linearized augmented Lagrangian method with ordered subsets. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:388-99. [PMID: 25248178 PMCID: PMC4315772 DOI: 10.1109/tmi.2014.2358499] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Augmented Lagrangian (AL) methods for solving convex optimization problems with linear constraints are attractive for imaging applications with composite cost functions due to the empirical fast convergence rate under weak conditions. However, for problems such as X-ray computed tomography (CT) image reconstruction, where the inner least-squares problem is challenging and requires iterations, AL methods can be slow. This paper focuses on solving regularized (weighted) least-squares problems using a linearized variant of AL methods that replaces the quadratic AL penalty term in the scaled augmented Lagrangian with its separable quadratic surrogate function, leading to a simpler ordered-subsets (OS) accelerable splitting-based algorithm, OS-LALM. To further accelerate the proposed algorithm, we use a second-order recursive system analysis to design a deterministic downward continuation approach that avoids tedious parameter tuning and provides fast convergence. Experimental results show that the proposed algorithm significantly accelerates the convergence of X-ray CT image reconstruction with negligible overhead and can reduce OS artifacts when using many subsets.
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