Compressed sensing MRI reconstruction from 3D multichannel data using GPUs. Issue 6 (15th February 2017)

• Record Type: Journal Article
• Authors: Chang, Ching‐Hua; Yu, Xiangdong; Ji, Jim X.
• Is Part Of: Magnetic resonance in medicine. Volume 78:Issue 6(2017)
• Page Start: 2265
• Page End: 2274
• Publication Date: 2017-02-15
• Subjects: image reconstruction -- compressed sensing -- parallel imaging -- graphics processing unit -- parallel computing; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.26636
• Languages: English
• ISSNs: 0740-3194

Abstract

Abstract : Purpose: To accelerate iterative reconstructions of compressed sensing (CS) MRI from 3D multichannel data using graphics processing units (GPUs). Methods: The sparsity of MRI signals and parallel array receivers can reduce the data acquisition requirements. However, iterative CS reconstructions from data acquired using an array system may take a significantly long time, especially for a large number of parallel channels. This paper presents an efficient method for CS‐MRI reconstruction from 3D multichannel data using GPUs. In this method, CS reconstructions were simultaneously processed in a channel‐by‐channel fashion on the GPU, in which the computations of multiple‐channel 3D‐CS reconstructions are highly parallelized. The final image was then produced by a sum‐of‐squares method on the central processing unit. Implementation details including algorithm, data/memory management, and parallelization schemes are reported in the paper. Results: Both simulated data and in vivo MRI array data were tested. The results showed that the proposed method can significantly improve the image reconstruction efficiency, typically shortening the runtime by a factor of 30. Conclusions: Using low‐cost GPUs and an efficient algorithm allowed the 3D multislice compressive‐sensing reconstruction to be performed in less than 1 s. The rapid reconstructions are expected to help bring high‐dimensional, multichannel parallel CS MRI closer to clinical applications. Magn Reson Med 78:2265–2274, 2017. © 2017 International Society for Magnetic Resonance in Medicine.