title: Compressed sensing MRI reconstruction from 3D multichannel data using GPUs.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
authors.forename: Ching-Hua
authors.initials: CH
authors.lastname: Chang
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
authors.forename: Xiangdong
authors.initials: X
authors.lastname: Yu
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA.
authors.forename: Jim X
authors.initials: JX
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D007717:Knee / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software
keywords: compressed sensing; graphics processing unit; image reconstruction; parallel computing; parallel imaging