Multi-GPU Distributed Magnetic Resonance Image Reconstruction Based on Gadgetron

doi:10.11938/cjmr20182626

Abstract

Abstract: Clinical magnetic resonance imaging (MRI) scanning requires fast image reconstruction. At present, most image reconstruction algorithms are implemented with central processing unit (CPU)-based processing. However, with massive amount of raw data and increasing complexity of the algorithm, CPU-based processing can be inefficient due to lack of computation parallelism and slow computation speed. For this reason, some advanced algorithms are difficult to implement in clinical practice. In this paper, a new image reconstruction algorithm was designed and implemented, which combined multiple cores CPU with multi-graphic processing unit (GPU) to reconstruct magnetic resonance images by distributed parallel computing on the Gadgetron software platform. Taking the Stack of Star (SOS) reconstruction as an example, it was demonstrated that the proposed method improved the reconstruction speed significantly without the requirement of expensive hardware.

Key words: magnetic resonance image reconstruction, Gadgetron, multi-GPU, distributed parallel computing, 3D MRI

CLC Number:

O482.53

XU Jia-wen, XU Jian, ZHOU Xiao-dong, ZHANG Cong, CHEN Qun. Multi-GPU Distributed Magnetic Resonance Image Reconstruction Based on Gadgetron[J]. Chinese Journal of Magnetic Resonance, 2018, 35(3): 303-317.

References

[1] KECSKEMETI S, JOHNSON K, WU Y J, et al. High resolution three-dimensional cine phase contrast MRI of small intracranial aneurysms using a stack of stars k-space trajectory[J]. J Magn Reson Imaging, 2012. 35(3):518-527.
[2] LUSTIG M, DONOHO D, PAULY J M. Sparse MRI:The application of compressed sensing for rapid MR imaging[J]. Magn Reson Med, 1999, 58(6):1182-1195.
[3] HANSEN M S, SØRENSEN T S. Gadgetron:an open source framework for medical image reconstruction[J]. Magn Reson Med, 2013, 69(6):1768-1776.
[4] XUE H, INATI S, SØRENSEN T S, et al. Distributed MRI reconstruction using Gadgetron-based cloud computing[J]. Magn Reson Med, 2015, 73(3):1015-1025.
[5] KALINOV A, LASTOVETSKY A, ROBERT Y. Heterogeneous computing[J]. Parallel Computing, 2005. 31(7):649-652.
[6] SORENSEN T S, SCHAEFFTER T, NOE K O, et al. Accelerating the nonequispaced fast Fourier transform on commodity graphics hardware[J]. IEEE Trans Med Imaging, 2008. 27(4):538-547.
[7] COOK S. CUDA programming:A developer's guide to parallel computing with GPUs[M]. Waltham MA:Elsevier, 2012.
[8] HANSEN M S, SØRENSEN T S. Gadgetron users guide v3.8.2[OL].[2014-11-06]. https://sourceforge.net/p/gadgetron/home/Manual/.
[9] NVIDIA. Programming guide[OL].[2018-3-5]. https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#compute-capability.
[10] STORTI D, YURTOGLU M. CUDA for engineers[M]. New Jersey:Addison-Wesley, 2016.
[11] WRIGHT K L, HAMILTON J I, GRISWOLD M A, et al. Non-cartesian parallel imaging reconstruction[J]. J Magn Reson Imaging, 2014, 40(5):1022-1040.
[12] PIPE J. Gridding for non-cartesian k-space sampling[C]//Seattle:International Society for Magnetic Resonance in Medicine(ISMRM), 2006.
[13] SEDARAT H, NISHIMURA D G. On the optimality of the gridding reconstruction algorithm[J]. IEEE Trans Med Imaging, 2000, 19(4):306-317.
[14] KANDROT E, SANDERS J. CUDA by example[M]. Amsterdam:Addison-Wesley Longman, 2010.
[15] LEA D. A Java fork/join framework[C]//Association for Computing Machinery(ACM) conference. Texas:ACM Press, 2000.
[16] BARLAS G. Multicore and GPU programming[M]. Waltham MA:Elsevier, 2015.