Wang Haifeng, Peng Hanchuan, Chang Yuchou, Liang Dong
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Allen Institute for Brain Science, Seattle, WA, USA.
Quant Imaging Med Surg. 2018 Mar;8(2):196-208. doi: 10.21037/qims.2018.03.07.
Image reconstruction in magnetic resonance imaging (MRI) clinical applications has become increasingly more complicated. However, diagnostic and treatment require very fast computational procedure. Modern competitive platforms of graphics processing unit (GPU) have been used to make high-performance parallel computations available, and attractive to common consumers for computing massively parallel reconstruction problems at commodity price. GPUs have also become more and more important for reconstruction computations, especially when deep learning starts to be applied into MRI reconstruction. The motivation of this survey is to review the image reconstruction schemes of GPU computing for MRI applications and provide a summary reference for researchers in MRI community.
磁共振成像（MRI）临床应用中的图像重建变得越来越复杂。然而，诊断和治疗需要非常快速的计算过程。现代具有竞争力的图形处理单元（GPU）平台已被用于实现高性能并行计算，并以商品价格吸引普通消费者来计算大规模并行重建问题。GPU对于重建计算也变得越来越重要，特别是当深度学习开始应用于MRI重建时。本次综述的目的是回顾用于MRI应用的GPU计算的图像重建方案，并为MRI领域的研究人员提供一份总结性参考资料。
