Gai Jiading, Obeid Nady, Holtrop Joseph L, Wu Xiao-Long, Lam Fan, Fu Maojing, Haldar Justin P, Hwu Wen-Mei W, Liang Zhi-Pei, Sutton Bradley P
Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
J Parallel Distrib Comput. 2013 May 1;73(5):686-697. doi: 10.1016/j.jpdc.2013.01.001.
Several recent methods have been proposed to obtain significant speed-ups in MRI image reconstruction by leveraging the computational power of GPUs. Previously, we implemented a GPU-based image reconstruction technique called the Illinois Massively Parallel Acquisition Toolkit for Image reconstruction with ENhanced Throughput in MRI (IMPATIENT MRI) for reconstructing data collected along arbitrary 3D trajectories. In this paper, we improve IMPATIENT by removing computational bottlenecks by using a gridding approach to accelerate the computation of various data structures needed by the previous routine. Further, we enhance the routine with capabilities for off-resonance correction and multi-sensor parallel imaging reconstruction. Through implementation of optimized gridding into our iterative reconstruction scheme, speed-ups of more than a factor of 200 are provided in the improved GPU implementation compared to the previous accelerated GPU code.
最近已经提出了几种方法,通过利用GPU的计算能力来显著加速MRI图像重建。此前,我们实现了一种基于GPU的图像重建技术,称为伊利诺伊州大规模并行采集图像重建工具包,用于在MRI中提高吞吐量(IMPATIENT MRI),以重建沿任意3D轨迹采集的数据。在本文中,我们通过使用网格化方法来加速先前例程所需的各种数据结构的计算,从而消除计算瓶颈,对IMPATIENT进行了改进。此外,我们还增强了该例程的失谐校正和多传感器并行成像重建能力。通过在我们的迭代重建方案中实现优化的网格化,与之前的加速GPU代码相比,改进后的GPU实现提供了超过200倍的加速。