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.

中文翻译：

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更急躁：一种基于网格加速 Toeplitz 的策略，用于 GPU 上的非笛卡尔高分辨率 3D MRI。

最近提出了几种方法，通过利用 GPU 的计算能力来显着提高 MRI 图像重建的速度。之前，我们实施了一种基于 GPU 的图像重建技术，称为伊利诺伊州大规模并行采集工具包，用于增强 MRI 吞吐量的图像重建 (IMPATIENT MRI)，用于重建沿任意 3D 轨迹收集的数据。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，从而改进 IMPATIENT。此外，我们增强了具有偏共振校正和多传感器并行成像重建功能的例程。通过在我们的迭代重建方案中实施优化网格，