IEEE Trans Med Imaging. 2014 Mar;33(3):607-17. doi: 10.1109/TMI.2013.2292119.
A new step-by-step comprehensive MR physics simulator (MRISIMUL) of the Bloch equations is presented. The aim was to develop a magnetic resonance imaging (MRI) simulator that makes no assumptions with respect to the underlying pulse sequence and also allows for complex large-scale analysis on a single computer without requiring simplifications of the MRI model. We hypothesized that such a simulation platform could be developed with parallel acceleration of the executable core within the graphic processing unit (GPU) environment. MRISIMUL integrates realistic aspects of the MRI experiment from signal generation to image formation and solves the entire complex problem for densely spaced isochromats and for a densely spaced time axis. The simulation platform was developed in MATLAB whereas the computationally demanding core services were developed in CUDA-C. The MRISIMUL simulator imaged three different computer models: a user-defined phantom, a human brain model and a human heart model. The high computational power of GPU-based simulations was compared against other computer configurations. A speedup of about 228 times was achieved when compared to serially executed C-code on the CPU whereas a speedup between 31 to 115 times was achieved when compared to the OpenMP parallel executed C-code on the CPU, depending on the number of threads used in multithreading (2-8 threads). The high performance of MRISIMUL allows its application in large-scale analysis and can bring the computational power of a supercomputer or a large computer cluster to a single GPU personal computer.
我们提出了一种新的逐步式全矩阵磁共振物理模拟（MRISIMUL）方法。该方法旨在开发一种磁共振成像（MRI）模拟器，它不针对基本脉冲序列做出任何假设，并且还允许在单个计算机上进行复杂的大规模分析，而无需简化 MRI 模型。我们假设可以在图形处理单元（GPU）环境中并行加速可执行核心来开发这样的模拟平台。MRISIMUL 集成了从信号生成到图像形成的 MRI 实验的实际方面，并为密集间隔的等旋体和密集间隔的时间轴解决了整个复杂问题。该模拟平台是在 MATLAB 中开发的，而计算要求高的核心服务则是在 CUDA-C 中开发的。MRISIMUL 模拟器对三个不同的计算机模型进行了成像：用户定义的体模、人脑模型和人心模型。将基于 GPU 的模拟的高计算能力与其他计算机配置进行了比较。与在 CPU 上串行执行的 C 代码相比，实现了约 228 倍的加速，而与在 CPU 上使用 OpenMP 并行执行的 C 代码相比，实现了 31 到 115 倍的加速，具体取决于在多线程（2-8 个线程）中使用的线程数。MRISIMUL 的高性能允许其在大规模分析中应用，并可以将超级计算机或大型计算机集群的计算能力引入到单个 GPU 个人计算机中。
