Background and objective: Recent research has reported methods that reconstruct cardiac MR images acquired with acceleration factors as high as 15 in Cartesian coordinates. However, the computational cost of these techniques is quite high, taking about 40 min of CPU time in a typical current machine. This delay between acquisition and final result can completely rule out the use of MRI in clinical environments in favor of other techniques, such as CT. In spite of this, reconstruction methods reported elsewhere can be parallelized to a high degree, a fact that makes them suitable for GPU-type computing devices. This paper contributes a vendor-independent, device-agnostic implementation of such a method to reconstruct 2D motion-compensated, compressed-sensing MRI sequences in clinically viable times.

Methods: By leveraging our OpenCLIPER framework, the proposed system works in any computing device (CPU, GPU, DSP, FPGA, etc.), as long as an OpenCL implementation is available, and development is significantly simplified versus a pure OpenCL implementation. In OpenCLIPER, the problem is partitioned in independent black boxes which may be connected as needed, while device initialization and maintenance is handled automatically. Parallel implementations of both a groupwise FFD-based registration method, as well as a multicoil extension of the NESTA algorithm have been carried out as processes of OpenCLIPER. Our platform also includes significant development and debugging aids. HIP code and precompiled libraries can be integrated seamlessly as well since OpenCLIPER makes data objects shareable between OpenCL and HIP. This also opens an opportunity to include CUDA source code (via HIP) in prospective developments.

Results: The proposed solution can reconstruct a whole 12–14 slice CINE volume acquired in 19–32 coils and 20 phases, with an acceleration factor of ranging 4–8, in a few seconds, with results comparable to another popular platform (BART). If motion compensation is included, reconstruction time is in the order of one minute.

Conclusions: We have obtained clinically-viable times in GPUs from different vendors, with delays in some platforms that do not have correspondence with its price in the market. We also contribute a parallel groupwise registration subsystem for motion estimation/compensation and a parallel multicoil NESTA subsystem for $l1-l2$-norm problem solving.

背景和目的：最近的研究报告了重建以笛卡尔坐标系中高达 15 的加速度因子获取的心脏 MR 图像的方法。然而，这些技术的计算成本相当高，在典型的当前机器上需要大约 40 分钟的 CPU 时间。采集和最终结果之间的这种延迟可以完全排除在临床环境中使用 MRI 以支持其他技术，例如 CT。尽管如此，其他地方报道的重建方法可以高度并行化，这一事实使它们适用于 GPU 类型的计算设备。本文贡献了这种方法的独立于供应商、设备不可知的实现，以在临床可行的时间内重建 2D 运动补偿、压缩传感 MRI 序列。

方法：通过利用我们的 OpenCLIPER 框架，所提议的系统可以在任何计算设备（CPU、GPU、DSP、FPGA 等）中运行，只要 OpenCL 实现可用，并且与纯 OpenCL 实现相比，开发得到显着简化。在 OpenCLIPER 中，问题被划分为独立的黑匣子，可以根据需要连接，同时自动处理设备初始化和维护。基于分组 FFD 的配准方法以及 NESTA 算法的多线圈扩展的并行实现已作为 OpenCLIPER 的进程进行。我们的平台还包括重要的开发和调试辅助工具。HIP 代码和预编译库也可以无缝集成，因为 OpenCLIPER 使数据对象可在 OpenCL 和 HIP 之间共享。

结果：所提出的解决方案可以在几秒钟内重建在 19-32 个线圈和 20 个阶段中获得的整个 12-14 切片 CINE 卷，加速因子范围为 4-8，其结果与另一个流行平台 (BART) 相当. 如果包括运动补偿，重建时间大约为一分钟。

结论：我们已经获得了来自不同供应商的 GPU 的临床可行性时间，在某些平台上出现了延迟，与其在市场上的价格不符。我们还贡献了一个用于运动估计/补偿的并行分组注册子系统和一个用于运动估计/补偿的并行多线圈 NESTA 子系统。$升1-升2$- 规范问题解决。