Dynamic MRI is useful to diagnose different diseases, e.g. cardiac ailments, by monitoring the structure and function of the heart and blood flow through the valves. Faster data acquisition is highly desirable in dynamic MRI, but this may lead to aliasing artifacts due to under-sampling. Advanced image reconstruction algorithms are required to obtain aliasing-free MR images from the acquired under-sampled data. One major limitation of using the advanced reconstruction algorithms is their computationally expensive and time-consuming nature, which make them infeasible for clinical use, especially for applications like cardiac MRI. L + S decomposition model is an approach provided in literature which separates the sparse and low-rank information in dynamic MRI. However, L + S decomposition model is a computationally complex process demanding significant computation time. In this paper, a parallel framework is proposed to accelerate the image reconstruction process of L + S decomposition model using GPU. Experiments are performed on cardiac perfusion dataset (\(256\, \times \,256\, \times \,40\, \times \,12\)) and cardiac cine dataset (\(256\, \times \,256\, \times \,11\, \times \,30\)) using NVIDIA’s GeForce GTX780 GPU and Core-i7 CPU. The results show that the proposed method provides up to 18 × speed-up including the memory transfer time (i.e. data transfer between the CPU and GPU) and ~ 46 × speed-up without memory transfer for the cardiac perfusion dataset in our experiments. This level of improvement in the reconstruction time will increase the usefulness of L + S reconstruction by making it feasible for clinical applications.

通过监测心脏的结构和功能以及流经瓣膜的血流，动态 MRI 可用于诊断不同的疾病，例如心脏疾病。在动态 MRI 中非常需要更快的数据采集，但这可能会由于采样不足而导致混叠伪影。需要先进的图像重建算法才能从采集的欠采样数据中获得无混叠的 MR 图像。使用高级重建算法的一个主要限制是其计算成本高昂且耗时，这使得它们不适用于临床应用，尤其是心脏 MRI 等应用。L  +  S分解模型是文献中提供的一种方法，用于分离动态 MRI 中的稀疏和低秩信息。然而，L +  S分解模型是一个计算复杂的过程，需要大量的计算时间。在本文中，提出了一种并行框架来加速使用 GPU的L  +  S分解模型的图像重建过程。实验在心脏灌注数据集（\(256\, \times \,256\, \times \,40\, \times \,12\)）和心脏电影数据集（\(256\, \times \,256 \, \times \,11\, \times \,30\)) 使用 NVIDIA 的 GeForce GTX780 GPU 和 Core-i7 CPU。结果表明，在我们的实验中，所提出的方法为心脏灌注数据集提供了高达 18 倍的加速，包括内存传输时间（即 CPU 和 GPU 之间的数据传输）和约 46 倍的加速，无内存传输。重建时间的这种改进水平将提高L  +  S重建的实用性，使其在临床应用中变得可行。