arXiv 2006.098082020-06-17physics.med-pheess.IV

基于GPU的原始-对偶算法的正对比敏感度MR成像

Positive Contrast Susceptibility MR Imaging Using GPU-based Primal-Dual Algorithm

Haifeng Wang, Fang Cai, Caiyun Shi, Jing Cheng, Shi Su, Zhilang Qiu, Guoxi Xie, Hanwei Chen, Xin Liu, Dong Liang

备注：: 4 pages, 6 figures, Accepted at the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)

摘要：

基于磁化率的正对比MR技术，采用基于正则化L-1最小化的核反卷积算法来估计金属器件的任意磁化率分布，与其他方法相比，一阶原对偶（PD）算法可以提供更快的重建时间来求解L-1最小化。在这里，我们建议使用图形处理器单元（GPU）的多核多线程特性来加速正对比度图像的PD算法。实验结果表明，基于GPU的PD算法可以达到与金属介入器械相当的准确性，在阳性造影成像与更少的计算时间。基于GPU的PD方法比以前基于CPU的方案快4~15倍。

英文摘要：

The susceptibility-based positive contrast MR technique was applied to estimate arbitrary magnetic susceptibility distributions of the metallic devices using a kernel deconvolution algorithm with a regularized L-1 minimization.Previously, the first-order primal-dual (PD) algorithm could provide a faster reconstruction time to solve the L-1 minimization, compared with other methods. Here, we propose to accelerate the PD algorithm of the positive contrast image using the multi-core multi-thread feature of graphics processor units (GPUs). The some experimental results showed that the GPU-based PD algorithm could achieve comparable accuracy of the metallic interventional devices in positive contrast imaging with less computational time. And the GPU-based PD approach was 4~15 times faster than the previous CPU-based scheme.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。