Wang Haifeng, Cai Fang, Shi Caiyun, Cheng Jing, Su Shi, Qiu Zhilang, Xie Guoxi, Chen Hanwei, Liu Xin, Liang Dong
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1485-1488. doi: 10.1109/EMBC44109.2020.9176223.
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 415 times faster than the previous CPU-based scheme.Clinical Relevance-This can estimate arbitrary magnetic susceptibility distributions of the metallic devices with the processing efficacy of 415 times faster than before.
基于磁化率的正性对比磁共振技术被应用于使用带有正则化L-1最小化的核反卷积算法来估计金属装置的任意磁化率分布。此前，与其他方法相比，一阶原始对偶（PD）算法能够提供更快的重建时间来解决L-1最小化问题。在此，我们提议利用图形处理器单元（GPU）的多核多线程特性来加速正性对比图像的PD算法。一些实验结果表明，基于GPU的PD算法在正性对比成像中能够以更少的计算时间实现与金属介入装置相当的精度。并且基于GPU的PD方法比之前基于CPU的方案快4至15倍。临床意义——这能够以比之前快4至15倍的处理效率来估计金属装置的任意磁化率分布。
