van Aart Evert, Sepasian Neda, Jalba Andrei, Vilanova Anna
Department of Mathematics and Computer Science, Eindhoven University of Technology, 5600 MB Eindhove, The Netherlands.
Int J Biomed Imaging. 2011;2011:698908. doi: 10.1155/2011/698908. Epub 2011 Sep 20.
Diffusion Tensor Imaging (DTI) allows to noninvasively measure the diffusion of water in fibrous tissue. By reconstructing the fibers from DTI data using a fiber-tracking algorithm, we can deduce the structure of the tissue. In this paper, we outline an approach to accelerating such a fiber-tracking algorithm using a Graphics Processing Unit (GPU). This algorithm, which is based on the calculation of geodesics, has shown promising results for both synthetic and real data, but is limited in its applicability by its high computational requirements. We present a solution which uses the parallelism offered by modern GPUs, in combination with the CUDA platform by NVIDIA, to significantly reduce the execution time of the fiber-tracking algorithm. Compared to a multithreaded CPU implementation of the same algorithm, our GPU mapping achieves a speedup factor of up to 40 times.
扩散张量成像(DTI)能够非侵入性地测量纤维组织中水分子的扩散情况。通过使用纤维追踪算法从DTI数据重建纤维,我们可以推断出组织的结构。在本文中,我们概述了一种使用图形处理器(GPU)加速这种纤维追踪算法的方法。该算法基于测地线的计算,对于合成数据和真实数据都已显示出有前景的结果,但因其高计算需求而在适用性方面受到限制。我们提出了一种解决方案,该方案利用现代GPU提供的并行性,并结合英伟达的CUDA平台,以显著减少纤维追踪算法的执行时间。与同一算法的多线程CPU实现相比,我们的GPU映射实现了高达40倍的加速因子。