基于GPU加速的磁共振血管造影图像的并行分割与追踪算法

在应用磁共振血管造影图像进行临床诊断时,临床医生往往需要提取感兴趣区域(Region Of Interest,ROI)的部分血管.这个工作传统上需要手工进行,费时费力.该文提出一种并行的血管分割与追踪算法,利用现代图形处理器(Graphics Processing Unit,GPU)所具备的大规模并行计算能力进行快速的血管分割.首先将三维图像网格化为共面的立方体,并行处理每个立方体,确定立方体中哪些表面有血管通过,以及立方体中哪些体素包含血管.之后再将该结果用于串行的全局分割与血管追踪处理.实验结果表明,利用这种先并行后串行的方法,可以在1 s之内完成全脑血管的分割,分割的结果也更准确.

Parallel Segmentation and Tracking Algorithm for Magnetic Resonance Angiography Images Based on GPU

Clinical magnetic resonance angiography (MRA) often involves extraction of images, which is often done manually by radiologists. The process can be tedious and time-consuming. In this study, we propose a new parallel vessel segmentation/tracking algorithm, utilizing large-scale parallel computing provided by graphics processing unit (GPU). The whole three-dimensional image volumes are first divided into small cubes, which share surface with their neighbors. Each cube is then processed separately to determine whether there are vessels passing through its surface. These results are then used for global segmentation and vessel tracking. Application of the algorithm to real MRA data showed that segmentation of a whole-brain MRA dataset could be achieved in less than 1 s.