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

doi:10.11938/cjmr20160406

波谱学杂志 ›› 2016, Vol. 33 ›› Issue (4): 570-580.doi: 10.11938/cjmr20160406

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

张雪莹¹, 王成龙¹, 谢海滨^1,2, 张成秀², 马超³, 陆建平³, 杨光^1,2

1. 华东师范大学物理与材料科学学院, 上海市磁共振重点实验室, 上海 200062;
2. 上海卡勒幅磁共振技术有限公司, 上海 201614;
3. 上海市第二军医大学附属长海医院放射科, 上海 200433

收稿日期:2016-04-01 修回日期:2016-11-01 出版日期:2016-12-05 发布日期:2016-12-05
通讯作者: 谢海滨,电话:021-62233873,E-mail:hbxie@phy.ecnu.edu.cn;杨光,电话:021-62233873,E-mail:gyang@phy.ecnu.edu.cn E-mail:hbxie@phy.ecnu.edu.cn;gyang@phy.ecnu.edu.cn
作者简介:张雪莹(1990-),女,上海人,硕士研究生,无线电物理专业
基金资助:
国家高技术研究发展计划资助项目（2014AA123400）．

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

ZHANG Xue-ying¹, WANG Cheng-long¹, XIE Hai-bin^1,2, ZHANG Cheng-xiu², MA Chao³, LU Jian-ping³, YANG Guang^1,2

1. Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Materials Science, East China Normal University, Shanghai 200062, China;
2. Shanghai Colorful Magnetic Resonance Technology Corporation Limited, Shanghai 201614, China;
3. Department of Radiology, Changhai Hospital, The Second Military Medical University, Shanghai 200433, China

Received:2016-04-01 Revised:2016-11-01 Online:2016-12-05 Published:2016-12-05

摘要/Abstract

摘要： 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．

关键词: 血管造影, 图形处理器(GPU), 统一计算设备架构(CUDA), 图像分割, 磁共振成像(MRI)

Abstract: 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.

Key words: graphics processing unit(GPU), magnetic resonance angiography(MRA), image segmentation, compute unified device architecture(CUDA), magnetic resonance imaging(MRI)

中图分类号:

O482.53

张雪莹, 王成龙, 谢海滨, 张成秀, 马超, 陆建平, 杨光. 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[J]. 波谱学杂志, 2016, 33(4): 570-580.

ZHANG Xue-ying, WANG Cheng-long, XIE Hai-bin, ZHANG Cheng-xiu, MA Chao, LU Jian-ping, YANG Guang. Parallel Segmentation and Tracking Algorithm for Magnetic Resonance Angiography Images Based on GPU[J]. Chinese Journal of Magnetic Resonance, 2016, 33(4): 570-580.

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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

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

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