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

doi:10.11938/cjmr20160406

Chinese Journal of Magnetic Resonance ›› 2016, Vol. 33 ›› Issue (4): 570-580.doi: 10.11938/cjmr20160406

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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

Abstract

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)

CLC Number:

O482.53

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

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