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.
MA Chao
,
ZHANG Xue-ying
,
WANG Cheng-long
,
XIE Hai-bin
,
LU Jian-ping
,
YANG Guang
,
ZHANG Cheng-xiu
. Parallel Segmentation and Tracking Algorithm for Magnetic Resonance Angiography Images Based on GPU[J]. Chinese Journal of Magnetic Resonance, 2016
, 33(4)
: 570
-580
.
DOI: 10.11938/cjmr20160406
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