3D assessment of the carotid artery vessel wall volume: an imaging biomarker for diagnosis of the atherosclerotic disease

Mariam Afshin; Tishan Maraj; Tina Binesh Marvasti; Navneet Singh; Alan Moody

doi:10.1117/12.2216890

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24 March 2016 3D assessment of the carotid artery vessel wall volume: an imaging biomarker for diagnosis of the atherosclerotic disease

Mariam Afshin, Tishan Maraj, Tina Binesh Marvasti, Navneet Singh, Alan Moody

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Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 978504 (2016) https://doi.org/10.1117/12.2216890
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

This study investigates a novel method of 3D evaluation of the carotid vessel wall using two different Magnetic Resonance (MR) sequences. The method focuses on energy minimization by level-set curve boundary evolution. The level-set framework allows for the introduction of prior knowledge that is learnt from some images on the solution. The lumen is detected using a 3D TOF sequence. The lumen MRA segmentation (contours) was then transferred and registered to the corresponding images on the Magnetic Resonance Imaging plaque hemorrhage (MRIPH) sequence. The 3D registration algorithm was applied to align the sequences. The same technique used for lumen detection was then applied to extract the outer wall boundary. Our preliminary results show that the segmentations are well correlated with those obtained from a 2D reference sequence (2D-T1W). The estimated Vessel Wall Volume (VWV) can be used as an imaging biomarker to help radiologists diagnose and monitor atherosclerotic disease. Furthermore, the 3D map of the Vessel Wall Thickness (WVT) and Vessel Wall Signal Intensity may be used as complementary information to monitor disease severity.

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Mariam Afshin, Tishan Maraj, Tina Binesh Marvasti, Navneet Singh, and Alan Moody "3D assessment of the carotid artery vessel wall volume: an imaging biomarker for diagnosis of the atherosclerotic disease", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978504 (24 March 2016); https://doi.org/10.1117/12.2216890

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