Carotid Artery Localization and Lesion Classification on 3D-MERGE MRI using NeuralNetwork and Object Tracking methods

Li Chen

, Huilin Zhao

, Niranjan Balu

, Xihai Zhao

, Rui Li

, Jianrong Xu

, Thomas S Hatsukami

, Jenq-Neng Hwang

, and ChunYuan

University of Washington, Seattle, WA, United States,

Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China,

Department of Biomedical Engineering, Tsinghua University School of Medicine, Beijing, China

Synopsis

Carotid vessel wall imaging (VWI) with MRI provides additional prognostic value for cerebro/cardiovascular ischemic events, beyond current clinicaldiagnostic imaging methods. While fast 3D carotid MRI is possible, manual review of the large 3D dataset is time consuming. Automaticidentification of artery locations and lesion categories are therefore required for VWI screening protocols. With neural network and object trackingmethods, we developed a fully automated analysis tool to find common/internal/external carotid arteries and flag possible high-risk lesion locations.The tool achieved 0.782 Intersection over Union (IoU) for artery localization, and 0.895 sensitivity for high-risk lesion classification.

ISMRM_LATTE4812.pdf