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Abstract #2097

Predicting the age from time of flight MR angiography using 3D convolutional neural network

Yoonho Nam1, Jaeho Lee2, Dong-Hyun Kim2, Jinhee Jang1, Bumsoo Kim1, and Kook-Jin Ahn1

1Seoul St.Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea, 2Yonsei University, Seoul, Republic of Korea

The age-related changes involve the vasculatures of the brain because the brain has rich blood supply. Previous studies using time of flight (TOF) MR angiography suggested that the aging intracranial arteries were tortuous, irregular and heterogeneous in shape. However, the use of these hand-crafted features and qualitative visual assessments are limited in practical clinical use. Vascular aging could be used as an imaging biomarker for the brain if we could distinguish various age-related vascular changes automatically and quickly from MR angiography. In this study, we investigate the feasibility of deep learning based feature extraction as a tool for analysis of age-related change of brain vasculatures.

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