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Abstract #0603

Deep Learning Segmentation of Lenticulostriate Arteries Using 3T and 7T 3D Black-Blood MRI

Samantha J Ma1,2, Mona Sharifi Sarabi2, Kai Wang2, Wenli Tan2, Huiting Wu3, Lei Hao3, Yulan Dong3, Hong Zhou3, Lirong Yan2, Yonggang Shi2, and Danny JJ Wang2
1Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States, 2University of Southern California, Los Angeles, CA, United States, 3Department of Radiology, The First Affiliated Hospital of University of South China, Hunan, China

Given the inaccessibility of cerebral small vessels to existing clinical in vivo imaging technologies, early cerebral microvascular morphological changes in small vessel disease (SVD) can be difficult to evaluate. In this study, we trained a deep learning (DL)-based algorithm with 3T and 7T black-blood images on two vendor platforms to automatically segment lenticulostriate arteries (LSAs) in the brain. Our results show that black-blood imaging in conjunction with DL is a promising approach to enable quantitative morphometric analysis in patients with cerebral SVD.

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