Abstract #0603

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

Samantha J Ma^1,2, 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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