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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