A novel MRI-based deep learning imaging biomarker for comprehensive assessment of the lenticulostriate artery-neural complex
- Published on 06/04/2025
- Reading time: 5 min.
Song Yan 1, Jin Yunlong 1, Wei Jianguo 2, Wang Jiajia 3, Zheng Zhong 4, Wang Ying 5, Zeng Ru 1, Lu Weiping 6, Huang Bingcang 6
1 https://ror.org/00ay9v204 Department of Control Science and Engineering University of Shanghai for Science and Technology 200093 Shanghai China
2 https://ror.org/006teas31 Department of PET/CT Shanghai Universal Medical Imaging Diagnostic Center No. 406 Guilin Road, Xuhui Area 200030 Shanghai China
3 https://ror.org/00ay9v204 School of Gongli Hospital Medical Technology University of Shanghai for Science and Technology 200093 Shanghai China
4 https://ror.org/02h8a1848 Shanghai Gongli Hospital, Ningxia Medical University No. 219 Miaopu Road, Pudong New Area 200135 Shanghai China
5 https://ror.org/04v5gcw55 Shanghai Health Commission Key Lab of Artificial Intelligence (AI)-Based Management of Inflammation and Chronic Diseases, Sino-French Cooperative Central Lab Shanghai Pudong New Area Gongli Hospital No. 219 Miaopu Road, Pudong New Area 200135 Shanghai China
6 https://ror.org/04v5gcw55 Department of Radiology Shanghai Pudong New Area Gongli Hospital No. 219 Miaopu Road, Pudong New Area 200135 Shanghai China
Abstract
Objectives To develop a deep learning network for extracting features from the blood-supplying regions of the lenticulostriate artery (LSA) and to establish these features as an imaging biomarker for the comprehensive assessment of the lenticulostriate artery-neural complex (LNC).
Materials and methods Automatic segmentation of brain regions on T1-weighted images was performed, followed by the development of the ResNet18 framework to extract and visualize...
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