BMC medical imaging · 2025
A novel MRI-based deep learning imaging biomarker for comprehensive assessment of the lenticulostriate artery-neural complex.
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Song Yan, Jin Yunlong, Wei Jianguo, Wang Jiajia, Zheng Zhong, Wang Ying, Zeng Ru, Lu Weiping, Huang Bingcang
Аннотация
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 deep learning features from three regions of interest (ROIs). The root mean squared error (RMSE) was then used to assess the correlation between these features and fractional anisotropy (FA) values from diffusion tensor imaging (DTI) and cerebral blood flow (CBF) values from arterial spin labeling (ASL). The correlation of these features with LSA root numbers and three disease categories was further validated using fine-tuning classification (Task1 and Task2).
RESULTS: Seventy-nine patients were enrolled and classified into three groups. No significant differences were found in the number of LSA roots between the right and left hemispheres, nor in the FA and CBF values of the ROIs. The RMSE loss, relative to the mean FA and CBF values across different ROI inputs, ranged from 0.154 to 0.213%. The model's accuracy in Task1 and Task2 fine-tuning classification reached 100%.
CONCLUSIONS: Deep learning features extracted from the basal ganglia nuclei effectively reflect cerebrovascular and neurological functions and reveal the damage status of the LSA. This approach holds promise as a novel imaging biomarker for the comprehensive assessment of the 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 deep learning features from three regions of interest (ROIs). The root mean squared error (RMSE) was then used to assess the correlation between these features and fractional anisotropy (FA) values from diffusion tensor imaging (DTI) and cerebral blood flow (CBF) values from arterial spin labeling (ASL). The correlation of these features with LSA root numbers and three disease categories was further validated using fine-tuning classification (Task1 and Task2).
RESULTS: Seventy-nine patients were enrolled and classified into three groups. No significant differences were found in the number of LSA roots between the right and left hemispheres, nor in the FA and CBF values of the ROIs. The RMSE loss, relative to the mean FA and CBF values across different ROI inputs, ranged from 0.154 to 0.213%. The model's accuracy in Task1 and Task2 fine-tuning classification reached 100%.
CONCLUSIONS: Deep learning features extracted from the basal ganglia nuclei effectively reflect cerebrovascular and neurological functions and reveal the damage status of the LSA. This approach holds promise as a novel imaging biomarker for the comprehensive assessment of the LNC.