Deep learning‐based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk

作者：Meiyi Yang, Lili Yang, Qi Zhang, Lifeng Xu, Bo Yang, Yingjie Li, Xudong Cheng, Feng Zhang, Ming Liu, Nengwei Yu · 发表于：Medical Physics · 年份：2024 · DOI：10.1002/mp.17237 · 被引用次数：5 · 研究领域：Neurological Disease Mechanisms and Treatments、Acute Ischemic Stroke Management、Advanced MRI Techniques and Applications

BACKGROUND: Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that poses a diagnostic challenge due to its nonspecific symptoms. PURPOSE: To enhance the diagnosis of CCH and non-CCH through Magnetic Resonance Imaging (MRI), offering support in clinical decision-making and recommendations to ultimately elevate diagnostic accuracy and optimize patient treatment outcomes. METHODS: In the retrospective research, we collected 204 routine brain magnetic resonance imaging (MRI) from March 1 to September 10 2022, as training and testing cohorts. And a validation cohort with 108 samples was collected from November 14 2022 to August 4 2023. MRI sequences were processed to obtain T1-weighted (T1WI) and T2-weighted (T2WI) sequence images for each patient. We propose CCH-Network (CCHNet), an end-to-end deep learning model, integrating convolution and Transformer modules to capture local and global structural information. Our novel adversarial training method improves feature knowledge capture, enhancing both generalization ability and efficiency in predicting CCH risk. We assessed the classification performance of the proposed model CCHNet by comparing it with existing state-of-the-art deep learning algorithms, including ResNet34, DenseNet121, VGG16, Convnext, ViT, Coat, and TransFG. To better validate model performance, we compared the results of the proposed model with eight neurologists to evaluate their consistency. RESULTS: CCHNet achieved an AUC of ...