MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.

To investigate a non-invasive magnetic resonance imaging (MRI)-based method for detecting amyloid-β (Aβ) protein deposition in different brain regions of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). This study included 80 patients with MCI and 62 patients with AD, who were randomly divided into training and testing sets at an 8:2 ratio. All participants underwent 18 F-florbetapir positron emission tomography (PET) imaging and three-dimensional T1-weighted MRI. The interval between MRI and PET examinations did not exceed 30 days. A deep learning-based three-dimensional VB-Net model was developed for brain region segmentation. All PET images were registered to the corresponding MRI images, and standardized uptake ratios for 109 brain regions were calculated and averaged. Following radiomics feature extraction and selection using multiple methods, six machine learning algorithms were applied to establish regression models. In addition, a lightweight transformer-based deep learning model was constructed by improving the original transformer architecture. A total of 1,409 features were extracted from each brain region in patients with MCI and AD. After feature selection, 46, 16, 47, 59, 17, and 72 features were retained for the construction of stochastic gradient regression (SGR), GBR, random forest regression (RFR), support vector regression (SVR), extreme gradient boosting (XGB), and k-nearest neighbor (KNN) models, respectively. Delong test analysis demonstrated that the RFR model achieved the best performance, with mean absolute error (MAE), mean squared error (MSE), R