MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.
Authors
Affiliations (4)
Affiliations (4)
- Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, No. 1 Jiankang Road, Yuzhong District, Chongqing, 400014, China.
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200030, China.
- Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, No. 1 Jiankang Road, Yuzhong District, Chongqing, 400014, China. [email protected].
- Department of Medical Imaging, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, No. 1 Jiankang Road, Yuzhong District, Chongqing, 400014, China. [email protected].
Abstract
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<sup>2</sup> score (RS), and Pearson correlation coefficient (PCC) values of 0.13 ± 0.05, 0.03 ± 0.02, 0.77 ± 0.22, and 0.89 ± 0.05 in the training set, and 0.23 ± 0.10, 0.09 ± 0.08, 0.36 ± 0.12, and 0.65 ± 0.09 in the testing set, respectively. For the deep learning model, the MAE, MSE, RS, and PCC in the testing set were 0.41 ± 0.17, 0.25 ± 0.18, - 0.83 ± 0.42, and - 0.01 ± 0.17, respectively. An artificial intelligence-based approach was successfully developed to quantitatively detect Aβ protein accumulation in different brain regions of patients with AD and MCI using MRI. This method is convenient and non-invasive and does not require cerebrospinal fluid puncture or exposure to ionizing radiation.