Effects of Artificial Intelligence-based Reconstruction on Image Quality and Voxel-based Morphometry Analysis of Atrophy in Accelerated MPRAGE.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiological Technology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan.
- Faculty of Dental Medicine, Department of Radiology, Hokkaido University, Sapporo, Hokkaido, Japan.
- Faculty of Health Sciences, Hokkaido University, Sapporo, Hokkaido, Japan.
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan.
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan.
Abstract
To investigate the effects of artificial intelligence (AI)-based reconstruction on image quality and voxel-based morphometry (VBM)-based atrophy analysis in 3D Magnetization Prepared Rapid Gradient Echo (MPRAGE). Ten healthy volunteers underwent 3T MPRAGE imaging using varying acceleration factors with sensitivity encoding (SENSE) (SE; 2,3), compressed sensing (CS; 2,3,5,8), and AI-based reconstruction (AI; 2,3,5,8). Quantitative assessment included SNR and contrast-to-noise ratio (CNR) in both superficial and deep brain regions. Visual assessment of image quality was performed by 2 experienced neuroradiologists. VBM-based Z-score analysis of regional atrophy was performed using Voxel-based Specific Regional Analysis System for Alzheimer's Disease (VSRAD). Images acquired using a widely recognized standard imaging protocol, SENSE acceleration factor 2 with 1.0-mm isotropic voxels, were used as the reference, and various reconstruction methods and acceleration factors were compared. Conventional SENSE and CS reconstructions showed stepwise decreases in SNR and CNR with increasing acceleration factors. SNR decrease was particularly pronounced in deep brain regions. Images with AI-based reconstruction maintained relatively stable SNR and CNR across acceleration factors, showing consistent performance in both superficial and deep regions. Visual assessment confirmed reduced noise in images with AI-based reconstruction, which were rated as more favorable for interpretation than SE or CS images. VSRAD analysis demonstrated high correlation with reference images and minimal systematic bias across all acceleration conditions. AI-based reconstruction may enable faster MPRAGE acquisition (up to 5-fold acceleration) while preserving image quality and the reproducibility of VBM-based atrophy analysis.