Effects of Artificial Intelligence–based Reconstruction on Image Quality and Voxel-based Morphometry Analysis of Atrophy in Accelerated MPRAGE
Abstract
PURPOSE: 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). METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Related
- Evaluation of accelerated whole-body diffusion weighted imaging with deep learning reconstruction in patients with metastatic prostate cancer: assessment of image quality and ADC estimates.
- AI-accelerated compressed sensing and deep learning reconstruction for colon MRI: prospective validation of image quality and preliminary assessment of staging accuracy versus contrast-enhanced CT
- A voice-guided real-time artificial intelligence system for navigation and quality control during standardized mediastinal endosonography scanning.
- Quality, transparency, and readability of artificial intelligence chatbot responses to fibrous dysplasia-related searches: a cross-sectional comparative study
- Quality of physicians responses using a conversational artificial intelligence system: a randomized vignette experiment in Latin America
BibTeX
@article{horie2026effects,
title = {{Effects of Artificial Intelligence–based Reconstruction on Image Quality and Voxel-based Morphometry Analysis of Atrophy in Accelerated MPRAGE}},
author = {Tatsunori Horie and Noriyuki Fujima and Hiroyuki Kameda and Tamotsu Kamishima and Kohsuke Kudo},
journal = {Magnetic Resonance in Medical Sciences},
year = {2026},
month = oct,
doi = {10.2463/mrms.mp.2026-0090},
url = {https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0090/_article},
note = {CC BY},
}
RIS
TY - JOUR
TI - Effects of Artificial Intelligence–based Reconstruction on Image Quality and Voxel-based Morphometry Analysis of Atrophy in Accelerated MPRAGE
AU - Tatsunori Horie
AU - Noriyuki Fujima
AU - Hiroyuki Kameda
AU - Tamotsu Kamishima
AU - Kohsuke Kudo
PY - 2026
DA - 2026/10/02
JO - Magnetic Resonance in Medical Sciences
DO - 10.2463/mrms.mp.2026-0090
UR - https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0090/_article
AB - PURPOSE: 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). METHODS: 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. RESULTS: 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. CONCLUSION: 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.
SN - 1880-2206
SN - 1347-3182
ER -