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Updated: May 10, 2026
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
Published on: April 14, 2014
Toward safe deployment of deep learning in MRI: A physics-driven uncertainty framework for automated quality control
Rong Liu1, ZengHan Zhou2, YingYing Song3
1Wuhan Puren Hospital (Affiliated to Wuhan University of Science and Technology), 1 Benxi Street, Jianshe Fourth Road, Qingshan District, 430080 Wuhan, Hubei, China; Institute of Medical Innovation and Transformation, Puren Hospital Affiliated to Wuhan University of Science and Technology, 1 Benxi Street, Jianshe Fourth Road, Qingshan District, 430080 Wuhan, Hubei, China; Wuhan Liu Sanwu Traditional Chinese Medicine Orthopedic Hospital, No. 388, Xingkeli, Wenchang Avenue, Zhucheng Street, Xinzhou District, 431400 Wuhan, Hubei, China.
Purpose:
Deep generative models in MRI are hindered by "hallucinations" and a lack of safety mechanisms. This study introduces a physics-driven framework for trustworthy Virtual Fat Suppression (VFS), enabling automated quality control and active risk tiering.
Methods:
A differentiable Bloch layer was embedded into an RRDB-RaGAN architecture to enforce physics-based signal consistency during synthesis. An uncertainty-guided training strategy with artifact-enriched supervision was further introduced to support a three-tier risk model for auto-pass, human review, and rejection. The framework was evaluated using quantitative fidelity metrics, real-world severe artifact detection, and blinded clinical reader assessment.
Results:
In real-world severe artifact detection, the image-level uncertainty score achieved an AUC of 0.9053 and a recall of 81.82% at a false-positive rate of 4.69%. In blinded clinical evaluation, agreement between AI-predicted and reader-mapped risk tiers was substantial (quadratic weighted Cohen's κ = 0.7938), and images assigned to the Low-Risk tier achieved a mean Likert score of 4.70 ± 0.61.
Conclusion:
By coupling physics-constrained synthesis with uncertainty-based risk governance, the proposed framework provides a practical and auditable safety layer for virtual fat suppression in clinical MRI workflows.