A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
2026-08-17 • Artificial Intelligence
Artificial IntelligenceComputer Vision and Pattern Recognition
AI summaryⓘ
The authors developed a new method called MSCNet to fix or fill in missing or poor-quality images in prostate MRI scans. Their system can recreate certain types of images that were unavailable or degraded, achieving better similarity to real images than other methods. In tests involving thousands of cases, MSCNet's image quality was close to original images for most types, and it also helped detect significant prostate cancer almost as well as using original scans. This method worked across multiple hospitals, suggesting it could be widely useful as a support tool for prostate MRI analysis.
Prostate MRIMultiparametric MRIImage reconstructionCross-modal learningStructural similarityDWIADCT2-weighted imagingDiagnostic accuracyDeep learning
Authors
Siyuan Ma, Liang He, Mengying Zhu, Yi Chai, Mengyao Lyu, Haowei Wang, Qizhen Lan, HaoBo Sun, Qixin Zhang, Jingli Chen, Xiaobing Wei, Jiaming Liu, Guiqin Liu, Qianwen Zhang, Yang Liu, Dacheng Tao, Guangyu Wu
Abstract
Missing or degraded sequences can limit prostate multiparametric MRI. We developed MSCNet, a sequence-conditioned cross-modal generative framework for reconstructing unavailable contrasts and restoring degraded acquisitions. Across ten completion tasks, task-specific MSCNet achieved mean structural similarity of 0.818 versus 0.798 for the strongest task-matched comparators; matched-capacity analyses showed larger differences in lesion fidelity and boundary preservation. In a blinded 1,000-case reader study, overall image quality met the prespecified non-inferiority criterion for DWI, ADC and T2W completion, but not T1W. In a separate 200-case diagnostic assessment, AUCs for clinically significant cancer were 0.860 with acquired images, 0.841 with MSCNet and 0.797 with baseline-generated images. A locked 186-case three-hospital cohort supported multicentre transportability. These retrospective results support quality-controlled cross-modal reconstruction as an adjunct to acquired prostate MRI.