A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation

• URL: http://arxiv.org/abs/2608.16233v1
• Date: Mon, 17 Aug 2026 08:11:25 GMT
• Title: A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
• Abstract summary: MSCNet is a sequence-conditioned cross-modal generative framework for reconstructing unavailable contrasts and restoring degraded acquisitions.<n>Overall image quality met the prespecified non-inferiority criterion for DWI, ADC and T2W completion, but not T1W.
• Score: 48.14053205497089
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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