A cross-modal generative model for incomplete and degradedprostate MRI with multicentre clinical validation
Ma, S.; He, L.; Zhu, M.; Chai, Y.; Lyu, M.; Wang, H.; Lan, Q.; Sun, H.; Zhang, Q.; Chen, J.; Wei, X.; Liu, J.; Liu, G.; Zhang, Q.; Liu, Y.; Tao, D.; Wu, G.
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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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