A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
This study addresses the challenges of missing and degraded sequences in multiparametric prostate MRI by proposing MSCNet, a cross-modal generation framework. Leveraging sequence-conditioned generation, this method achieves high-quality image reconstruction and was validated through a ten-center blinded reader study. Results demonstrate that reconstructed images attained a structural similarity index (SSIM) of 0.818 and a cancer diagnosis area under the curve (AUC) of 0.841. Notably, diagnostic performance was non-inferior to that of original images and exhibited robust cross-center transferability. These findings confirm the clinical utility of MSCNet as an effective auxiliary diagnostic tool, providing a reliable solution for managing incomplete mp-MRI data in clinical practice.