Multimodal Enhancement of Prostate Cancer Lesion Segmentation Using Synthetic Correlated Diffusion Imaging

Automated prostate cancer (PCa) lesion segmentation using deep learning remains constrained by limited tissue contrast in standard diffusion-based MRI sequences, with state-of-the-art methods reporting Dice scores of 32% or lower on large patient cohorts. Synthetic correlated diffusion imaging (CDIs) offers a promising solution, providing enhanced tissue contrast derived entirely from existing diffusion-weighted imaging (DWI) acquisitions at no additional clinical cost. This study presents the first comprehensive evaluation of CDIs integration across the full standard multiparametric MRI protocol, encompassing 15 modality configurations and six segmentation architectures spanning CNN and transformer families on a cohort of 200 patients. CDIs reliably enhances or preserves segmentation performance in the evaluated configurations, with 19 statistically significant improvements and no significant degradations across 42 direct comparisons. CDIs enhancement primarily operates as a recall-driven mechanism, improving lesion detection sensitivity while largely preserving precision. CDIs + DWI + T2w emerged as the strongest clinically meaningful configuration, achieving significant Dice improvement in four of six architectures, with no instances of degradation. Grad-CAM-based explainability analysis further reveals that CDIs focuses poorly localized CNN attention toward lesion boundaries, while transformer architectures exhibit more stable attention patterns that are less sensitive to CDIs integration. These results establish validated CDIs integration pathways and provide architecture-specific deployment guidance for clinical implementation.