Deep learning reconstruction in prostate MRI: technical principles, clinical applications, and future directions

Multiparametric magnetic resonance imaging (mpMRI) has become integral to the detection, localization, risk stratification, and management of prostate cancer. Increasing utilization of prostate MRI in screening, active surveillance, targeted biopsy guidance, and treatment planning has amplified the demand for faster imaging protocols while maintaining high diagnostic quality. Conventional MRI acceleration strategies, including parallel imaging and compressed sensing, improve acquisition efficiency but remain limited by trade-offs involving signal-to-noise ratio (SNR), image sharpness, artifacts, and reconstruction complexity. Deep learning reconstruction (DLR) has emerged as a promising artificial intelligence-based approach for MRI acceleration and image enhancement. Unlike traditional reconstruction methods that rely primarily on predefined mathematical models, DLR uses neural networks to learn image priors and reconstruction features from MRI data. Published studies suggest that DLR may enable accelerated acquisitions while maintaining or, in some settings, improving image quality through denoising, artifact reduction, and improved spatial resolution and lesion conspicuity. However, these effects vary according to the reconstruction algorithm, sequence, acceleration factor, and clinical setting. Variational networks combine physics-based MRI constraints with learned regularization and represent an important framework for DLR. In prostate MRI, available studies have reported substantial reductions in acquisition time for T2-weighted and diffusion-weighted imaging while generally maintaining perceived image quality. Reported benefits include improved lesion conspicuity, reader confidence, and delineation of anatomic structures. Current evidence suggests that DLR generally preserves PI-RADS assessment and diagnostic performance rather than systematically altering lesion categorization; however, evidence demonstrating improvements in diagnostic accuracy or clinical outcomes remains limited. Important limitations include vendor and algorithm dependence, potential reconstruction artifacts, variability in quantitative imaging metrics, limited multicenter validation, and uncertain generalizability across scanners, protocols, and clinical settings. This review summarizes the technical principles underlying DLR, discusses current clinical applications in prostate MRI, highlights representative studies and workflow implications, reviews limitations, and outlines future directions for this rapidly evolving technology.