Microstructure imaging of prostate cancer by diffusion MRI

Prostate cancer remains one of the most prevalent malignancies in men worldwide, and early detection, accurate characterization, and staging are critical for optimal patient management. Diffusion magnetic resonance imaging (dMRI) has emerged as a powerful technique for non-invasive in vivo imaging of tissue microstructure, promising to enable unprecedented insights into the pathophysiology of prostate cancer. In this review, we examine the current state and future directions of dMRI techniques for microstructure MRI in prostate cancer applications. We describe the technical challenges of prostate imaging along with potential remedies, as well as techniques that go beyond measuring the conventional apparent diffusion coefficient. These include more sophisticated representations and models, such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), methods based on tensor-valued diffusion encoding such as q-space trajectory imaging (QTI), as well as time-dependent dMRI. Models, such as VERDICT (vascular, extracellular, and restricted diffusion for cytometry in tumors) and IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion), are highlighted for their ability to quantify the composition of prostatic tissues in the clinical setting. This review highlights the transformative potential of microstructure MRI in prostate cancer, while identifying key research priorities and technical developments needed to realize its clinical impact. Future directions include the adoption of high-performance MRI systems, improved image readout and processing techniques, integration of multiparametric and multidimensional approaches, development of parsimonious protocols, and standardization of robust biomarkers for personalized treatment stratification. Although there is yet to be a consensus of how microstructure MRI should be deployed, current evidence suggests that it can provide superior diagnostic performance compared to conventional measurements, with improved correlation to tumor aggressiveness and potential downstream advantages for prostate cancer diagnostics and treatment monitoring.