Project Summary – Pilot Project Prostate cancer is a significant disease in men, particularly among African Americans who face higher incidence and mortality rates than Caucasians. Late detection contributes to increased mortality, with current screening methods having limitations. Our proposed solution involves developing a non-invasive imaging modality utilizing molecular CEST MRI on standard 3T MRI scanners, combined with a machine learning- based metabolic imaging processing pipeline. This aims to provide a precise, cost-effective tool for detecting and prognosing prostate cancer in underserved communities. Molecular MRI, though challenging, can be enhanced using machine learning superresolution techniques. By simulating metabolic imaging in organ-scale vascular networks, it is possible to obtain high resolution images for training a superresolution neural network. We hypothesize that CEST MRI with subject-specific metabolism simulation will enable detection of metabolic activity at small scales, which will improve prediction of cancer progression. Our study aligns with the Howard-Hopkins Partnership priorities, addressing the urgent need for accurate detection and prediction of cancer aggressiveness in African American men, ultimately aiming to reduce mortality rates and disparities. The specific aims of our study include: 1) using dynamic CEST MRI to analyze energy metabolism variations in aggressive prostate tumors, mapping glucose uptake in PSMA-positive and negative cancers; 2) developing a superresolution machine learning model for improved spatial resolution in metabolic imaging using subject- specific metabolism simulation; and 3) validating patient-specific metabolic/perfusion models generated from magnetic resonance angiography (MRA) and anatomic images in a small pilot project and assessing the quality of superresolution results through image similarity metrics and ratings.