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abstract: 'Magnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis. It can spare men with a normal exam from undergoing invasive biopsy while making biopsies more accurate in men with lesions suspicious for cancer. Yet, the subtle differences between cancer and confounding conditions, render the interpretation of MRI challenging. The tissue collected from patients that undergo pre-surgical MRI and radical prostatectomy provides a unique opportunity to correlate histopathology images of the entire prostate with MRI in order to accurately map the extent of prostate cancer onto MRI. Such mapping will help improve existing MRI interpretation schemes, e.g. PIRADS, and will facilitate the development of quantitative image analysis methods to assess the imaging characteristics of prostate cancer on MRI. Here, we introduce the RAPSODI (diology athology patial pen-Source multi-imensional ntegration) framework for the registration of radiology and pathology images. RAPSODI relies on a three-step procedure that first reconstructs in three dimensions (3D) the resected tissue using the serial whole-mount histopathology slices, then registers corresponding histopathology and MRI slices, and finally maps the cancer outlines from the histopathology slices onto MRI. We tested RAPSODI in a phantom study where we simulated various conditions, e.g., tissue specimen rotation upon 