mm$^3$.

Here, we introduce the RAPSODI (diology athology patial pen-Source multi-imensional ntegration) framework for the registration of histopathology slices and pre-operative MRI. RAPSODI includes a dictionary-based data management system, a memory-efficient registration methodology and a Graphical User Interface Plugin to 3D Slicer [@fedorov_3d_2012]. Our registration approach relies on the 3D reconstruction of the histopathology specimen to create a digital representation of the tissue before gross sectioning. Next, RAPSODI registers corresponding histopathology and MRI slices. Finally, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the pre-operative MRI.

We evaluated our methodology using a digital phantom study where we simulated various conditions resulting from the histologic preparation of the excised tissue, e.g., rotation of the tissue when mounting on the glass slide or shrinking of the tissue. Moreover, we tested RAPSODI in 89 prostate cancer patients that underwent radical prostatectomy from two institutions, ours and a public cohort [@madabhushi_fused_2016]. RAPSODI is open-source and can be downloaded from <https://github.com/pimed/Slicer-RadPathFusion>, while the phantom data is available at <https://github.com/pimed/rad-path-phantom>.

Prior Work and Our Contribution
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Although numerous automated approaches for the registration of radiology and histopathology images have been developed, manual approaches are still 