the Insight Toolkit (ITK) [@johnson_itk_2013]and its Simple ITK API in python. The approach is available as a 3D Slicer python plugin [@fedorov_3d_2012] ( \[fig:slicer\]) or as a stand-alone application to be run in batch mode. RAPSODI can be downloaded from <https://github.com/pimed/Slicer-RadPathFusion>. We measured the performance of the approach on an Intel i7-8700 CPU, 3.70GHz, 64GB Memory Computer.

![image](figures/Slicer_Plugin_red.pdf){width="\linewidth"}

Digital Phantom for Radiology-Pathology Registration {#digital-phantom-for-radiology-pathology-registration .unnumbered}
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We created a digital phantom to assess the quality of the alignment during the development of RAPSODI and to evaluate its performance when ground truth exists. The phantom is used to simulate artifacts known to affect the histopathology sample. We constructed the phantom by first outlining different prostatic regions, peripheral zone, cancer, and urethra in a 3D T2w MRI ( \[fig:Phantom\]a-c). Then, we synthesized the phantom T2 MRI by filling the segmented regions with the average intensities from the input T2 image ( \[fig:Phantom\]d). Moreover, we created the pathology phantom based on the histopathology images already registered to the T2w MRI (data not shown), by averaging their color intensities within the segmented regions ( \[fig:Phantom\]e-f). Our simulations included Gaussian noise on both the MRI and histopathology phantom slices.

