Multiparametric MRI (mpMRI) has been shown to improve detection, localization and characterization in patients with suspected prostate cancer (PCa). The Prostate Imaging Reporting and Data System (PIRADS) aims to establish technical parameters and simplify reporting for mpMRI. While computer-aided evaluation (CAE) technology is not required for prostate mpMRI interpretation, the current PIRADS guideline (v2, 2015) also states that CAE may improve workflow, provide quantitative perfusion data and enhance discrimination performance for less experienced radiologists and can also facilitate integration of MRI data for some biopsy systems [1]. The goal of this work was to illustrate and report on features and benefits of an investigational PC-based CAE tool for mpMRI of the prostate. The custom-made software was developed under IDL (Exelis VIS, Boulder, CO) and runs under a freely available virtual machine. It consists of a preparation tool that automatically identifies all mpMRI data from randomly ordered/ named DICOM input data and places them into key folders (labelled _ADC, _DCE, _DWI and _T2W) along with the original (renamed) series folders (e.g., #013 – ep2d_diff_b50_500_800). Standardized file names are built from the DICOM tag entries patient name, slice and acquisition number (e.g., Doe, John – SlcAcq=013.005 for 5th DCE time point of slice #13). The workflow of the CAE tool is shown in Fig. 1. A screenshot of the user interface is shown in Fig. 2. Figure 3 illustrates how sigmoid function and linear fits (to delayed phase) are used to compute semiquantitative DCE maps from the corresponding time curves (pixel by pixel). Heuristic filtering is used to improve visual map appearance (suppress areas with implausible results, e.g., very low contrast enhancement CE or long arrival time Δt). Our mpMRI protocol (3 T, Magnetom Tim Trio, Siemens) minimally involved T2W, DWI and DCE, in accordance with PIRADS v2. Illustrative cases with proper histological validation (under IRB approval, with informed consent) were selected from over 200 mpMRI datasets; a sample case is shown in Fig. 4. Pixel-based computation of the DCE maps took about 45 s for a typical dataset (18 slices, 20 time points) on a quad-core CPU (Intel i7). The tool can be readily implemented on any PC. Compressed images should be preprocessed by a client or additional IDL tool. The main users (3 and 5 years’ experience in prostate mpMRI) considered navigation between slices, time points and b‑values to be intuitive. In combination with fully automatic processing, also of multiple patients (batch mode), the tool was found to be highly beneficial for the radiological workflow. All major processing and visualization parameters could be easily configured in a simple text file. Simultaneous display may facilitate PIRADS assignment around scores of 3 or in cases with equivocal findings between dominant and subsequent sequence (DWI<> T2W) under v2. DCE maps with interactive time curves (raw data) were found to increase confidence in dominant T2W and DWI/ADC sequences and also assist with visual correction of potential motion between sequences (Fig. 4). In addition, this tool also accounts for potential slice offsets (different thickness or gap) between sequences. The presented CAE tool can be used on any PC and may provide an improved workflow for the display, analysis, interpretation and communication of mpMRI data of the prostate, in particular for equivocal findings and less experienced radiologists. As an investigational tool, it does not provide the full range of certified products nor replace them.