QUIBIM Precision Prostate (qp-Prostate)

qp-Prostate is an image processing software package intended for trained medical professionals to view, process, and analyze prostate MR images using a client-server architecture and DICOM-compliant data. The device utilizes Apparent Diffusion Coefficient (ADC) post-processing with a mono-exponential model and Perfusion – Pharmacokinetics (PKM) post-processing via the Tofts model to generate quantitative parametric maps and facilitate structured PI-RADS v2.1 reporting, serving as an analytical tool rather than a diagnostic one.

qp-Prostate is a medical image processing software package designed for trained professionals, including radiologists specialized in prostate imaging, urologists, and oncologists, to aid in the visualization and analysis of prostate MR images. The software operates on a standard 'off-the-shelf' workstation, functioning via a client-server model where the server, installed within the hospital network, communicates with PACS through the DICOM protocol, and users access the platform via a web browser. Its core purpose is to facilitate image viewing, processing, and analysis of DICOM-compliant prostate MR images, but it explicitly does not perform a diagnostic function; patient management decisions should not solely rely on its results. Key features include query/retrieve interaction with PACS, a DICOM viewer with image manipulation and segmentation capabilities, and a structured reporting option based on the PI-RADS® v2.1 template. The system incorporates two main quantitative analysis plug-ins: Apparent Diffusion Coefficient (ADC) post-processing and Perfusion – Pharmacokinetics (PKM) post-processing. For ADC analysis, requiring T2-weighted and Diffusion Weighted Imaging (DWI) sequences, it applies a mono-exponential model after spatial smoothing and motion correction to calculate ADC values, representing water molecule mobility in tissues, and generates ADC parametric maps. For PKM analysis, requiring T2-weighted and Dynamic Contrast Enhanced (DCE) sequences, it uses the standard Tofts model (after smoothing, motion correction, and Arterial Input Function selection) to calculate Ktrans, kep, and ve parameters, reflecting tissue vascular environment, and produces corresponding parametric maps. Performance was validated using digital reference objects (DROs) for technical accuracy and a dataset of clinical prostate MR cases to assess algorithm performance (spatial smoothing, registration, segmentation, motion correction, AIF selection) and for comparison against a predicate device using 157 cases from various MRI vendors and field strengths.

AI Regulatory Concerns

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Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

• Q1. How does the performance of the automated Arterial Input Function (AIF) selection algorithm in the Perfusion-Pharmacokinetics module vary across different MR scanner vendors and field strengths, beyond the tested Siemens, GE, and Philips units?; What is the typical processing time for a complete prostate MR study, including ADC and Perfusion-Pharmacokinetics analysis, on a standard 'off-the-shelf' workstation meeting the recommended specifications?; Can the quantitative parameters (ADC, Ktrans, kep, ve) derived by qp-Prostate be reliably correlated with specific Gleason scores or other histopathological findings to improve risk stratification?; What level of training or expertise is recommended for users to effectively utilize the advanced segmentation tools and interpret the quantitative outputs of qp-Prostate for PI-RADS v2.1 reporting?; Are there plans to incorporate machine learning or AI-driven algorithms to further enhance the accuracy of segmentations or the predictive value of the quantitative parameters generated by qp-Prostate?