p ‐Brain: A Modular Open‐Source Framework for Automated Quantitative DCE‐MRI of Cerebral Perfusion, Microvasculature, and Blood–Brain Barrier Permeability

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ABSTRACT We present p ‐Brain, a modular, open‐source framework for reproducible, automated quantitative DCE‐MRI at scale. Rather than a fixed pipeline, p ‐Brain is built from interchangeable stages (ingestion, fitting, vascular and tissue ROI extraction, signal‐to‐concentration conversion, kinetic modeling, and quality control), each selected and configured through a single file‐based interface, so any stage can be swapped or extended without modifying the surrounding code. In its default configuration, p ‐Brain converts signal to gadolinium concentration, derives arterial and venous input functions using convolutional neural network (CNN) slice selection and ROI segmentation, and produces voxelwise, regional, and whole‐brain maps. It implements Patlak graphical analysis for the blood–brain barrier influx constant () and blood volume (), and model‐free Tikhonov‐regularised residue deconvolution for cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT), with structured metadata and stage‐level quality‐control artifacts for auditability. We validate p ‐Brain against an established reference workflow in two ways: On identical inputs its estimators reproduce the reference algorithms to machine precision, and as a fully automated pipeline it agrees with the reference voxelwise (, ICC) across all five maps (CBF, CBV, MTT, , ) in 12 healthy controls. p ‐Brain runs on Linux, macOS, and Windows as a Python package and command‐line tool, and is open and extensible to additional segmentation tools, input‐function providers, and kinetic models.

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