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

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, (Formula presented.) 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 ((Formula presented.)) and blood volume ((Formula presented.)), 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 ((Formula presented.), ICC (Formula presented.)) across all five maps (CBF, CBV, MTT, (Formula presented.), (Formula presented.)) 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.