p-Brain: A Modular Open-Source Framework for Automated Quantitative DCE-MRI of Cerebral Perfusion, Microvasculature,...

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, $T_1/M_0$ 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 ( $K_i$ ) and blood volume ( $v_b$ ), 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 ( $r>0.96$ , ICC $>0.96$ ) across all five maps (CBF, CBV, MTT, $K_i$ , $v_b$ ) 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.