Automatic detection of arterial input function for brain DCE-MRI in multi-site cohorts.

ABSTRACT:

Purpose

Arterial input function (AIF) extraction is a crucial step in quantitative pharmacokinetic modeling of DCE-MRI. This work proposes a robust deep learning model that can precisely extract an AIF from DCE-MRI images.

Methods

A diverse dataset of human brain DCE-MRI images from 289 participants, totaling 384 scans, from five different institutions with extracted gadolinium-based contrast agent curves from large penetrating arteries, and with most data collected for blood-brain barrier (BBB) permeability measurement, was retrospectively analyzed. A 3D UNet model was implemented and trained on manually drawn AIF regions. The testing cohort was compared using proposed AIF quality metric AIFitness and K^trans values from a standard DCE pipeline. This UNet was then

SUBMITTER: Saca L 

PROVIDER: S-EPMC12501662 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature