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Related Experiment Video

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Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
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Automatic detection of arterial input function for brain DCE-MRI in multi-site cohorts.

Lucas Saca1, Raghav Gaggar2,3, Ioannis Pappas4

  • 1Department of Radiology, Loma Linda University, Loma Linda, California, USA.

Magnetic Resonance in Medicine
|August 14, 2025
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Summary

A deep learning model accurately extracts arterial input functions (AIFs) from dynamic contrast-enhanced MRI (DCE-MRI) scans. This automated method shows comparable results to manual extraction for pharmacokinetic modeling, improving efficiency and consistency.

Keywords:
3D UNetAIF detectionDCE‐MRIartificial intelligenceblood–brain barriermulti‐site data

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate arterial input function (AIF) extraction is essential for quantitative pharmacokinetic modeling in dynamic contrast-enhanced MRI (DCE-MRI).
  • Traditional AIF extraction relies on manual selection, which can be time-consuming and prone to inter-observer variability.
  • Developing automated methods is crucial for improving the efficiency and reliability of DCE-MRI analysis.

Purpose of the Study:

  • To develop and validate a robust deep learning model for precise arterial input function (AIF) extraction from DCE-MRI images.
  • To assess the performance of the proposed model against manual AIF extraction using quantitative metrics.

Main Methods:

  • A 3D UNet deep learning model was implemented and trained on manually delineated AIF regions from a diverse dataset of 384 human brain DCE-MRI scans across five institutions.
  • The model's performance was evaluated using a novel AIF quality metric (AIFitness) and by comparing Ktrans values derived from standard DCE pipelines.
  • The validated model was then applied to a separate, independent dataset of 421 DCE-MRI scans for replication.

Main Results:

  • The 3D UNet model achieved high AIFitness scores (93.9 on the primary test set, 97.0 on the replication set), closely approaching the performance of manual selections (99.7).
  • Intraclass correlation for Ktrans values between automated and manual AIFs was strong (0.89), indicating excellent agreement.
  • Quantitative analysis showed no significant differences in white matter Ktrans values between automated and manual AIF extraction methods.

Conclusions:

  • The proposed 3D UNet model, incorporating enhanced convolutional kernels and a modified Huber loss function, demonstrates superior performance for automated AIF extraction from multi-center DCE-MRI data.
  • The automated AIF extraction method provides reliable and consistent results comparable to manual selection, as evidenced by AIFitness scores and DCE-MRI-derived metrics like Ktrans maps.
  • This deep learning approach offers a promising solution for efficient and accurate pharmacokinetic analysis in DCE-MRI studies, particularly for blood-brain barrier permeability measurements.