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Evaluation of Capillary and Other Vessel Contribution to Macular Perfusion Density Measured with Optical Coherence Tomography Angiography

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AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning.

Ezequiel de la Rosa¹, Diana M Sima², Bjoern Menze³

¹icometrix, Leuven, Belgium; Department of Computer Science, Technical University of Munich, Munich, Germany.

Medical Image Analysis

|August 23, 2021

Summary

This summary is machine-generated.

Automating arterial input function (AIF) estimation in perfusion CT imaging using AIFNet, a deep learning model, improves the accuracy and reproducibility of stroke lesion quantification, aiding critical treatment decisions.

Keywords:

Arterial input function Deep learning Ischemic stroke Perfusion imaging

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Last Updated: Oct 23, 2025

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A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology

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Published on: February 7, 2015

Area of Science:

Medical Imaging
Radiology
Computational Neuroscience

Background:

Perfusion CT imaging is vital for acute ischemic stroke assessment, quantifying salvageable penumbra and core lesions to guide reperfusion therapy.
Deconvolution methods in perfusion CT rely on accurate arterial input function (AIF) and venous output function estimation.
Manual AIF selection is time-consuming, lacks reproducibility, and depends on operator expertise, potentially compromising stroke lesion quantification and treatment decisions.

Purpose of the Study:

To develop and validate AIFNet, a fully automatic deep learning approach for estimating vascular input functions in perfusion CT.
To enhance the accuracy and reproducibility of perfusion parameter quantification in acute ischemic stroke analysis.
To improve the reliability of core and penumbra lesion volume assessment for clinical decision-making.

Main Methods:

AIFNet, an end-to-end trainable deep learning model, was developed for automated arterial input function (AIF) estimation.
The model directly optimizes vascular function estimation, improving recognition of time-curve profiles compared to traditional voxel selection methods.
Validation was performed on the public ISLES18 stroke database.

Main Results:

AIFNet demonstrated near inter-rater performance in estimating vascular functions.
The automated approach yielded comparable parameter maps and core lesion quantification to manual methods.
Deep learning-based AIF estimation significantly improved the reproducibility of perfusion analysis.

Conclusions:

AIFNet offers a robust and automated solution for perfusion analysis in acute ischemic stroke.
The deep learning approach shows potential for clinical integration into perfusion deconvolution software.
Automated AIF estimation by AIFNet can enhance the reliability of stroke imaging biomarkers and support treatment decisions.