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Updated: Jun 16, 2025
Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders
Published on: May 31, 2024
perfDSA: Automatic Perfusion Imaging in Cerebral Digital Subtraction Angiography
Ruisheng Su1,2, P Matthijs van der Sluijs3, Flavius-Gabriel Marc4
1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. r.su@tue.nl.
Purpose:
Cerebral digital subtraction angiography (DSA) is a standard imaging technique in image-guided interventions for visualizing cerebral blood flow and therapeutic guidance thanks to its high spatio-temporal resolution. To date, cerebral perfusion characteristics in DSA are primarily assessed visually by interventionists, which is time-consuming, error-prone, and subjective. To facilitate fast and reproducible assessment of cerebral perfusion, this work aims to develop and validate a fully automatic and quantitative framework for perfusion DSA.
Methods:
We put forward a framework, perfDSA, that automatically generates deconvolution-based perfusion parametric images from cerebral DSA. It automatically extracts the arterial input function from the supraclinoid internal carotid artery (ICA) and computes deconvolution-based perfusion parametric images including cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and Tmax.
Results:
On a DSA dataset with 1006 patients from the multicenter MR CLEAN registry, the proposed perfDSA achieves a Dice of 0.73(±0.21) in segmenting the supraclinoid ICA, resulting in high accuracy of arterial input function (AIF) curves similar to manual extraction. Moreover, some extracted perfusion images show statistically significant associations (P=2.62e 5) with favorable functional outcomes in stroke patients.
Conclusion:
The proposed perfDSA framework promises to aid therapeutic decision-making in cerebrovascular interventions and facilitate discoveries of novel quantitative biomarkers in clinical practice. The code is available at https://github.com/RuishengSu/perfDSA .
