Spatiotemporal disentanglement of arteriovenous malformations in digital subtraction angiography

Kathleen Baur; Xin Xiong; Erickson Torio; Rose Du; Parikshit Juvekar; Reuben Dorent; Alexandra Golby; Sarah Frisken; Nazim Haouchine

doi:10.1117/12.3006740

2 April 2024 Spatiotemporal disentanglement of arteriovenous malformations in digital subtraction angiography

Kathleen Baur, Xin Xiong, Erickson Torio, Rose Du, Parikshit Juvekar, Reuben Dorent, Alexandra Golby, Sarah Frisken, Nazim Haouchine

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129263B (2024) https://doi.org/10.1117/12.3006740
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Although Digital Subtraction Angiography (DSA) is the most important imaging for visualizing cerebrovascular anatomy, its interpretation by clinicians remains difficult. This is particularly true when treating arteriovenous malformations (AVMs), where entangled vasculature connecting arteries and veins needs to be carefully identified. The presented method aims to enhance DSA image series by highlighting critical information via automatic classification of vessels using a combination of two learning models: An unsupervised machine learning method based on Independent Component Analysis that decomposes the phases of flow and a convolutional neural network that automatically delineates the vessels in image space. The proposed method was tested on clinical DSA images series and demonstrated efficient differentiation between arteries and veins that provides a viable solution to enhance visualizations for clinical use.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kathleen Baur, Xin Xiong, Erickson Torio, Rose Du, Parikshit Juvekar, Reuben Dorent, Alexandra Golby, Sarah Frisken, and Nazim Haouchine "Spatiotemporal disentanglement of arteriovenous malformations in digital subtraction angiography", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129263B (2 April 2024); https://doi.org/10.1117/12.3006740

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KEYWORDS

Image segmentation

Visualization

Education and training

Veins

Arteries

Image enhancement

Angiography

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