1. Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography
- Author
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Baur, Kathleen, Xiong, Xin, Torio, Erickson, Du, Rose, Juvekar, Parikshit, Dorent, Reuben, Golby, Alexandra, Frisken, Sarah, and Haouchine, Nazim
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Computer Vision and Pattern Recognition - 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., Comment: Paper accepted for publication at SPIE Medical Imaging 2024
- Published
- 2024