Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography
<|reference_start|>Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography: 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.<|reference_end|>
@article{baur2024spatiotemporal,
    title={Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital
  Subtraction Angiography},
    author={Kathleen Baur, Xin Xiong, Erickson Torio, Rose Du, Parikshit Juvekar,
  Reuben Dorent, Alexandra Golby, Sarah Frisken, Nazim Haouchine},
    journal={arXiv preprint arXiv:2402.09636},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09636},
    primaryClass={eess.IV cs.CV}
}