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
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.