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.

翻译：尽管数字减影血管造影（DSA）是可视化脑血管解剖最重要的影像技术，但临床医师对其判读仍存在困难。在治疗动静脉畸形（AVMs）时尤其如此，连接动脉与静脉的血管网络往往相互缠绕，需要仔细识别。本文提出一种方法，通过结合两种学习模型自动分类血管来突显DSA图像序列中的关键信息：基于独立成分分析的无监督机器学习方法用于分解血流时相，以及能够自动勾画图像空间血管结构的卷积神经网络。所提方法在临床DSA图像序列上进行了测试，验证了其能有效区分动静脉，为增强临床可视化提供了可行方案。