AngioMoCo: Learning-Based Motion Correction in Cerebral Digital Subtraction Angiography

Abstract

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

Untitled Document

“…The motion alignment algorithms [9], [10] detected differences between live and mask images firstly. Subsequently, the mask images were distorted on the basis of the corresponding live images.…”

“…There currently exist two main research directions in the DSA image generation task. The first step is to register the mask images and live images with the motion alignment [9], [10]. The second-fold is named as virtual DSA, with the aim of generating DSA images from the live images without mask images by using Generative Adversarial Nets(GANs).…”