@inbook{874f37d66b5b40248b10bcf3becfbf19,
title = "Unsupervised Adversarial Correction of Rigid MR Motion Artifacts",
abstract = "Motion is one of the main sources for artifacts in magnetic resonance (MR) images. It can have significant consequences on the diagnostic quality of the resultant scans. Previously, supervised adversarial approaches have been suggested for the correction of MR motion artifacts. However, these approaches suffer from the limitation of required paired co-registered datasets for training which are often hard or impossible to acquire. Building upon our previous work, we introduce a new adversarial framework with a new generator architecture and loss function for the unsupervised correction of severe rigid motion artifacts in the brain region. Quantitative and qualitative comparisons with other supervised and unsupervised translation approaches showcase the enhanced performance of the introduced framework.",
keywords = "Deep Learning, Generative Adversarial Networks, MR Motion Correction, Unsupervised Learning",
author = "Karim Armanious and Aastha Tanwar and Sherif Abdulatif and Thomas Kustner and Sergios Gatidis and Bin Yang",
year = "2020",
month = apr,
doi = "10.1109/ISBI45749.2020.9098570",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1494--1498",
booktitle = "ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging",
note = "17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 ; Conference date: 03-04-2020 Through 07-04-2020",
}