@inproceedings{6b3382fedca345498d21460c3344a24f,
title = "Deep image reconstruction using unregistered measurements without groundtruth",
abstract = "One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images. This paper addresses this limitation by proposing a novel unsupervised deep registration-augmented reconstruction method (U-Dream) for training deep neural nets to reconstruct high-quality images by directly mapping pairs of unregistered and artifact-corrupted images. The ability of U-Dream to circumvent the need for accurately registered data makes it widely applicable to many biomedical image reconstruction tasks. We validate it in accelerated magnetic resonance imaging (MRI) by training an image reconstruction model directly on pairs of undersampled measurements from images that have undergone nonrigid deformations. ",
keywords = "Deep learning, Deformable image registration, Image reconstruction, Magnetic resonance imaging",
author = "Weijie Gan and Yu Sun and Cihat Eldeniz and Jiaming Liu and Hongyu An and Kamilov, {Ulugbek S.}",
note = "Funding Information: Research reported in this publication was supported by the Washington University Institute of Clinical and Translational Sciences grant UL1TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH. Publisher Copyright: {\textcopyright} 2021 IEEE.; null ; Conference date: 13-04-2021 Through 16-04-2021",
year = "2021",
month = apr,
day = "13",
doi = "10.1109/ISBI48211.2021.9434079",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1531--1534",
booktitle = "2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021",
}