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The UK\u00a0Biobank is acquiring whole-body Dixon\u00a0MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon\u00a0MRI acquisitions in the UK\u00a0Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK\u00a0Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon\u00a0MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK\u00a0Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon\u00a0MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.<\/jats:p>","DOI":"10.1186\/s40537-022-00677-1","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T11:11:21Z","timestamp":1673521881000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Artifact-free fat-water separation in Dixon MRI using deep learning"],"prefix":"10.1186","volume":"10","author":[{"given":"Nicolas","family":"Basty","sequence":"first","affiliation":[]},{"given":"Marjola","family":"Thanaj","sequence":"additional","affiliation":[]},{"given":"Madeleine","family":"Cule","sequence":"additional","affiliation":[]},{"given":"Elena P.","family":"Sorokin","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"E. 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YL is a former employee of Calico Life Sciences LLC. BW, MT, NB, JDB and ELT declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"4"}}