{"doi":"10.1002/mrm.28546","title":"Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training","abstract":"Purpose To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. Methods The current ‐IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization. However, this approach requires supervised training of DNN using the reference water/fat separation images. Here we propose 2 novel DNN water/fat separation methods: 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no training of DNN using physical cost and backpropagation to directly reconstruct a single dataset. The supervised training of DNN, unsupervised training of DNN, and no training of DNN methods were compared with the reference ‐IDEAL. Results All DNN methods generated consistent water/fat separation results that agreed well with ‐IDEAL under proper initialization. Conclusion The water/fat separation problem can be solved using unsupervised deep neural networks.","journal":"Magnetic Resonance in Medicine","year":2020,"id":62807,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":49,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9502,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":314690,"name":"Pascal Spincemaille","orcid":"0000-0002-8821-1341","position":1,"is_corresponding":false},{"id":332008,"name":"Jinwei Zhang","orcid":"0000-0002-9116-358X","position":2,"is_corresponding":false},{"id":314691,"name":"Thanh D. Nguyen","orcid":"0000-0002-1411-7694","position":3,"is_corresponding":false},{"id":332009,"name":"Xianfu Luo","orcid":"0000-0001-6506-1267","position":4,"is_corresponding":false},{"id":315431,"name":"Junghun Cho","orcid":"0000-0002-0826-5463","position":5,"is_corresponding":false},{"id":332010,"name":"Daniel Margolis","orcid":"0000-0001-8212-707X","position":6,"is_corresponding":false},{"id":332011,"name":"Martin R. Prince","orcid":"0000-0002-9883-0584","position":7,"is_corresponding":false},{"id":314692,"name":"Yi Wang","orcid":"0000-0003-1404-8526","position":8,"is_corresponding":false},{"id":332007,"name":"Ramin Jafari","orcid":"0000-0002-1853-6677","position":0,"is_corresponding":true}],"reference_count":28,"raw_metadata":null,"created_at":"2026-07-18T21:10:35.996198Z","pmid":"33107127","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}