{"doi":"10.58530/2023/0423","title":"Semi-Supervision for Clinical Contrast-Weighted Image Synthesis from Magnetic Resonance Fingerprinting","abstract":"Previous works have introduced deep models to synthesize clinical contrast-weighted images from magnetic resonance fingerprinting (MRF). Although these models achieve high synthesis accuracy, they demand full-supervision from fully-sampled training data of clinical contrasts which might become difficult to acquire across diverse sets due to scan costs. To eliminate undesirable reliance on full-supervision, we introduce a semi-supervised model, ssMRF, that allows training using accelerated references. ssMRF introduces a semi-supervised loss function based only on collected k-space samples of clinical contrasts, and further leverages complementary Poisson disc masks, via a multi-task learning protocol to synergistically synthesize multiple contrasts.","journal":"Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition","year":2024,"id":502075,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9416,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1252928,"name":"Cagan Alkan","orcid":"0000-0002-0241-0631","position":1,"is_corresponding":false},{"id":1046284,"name":"Sophie Schauman","orcid":"0000-0002-3744-2553","position":2,"is_corresponding":false},{"id":656688,"name":"Xiaozhi Cao","orcid":"0000-0001-5095-648X","position":3,"is_corresponding":false},{"id":263265,"name":"Congyu Liao","orcid":"0000-0003-2270-276X","position":4,"is_corresponding":false},{"id":344034,"name":"Siddharth Iyer","orcid":"0000-0002-6432-0850","position":5,"is_corresponding":false},{"id":1044394,"name":"Tolga Çukur","orcid":"0000-0002-2296-851X","position":6,"is_corresponding":false},{"id":301561,"name":"Shreyas Vasanawala","orcid":"0000-0002-1999-6595","position":7,"is_corresponding":false},{"id":92433,"name":"John M. Pauly","orcid":"0000-0001-5918-4172","position":8,"is_corresponding":false},{"id":263269,"name":"Kawin Setsompop","orcid":"0000-0003-0455-7634","position":9,"is_corresponding":false},{"id":1046283,"name":"Mahmut Yurt","orcid":"0000-0003-3280-4217","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:10:19.647285Z","pmid":null,"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":[]}