{"doi":"10.1101/2023.12.28.23300409","title":"Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired data","abstract":"Introduction: Portable low-field strength (64mT) MRI scanners promise to increase access to neuroimaging for clinical and research purposes, however these devices produce lower quality images compared to high-field scanners. In this study, we developed and evaluated a deep learning architecture to generate high-field quality brain images from low-field inputs using a paired dataset of multiple sclerosis (MS) patients scanned at 64mT and 3T. Methods: A total of 49 MS patients were scanned on portable 64mT and standard 3T scanners at Penn (n=25) or the National Institutes of Health (NIH, n=24) with T1-weighted, T2-weighted and FLAIR acquisitions. Using this paired data, we developed a generative adversarial network (GAN) architecture for low- to high-field image translation (LowGAN). We then evaluated synthesized images with respect to image quality, brain morphometry, and white matter lesions. Results: Synthetic high-field images demonstrated visually superior quality compared to low-field inputs and significantly higher normalized cross-correlation (NCC) to actual high-field images for T1 (p=0.001) and FLAIR (p<0.001) contrasts. LowGAN generally outperformed the current state-of-the-art for low-field volumetrics. For example, thalamic, lateral ventricle, and total cortical volumes in LowGAN outputs did not differ significantly from 3T measurements. Synthetic outputs preserved MS lesions and captured a known inverse relationship between total lesion volume and thalamic volume. Conclusions: LowGAN generates synthetic high-field images with comparable visual and quantitative quality to actual high-field scans. Enhancing portable MRI image quality could add value and boost clinician confidence, enabling wider adoption of this technology.","journal":"medRxiv","year":2023,"id":389961,"datarank":0.42498200160843247,"base_score":2.833213344056216,"endowment":2.833213344056216,"self_citation_contribution":0.42498200160843247,"citation_network_contribution":0.0,"self_endowment_contribution":0.42498200160843247,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9446,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":585419,"name":"Thomas Arnold","orcid":"0000-0001-8226-8497","position":1,"is_corresponding":false},{"id":845983,"name":"Serhat V. Okar","orcid":"0000-0003-3716-2196","position":2,"is_corresponding":false},{"id":1161188,"name":"Chetan Vadali","orcid":"0009-0008-7085-1771","position":3,"is_corresponding":false},{"id":861032,"name":"Karan D. Kawatra","orcid":null,"position":4,"is_corresponding":false},{"id":1161189,"name":"Zheng Ren","orcid":"0009-0009-1779-2327","position":5,"is_corresponding":false},{"id":622982,"name":"Quy Cao","orcid":"0000-0002-6204-1305","position":6,"is_corresponding":false},{"id":52271,"name":"Russell T. Shinohara","orcid":"0000-0001-8627-8203","position":7,"is_corresponding":false},{"id":484419,"name":"Matthew K. Schindler","orcid":"0000-0003-0157-5669","position":8,"is_corresponding":false},{"id":1161190,"name":"Kathryn A. Davis","orcid":"0000-0001-5830-6676","position":9,"is_corresponding":false},{"id":392434,"name":"Brian Litt","orcid":"0000-0003-2732-6927","position":10,"is_corresponding":false},{"id":230800,"name":"Daniel S. Reich","orcid":"0000-0002-2628-4334","position":11,"is_corresponding":false},{"id":240420,"name":"Joel M. Stein","orcid":"0000-0002-0741-1780","position":12,"is_corresponding":false},{"id":324340,"name":"Alfredo Lucas","orcid":"0000-0001-9439-735X","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:18:32.854511Z","pmid":"38234785","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":[]}