{"doi":"10.1002/mrm.30338","title":"<scp>MR</scp> electrical properties mapping using vision transformers and canny edge detectors","abstract":"PURPOSE: We developed a 3D vision transformer-based neural network to reconstruct electrical properties (EP) from magnetic resonance measurements. THEORY AND METHODS: Our network uses the magnitude of the transmit magnetic field of a birdcage coil, the associated transceive phase, and a Canny edge mask that identifies the object boundaries as inputs to compute the EP maps. We trained our network on a dataset of 10 000 synthetic tissue-mimicking phantoms and fine-tuned it on a dataset of 11 000 realistic head models. We assessed performance in-distribution simulated data and out-of-distribution head models, with and without synthetic lesions. We further evaluated our network in experiments for an inhomogeneous phantom and a volunteer. RESULTS: The conductivity and permittivity maps had an average peak normalized absolute error (PNAE) of 1.3% and 1.7% for the synthetic phantoms, respectively. For the realistic heads, the average PNAE for the conductivity and permittivity was 1.8% and 2.7%, respectively. The location of synthetic lesions was accurately identified, with reconstructed conductivity and permittivity values within 15% and 25% of the ground-truth, respectively. The conductivity and permittivity for the phantom experiment yielded 2.7% and 2.1% average PNAEs with respect to probe-measured values, respectively. The in vivo EP reconstruction truthfully preserved the subject's anatomy with average values over the entire head similar to the expected literature values. CONCLUSION: We introduced a new learning-based approach for reconstructing EP from MR measurements obtained with a birdcage coil, marking an important step towards the development of clinically-usable in vivo EP reconstruction protocols.","journal":"Magnetic Resonance in Medicine","year":2024,"id":443474,"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":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9259,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"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":757421,"name":"Giuseppe Carluccio","orcid":"0000-0001-5376-3843","position":1,"is_corresponding":false},{"id":381491,"name":"Mahesh Keerthivasan","orcid":"0000-0002-7841-9333","position":2,"is_corresponding":false},{"id":1257631,"name":"Gregor Koerzdoerfer","orcid":"0000-0002-8881-9517","position":3,"is_corresponding":false},{"id":489168,"name":"Karthik Lakshmanan","orcid":"0000-0003-0187-495X","position":4,"is_corresponding":false},{"id":936033,"name":"Hector Lise de Moura","orcid":"0000-0002-6620-9814","position":5,"is_corresponding":false},{"id":580450,"name":"José E. Cruz Serrallés","orcid":"0000-0002-3323-5688","position":6,"is_corresponding":false},{"id":439963,"name":"Riccardo Lattanzi","orcid":"0000-0002-8240-5903","position":7,"is_corresponding":false},{"id":580449,"name":"Ilias I. Giannakopoulos","orcid":"0000-0003-2180-5898","position":0,"is_corresponding":true}],"reference_count":74,"raw_metadata":null,"created_at":"2026-07-19T02:01:24.471942Z","pmid":"39415436","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":[]}