{"doi":"10.1002/mrm.28788","title":"A self‐decoupled 32‐channel receive array for human‐brain MRI at 10.5 T","abstract":"PURPOSE: field strength are crucial contributors to SNR and parallel-imaging performance. Here, we investigate SNR and parallel-imaging gains at 10.5 T compared with 7 T using 32-channel receive arrays at both fields. METHODS: A self-decoupled 32-channel receive array for human brain imaging at 10.5 T (10.5T-32Rx), consisting of 31 loops and one cloverleaf element, was co-designed and built in tandem with a 16-channel dual-row loop transmitter. Novel receive array design and self-decoupling techniques were implemented. Parallel imaging performance, in terms of SNR and noise amplification (g-factor), of the 10.5T-32Rx was compared with the performance of an industry-standard 32-channel receiver at 7 T (7T-32Rx) through experimental phantom measurements. RESULTS: Compared with the 7T-32Rx, the 10.5T-32Rx provided 1.46 times the central SNR and 2.08 times the peripheral SNR. Minimum inverse g-factor value of the 10.5T-32Rx (min[1/g] = 0.56) was 51% higher than that of the 7T-32Rx (min[1/g] = 0.37) with R = 4 × 4 2D acceleration, resulting in significantly enhanced parallel-imaging performance at 10.5 T compared with 7 T. The g-factor values of 10.5 T-32 Rx were on par with those of a 64-channel receiver at 7 T (eg, 1.8 vs 1.9, respectively, with R = 4 × 4 axial acceleration). CONCLUSION: Experimental measurements demonstrated effective self-decoupling of the receive array as well as substantial gains in SNR and parallel-imaging performance at 10.5 T compared with 7 T.","journal":"Magnetic Resonance in Medicine","year":2021,"id":186560,"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":20,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9443,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":745413,"name":"Russell L. Lagore","orcid":null,"position":1,"is_corresponding":false},{"id":393369,"name":"Steve Jungst","orcid":"0000-0001-8374-7924","position":2,"is_corresponding":false},{"id":744752,"name":"Shajan Gunamony","orcid":"0000-0002-3146-6079","position":3,"is_corresponding":false},{"id":507670,"name":"Jerahmie Radder","orcid":null,"position":4,"is_corresponding":false},{"id":399489,"name":"Andrea Grant","orcid":"0000-0002-1553-596X","position":5,"is_corresponding":false},{"id":256594,"name":"Steen Moeller","orcid":"0000-0003-1698-7260","position":6,"is_corresponding":false},{"id":320266,"name":"Edward J. Auerbach","orcid":"0000-0003-4553-1545","position":7,"is_corresponding":false},{"id":159768,"name":"Kamil Ugurbil","orcid":null,"position":8,"is_corresponding":false},{"id":320269,"name":"Gregor Adriany","orcid":"0000-0002-6428-9005","position":9,"is_corresponding":false},{"id":393370,"name":"Pierre‐François Van de Moortele","orcid":"0000-0002-6941-5947","position":10,"is_corresponding":false},{"id":744751,"name":"Nader Tavaf","orcid":"0000-0002-3002-3794","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":null,"created_at":"2026-07-18T23:48:46.850806Z","pmid":"33780032","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":[]}