{"doi":"10.1002/mrm.29798","title":"A 128‐channel receive array for cortical brain imaging at 7 T","abstract":"PURPOSE: A 128-channel receive-only array for brain imaging at 7 T was simulated, designed, constructed, and tested within a high-performance head gradient designed for high-resolution functional imaging. METHODS: The coil used a tight-fitting helmet geometry populated with 128 loop elements and preamplifiers to fit into a 39 cm diameter space inside a built-in gradient. The signal-to-noise ratio (SNR) and parallel imaging performance (1/g) were measured in vivo and simulated using electromagnetic modeling. The histogram of 1/g factors was analyzed to assess the range of performance. The array's performance was compared to the industry-standard 32-channel receive array and a 64-channel research array. RESULTS: It was possible to construct the 128-channel array with body noise-dominated loops producing an average noise correlation of 5.4%. Measurements showed increased sensitivity compared with the 32-channel and 64-channel array through a combination of higher intrinsic SNR and g-factor improvements. For unaccelerated imaging, the 128-channel array showed SNR gains of 17.6% and 9.3% compared to the 32-channel and 64-channel array, respectively, at the center of the brain and 42% and 18% higher SNR in the peripheral brain regions including the cortex. For R = 5 accelerated imaging, these gains were 44.2% and 24.3% at the brain center and 86.7% and 48.7% in the cortex. The 1/g-factor histograms show both an improved mean and a tighter distribution by increasing the channel count, with both effects becoming more pronounced at higher accelerations. CONCLUSION: The experimental results confirm that increasing the channel count to 128 channels is beneficial for 7T brain imaging, both for increasing SNR in peripheral brain regions and for accelerated imaging.","journal":"Magnetic Resonance in Medicine","year":2023,"id":327367,"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":26,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9363,"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":241410,"name":"Jason Stockmann","orcid":"0000-0001-8454-5347","position":1,"is_corresponding":false},{"id":597570,"name":"Azma Mareyam","orcid":"0000-0002-2893-7386","position":2,"is_corresponding":false},{"id":492626,"name":"Boris Keil","orcid":"0000-0003-0805-8330","position":3,"is_corresponding":false},{"id":263263,"name":"Berkin Bilgic̦","orcid":"0000-0002-9080-7865","position":4,"is_corresponding":false},{"id":890790,"name":"Yulin V. Chang","orcid":"0000-0003-2714-2634","position":5,"is_corresponding":false},{"id":492627,"name":"Ehsan Kazemivalipour","orcid":"0000-0003-4221-2397","position":6,"is_corresponding":false},{"id":1046442,"name":"Alexander Beckett","orcid":"0000-0002-7123-9559","position":7,"is_corresponding":false},{"id":828538,"name":"An T. Vu","orcid":"0000-0003-2463-7789","position":8,"is_corresponding":false},{"id":828539,"name":"David A. Feinberg","orcid":"0009-0002-5016-9165","position":9,"is_corresponding":false},{"id":241418,"name":"Lawrence L. Wald","orcid":"0000-0001-8278-6307","position":10,"is_corresponding":false},{"id":1047506,"name":"Bernhard Gruber","orcid":"0000-0003-4901-5732","position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T01:08:42.846627Z","pmid":"37582214","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":[]}