{"doi":"10.1101/2022.03.08.483513","title":"Impact of Autocalibration Method on Accelerated Echo-Planar Imaging of the Cervical Spinal Cord at 7T","abstract":"Abstract Purpose The spinal cord contains sensorimotor neural circuits of scientific and clinical interest. However, spinal cord fMRI is significantly more technically demanding than brain fMRI due primarily to its proximity to the lungs. Accelerated EPI at 7T is particularly vulnerable to k-space phase inconsistencies induced by motion or B 0 fluctuation, during either autocalibration signal (ACS) or timeseries acquisition. For 7T brain fMRI, sensitivity to motion and B 0 fluctuation can be reduced using a re-ordered segmented EPI ACS based on the fast low-angle excitation echo-planar technique (FLEET). However, respiration-induced B 0 fluctuations (exceeding 100Hz at C7) are greater, and fewer k-space lines per slice are required, for cervical spinal cord fMRI at 7T, necessitating a separate evaluation of ACS methods. Methods We compared 24-line single-shot EPI, and 48-line two-shot segmented EPI, two-shot FLEET, and GRE-based ACS acquisition methods, performed under various physiological conditions, in terms of temporal SNR (tSNR) and prevalence of artifacts in GRAPPA-accelerated EPI of the cervical spinal cord at 7T. Results Segmented EPI and FLEET ACS produce images with nearly identical patterns of severe image artifacts. GRE and single-shot EPI ACS consistently produce images free from significant artifacts, and tSNR is significantly greater for GRE ACS, particularly in lower slices where through-slice dephasing is most severe. Conclusion GRE and single-shot EPI ACS acquisition methods, which are robust to respiration-induced phase errors between k-space segments, produce images with fewer and less severe artifacts than either FLEET or conventionally segmented EPI for accelerated EPI of the cervical spinal cord at 7T.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":306442,"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":0.9696,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":12946,"name":"Junqian Xu","orcid":"0000-0001-8438-2066","position":1,"is_corresponding":false},{"id":628026,"name":"Alan C. Seifert","orcid":"0000-0001-7877-4813","position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T00:32:48.984902Z","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":[]}