{"doi":"10.1155/cmr/8276955","title":"Comparison of Coil Combination Technique Performance for Phase Preservation in bSSFP","abstract":"MR images are often acquired using a phased array of radiofrequency (RF) channels, with each RF channel being sensitive to only part of the object being scanned. The images collected from each RF channel are combined to generate a single image with higher SNR and more uniform sensitivity than can be obtained with a single channel alone. It is generally desirable to combine the images before performing any analysis in a quantitative imaging (QI) experiment—this way, the voxel‐level signals input into the fitting model have high SNR. Computationally, it is often also more efficient than performing a quantitative fitting process on each channel image individually. Although fitting is typically performed on a voxel‐level signal magnitude, certain pulse sequences like phase‐cycled balanced steady‐state free precession (pc‐bSSFP) encode important information about tissue properties in the signal phase as well. Therefore, it is desirable to preserve the complex signal during the coil combination process in order for QI analyses to be reliable. While a variety of different coil combination techniques exist, there is little information on which ones best preserve phase for pc‐bSSFP. Pc‐bSSFP is of particular interest as the complex‐valued images are used for relaxometry. This study compared the phase preservation performance of various coil combination techniques: Eigenvalue‐based approach for iterative self‐consistent parallel imaging reconstruction (ESPIRiT), simple phase robust coil combination (SRCC), full phase robust coil combination (FRCC), adaptive reconstruction (AR), and intrinsic multi‐channel phase alignment (IMPA). These techniques were tested on pc‐bSSFP data in both a simulated phantom and in vivo knee cartilage. The comparisons were conducted across a range of SNR levels to reflect realistic scenarios. Results showed that ESPIRiT, AR, and IMPA best preserved phase across the range of SNR levels tested.","journal":"Concepts in Magnetic Resonance Part A","year":2025,"id":584113,"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":0.9522,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1496993,"name":"Zimu Huo","orcid":"0009-0008-3426-326X","position":1,"is_corresponding":false},{"id":1496994,"name":"Michael Mendoza","orcid":"0009-0009-3426-8834","position":2,"is_corresponding":false},{"id":1225046,"name":"Michael N. Hoff","orcid":"0000-0002-3498-0591","position":3,"is_corresponding":false},{"id":1496995,"name":"Anil A. Bharath","orcid":"0000-0001-8808-2714","position":4,"is_corresponding":false},{"id":526715,"name":"Peter Lally","orcid":"0000-0003-0075-0103","position":5,"is_corresponding":false},{"id":12943,"name":"Neal K. Bangerter","orcid":"0000-0001-6444-1882","position":6,"is_corresponding":false},{"id":1496992,"name":"K. Balaji","orcid":"0009-0004-4575-767X","position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-19T02:59:11.978098Z","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":[]}