{"doi":"10.1002/mrm.29573","title":"Reconstruction for <scp>7T</scp> high‐resolution whole‐brain diffusion <scp>MRI</scp> using two‐stage N/2 ghost correction and <scp>L1‐SPIRiT</scp> without single‐band reference","abstract":"PURPOSE: To combine a new two-stage N/2 ghost correction and an adapted L1-SPIRiT method for reconstruction of 7T highly accelerated whole-brain diffusion MRI (dMRI) using only autocalibration scans (ACS) without the need of additional single-band reference (SBref) scans. METHODS: The proposed ghost correction consisted of a 3-line reference approach in stage 1 and the reference-free entropy method in stage 2. The adapted L1-SPIRiT method was formulated within the 3D k-space framework. Its efficacy was examined by acquiring two dMRI data sets at 1.05-mm isotropic resolutions with a total acceleration of 6 or 9 (i.e., 2-fold or 3-fold slice and 3-fold in-plane acceleration). Diffusion analysis was performed to derive DTI metrics and estimate fiber orientation distribution functions (fODFs). The results were compared with those of 3D k-space GRAPPA using only ACS, all in reference to 3D k-space GRAPPA using both ACS and SBref (serving as a reference). RESULTS: The proposed ghost correction eliminated artifacts more robustly than conventional approaches. Our adapted L1-SPIRiT method outperformed 3D k-space GRAPPA when using only ACS, improving image quality to what was achievable with 3D k-space GRAPPA using both ACS and SBref scans. The improvement in image quality further resulted in an improvement in estimation performances for DTI and fODFs. CONCLUSION: The combination of our new ghost correction and adapted L1-SPIRiT method can reliably reconstruct 7T highly accelerated whole-brain dMRI without the need of SBref scans, increasing acquisition efficiency and reducing motion sensitivity.","journal":"Magnetic Resonance in Medicine","year":2023,"id":363350,"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":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.938,"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":459246,"name":"Xiaodong Ma","orcid":"0000-0001-7158-8073","position":1,"is_corresponding":false},{"id":760959,"name":"Erpeng Dai","orcid":"0000-0002-1032-5883","position":2,"is_corresponding":false},{"id":320266,"name":"Edward J. Auerbach","orcid":"0000-0003-4553-1545","position":3,"is_corresponding":false},{"id":236958,"name":"Hua Guo","orcid":"0000-0002-0482-1493","position":4,"is_corresponding":false},{"id":256599,"name":"Kâmil Uǧurbil","orcid":"0000-0002-8475-9334","position":5,"is_corresponding":false},{"id":256597,"name":"Xiaoping Wu","orcid":"0000-0001-6021-9088","position":6,"is_corresponding":false},{"id":1117385,"name":"Ziyi Pan","orcid":"0000-0002-7138-238X","position":0,"is_corresponding":true}],"reference_count":57,"raw_metadata":null,"created_at":"2026-07-19T01:14:28.054440Z","pmid":"36594439","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":[]}