{"doi":"10.1109/tbme.2025.3576330","title":"Phase Correction of MR Spectroscopic Imaging Data Using Model-Based Signal Estimation and Extrapolation","abstract":"OBJECTIVE: To develop an effective method for phase correction of magnetic resonance spectroscopic imaging (MRSI) data. METHODS: In many MRSI applications, it is desirable to generate absorption-mode spectra, which requires correction of phase errors in the measured MRSI data. Conventional phase correction methods are sensitive to measurement noise and baseline distortion, often resulting in distorted absorption-mode spectra from MRSI data with low-SNR and long acquisition dead time. This paper proposed a novel model-based method for improved phase correction of MRSI data. The proposed method determined the zeroth-order phase and acquisition dead time using a Lorentzian-based spectral model and performed signal extrapolation using a generalized series model. Absorption-mode spectra were then generated from the phase-corrected and extrapolated MRSI data. RESULTS: H) MRSI experiments. Simulation results demonstrated improved parameter estimation accuracy by the proposed method under various noise levels and dead times. The proposed method also consistently generated high-quality absorption-mode spectra with minimal spectral distortions from experimental data. The proposed method was compared with state-of-the-art methods (including the entropy method and LCModel method) and showed more robust phase correction performance with less spectral distortions. CONCLUSION: This paper introduced a novel method for phase correction of MRSI data. Results from simulated and in vivo data demonstrated that high-quality absorption-mode spectra could be obtained using the proposed method. SIGNIFICANCE: This method will provide a useful tool for processing MRSI data.","journal":"IEEE Transactions on Biomedical Engineering","year":2025,"id":553044,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9576,"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":516382,"name":"Rong Guo","orcid":"0000-0003-0405-3268","position":1,"is_corresponding":false},{"id":379448,"name":"Yudu Li","orcid":"0000-0003-2061-2306","position":2,"is_corresponding":false},{"id":516381,"name":"Yibo Zhao","orcid":"0000-0002-0848-7808","position":3,"is_corresponding":false},{"id":366265,"name":"Xin Li","orcid":"0000-0001-6999-0610","position":4,"is_corresponding":false},{"id":532051,"name":"Xiao‐Hong Zhu","orcid":"0009-0008-5926-1869","position":5,"is_corresponding":false},{"id":384839,"name":"Wei Chen","orcid":"0000-0002-9608-1444","position":6,"is_corresponding":false},{"id":379450,"name":"Zhi‐Pei Liang","orcid":"0000-0003-4586-3056","position":7,"is_corresponding":false},{"id":1406239,"name":"Wen Jin","orcid":"0000-0002-0625-8876","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:54:37.527189Z","pmid":"40465452","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":[]}