{"doi":"10.1002/nbm.70182","title":"Gradient Scheme Optimization for PRESS‐Localized Edited MRS Using Weighted Pathway Suppression","abstract":"This study aimed to design and implement an optimized gradient scheme for PRESS-localized edited magnetic resonance spectroscopy (MRS) to enhance suppression of out-of-voxel (OOV) artifacts. These artifacts, which originate from insufficient crushing of unwanted coherence transfer pathways (CTPs), are particularly challenging in editing schemes for metabolites like gamma-aminobutyric acid and glutathione. To address this, a volume-based likelihood model was developed to guide gradient scheme optimization, prioritizing suppression of CTPs based on likelihood. The volume-based likelihood model for CTP weighting was integrated into a Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient optimization. Using a genetic algorithm with a weighted dual-penalty cost function, gradient schemes were optimized to maximize pathway-specific suppression. Hardware and sequence constraints, maximum gradient amplitudes and delay durations respectively, informed the optimization. Validation of the optimized scheme was performed with simulations by calculating the k-space crushing efficiency analytically with k-space trajectory and in vivo using an edited MRS sequence in three brain regions (posterior cingulate cortex PCC, thalamus, and medial prefrontal cortex [mPFC]), with particular focus on OOV artifact reduction and spectral quality improvements. A three-way Analysis of Variance was used to assess the significance level of OOV artifact reduction. The optimized gradient scheme demonstrated improved k-space crushing efficiency (by an average of 197%). OOV artifacts were reduced in all brain regions, particularly in highly OOV-susceptible regions (thalamus and mPFC). Improvements were most notable around 4.3 ppm with significant OOV artifact amplitude reductions (p < 0.001). By using a volume-based likelihood model for CTP prioritization, the optimized DOTCOPS scheme ensures robust and region-agnostic performance in reducing OOV artifacts.","journal":"NMR in Biomedicine","year":2025,"id":537213,"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":0.9562,"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":1423017,"name":"Zahra Shams","orcid":"0000-0002-0311-0665","position":1,"is_corresponding":false},{"id":699065,"name":"Saipavitra Murali‐Manohar","orcid":"0000-0002-4978-0736","position":2,"is_corresponding":false},{"id":691233,"name":"Dunja Simičić","orcid":"0000-0002-6600-2696","position":3,"is_corresponding":false},{"id":1423018,"name":"Abdelrahman Gamil Gad","orcid":"0000-0002-4461-3517","position":4,"is_corresponding":false},{"id":417910,"name":"Yulu Song","orcid":"0000-0002-4416-7959","position":5,"is_corresponding":false},{"id":514852,"name":"Vivek Yedavalli","orcid":"0000-0002-2450-4014","position":6,"is_corresponding":false},{"id":664556,"name":"Christopher W. Davies‐Jenkins","orcid":"0000-0002-6015-762X","position":7,"is_corresponding":false},{"id":870230,"name":"Aaron T. Gudmundson","orcid":"0000-0001-5104-0959","position":8,"is_corresponding":false},{"id":238011,"name":"Helge J. Zöllner","orcid":"0000-0002-7148-292X","position":9,"is_corresponding":false},{"id":238010,"name":"Georg Oeltzschner","orcid":"0000-0003-3083-9811","position":10,"is_corresponding":false},{"id":238015,"name":"Richard A.E. Edden","orcid":"0000-0002-0671-7374","position":11,"is_corresponding":false},{"id":531896,"name":"Gizeaddis Lamesgin Simegn","orcid":"0000-0003-1333-4555","position":0,"is_corresponding":true}],"reference_count":29,"raw_metadata":null,"created_at":"2026-07-19T02:52:12.997494Z","pmid":"41261502","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":[]}