{"doi":"10.1002/mrm.29985","title":"Hyperpolarized <scp><sup>13</sup>C</scp> metabolic imaging of the human abdomen with spatiotemporal denoising","abstract":"Abstract Purpose Improving the quality and maintaining the fidelity of large coverage abdominal hyperpolarized (HP) 13 C MRI studies with a patch based global–local higher‐order singular value decomposition (GL‐HOVSD) spatiotemporal denoising approach. Methods Denoising performance was first evaluated using the simulated [1‐ 13 C]pyruvate dynamics at different noise levels to determine optimal k global and k local parameters. The GL‐HOSVD spatiotemporal denoising method with the optimized parameters was then applied to two HP [1‐ 13 C]pyruvate EPI abdominal human cohorts ( n = 7 healthy volunteers and n = 8 pancreatic cancer patients). Results The parameterization of k global = 0.2 and k local = 0.9 denoises abdominal HP data while retaining image fidelity when evaluated by RMSE. The k PX (conversion rate of pyruvate‐to‐metabolite, X = lactate or alanine) difference was shown to be &lt;20% with respect to ground‐truth metabolic conversion rates when there is adequate SNR (SNR AUC &gt; 5) for downstream metabolites. In both human cohorts, there was a greater than nine‐fold gain in peak [1‐ 13 C]pyruvate, [1‐ 13 C]lactate, and [1‐ 13 C]alanine apparent SNR AUC . The improvement in metabolite SNR enabled a more robust quantification of k PL and k PA . After denoising, we observed a 2.1 ± 0.4 and 4.8 ± 2.5‐fold increase in the number of voxels reliably fit across abdominal FOVs for k PL and k PA quantification maps. Conclusion Spatiotemporal denoising greatly improves visualization of low SNR metabolites particularly [1‐ 13 C]alanine and quantification of [1‐ 13 C]pyruvate metabolism in large FOV HP 13 C MRI studies of the human abdomen.","journal":"Magnetic Resonance in Medicine","year":2024,"id":464571,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9574,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":495583,"name":"Yaewon Kim","orcid":"0000-0003-1016-1572","position":1,"is_corresponding":false},{"id":755983,"name":"Philip Lee","orcid":"0000-0002-5783-1599","position":2,"is_corresponding":false},{"id":460831,"name":"Hsin‐Yu Chen","orcid":"0000-0002-2765-1685","position":3,"is_corresponding":false},{"id":535118,"name":"Michael A. Ohliger","orcid":"0000-0001-6878-8189","position":4,"is_corresponding":false},{"id":345505,"name":"Robert Bok","orcid":"0000-0003-1737-9056","position":5,"is_corresponding":false},{"id":264063,"name":"Zhen J. Wang","orcid":"0000-0002-2065-5296","position":6,"is_corresponding":false},{"id":313995,"name":"Peder E. Z. Larson","orcid":"0000-0003-4183-3634","position":7,"is_corresponding":false},{"id":345507,"name":"Daniel B. Vigneron","orcid":"0000-0001-5795-8699","position":8,"is_corresponding":false},{"id":345502,"name":"Jeremy W. Gordon","orcid":"0000-0003-2760-4886","position":9,"is_corresponding":false},{"id":884983,"name":"Tanner Nickles","orcid":"0000-0003-4322-2431","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:04:42.082401Z","pmid":"38193310","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":[]}