{"doi":"10.1002/mrm.29817","title":"A user independent denoising method for x‐nuclei <scp>MRI</scp> and <scp>MRS</scp>","abstract":"PURPOSE: X-nuclei (also called non-proton MRI) MRI and spectroscopy are limited by the intrinsic low SNR as compared to conventional proton imaging. Clinical translation of x-nuclei examination warrants the need of a robust and versatile tool improving image quality for diagnostic use. In this work, we compare a novel denoising method with fewer inputs to the current state-of-the-art denoising method. METHODS: C brain scans, with and without additional noise. The current state-of-the-art denoising method Global-local higher order singular value decomposition (GL-HOSVD) was compared to the few-input method tensor Marchenko-Pastur principal component analysis (tMPPCA). Noise-removal was quantified by residual distributions, and statistical analyses evaluated the differences in mean-square-error and Bland-Altman analysis to quantify agreement between original and denoised results of noise-added data. RESULTS: GL-HOSVD and tMPPCA showed similar performance for the variety of x-nuclei data analyzed in this work, with tMPPCA removing ˜5% more noise on average over GL-HOSVD. The mean ratio between noise-added and denoising reproducibility coefficients of the Bland-Altman analysis when compared to the original are also similar for the two methods with 3.09 ± 1.03 and 2.83 ± 0.79 for GL-HOSVD and tMPPCA, respectively. CONCLUSION: The strength of tMPPCA lies in the few-input approach, which generalizes well to different data sources. This makes the use of tMPPCA denoising a robust and versatile tool in x-nuclei imaging improvements and the preferred denoising method.","journal":"Magnetic Resonance in Medicine","year":2023,"id":327726,"datarank":0.7279022513962379,"base_score":3.258096538021482,"endowment":3.258096538021482,"self_citation_contribution":0.4887144807032224,"citation_network_contribution":0.23918777069301544,"self_endowment_contribution":0.4887144807032224,"citer_contribution":0.23918777069301544,"corpus_percentile":null,"corpus_rank":null,"citation_count":25,"citer_count":11,"citers_with_citation_signal":9,"citers_with_endowment":9,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9427,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT05215938"]},"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":1048451,"name":"Michael Væggemose","orcid":"0000-0001-7666-6819","position":1,"is_corresponding":false},{"id":931690,"name":"Nikolaj Bøgh","orcid":"0000-0002-0321-3269","position":2,"is_corresponding":false},{"id":931691,"name":"Esben Søvsø Szocska Hansen","orcid":"0000-0001-5512-9870","position":3,"is_corresponding":false},{"id":856125,"name":"Jonas Lynge Olesen","orcid":"0000-0003-4624-9816","position":4,"is_corresponding":false},{"id":495583,"name":"Yaewon Kim","orcid":"0000-0003-1016-1572","position":5,"is_corresponding":false},{"id":345507,"name":"Daniel B. Vigneron","orcid":"0000-0001-5795-8699","position":6,"is_corresponding":false},{"id":345502,"name":"Jeremy W. Gordon","orcid":"0000-0003-2760-4886","position":7,"is_corresponding":false},{"id":304582,"name":"Sune Nørhøj Jespersen","orcid":"0000-0003-3146-4329","position":8,"is_corresponding":false},{"id":339867,"name":"Christoffer Laustsen","orcid":"0000-0002-0317-2911","position":9,"is_corresponding":false},{"id":1048450,"name":"Nichlas Vous Christensen","orcid":"0000-0001-7390-6071","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T01:08:47.581262Z","pmid":"37526128","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":[]}