{"doi":"10.1007/s10334-024-01190-7","title":"Towards assessing and improving the reliability of ultrashort echo time quantitative magnetization transfer (UTE-qMT) MRI of cortical bone: In silico and ex vivo study","abstract":"OBJECTIVE: To assess and improve the reliability of the ultrashort echo time quantitative magnetization transfer (UTE-qMT) modeling of the cortical bone. MATERIALS AND METHODS: Simulation-based digital phantoms were created that mimic the UTE-qMT properties of cortical bones. A wide range of SNR from 25 to 200 was simulated by adding different levels of noise to the synthesized MT-weighted images to assess the effect of SNR on UTE-qMT fitting results. Tensor-based denoising algorithm was applied to improve the fitting results. These results from digital phantom studies were validated via ex vivo rat leg bone scans. RESULTS: The selection of initial points for nonlinear fitting and the number of data points tested for qMT analysis have minimal effect on the fitting result. Magnetization exchange rate measurements are highly dependent on the SNR of raw images, which can be substantially improved with an appropriate denoising algorithm that gives similar fitting results from the raw images with an 8-fold higher SNR. DISCUSSION: The digital phantom approach enables the assessment of the reliability of bone UTE-qMT fitting by providing the known ground truth. These findings can be utilized for optimizing the data acquisition and analysis pipeline for UTE-qMT imaging of cortical bones.","journal":"Magnetic Resonance Materials in Physics Biology and Medicine","year":2024,"id":451650,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":35.18991258606018,"corpus_rank":8206,"citation_count":3,"citer_count":1,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.5808,"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":2.0833,"fair_percentile":1.4062977682665851,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":896077,"name":"Dina Moazamian","orcid":"0000-0002-8815-4535","position":1,"is_corresponding":false},{"id":774967,"name":"Qingbo Tang","orcid":"0000-0002-7995-504X","position":2,"is_corresponding":false},{"id":310555,"name":"Saeed Jerban","orcid":"0000-0001-6450-2892","position":3,"is_corresponding":false},{"id":310553,"name":"Yajun Ma","orcid":"0000-0003-0830-9232","position":4,"is_corresponding":false},{"id":310558,"name":"Jiang Du","orcid":"0000-0002-9203-2450","position":5,"is_corresponding":false},{"id":1166984,"name":"Eric Chang","orcid":"0000-0003-0446-7399","position":6,"is_corresponding":false},{"id":794956,"name":"Soo Hyun Shin","orcid":"0000-0003-1875-2908","position":0,"is_corresponding":true}],"reference_count":54,"raw_metadata":null,"created_at":"2026-07-19T02:02:45.951080Z","pmid":"39126439","pmcid":"PMC11582156","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":11.1111,"fair_a":12.5,"fair_i":0.0,"fair_r":20.8333,"fair_zscore":-1.2811,"fair_rationale":{"fair_score":2.08,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":11.11,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"The data supporting the reported findings and the code used for analyzing the data are available from the corresponding authors upon reasonable request.","grounded":true,"rationale":"No persistent identifier (DOI, Handle, ARK, URN, or repository accession) is given for this study's data.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"The data supporting the reported findings and the code used for analyzing the data are available from the corresponding authors upon reasonable request.","grounded":true,"rationale":"No repository is named; the data are held by the authors.","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"The data supporting the reported findings and the code used for analyzing the data are available from the corresponding authors upon reasonable request.","grounded":true,"rationale":"The statement points to a person (corresponding author), which is Colavizza category 1. 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For neuroimaging data, deposit in OpenNeuro or NeuroVault.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"The data supporting the reported findings and the code used for analyzing the data are available from the corresponding authors upon reasonable request.","why":"No persistent identifier (DOI, Handle, ARK, URN, or repository accession) is given for this study's data.","gain":16.67,"priority":"essential","scored":true},{"key":"f_repository_named","dimension":"F","label":"Named repository","action":"Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. 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'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No licence or reuse terms for the data are named; the article's CC BY licence does not apply to the data.","gain":16.67,"priority":"essential","scored":true},{"key":"f_dataset_cited","dimension":"F","label":"Dataset formally cited","action":"Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"The data supporting the reported findings and the code used for analyzing the data are available from the corresponding authors upon reasonable request.","why":"No identifier or link for the dataset appears in the paper; the only mention is the data availability statement without a resolvable locator.","gain":8.33,"priority":"important","scored":true},{"key":"i_open_nonproprietary_format","dimension":"I","label":"Open file format","action":"Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable. Prefer open neuroimaging formats such as NIfTI or BIDS.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No file format for the released data is named in the text.","gain":8.33,"priority":"important","scored":true},{"key":"x_code_availability","dimension":"R","label":"Analysis code available","action":"Publish the analysis code in a public forge, archive a tagged release with a DOI (Zenodo/Software Heritage), and cite that DOI in the paper. NIH DMS Element 2 asks for the tools and code, not only the data — and 'available on request' is not a locator. Archive the analysis code in a versioned repository (GitHub + a Zenodo release DOI).","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":"The data supporting the reported findings and the code used for analyzing the data are available from the corresponding authors upon reasonable request.","why":"The code is offered through a discretionary request to a person, not a machine-resolvable locator.","gain":8.33,"priority":"important","scored":true},{"key":"r_versioning","dimension":"R","label":"Snapshot identified","action":"Version the deposit and cite the exact version analysed (a version-specific DOI, or an accession with its version suffix). A reader reproducing your work against 'the current release' is reproducing it against a different dataset.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No version token or date is given to pin a snapshot of the data.","gain":4.17,"priority":"useful","scored":true},{"key":"x_funding_attribution","dimension":"R","label":"Funder and award number","action":"State the funder AND the award number in the paper, and put them in the dataset's FundingReference metadata. A funder name alone cannot be linked back to the award, so the funding provenance of the data is lost the moment the paper is indexed.","anchors":["yes","partial","no"],"verdict":"partial","current":0.5,"evidence":"The authors acknowledge grant support from the Veterans Affairs Research and Development Services (Merit Awards I01BX005952, I01CX001388, and I01CX002211); National Institutes of Health (R01AR068987, R01AR079484, RF1AG075717, and K01AR080257); and University of California San Diego (RG114138).","why":"Specific grant numbers are attached to named funders. [downgraded to 'partial' — no verifiable quote from the paper] [majority verdict 'partial' (4/5 passes agreed)]","gain":2.08,"priority":"useful","scored":true},{"key":"f_data_availability_statement","dimension":"F","label":"Data-availability statement","action":"Replace the statement with the repository template: name the repository and give the accession or DOI (Colavizza category 3). 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A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No identifier (accession, DOI, RRID) for any external resource appears in the text.","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No sentence states when the data become available or how long they persist.","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For neuroimaging data, deposit in OpenNeuro or NeuroVault.","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the neuroimaging repository accession (e.g. from OpenNeuro or NeuroVault) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T13:18:05.926540Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}