Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP).
- DOI
- 10.21037/qims-2025-1563
- Published
- 2026 Jul 1
- Container
- Quantitative imaging in medicine and surgery
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.21037/qims-2025-1563,
title = {Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP).},
author = {Yang R and Sun H and Lin X and Li H and Chen H},
year = {2026},
journal = {Quantitative imaging in medicine and surgery},
doi = {10.21037/qims-2025-1563},
url = {https://doi.org/10.21037/qims-2025-1563}
}RIS
TY - JOUR TI - Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP). AU - Yang R AU - Sun H AU - Lin X AU - Li H AU - Chen H PY - 2026 JO - Quantitative imaging in medicine and surgery DO - 10.21037/qims-2025-1563 UR - https://doi.org/10.21037/qims-2025-1563 ER -
APA
R, Y., H, S., X, L., H, L., & H, C. (2026). Spatial-temporal and physical constrained deep learning model for simultaneous T1 and T2 reconstruction and mapping (STEP).. Quantitative imaging in medicine and surgery. https://doi.org/10.21037/qims-2025-1563
Source records
- pubmed · retrieved 2026-09-25T06:52:23.535Z