Evidence map›Paper›PMID 40968299›Full record

ArticleSkeletal radiology2026

Rapid and robust quantitative cartilage assessment for the clinical setting: deep learning-enhanced accelerated T2 mapping.

Laura Carretero-Gómez, Florian Wiesinger, Maggie Fung, Bruno Nunes, Valentina Pedoia, Sharmila Majumdar, Arjun D Desai, Anthony Gatti, Akshay Chaudhari, Eugenia Sánchez-Lacalle and 2 more

Abstract read
In one paragraph

Article in Skeletal radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Laura Carretero-GómezGE HealthCare, Madrid, Spain. laura.carreterogomez@gehealthcare.com.ORCID http://orcid.org/0009-0007-6998-1045
Florian WiesingerGE HealthCare, Munich, Germany.
Maggie FungGE HealthCare, New York, NY, USA.
Bruno NunesGE HealthCare, San Ramon, CA, USA.
Valentina PedoiaDepartment of Radiology and Biomedical Imaging, San Francisco (UCSF), University of California, San Francisco, CA, USA.
Sharmila MajumdarDepartment of Radiology and Biomedical Imaging, San Francisco (UCSF), University of California, San Francisco, CA, USA.
Arjun D DesaiRadiology, Stanford University, Stanford, CA, USA.
Anthony GattiRadiology, Stanford University, Stanford, CA, USA.
Akshay ChaudhariRadiology, Stanford University, Stanford, CA, USA.
Eugenia Sánchez-LacalleDepartment of Radiology, Clínica CEMTRO, Madrid, Spain.
Norberto MalpicaMedical Image Analysis and Biometry Lab, Rey Juan Carlos University, Madrid, Spain.
Mario PadrónDepartment of Radiology, Clínica CEMTRO, Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveClinical adoption of T2 mapping is limited by poor reproducibility, lengthy examination times, and cumbersome image analysis. This study aimed to develop an accelerated deep learning (DL)-enhanced cartilage T2 mapping sequence (DL CartiGram), demonstrate its repeatability and reproducibility, and evaluate its accuracy compared to conventional T2 mapping using a semi-automatic pipeline.

methodsDL CartiGram was implemented using a modified 2D Multi-Echo Spin-Echo sequence at 3 T, incorporating parallel imaging and DL-based image reconstruction. Phantom tests were performed at two sites to obtain test-retest T2 maps, using single-echo spin-echo (SE) measurements as reference values. At one site, DL CartiGram and conventional T2 mapping were performed on 43 patients. T2 values were extracted from 52 patellar and femoral compartments using DL knee segmentation and the DOSMA framework. Repeatability and reproducibility were assessed using coefficients of variation (CV), Bland-Altman analysis, and concordance correlation coefficients (CCC). T2 differences were evaluated with Wilcoxon signed-rank tests, paired t tests, and accuracy CV.

resultsPhantom tests showed intra-site repeatability with CVs ≤ 2.52% and T2 precision ≤ 1 ms. Inter-site reproducibility showed a CV of 2.74% and a CCC of 99% (CI 92-100%). Bland-Altman analysis showed a bias of 1.56 ms between sites (p = 0.03), likely due to temperature effects. In vivo, DL CartiGram reduced scan time by 40%, yielding accurate cartilage T2 measurements (CV = 0.97%) with no significant differences compared to conventional T2 mapping (p = 0.1).

conclusionsDL CartiGram significantly accelerates T2 mapping, while still assuring excellent repeatability and reproducibility. Combined with the semi-automatic post-processing pipeline, it emerges as a promising tool for quantitative T2 cartilage biomarker assessment in clinical settings.

Indexed as

Cartilage, ArticularDeep LearningImage Interpretation, Computer-AssistedMagnetic Resonance ImagingAdultAgedFemaleHumansKnee JointMaleMiddle AgedPhantoms, ImagingReproducibility of ResultsDeep learningKnee cartilageQuantitative MRIRepeatabilityReproducibilityT2 mapping

Identifiers

PMID40968299
PMCPMC12743111

What Socratic holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.