ArticleQuantitative imaging in medicine and surgery2023
Comparison of compositional MRI techniques to quantify the regenerative potential of articular cartilage: a preclinical minipig model after osteochondral defect treatments with autologous mesenchymal stromal cells and unseeded scaffolds.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Models of cartilage repair with autologous mesenchymal stem cells seeded on scaffolds: a systematic narrative review.Frontiers in bioengineering and biotechnology · 2026Review
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Authors and funding
15 authors at 3 institutions in 1 country.
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Abstract
Background: The field of orthopedics seeks effective, safer methods for evaluating articular cartilage regeneration. Despite various treatment innovations, non-invasive, contrast-free full quantitative assessments of hyaline articular cartilage's regenerative potential using compositional magnetic resonance (MR) sequences remain challenging. In this context, our aim was to investigate the effectiveness of different MR sequences for quantitative assessment of cartilage and to compare them with the current gold standard delayed gadolinium-enhanced MR imaging of cartilage (dGEMRIC) measurements. Methods: We employed Results: In the untreated defect group, we observed significant differences in the defect region, with dGEMRIC values significantly lower (404.86±64.2 ms, P=0.018) and T Conclusions: As expected, dGEMRIC is well suited for monitoring cartilage regeneration. Interestingly, T
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Registered trials
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 OpenQuestion graph.