<i>In vivo</i> morphological characterisation of skin by MRI micro‐imaging methods
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Quantitative assessments in skin layers using images obtained with standard magnetic resonance imaging (MRI) sequences are limited, since the stratum corneum and dermis, the layers of most clinical interest, have low signal due to their short spin-spin relaxation, T2. METHODS: In the present work, different methods of MRI contrast, such as magnetisation transfer contrast (MTC), T1-weighting (where T1 is spin-lattice relaxation time), T2*-weighting (where T2* is the combination of T2 and magnetic field in-homogeneity effect) and chemical shift, were used. These techniques were combined with high-resolution MRI. RESULTS: We found that skin is a very MT active tissue, and MTC provides data enabling the evaluation of how the tissue in skin layers interacts with the interstitial fluids. Details obtained from high-resolution high-quality in vivo skin images with different contrast allowed for differentiation of skin layers, sub-layers and excellent correlation of MR data with known histological features and water constituent of skin layers. CONCLUSION: Combining MT and other MRI data employing other contrast mechanisms provides a superior non-invasive in vivo technique for visualisation and also quantitative assessment of the constituents of the stratum corneum, epidermis, papillary dermis, reticular dermis and hypodermis as major structural layers of the skin. This type of study can be extended to cutaneous disease states or skin ageing, where defects in water mobility, concentration and/or macromolecular structural changes are expected.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".