Quantitative susceptibility and T1<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:mi mathvariant="bold-italic">ρ</mml:mi></mml:mrow></mml:math> mapping of knee articular cartilage at 3T
Bibliographic record
Abstract
T1 ρ and Quantitative Susceptibility Mapping (QSM) are evolving as substrates for quantifying the progressive nature of knee osteoarthritis. Objective To evaluate the effects of spin lock time combinations on depth-dependent T1 ρ estimation, in adjunct to QSM, and characterize the degree of shared variance in QSM and T1 ρ for the quantitative measurement of articular cartilage. Design Twenty healthy participants (10 M/10F, 22.2 ± 3.4 years) underwent bilateral knee MRI using T1 ρ MAPPS sequences with varying TSLs ([0–120] ms), along with a 3D spoiled gradient echo for QSM. Five total TSL combinations were used for T1 ρ computation, and direct depth-based comparison. Depth-wide variance was assessed in comparison to QSM as a basis to assess for depth-specific variation in T1 ρ computations across healthy cartilage. Results Longer T1 ρ relaxation times were observed for TSL combinations with higher spin lock times. Depth-specific differences were documented for both QSM and T1 ρ , with most change found at ∼60% depth of the cartilage, relative to the surface. Direct squared linear correlation revealed that most T1 ρ TSL combinations can explain over 30% of the variability in QSM, suggesting inherent shared sensitivity to cartilage microstructure. Conclusions T1 ρ mapping is subjective to the spin lock time combinations used for computation of relaxation times. When paired with QSM, both similarities and differences in signal sensitivity may be complementary to capture depth-wide changes in articular cartilage.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".