An application of algebraic method on MR T2 imaging of knee articular cartilage
Bibliographic record
Abstract
The computation of relaxation time from quantitative magnetic resonance (MR) imaging depends on the applied algorithms. The purpose of this project was to use the algebraic curve fitting algorithm to quantify T2 mapping of knee articular cartilage for T2 relaxation time calculation. The T2 images of a healthy male volunteer's right knee tibiofemoral joint cartilage were generated by a 3T MR imaging scanner using a spin echo multislice multiecho (MSME) Carr-Purcell Meiboom-Gill (CPMG) sequence. The medial and lateral condyle cartilage regions were further subdivided into three compartments - anterior, middle and posterior for identifying T2 values variation in between them. The T2 relaxation time mean and standard deviation in each region of interest (ROI) was calculated using the algebraic fitting algorithm and compared with conventional nonlinear algorithms. The results show that the algebraic fitting algorithm is feasible for T2 relaxation time calculation of knee tibiofemoral condyle cartilage. It is not only clear but also sensitive to T2 MR imaging of knee 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".