Multi‐echo GRE imaging of knee cartilage
Bibliographic record
Abstract
PURPOSE: To visualize healthy and abnormal articular cartilage, we investigated the potential of using the 3D multi-echo gradient echo (GRE) signal's magnitude and frequency and maps of T2* relaxation. MATERIALS AND METHODS: After optimizing imaging parameters in five healthy volunteers, 3D multi-echo GRE magnetic resonance (MR) images were acquired at 3T in four patients with chondral damage prior to their arthroscopic surgery. Average magnitude and frequency information was extracted from the GRE images, and T2* maps were generated. Cartilage abnormalities were confirmed after arthroscopy and were graded using the Outerbridge classification scheme. Regions of interest were identified on average magnitude GRE images and compared to arthroscopy. RESULTS: All four patients presented with regions of Outerbridge Grade I and II cartilage damage on arthroscopy. One patient had Grade III changes. Grade I, II, and III changes were detectable on average magnitude and T2* maps, while Grade II and higher changes were also observable on MR frequency maps. For average magnitude images of healthy volunteers, the signal-to-noise ratio of the magnitude image averaged over three echoes was 4.26 ± 0.32, 12.26 ± 1.09, 14.31 ± 1.93, and 13.36 ± 1.13 in bone, femoral, tibial, and patellar cartilage, respectively. CONCLUSION: This proof-of-principle study demonstrates the feasibility of using different imaging contrasts from the 3D multi-echo GRE scan to visualize abnormalities of the articular cartilage. © 2016 International Society for Magnetic Resonance in Medicine Level of Evidence: 1 J. MAGN. RESON. IMAGING 2017;45:1502-1513.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".