Multiplanar reconstruction recovers morphological cartilage assessment reproducibility from maloriented coronal MRI scans
Bibliographic record
Abstract
The study's purpose was to assess the effect of multiplanar reconstruction on precision of weight-bearing medial and lateral femoral cartilage (cMF, cLF) morphometry in maloriented coronal MR images. Twenty knees were scanned four times with a 1.0 Tesla extremity imager using a fat-suppressed T1-weighted three-dimensional spoiled gradient recalled echo sequence; twice with "best as" double bull's-eye orientation of the femoral condyles, and once each with 5° internal and external rotation. Multiplanar reconstruction was applied to maloriented scans to recover double bull's-eye orientation. Medial and lateral femoral cartilages were segmented and precision of bone area, cartilage volume and thickness (ThCtAB) evaluated for all scans. Test-retest precision (RMSCV%) of the double bull's-eye scans was 1.1% for total bone area and 4.1% for cartilage volume. Differences in precision between double bull's-eye and maloriented images were assessed. Higher precision errors were observed in malorientated images for all outcomes (1.7-4.8% for internally rotation scans; 1.7-4.8% for external rotation scans). Precision generally improved with multiplanar reconstruction correction (1.7-5.6% for internally rotated scans; 1.2-3.5% for external rotation scans). Precision of femoral cartilage morphometry is generally reduced when maloriented images are acquired. Multiplanar reconstruction can correct malorientated scans and recover precision losses. Measurements are affected in a rotationally and compartmentally dependent manner.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".