The utility of virtual monochromatic dual-energy computed tomography (DECT) in meniscal imaging: a technical evaluation
Bibliographic record
Abstract
Purpose To determine the feasibility of meniscal imaging using virtual monochromatic images obtained through dual-energy computed tomography (DECT) technique, and to determine which keV levels optimise contrast resolution. Material and methods All DECT exams were performed on a Discovery CT750 HD system from GE Healthcare. Virtual monochromatic images were reconstructed at 40 keV, 73 keV, 106 keV, and 139 keV. Contrast resolution of the medial and lateral menisci using a 5-point Likert scale at each keV level was determined through a consensus agreement by 2 fellowship-trained musculoskeletal radiologists. Friedman’s and Wilcoxon signed rank tests were used to compare visualisation scores across different keV levels. Results Seventeen knee exams from 10 patients met criteria for inclusion in the study. All patients included in the study cohort were male. The median age of patients was 46 years (interquartile range, 35-53 years). Virtual monochromatic images at 40 keV demonstrated highest contrast resolution of the menisci, with a statistically significant difference between contrast resolution scores at 40 keV and 76 keV, Friedman test: p < 0.0001. Conclusions Meniscal imaging is feasible using DECT virtual monochromatic images at low keV levels. Improved contrast resolution at these specified KeV values could pave the way for further research in this field to determine its role in the future as an alternative option for assessment of the menisci in patients with contraindications to MRI or in the setting of a periarticular ferromagnetic foreign body obscuring the field of view.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".