Dual-energy CT applications in musculoskeletal disorders
Bibliographic record
Abstract
Dual-energy CT (DECT) is an exciting application in CT technology conferring many advantages over conventional single-energy CT at no additional with comparable radiation dose to the patient. Various emerging and increasingly established clinical DECT applications in musculoskeletal (MSK) imaging such as bone marrow oedema detection, metal artefact reduction, monosodium urate analysis, and collagen analysis for ligamentous, meniscal, and disc injuries are made possible through its advanced DECT post-processing capabilities. These provide superior information on tissue composition, artefact reduction and image optimization. Newer DECT applications to evaluate fat fraction for sarcopenia, Rho/Z application for soft tissue calcification differentiation, 3D rendering, and AI integration are being assessed for future use. In this article, we will discuss the established and developing applications of DECT in the setting of MSK radiology as well as the basic principles of DECT which facilitate them.
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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 | 0.000 | 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".