The Impact of Virtual Monoenergetic Imaging on Visualization of the Cervical Spinal Canal
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: Our purpose is to explore the role of dual-energy computed tomography (DECT) and virtual monoenergetic energy levels in reducing shoulder artifact to improve visualization of the cervical spinal canal. MATERIALS AND METHODS: A retrospective review of 171 consecutive DECT scans of the neck (95 male, 65 female; mean age, 60.9 years, ranging from 18 to 88 years; with 11 excluded because of nondiagnostic image quality) during an 8-month period was performed with postprocessing of monoenergetic images at 50, 70, 100, and 140 keV. Subjective comparisons and objective image noise between the monoenergetic images and standard computed tomography (CT) were analyzed by 1-way analysis of variance to determine the optimal DECT energy level with the highest image quality. RESULTS: Subjectively, 100-keV DECT best visualizes the spinal canal relative to standard CT, 50 and 70 keV ( P < 0.01), and was superior to 140 keV for reader 1 ( P < 0.01). Objectively, 100 keV demonstrated less noise relative to 50 keV (72.02; P < 0.01). There was no difference in noise between 100 keV and 70 keV, or between 100 keV and standard CT, which also demonstrated lower noise relative to 50-, 70-, and 140-keV levels (91.53, P < 0.01; 29.84, P < 0.01; and 22.66, P < 0.03). CONCLUSION: Dual-energy CT at 100 keV may be the preferred DECT monoenergetic level for soft tissue assessment. Increasing energy level is associated with reduction in shoulder artifact, with no difference in noise between 100 keV and standard CT, although 100-keV images may be subjectively better.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".