Dual-Source Computed Tomography of the Chest in Blunt Thoracic Trauma
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the clinical utility of temporal resolution optimization (TR-Opt), a computed tomography (CT) postprocessing technique, in reducing aortic motion artifacts in blunt thoracic trauma patients. MATERIALS AND METHODS: This was an IRB-approved study of 61 patients with blunt thoracic trauma carried out between February 18 and September 6, 2014; the patients had been imaged using a standardized dual-source high-pitch (DSHP) CT protocol. Image raw data were retrospectively postprocessed using the TR-Opt algorithm (DSHP-TR-Opt) and compared with conventional images (DSHP). Diagnostic ability to confidently identify and exclude potential injuries and qualitative aortic motion artifacts using a 5-point Likert scale (1=absence of motion artifacts; 5=severe motion artifact) was graded by 2 readers at multiple thoracic locations. Signal-to-noise and contrast-to-noise ratios were generated as quantitative indices of image quality. RESULTS: Motion artifacts degrading interpretation and limiting diagnosis of aortic injuries were present in 45% (442/976) of the assessed regions on DSHP. TR-Opt algorithm eliminated motion artifacts in 85% of the motion-degraded areas (375/442), leaving persistent motion artifacts in only 15% (67/442). Motion artifacts were most improved at the interventricular septum (1±1 vs. 3±1), aortic valve (2±1 vs. 4±1.5), and ascending aorta (1±1 vs. 3±2, P<0.005). Mean aorta noise (NAo) was 41.7% higher in the DSHP-TR-Opt images (26.5 vs. 18.7 HU, P<0.0001). CONCLUSIONS: Temporal resolution optimized reconstruction is a raw data-based CT postprocessing technique that can be used to remove the majority of thoracic aortic motion artifacts that commonly degrade interpretation when imaging blunt thoracic trauma patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".