Dual-Source Computed Tomography of the Chest in Blunt Thoracic Trauma
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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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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 | 0.001 | 0.001 |
| 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 it