Prototype Ultrahigh-Resolution Computed Tomography for Chest Imaging: Initial Human Experience
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate a prototype, ultrahigh-resolution computed tomography offering higher reconstruction matrix (1024 × 1024) and spatial resolution (0.15 mm) for chest imaging. METHODS: Higher (1024) matrix reconstruction enabled by ultrahigh-resolution computed tomography scanner (128-detector rows; detector width, 0.25 mm; spatial resolution, 0.15 mm) was compared with conventional (512) reconstruction with image quality grading on a Likert scale (1, excellent; 5, nondiagnostic) for image noise, artifacts, contrast, small detail, lesion conspicuity, image sharpness, and diagnostic confidence. Image noise and signal-to-noise ratio were quantified. RESULTS: Diagnostic image quality was achieved for all scans on 101 patients. The 1024 reconstruction demonstrated increased image noise (20.2 ± 4.0 vs 17.2 ± 3.8, P < 0.001) and a worse noise rating (1.98 ± 0.63 vs 1.75 ± 0.61, P < 0.001) but performed significantly better than conventional 512 matrix with fewer artifacts (1.37 ± 0.43 vs 1.50 ± 0.48, P < 0.001), better contrast (1.50 ± 0.56 vs 1.62 ± 0.57, P < 0.001), small detail detection (1.06 ± 0.19 vs 2.02 ± 0.22, P < 0.001), lesion conspicuity (1.08 ± 0.23 vs 2.02 ± 0.24, P < 0.001), sharpness (1.09 ± 0.24 vs 2.02 ± 0.28, P < 0.001), and overall diagnostic confidence (1.09 ± 0.25 vs 1.18 ± 0.34, P < 0.001). CONCLUSIONS: Ultrahigh-resolution computed tomography enabled a higher reconstruction matrix and improved image quality compared with conventional matrix reconstruction, with a minor increase in noise.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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