Prototype Ultrahigh-Resolution Computed Tomography for Chest Imaging: Initial Human Experience
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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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".