The reduction of image noise and streak artifact in the thoracic inlet during low dose and ultra-low dose thoracic CT
Bibliographic record
Abstract
Increased pixel noise and streak artifact reduce CT image quality and limit the potential for radiation dose reduction during CT of the thoracic inlet. We propose to quantify the pixel noise of mediastinal structures in the thoracic inlet, during low-dose (LDCT) and ultralow-dose (uLDCT) thoracic CT, and assess the utility of new software (quantum denoising system and BOOST3D) in addressing these limitations. Twelve patients had LDCT (120 kV, 25 mAs) and uLDCT (120 kV, 10 mAs) images reconstructed initially using standard mediastinal and lung filters followed by the quantum denoising system (QDS) to reduce pixel noise and BOOST3D (B3D) software to correct photon starvation noise as follows: group 1 no QDS, no B3D; group 2 B3D alone; group 3 QDS alone and group 4 both QDS and B3D. Nine regions of interest (ROIs) were replicated on mediastinal anatomy in the thoracic inlet, for each patient resulting in 3456 data points to calculate pixel noise and attenuation. QDS reduced pixel noise by 18.4% (lung images) and 15.8% (mediastinal images) at 25 mAs. B3D reduced pixel noise by approximately 8% in the posterior thorax and in combination there was a 35.5% reduction in effective radiation dose (E) for LDCT (1.63-1.05 mSv) in lung images and 32.2% (1.55-1.05 mSv) in mediastinal images. The same combination produced 20.7% reduction (0.53-0.42 mSv) in E for uLDCT, for lung images and 17.3% (0.51-0.42) for mediastinal images. This quantitative analysis of image quality confirms the utility of dedicated processing software in targeting image noise and streak artifact in thoracic LDCT and uLDCT images taken in the thoracic inlet. This processing software potentiates substantial reductions in radiation dose during thoracic LDCT and uLDCT.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".