TH‐C‐330A‐08: Soft‐Tissue Detectability Limits in Cone‐Beam CT: 2AFC Tests of Human Observer Performance in Relation to Contrast, Spatial Resolution, and the 3D Noise‐Power Spectrum
Bibliographic record
Abstract
Purpose: To quantify the contrast‐detail detectability limits of soft‐tissue structures in cone‐beam CT (CBCT) and to investigate the influence of the plane of visualization (axial/sagittal) and level of prior knowledge on observer performance. Method and Materials: Custom‐built cylindrical phantoms containing spherical lesions of varying size and contrast were imaged on a CBCT bench across a broad range of dose. Two‐alternative forced choice (2AFC) tests were conducted under controlled conditions using 7 observers (physicists and radiation therapists). For each 2AFC test, the proportion of correct responses, Pcorr, was analyzed as a function of lesion size (1.6 – 12.7mm) and contrast (20 – 165HU), dose (2.1 – 6.4mGy), plane of visualization (axial/sagittal), apodization filter (smooth Hanning to sharp Ram‐Lak), and degree of prior knowledge provided to the observer (ranging from Signal‐Known‐Exactly (SKE) to Signal‐Unknown (SUK)). Results: 2AFC analysis provided valuable quantitation of contrast‐detail detectability limits. For example, the lowest contrast lesion (20 HU) was detected at Pcorr>70% for diameters down to ∼6mm at doses >2mGy, but smaller 20 HU lesions (<3.2mm) were barely detectable (Pcorr<60%) at any dose. Detectability was significantly improved in axial versus sagittal planes, and the effect was amplified by sharper apodization filters in a manner consistent with 3D noise‐power spectrum asymmetry. Prior knowledge had a marked influence on detectability — e.g., a ∼6mm (20 HU) sphere was detected at Pcorr∼70–85% for SKE conditions, compared to Pcorr∼55–65% under SUK conditions across the same range of dose. Conclusion: Comprehensive human observer tests provide valuable quantitation of soft‐tissue detectability limits in CBCT and help to define low‐dose techniques for specific imaging tasks. Two factors in particular — plane of visualization and prior knowledge — hold significant practical implications: axial planes typically offer improved detectability, and performance is maintained at significantly lower dose under SKE conditions (e.g., lesion‐known image guidance) than in SUK conditions (lesion‐unknown diagnostic imaging).
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".