Soft‐tissue detectability in cone‐beam CT: Evaluation by 2AFC tests in relation to physical performance metrics
Bibliographic record
Abstract
Soft-tissue detectability in cone-beam computed tomography (CBCT) was evaluated via two-alternative forced-choice (2AFC) tests. Investigations included the dependence of detectability on radiation dose, the influence of the asymmetric three-dimensional (3D) noise-power spectrum (NPS) in axial and sagittal or coronal planes, and the effect of prior knowledge on detectability. Custom-built phantoms (approximately 15 cm diameter cylinders) containing soft-tissue-simulating spheres of variable contrast and diameter were imaged on an experimental CBCT bench. The proportion of correct responses (Pcorr) in 2AFC tests was analyzed as a figure of merit, ideally equal to the area under the receiver operating characteristic curve. Pcorr was evaluated as a function of the sphere diameter (1.6-12.7 mm), contrast (20-165 HU), dose (1-7 mGy), plane of visualization (axial/sagittal), apodization filter (Hanning and Ram-Lak), and prior knowledge provided to the observer [ranging from stimulus known exactly (SKE) to stimulus unknown (SUK)]. Detectability limits were characterized in terms of the dose required to achieve a given level of Pcorr (e.g., 70%). For example, a 20 HU stimulus of diameter down to approximately 6 mm was detected with Pcorr 70% at dose > or =2 mGy. Detectability tended to be greater in axial than in sagittal planes, an effect amplified by sharper apodization filters in a manner consistent with 3D NPS asymmetry. Prior knowledge had a marked influence on detectability--e.g., Pcorr for a approximately 6 mm (20 HU) sphere was approximately 55%-65% under SUK conditions, compared to approximately 70%-85% for SKE conditions. Human observer tests suggest practical implications for implementation of CBCT: (i) Detectability limits help to define minimum-dose imaging techniques for specific imaging tasks; (ii) detectability of a given structure can vary between axial and sagittal/coronal planes, owing to the spatial-frequency content of the 3D NPS in relation to the imaging task; and (iii) performance under SKE conditions (e.g., image guidance tasks in which lesion characteristics are known) is maintained at a lower dose than in SUK conditions (e.g., diagnostic tasks in which lesion characteristics are unknown).
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".