Scatter point models for breast cone-beam computed tomography: preliminary study
Bibliographic record
Abstract
Simulations with a single scatter point per incident beamlet (SSP) model applied to homogeneous phantoms could provide scatter signals that can be subtracted from cone beam breast CT projections so as to minimize the effects caused by scattered photons. Consider a heterogeneous 14 cm diameter 10.5 cm long cylindrical fat cylinder with 5 embedded cylinders of fibroglandular (fib) such that the percent volume of fib is 15%. A 60 kV beam delivering a total dose of 7 mGy over 300 projections was used for interrogation. A transmission model and a many scattering point per incident beamlet (MSP) model incorporating a scattering point every 1 cm of depth of beamlet were used to estimate the energy integrated signals (EIS) due to primary (p) and single scattered (s) photons on each pixel. These models were also used to calculate the EIS p and EIS s for a homogeneous phantom of the same size and fib:fat mass fractions. The SSP model was then applied with the scatter point of interaction being at a depth = d k × δ where d k = length of beamlet k in the phantom and δ was found by matching its peak scatter-to-primary ratio with that obtained with the MSP homogeneous model. The SSP was tested to correct for the effects of the single scatter during cone beam CT of the heterogeneous phantom. The Hounsfield unit deviations from the ideal primary for fib and fat were −95.1 and −56 via no correction for scatter whereas with correction they were 3.8 and −2. It was encouraging to see how a simple model could minimize the effects of single scattered photons during a cone beam CT imaging task. The preliminary findings encourage further efforts for thoroughly testing its applicability for obtaining higher quality images.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".