Poster - Thur Eve - 14: The effect of fluence and detector size on image quality in multi-projection compton scatter tomography
Bibliographic record
Abstract
Purpose: To assess how radiation dose and size of energy sensitive detectors affects image quality in multi-projection Compton scatter tomography. Methods and Materials: A Compton scatter tomography system was simulated in Maltab. The system consists of a point source generated x-ray fan beam and energy sensitive photon counting detectors, placed along a line with the source outside the periphery of the primary beam. Single scattered photons from a low contrast phantom simulating breast tissues were simulated. Simulation parameters are dose-limited and closely matched to typical breast CT. Poisson distributed noise was added to simulate quantum noise. Results: We have successfully reconstructed electron density images in a clinical fan-beam breast CT system, in the presence of noise. The reconstruction illustrates accurate spatial alignment of the structures of interest in the phantom. The increase in MSE due to noise was ∼11%. The optimal detector size of 2 × 2 mm2 is a trade off between the increased noise, that is present when smaller detector sizes are used, and the blurring of the image that occurs as larger detectors are employed. Conclusions: For breast CT dose of 4–12 mGy, the optimal detector size for a Compton scatter reconstruction using 360 projections and 1000 eV energy resolution was found to be 2 × 2 mm2. The ability to visualize large low contrast (9%) and small (2 mm diameter) high contrast objects was demonstrated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".