Poster — Thur Eve — 01: The effect of the number of projections on MTF and CNR in Compton scatter tomography
Bibliographic record
Abstract
Purpose: To investigate the dependence of the reconstructed image quality on the number of projections in multi‐projection Compton scatter tomography (MPCST). The conventional relationship between the projection number used for reconstruction and reconstructed image quality pertained to CT does not necessarily apply to MPCST, which can produce images from a single projection if the detectors have sufficiently high energy and spatial resolution. Methods: The electron density image was obtained using filtered‐backprojection of the scatter signal over circular arcs formed using Compton equation. The behavior of the reconstructed image quality as a function of the projection number was evaluated through analytical simulations and characterized by CNR and MTF. Results: The increase of the projection number improves the contrast with this dependence being a function of fluence. The number of projections required to approach the asymptotic maximum contrast decreases as the fluence increases. Increasing projection number increases the CNR but not spatial resolution. Conclusions: For MPCST using a 500eV energy resolution and a 2×2mm2 size detector, an adequate image quality can be obtained with a small number of projections provided the incident fluence is high enough. This is conceptually different from conventional CT where a minimum number of projections is required to obtain an adequate image quality. While increasing projection number, even for the lowest dose value, the CNR increases even though the number of photons per projection decreases. The spatial resolution of the image is improved by increasing the sampling within a projection rather than by increasing the number of projections.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".