SU‐FF‐I‐111: Exploration of the Scatter Distribution in Cone‐Beam CT Using Monte Carlo Techniques
Bibliographic record
Abstract
Purpose: To validate our current Monte Carlo (MC) model and explore the scatter distribution in cone‐beam CT (CBCT) projection images. The MC data will be used to garner a better understanding of the relationship between imaging parameters and the resulting scatter distribution. Method and Materials: Measured images to validate the CBCT MC model where collected using a flexible bench‐top CBCT system. The x‐ray tube was modeled using the BEAMnrc MC code system and the imaging geometries using a modified DOSXYZnrc program that differentiates primary from scattered particles. Two objects were investigated, a 16.4 and 30.6 cm diameter water cylinder. Projections were simulated for three different cone angles (1.4°, 5.7°, and 11.3°), three different source‐to‐axis distances (SAD: 50, 75, and 100 cm), and four different axis‐to‐detector distances (ADD: 9, 18, 30, and 56 cm). All simulations and measurements were done with an energy of 100 kVp. In order to validate the simulated scatter distributions experiments were conducted using beam‐blocking techniques to estimate the scatter for a 16.4 cm and 30.6 cm water cylinder for a single geometry (SAD=100, ADD=56 cm). Results: The simulated projections had a mean local absolute difference of 3.7 +/− 2.1% and 8.7 +/− 3.8% with the measured projections for the 16.4 and 30.6 cm diameter water cylinder respectively. Scatter simulations had a mean local absolute difference of 9.6 +/− 4.1% and 5.4 +/− 3.3% with the scatter measurements for the 16.4 cm and 30.6 cm diameter water cylinders respectively. Conclusion: The results support the use of MC to gain a further understanding of scatter and develop new techniques to correct for scatter induced image quality artifacts in CBCT. The scatter database created will be a valuable tool in exploring the relationships between imaging geometry parameters and the scatter distribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".