The effects of compensator and imaging geometry on the distribution of x‐ray scatter in CBCT
Bibliographic record
Abstract
PURPOSE: X-ray scatter contributes significantly to image degradation in cone-beam CT (CBCT) reconstructed images in the form of CT number inaccuracy image artifacts and loss of contrast. The need for an understanding of the relationship between the scatter distribution and common imaging parameters (cone angle, air gap, filtration, object size) is an essential element to developing methods for efficiently correcting for the effects of scatter in CBCT. The first objective of this study is to validate the scatter distributions calculated using a CBCT Monte Carlo (MC) model against measured scatter estimates. The second objective is to use the CBCT MC model to investigate the effects of common imaging parameters and bowtie compensators on the resulting scatter distribution. METHODS: This investigation employs the use of a CBCT MC model, developed using the EGSnrc code, to simulate the primary and scatter fluence arriving at the detector. The simulation is validated against projection images, scatter-to-open field ratio (SOR), and scatter-to-primary ratio (SPR) measurements taken using a bench-top CBCT system. The CBCT MC model is used to simulate the scatter distribution arriving at the detector for different cone angles {1.4 degrees, 2.8 degrees, 5.7 degrees, and 11.3 degrees}, source-to-axis distances (SADs) {50, 75 and 100 cm}, and axis-to-detector distances (ADDs) {9, 18, 30, 44, 56 cm} for both a 16.4 and 30.6 cm diameter water cylinder. The effects of different bowtie filters are also simulated using the CBCT MC model for the aforementioned cylinder sizes. RESULTS: Profiles of the simulated and measured projection images agree within 6%. The measurements of the SOR and SPR, taken using beam stop techniques, show good agreement with the simulated results. Limitations of current beam stop techniques in estimating scatter profiles of objects with varying thickness are also demonstrated. A functional relationship between scatter (SOR, SPR) and air gap, cone angle is reported. The bowtie filter was found to have the beneficial effect of decreasing the magnitude of scatter in the projection images (SPR decreased by 56%) as well as altering the spatial distribution of the scatter. CONCLUSIONS: The CBCT MC model accurately simulates scatter and primary fluences in the CBCT imaging components and geometry. The CBCT MC model provides a useful tool in investigating the effects of varying CBCT imaging parameters on the scatter distribution. Increasing the air gap, decreasing the cone angle, and the use of bowtie filtration were all found to be effective ways to minimize scatter in CBCT. The bowtie filter was particularly effective in both minimizing the magnitude and modifying the spatial distribution of the scattered photons. This observation directs further research in optimizing bowtie filter design in an effort to minimize scatter induced artifacts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".