Monte Carlo analysis of beam blocking grid design parameters: Scatter estimation and the importance of electron backscatter
Bibliographic record
Abstract
PURPOSE: Beam blocking grids provide a simple and direct measurement of the scattered photon signal which degrades image quality in x-ray imaging systems, such as cone-beam CT (CBCT). This study evaluates the scatter estimation accuracy of the beam blocking method to optimize the design parameters of the grid system (e.g., grid thickness, source-to-grid distance (SGD), septa width, air interspace, and grid ratio) using Monte Carlo (MC) simulations. METHOD: A MC model of a CBCT imaging system with a beam blocking grid in place is made using code based on EGSnrc, with the x-ray tube portion of the simulation including electron backscatter between the anode and cathode. The inclusion of the electron backscatter allowed a more complete model of the contamination signal to be estimated. The contamination signal consists of the off-focal radiation (OFR) and source component scatter (photon scatter in source components such as tube housing, filters, and collimators). The MC model was validated against measurements collected on a bench top imaging system with a grid in place. The MC model was used to simulate 11 different grid design configurations in addition to a case with no grid. For each design a simulated projection with and without a phantom in place was computed. The simulated projections were then used to estimate the scatter and contamination portion of the signal using the signal behind the grid septa. The estimated signals from the grid data were compared to the actual signals labeled during the MC simulation. RESULTS: Simulated results showed good agreeance with measured results with the importance of including electron backscatter resulting in off-focal radiation in the simulation being highlighted. When the source was free of contamination photons all grids performed with an error less than 8% when estimating just the scatter from the object. When the contamination photons were included in the simulation, the error in estimating both the scatter and contamination signal rose by a factor of 4 on average. In the case when both signals are present, increasing the grid thickness, changing the SGD, and reducing septa width and air interspace sizes all showed the ability to improve the grid-based estimates of the object scatter and contamination portion signal. CONCLUSIONS: The inclusion of the contamination signal in MC simulations of x-ray imaging systems is important in the design, validation, and evaluation of measurement-based scatter methods. Beam blocking grids show potential not only in object scatter estimation but in the estimation of the contamination signal, but appropriate interpolation functions must be used to account for higher frequencies found in contamination signal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".