SU‐E‐I‐04: Implementation of a Fast Monte Carlo Scatter Correction for Cone‐ Beam Computed Tomography
Bibliographic record
Abstract
PURPOSE: To improve image quality in cone-beam computed tomography (CBCT) scans by implementation of a fast and accurate MC-based scatter correction algorithm. METHODS: A Solid WaterTM phantom was imaged on a Varian OBI CBCT scanner using the standard-dose head protocol (100 kVp, 151 mAs, partial-angle). A fast Monte Carlo simulation developed in the EGSnrc framework was used to transport photons through the uncorrected CBCT scan. From the simulation output, the contribution from both primary and scattered photons for each projection image was estimated. Using these estimates, a subtractive scatter correction was performed on the CBCT projection data. This correction procedure was repeated iteratively, using the previous scatter corrected scan as input to the Monte Carlo simulation. RESULTS: Implementation of the scatter correction algorithm on real CBCT data was shown to help mitigate scatter-induced artifacts, such as cupping and streaking. The scatter corrected images were also shown to have improved accuracy in reconstructed attenuation coefficient values. In a region of interest centered on the Solid Water phantom, the number of voxels agreeing to within 10% of the theoretical attenuation coefficient increased from 46% to 97% after two iterations of the scatter correction. CONCLUSIONS: These results suggest that the proposed scatter correction algorithm is successful in improving image quality in real CBCT images. The accuracy of the attenuation coefficients extracted from the corrected CBCT scan renders the data suitable for on-the-fly dose recalculations, as well as vastly improved image registration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".