Sci—Fri PM: Delivery — 12: Scatter‐B‐Gon: Implementing a fast Monte Carlo cone‐beam computed tomography scatter correction on real data
Bibliographic record
Abstract
A fast and accurate MC-based scatter correction algorithm was implemented on real cone-beam computed tomography (CBCT) data. An ACR CT accreditation 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. 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 three regions of interest centered on material inserts in the ACR phantom, the reconstructed CT numbers agreed with clinical CT scan data to within 35 Hounsfield units after scatter correction. 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 adaptive on the fly dose calculations on individual fractions, 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.009 |
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".