Implementation of an efficient Monte Carlo calculation for CBCT scatter correction: phantom study
Bibliographic record
Abstract
Cone-beam computed tomography (CBCT) images suffer from poor image quality, in a large part due to contamination from scattered X-rays. In this work, a Monte Carlo (MC)-based iterative scatter correction algorithm was implemented on measured phantom data acquired from a clinical on-board CBCT scanner. An efficient EGSnrc user code (egs_cbct) was used to transport photons through an uncorrected CBCT scan of a Catphan 600 phantom. From the simulation output, the contribution from primary and scattered photons was estimated in each projection image. From these estimates, an iterative scatter correction was performed on the raw CBCT projection data. The results of the scatter correction were compared with the default vendor reconstruction. The scatter correction was found to reduce the error in CT number for selected regions of interest, while improving contrast-to-noise ratio (CNR) by 18%. These results demonstrate the performance of the proposed scatter correction algorithm in improving image quality for clinical CBCT images.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".