Sci‐Sat AM(1): Imaging‐05: Analytical scatter estimation for cone‐beam computed tomography
Bibliographic record
Abstract
A significant challenge to the implementation of cone-beam computed tomography (CBCT) for high-resolution imaging is the high scatter to primary ratio. Scatter causes cupping and shading artifacts, increased noise and decreased contrast in reconstructed images. Methods to reduce the impact of scatter in CBCT are thus very desirable. We are investigating methods for computational scatter estimation and compensation for CBCT, with the goal of incorporating a scatter estimator within a statistical reconstruction algorithm. We have developed an analytical method for estimating single scatter, based on Klein-Nishina cross-sections. We have compared scatter estimates generated with this method with the results of high-count EGSnrc Monte Carlo simulations. The analytical estimates compare favorably with the Monte Carlo estimates. The paper will discuss our method for analytical estimation of single scatter, including the assumptions and simplifications required to render it computationally tractable, along with the results of the comparison between the analytical method and Monte Carlo simulations. The paper will extend previous results obtained with small (40 × 40 × 40 voxel) homogeneous computational phantoms to include results for larger, more clinically relevant phantoms (128 × 128 × 128 voxels, simulated 50/50 breast tissue with inserts of varying contrast). The paper will also discuss computational acceleration obtained through the use of parallel processing via the WestGrid High-Performance Computing network.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".