Poster — Thur Eve — 34: Accelerated Analytical Scatter Estimation with Graphics Processing Units
Bibliographic record
Abstract
Image degradation due to scatter can be a serious problem in x‐ray imaging, particularly in cone‐beam computed tomography because of the high scatter to primary ratio. Computational methods to estimate scatter are useful both for system modeling and optimization as well as algorithmic scatter correction. Computational scatter estimators are generally based on either Monte Carlo simulation or analytical calculations. Monte Carlo methods can incorporate very accurate models of interaction physics, but are typically very time consuming. Analytical methods, while usually less accurate than Monte Carlo due to simplifications required to render them computationally tractable, are more amenable to acceleration by parallelization. We previously developed an analytical method for estimating Compton and Rayleigh single scatter for a voxelized phantom in a cone beam geometry. Scatter estimates produced with our initial Matlab code showed good agreement with those obtained from Monte Carlo simulation of an identical imaging geometry in EGSnrc. Computation time with the analytical code was still significant, however, especially when using a high‐resolution phantom with small voxels. Our goal for this project was to accelerate our analytical scatter estimator, without loss of accuracy, by porting the code for use with Nvidia graphics processing units (GPUs) with the CUDA programming environment. Using four GPUs, we obtained speed‐up factors of approximately 700X relative to the original Matlab code running on a single CPU while maintaining good agreement with our reference Monte Carlo results. We plan to apply our GPU‐based analytical scatter estimator to a scatter correction algorithm for cone beam computed tomography.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".