WE-G-141-07: Feasibility of Using a Grid to Detect and Correct the Geometric Variations in Flat-Panel Based Cone Beam CT
Bibliographic record
Abstract
Purpose: To study the feasibility of using a grid, shown previously to mitigate the scatter problem, to detect and correct geometric variations in flat-panel-based cone beam CT (CBCT) imaging. Methods: The study was performed on the CBCT system of a Varian Trilogy linac. The grid was located before the imaging object and attached to the bowtie-filter with a source-to-grid distance (SGD) of 40–50 cm. We first studied the correlations in geometric variation between the grid, bowtie-filter, source and imager. The grid position was represented by BBs in a plastic-plate replacing the grid. The bowtie-filter position was determined by a BB placed on its surface. The source and imager geometries were derived from the projections of 16 BBs in an IsoCal phantom using an in-house-developed computer program. Multiple scans were performed with different SGD in a 2-month period. Correlations were analyzed and used to predict the geometric variations of the bowtie-filter, source and imager from the grid variation obtained using a grid in a CatPhan scan. Results: The grid and bowtie-filter showed exactly same variations (up to 5 mm) in Y-direction during a 360° rotation, with a sine-like pattern superimposed by 1–2 mm random vibrations. The variation patterns were also sine-like curves in the X-direction. However, they were smooth and repeatable, with differences in phase and amplitude between the two, and the amplitude varying with SGD. The source and imager showed similar and repeatable 0.3–0.5 mm sine-like variation patterns. Based on these results, a model was developed to predict and correct the geometric variations in a CBCT scan with the grid. Conclusion: The preliminary results suggest that it is able to predict geometric variations of the CBCT system. Future study is underway to verify whether correction of these variations will improve the spatial resolution and mitigate the bowtie-filter variation-induced crescent artifact. The study is supported by NIH Grant Number 5 R01 CA166948-02.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".