An investigation of kV CBCT image quality and dose reduction for volume‐of‐interest imaging using dynamic collimation
Bibliographic record
Abstract
PURPOSE: The focus of this work was to investigate the improvements in image quality and dose reduction for volume-of-interest (VOI) kilovoltage-cone beam CT (CBCT) using dynamic collimation. METHODS: A prototype iris aperture was used to track a VOI during a CBCT acquisition. The current aperture design is capable of 1D translation as a function of gantry angle and dynamic adjustment of the iris radius. The aperture occupies the location of the bow-tie filter on a Varian On-Board Imager system. CBCT and planar image quality were investigated as a function of aperture radius, while maintaining the same dose to the VOI, for a 20 cm diameter cylindrical water phantom with a 9 mm diameter bone insert centered on isocenter. Corresponding scatter-to-primary ratios (SPR) were determined at the detector plane with Monte Carlo simulation using EGSnrc. Dose distributions for various sizes VOI were modeled using a dynamic BEAMnrc library and DOSXYZnrc. The resulting VOI dose distributions were compared to full-field distributions. RESULTS: SPR was reduced by a factor of 8.4 when decreasing iris diameter from 21.2 to 2.4 cm (at isocenter). Depending upon VOI location and size, dose was reduced to 16%-90% of the full-field value along the central axis plane and down to 4% along the axis of rotation, while maintaining the same dose to the VOI compared to full-field techniques. When maintaining constant dose to the VOI, this change in iris diameter corresponds to a factor increase of approximately 1.6 in image contrast and a factor decrease in image noise of approximately 1.2. This results in a measured gain in contrast-to-noise ratio by a factor of approximately 2.0. CONCLUSIONS: The presented VOI technique offers improved image quality for image-guided radiotherapy while sparing the surrounding volume of unnecessary dose compared to full-field techniques.
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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 | 0.000 | 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) | 0.001 | 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 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".