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 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 | 0.000 |
| 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) | 0.000 | 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".