Experimental evaluation of x‐ray acoustic computed tomography for radiotherapy dosimetry applications
Bibliographic record
Abstract
PURPOSE: The aim of this work was to experimentally demonstrate the feasibility of x-ray acoustic computed tomography (XACT) as a dosimetry tool in a clinical radiotherapy environment. METHODS: The acoustic waves induced following a single pulse of linear accelerator irradiation in a water tank were detected with an immersion ultrasound transducer. By rotating the collimator and keeping the transducer stationary, acoustic signals at varying angles surrounding the field were detected and reconstructed to form an XACT image. Simulated XACT images were obtained using a previously developed simulation workflow. Profiles extracted from experimental and simulated XACT images were compared to profiles measured with an ion chamber. A variety of radiation field sizes and shapes were investigated. RESULTS: XACT images resembling the geometry of the delivered radiation field were obtained for fields ranging from simple squares to more complex shapes. When comparing profiles extracted from simulated and experimental XACT images of a 4 cm × 4 cm field, 97% of points were found to pass a 3%/3 mm gamma test. Agreement between simulated and experimental XACT images worsened when comparing fields with fine details. Profiles extracted from experimental XACT images were compared to profiles obtained through clinical ion chamber measurements, confirming that the intensity of XACT images is related to deposited radiation dose. Seventy-seven percent of the points in a profile extracted from an experimental XACT image of a 4 cm × 4 cm field passed a 7%/4 mm gamma test when compared to an ion chamber measured profile. In a complicated puzzle-piece shaped field, 86% of the points in an XACT extracted profile passed a 7%/4 mm gamma test. CONCLUSIONS: XACT images with intensity related to the spatial distribution of deposited dose in a water tank were formed for a variety of field sizes and shapes. XACT has the potential to be a useful tool for absolute, relative and in vivo dosimetry.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".