MO‐A‐BRD‐07: Feasibility of X‐Ray Acoustic Computed Tomography as a Tool for Calibration and <i>In Vivo </i> Dosimetry of Radiotherapy Electron and Photon Beams
Bibliographic record
Abstract
Purpose: This work simulates radiation‐induced acoustic waves to assess the feasibility of x‐ray acoustic computed tomography (XACT) as a dosimeter. XACT exploits the phenomenon that acoustic waves with amplitude proportional to the dose deposited are induced following a radiation pulse. After detecting these acoustic waves with an ultrasound transducer, an image of the dose distribution can be reconstructed in realtime. Methods: Monte Carlo was used to simulate the dose distribution for monoenergetic 6 MeV photon and 9 MeV electron beams incident on a water tank. The dose distribution for a prostate patient planned with a photon 4‐field box technique was calculated using clinical treatment planning software. All three dose distributions were converted into initial pressure distributions, and transportation of the induced acoustic waves was simulated using an open‐source toolkit. Ideal transducers were placed around the circumference of the target to detect the acoustic waves, and a time reversal reconstruction algorithm was used to obtain an XACT image of the dose for each radiation pulse. Results: For the photon water tank relative dosimetry case, it was found that the normalized acoustic signal amplitude agreed with the normalized dose at depths from 0 cm to 10 cm, with an average percent difference of 0.5%. For the reconstructed in‐plane dose distribution of an electron water tank irradiation, all pixels passed a 3%–3 mm 2D gamma test. The reconstructed prostate dose distribution closely resembled the plan, with 89% of pixels passing a 3%–3 mm 2D gamma test. For all situations, the amplitude of the induced acoustic waves ranged from 0.01 Pa to 1 Pa. Conclusion: Based on the amplitude of the radiation‐induced acoustic waves and accuracy of the reconstructed dose distributions, XACT is a feasible technique for dosimetry in both calibration and in vivo environments for photon and electron beams and merits further investigation. Funding from NSERC, CIHR and McGill University. SH acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".