Ionization chamber volume determination and quality assurance using micro-CT imaging
Bibliographic record
Abstract
Ion chamber dosimetry requires a high degree of precision, at all steps within the dosimetric process, in order to ensure accurate dose measurements. This work presents a novel technique for ion chamber volume determination and quality assurance, using micro-computed tomography (micro-CT). Four nominally identical Exradin A1SL chambers (0.056 cm(3)) (Standard Imaging, WI, USA) were imaged using a micro-CT system (GE Locus, GE Healthcare, London, Ontario) and irradiated in a 6 MV x-ray reference field. Air volumes were calculated from the CT datasets using 3D analysis software (Microview 2.1.1, General Electric Healthcare, London, Ontario). Differences in the volumes of each chamber determined using micro-CT images agreed with differences in the ionization response within 1% for each chamber. Calibration coefficients were then compared through cross-calibration with a calibrated ion chamber and from the CT-measured volumes. The average ratio of these values was found to be 0.958 +/- 0.009 indicating good correlation. The results demonstrate the promise of using micro-CT imaging for the absolute volumetric characterization of ion chambers. The images have the potential to be an important clinical tool for quality assurance of ion chamber construction and integrity after routine clinical usage.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".