SU‐FF‐T‐321: Micro‐Computed Tomography: A Tool for the Determination of the Sensitive Volume of Cylindrical and Plane Parallel Ion Chambers
Bibliographic record
Abstract
Purpose: To use micro‐computed tomography (micro‐CT) as a tool for non‐destructive imaging of air ionization chambers for independent sensitive volume determination and quality assurance of the chambers. Methods and Materials: A GE Locus Micro‐CT imaging system was used to acquire high‐resolution images of several common small‐volume chambers (Exradin T11 plane‐parallel chamber and Exradin A1SL cylindrical chamber). Initial scans were taken of these chambers with a 4cm field of view, 80kVp energy, 450μA tube current, 399 views, and 400ms exposure time per view. Images were reconstructed with 89μm pixel size. GE MicroView visualization software was used to determine the chambers' air volumes for comparison with the manufacturer's specifications. The ability of micro‐CT to differentiate materials and the effect of high density materials on image quality was assessed. In addition, four Exradin A1SL cylindrical chambers were imaged with an increased number of views (720) and acquisition angles (360°) and reconstructed with 20μm pixel size. Air volumes from all of the chambers were compared to the chamber relative ionization signals generated in a 10×10cm2 reference field from a Varian 2100C/D linac. Results: The air cavity volumes derived from the micro‐CT images agreed with nominal volumes given by the manufacturers within 5% for both the cylindrical and plane parallel chambers. The relative response of the cylindrical chambers agreed with the relative volumes (semi‐automatic method) within 2%. The presence of the connecting cables or pins within the chambers did not affect the ability to accurately visualize the sensitive air volumes. Conclusions: Micro‐CT is a promising tool for the measurement of ion chamber air volume and potentially for determining calibration factors for use in dosimetry. These high resolution images could also prove useful as input to Monte Carlo simulations and the calculation of ion chamber response factors from first principles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".