Fixed, object-specific intensity compensation for cone beam optical CT radiation dosimetry
Bibliographic record
Abstract
Optical cone beam computed tomography (CT) scanning of radiochromic gel dosimeters, using a CCD camera and a low stray light convergent source, provides fast, truly 3D radiation dosimetry with high accuracy. However, a key limiting factor in radiochromic gel dosimetry at large (⩾10 cm diameter) volumes is the initial attenuation of the dosimeters. It is not unusual to observe a 5-10× difference in signal intensity through the dosimeter center versus through the surrounding medium in pre-irradiation images. Thus, all dosimetric information in a typical experiment is measured within the lower 10%-20% of the camera sensor's range, and re-use of gels is often not possible due to a lack of transmission. To counteract this, in this note we describe a simple method to create source compensators by printing on transparent films. This technique, which is easily implemented and inexpensive, is an optical analogue to the bowtie filter in x-ray CT. We present transmission images and solution phantom reconstructions to demonstrate that (1) placing compensators beyond the focal zone of the imaging lens prevents high spatial frequency features of the printed films from generating reconstruction artifacts, and (2) object-specific compensation considerably reduces the range of intensities measured in projection images. This will improve the measurable dose range in optical CT dosimetry, and will enable imaging of larger gel volumes (∼15 cm diameter). Additionally, it should enable re-use of dosimeters by printing a new compensator for a second experiment.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".