Poster — Thur Eve — 02: Measurement of CT radiation profile width using Fuji CR imaging plate raw data
Bibliographic record
Abstract
Measuring the CT collimation width and assessing the shape of the overall profile is a relatively straightforward quality control (QC) measure that impacts both image quality and patient dose, and is often required at acceptance and routine testing. Most CT facilities have access to computed radiography (CR) systems, so performing CT collimation profile assessments using CR plates requires no additional equipment. Previous studies have shown how to effectively use CR plates to measure the radiation profile width. However, a major limitation of the previous work is that the full dynamic range of CR detector plates are not used, since the CR processing technology reduces the dynamic range of the DICOM output to 210, requiring the sensitivity and latitude settings of CR reader to be adjusted to prevent clipping of the CT profile data. Such adjustments to CR readers unnecessarily complicate the QC procedure. These clipping artefacts hinder the ability to accurately assess CT collimation width because the full‐width at half maximum value of the penumbras are not properly determined if the maximum dose of the profile is not available. Furthermore, any inconsistencies in the radiation profile shape are lost if the profile plateau is clipped off. In this work we developed an opensource Matlab script for straightforward CT profile width measurements using raw CR data that also allows assessment of the profile shape without clipping, and applied this approach during CT QC.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.016 |
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".