Poster — Thur Eve — 43: Is faster always better? An evaluation of frame rate effects on continuous acquisition mode EPID imaging for dose verification
Bibliographic record
Abstract
INTRODUCTION: As radiotherapy moves towards intensity modulated arc therapy (arc-IMRT), there is a need for electronic portal imaging (EPID) to move towards continuous acquisition (cine) mode for dosimetric verification purposes. However, as the EPID resolution and frame rate (fps) increase, so does the computational burden of image processing. We investigated the reliability of cine mode EPID imaging in IMRT as a function of frame rate. METHODS: We acquired EPID images continuously while running an IMRT plan (6MV photons, 150MU, dose rate = 300MU/min) with frame rates ranging from 1-12 fps, as well as a single integrated mode image. Each cine dataset was then averaged to form a single image, which was compared with the integrated mode image by means of a pixel-by-pixel absolute value subtraction. RESULTS: Although a greater frame rate gave better agreement with the integrated mode image in all cases, the relative benefit diminished with increasing frame rate. In particular, for the IMRT plan delivered, there was little benefit of imaging faster than 6 fps, and virtually no benefit in increasing from 9 to 12 fps. In contrast, 12 fps produces twice the number of images as 6 fps which significantly increases the image processing and data storage burdens. CONCLUSION: Increasing frame rate in cine mode EPID imaging may be beneficial in some cases, but there is likely a threshold level above which no relevant additional information is obtained. Further research to determine the ideal frame rate for any particular IMRT or arc-IMRT plan is warranted.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".