Dosimetric investigation and portal dose image prediction using an amorphous silicon electronic portal imaging device
Bibliographic record
Abstract
A two step algorithm to predict portal dose images in arbitrary detector systems has been developed recently. The current work provides a validation of this algorithm on a clinically available, amorphous silicon flat panel imager. The high-atomic number, indirect amorphous silicon detector incorporates a gadolinium oxysulfide phosphor scintillating screen to convert deposited radiation energy to optical photons which form the portal image. A water equivalent solid slab phantom and an anthropomorphic phantom were examined at beam energies of 6 and 18 MV and over a range of air gaps (approximately 20-50 cm). In the many examples presented here, portal dose images in the phosphor were predicted to within 5% in low-dose gradient regions, and to within 5 mm (isodose line shift) in high-dose gradient regions. Other basic dosimetric characteristics of the amorphous silicon detector were investigated, such as linearity with dose rate (+/- 0.5%), repeatability (+/- 2%), and response with variations in gantry rotation and source to detector distance. The latter investigation revealed a significant contribution to the image from optical photon spread in the phosphor layer of the detector. This phenomenon is generally known as "glare," and has been characterized and modeled here as a radially symmetric blurring kernel. This kernel is applied to the calculated dose images as a convolution, and is successfully demonstrated to account for the optical photon spread. This work demonstrates the flexibility and accuracy of the two step algorithm for a high-atomic number detector. The algorithm may be applied to improve performance of dosimetric treatment verification applications, such as direct image comparison, backprojected patient dose calculation, and scatter correction in megavoltage computed tomography. The algorithm allows for dosimetric applications of the new, flat panel portal imager technology in the indirect configuration, taking advantage of a greater than tenfold increase in detector sensitivity over a direct configuration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".