Sci‐PM Sat ‐ 02: Development of a novel high quantum efficiency flat panel detector for megavoltage cone beam CT: An experimental study
Bibliographic record
Abstract
Soft tissue imaging in the treatment room is one of the main challenges faced today in high precision radiotherapy. Megavoltage cone‐beam CT (MVCT) is a promising new imaging technique for image‐guided radiotherapy due to its simplicity and its potentially higher accuracy. However, currently the dose required to achieve sufficient soft tissue contrast to visualize and delineate, e.g., the prostate using MVCT is prohibitively large for daily verification. This is due to the low x‐ray absorption of the electronic portal imaging devices (EPIDs) used, i.e., low quantum efficiency (QE), which is typically on the order of 2–4%. Our overall goal is to develop a new generation of area detectors for MVCT, with a QE an order of magnitude higher than that of current EPIDs and yet an equivalent spatial resolution. With this new generation of detectors, the large dose currently required to visualize and delineate soft‐tissue targets with MVCT will be significantly reduced, and image‐guided radiotherapy using MVCT can be realized. In this work, we constructed a prototype single‐pixel detector based on the novel design introduced recently by Pang and Rowlands. Some fundamental imaging properties including the QE, spatial resolution, and sensitivity of the prototype detector were measured with a 6MV beam. It has been shown that the experimental results agree with our theoretical predictions and further development based on the novel design including the construction of a prototype area detector is warranted. This work was supported by the Department of Defense Prostate Cancer Research Program (DAMD17‐04‐1‐0276).
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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 | 0.000 |
| 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 | 0.001 |
| 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".