Development of a novel high quantum efficiency MV x‐ray detector for image‐guided radiotherapy: A feasibility study
Bibliographic record
Abstract
PURPOSE: To develop a new scintillating fiber-based electronic portal imaging device (EPID) with a high quantum efficiency (QE) while preserving an adequate spatial resolution. METHODS: Two prototypes were built: one with a single pixel readout and the other with an active matrix flat-panel imager (AMFPI) for readout. The energy conversion layer of both prototypes was made of scintillating fiber layers interleaved with corrugated lead sheets to form a honeycomb pattern. The scintillating fibers have a diameter of 1 mm and the distance between the centers of neighboring fibers on the same layer is 1.35 mm. The layers have 1.22 mm spacing between them. The energy conversion layer has a thickness of 2 cm. The modulation transfer function (MTF), antiscatter properties and sensitivity of the detector with a single pixel readout were measured using a 6-MV beam on a LINAC machine. In addition, a Monte Carlo simulation was conducted to calculate the zero-frequency detective quantum efficiency (DQE(0)) of the proposed detector with an active matrix flat-panel imager for readout. RESULTS: field size is approximately 30% lower than that of ionization chamber-based detectors with a comparable QE. The detector noise which includes the x-ray quantum noise and absorption noise is much larger than the electronic noise per pixel of the flat-panel imager at a dose of less than two LINAC pulses. Thus, the proposed detector is quantum noise limited down to very low doses (∼a couple of radiation pulses of the LINAC). A proof-of-concept image has been obtained using a 6-MV beam. CONCLUSIONS: This work indicates that by using scintillating fibers and lead layers it is possible to increase the thickness of the detecting materials, and therefore the QE or the DQE(0) of the detector, while maintaining an adequate spatial resolution for MV x-ray imaging. Due to the use of lead as the spacing material, the new detector also has antiscatter property, which will help improve the signal-to-noise ratio of the images. Further investigation to optimize the design of the detector and achieve a better combination of DQE and spatial resolution 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 | 0.001 | 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) | 0.001 | 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".