Development of a new generation of area detectors for portal imaging: high-quantum-efficiency direct-conversion MV flat-panel imagers
Bibliographic record
Abstract
Recently developed flat-panel detectors have been proven to have a much better image quality than conventional electronic portal imaging devices (EPIDs) used in radiation therapy. They are, however, not yet ideal for portal imaging application primarily due to the low x-ray absorption for megavoltage(MV) x-rays, i.e., low quantum efficiency (QE), typically on the order of 2-4% as compared to the theoretical limit of 100%. A significant increase of QE is desirable for applications such as MV cone-beam computed tomography (MVCT) and MV fluoroscopy. Our goal is to develop a new generation of area detectors for radiotherapy treatment verification, with a QE an order of magnitude higher than that of current flat-panel systems and an equivalent spatial resolution. In this paper, we will first discuss the rationale and the challenges in designing a high QE detector for portal imaging application and give an overview of previous designs and their limitations. We will then introduce our novel design for a high QE detector, which has a thick, dense x-ray direct-conversion layer coupled to a 2D active matrix for image storage and readout. The conversion layer is made of high-density metal elements to convert x-rays to electrons and sub-pixel sized cavities filled with an ionization medium (e.g., gas or a-Se) to convert the electrons to free charges that are collected on electrodes connected to the active matrix. The QE, spatial resolution, and sensitivity of the proposed detector have been modeled, and results will be presented. It is shown that this new detector will be quantum noise limited and have both a high QE and a high resolution. Thus, further development based on this novel design is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".