A bench‐top megavoltage fan‐beam CT using ‐photodiode detectors. II. Image performance evaluation
Bibliographic record
Abstract
Megavoltage computed tomography (MVCT) is a potential imaging tool for positioning and dose delivery verification during image guided radiotherapy. The problem with many MVCT detectors, however, is their low detective quantum efficiency (DQE) which leads to poor low contrast resolution (LCR) and high image noise. This makes separating the tumors from the soft tissue background difficult. This manuscript describes the imaging performance of our bench-top MVCT scanner that uses an 80-element detector array consisting of CdWO4-photodiode elements with a DQE of 19% in 6 MV and 26% in Co60 beams [T. T. Monajemi, S. Steciw, B. G. Fallone, and S. Rathee, "Modelling scintillator-photodiodes as detectors for megavoltage CT," Med. Phys. 31, 1225-1234 (2004)] at zero frequency. The imaging experiments presented were carried out mainly in a Co60 teletherapy unit, while the beam hardening characteristics of the system were also presented for a 6 MV beam. During image evaluation, persistent ring artifacts, caused by air gaps at the ends of the eight-element detector blocks, were removed by using a calibration procedure. The measured contrast of a low contrast target with a 20 mm diameter was determined to be independent of dose, between 2.1 and 17 cGy. The measured LCR of a target with a nominal contrast of 2.8% was reduced from 2.3% to 1.2% when the contrast target diameter was reduced from 15 to 5 mm, using 17 cGy for imaging. The signal to noise ratio of this system is shown to be proportional to the square root of dose. Most importantly, a low contrast target with a diameter of 6 mm and a nominal contrast level of 1.5% is resolved with a radiation dose of 2.1 cGy in the Co60 beam. The spatial resolution in the Co60 beam is limited to one line pair per centimeter mainly due to the size of the Co60 source.
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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) | 0.001 | 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 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".