Performance characterization of a MVCT scanner using multislice thick, segmented cadmium tungstate‐photodiode detectors
Bibliographic record
Abstract
PURPOSE: Megavoltage computed tomography (MVCT) and megavoltage cone beam computed tomography (MVCBCT) can be used for visualizing anatomical structures prior to radiation therapy treatments to assist in patient setup and target localization. These systems are less susceptible to metal artifacts and provide better CT number linearity than conventional CT scanners. However, their contrast is limited by the properties of the megavoltage photons and the low detective quantum efficiency (DQE) of flat panel detector systems currently available. By using higher DQE, thick, segmented cadmium tungstate detectors, the authors can improve the low contrast detectability of a MVCT system. This in turn would permit greater soft tissue visualization for a given radiation dose, allowing MVCT to be used in more clinical situations. METHODS: This article describes the evaluation of our prototype system that uses thick, segmented detectors. In order to create images using a dose that would be acceptable for day to day patient imaging, the authors evaluated their system using the low intensity bremsstrahlung component of a 6 MeV electron beam. The system was evaluated for its uniformity, high contrast resolution, low contrast detectability, signal to noise ratio, contrast to noise ratio, and CT number linearity. RESULTS: The prototype system was found to have a high contrast spatial resolution of about 5 line pairs per cm, and to be able to visualize a 15 mm 1.5% contrast target with 2 cGy of radiation dose delivered. SNR2 vs radiation dose and mean pixel value vs electron density curves were linear. CONCLUSIONS: This prototype system shows a large improvement in low contrast detectability over current MVCBCT systems.
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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 | 0.002 | 0.004 |
| 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".