A quality assurance program for image quality of cone‐beam CT guidance in radiation therapy
Bibliographic record
Abstract
The clinical introduction of volumetric x-ray image-guided radiotherapy systems necessitates formal commissioning of the hardware and image-guided processes to be used and drafts quality assurance (QA) for both hardware and processes. Satisfying both requirements provides confidence on the system's ability to manage geometric variations in patient setup and internal organ motion. As these systems become a routine clinical modality, the authors present data from their QA program tracking the image quality performance of ten volumetric systems over a period of 3 years. These data are subsequently used to establish evidence-based tolerances for a QA program. The volumetric imaging systems used in this work combines a linear accelerator with conventional x-ray tube and an amorphous silicon flat-panel detector mounted orthogonally from the accelerator central beam axis, in a cone-beam computed tomography (CBCT) configuration. In the spirit of the AAPM Report No. 74, the present work presents the image quality portion of their QA program; the aspects of the QA protocol addressing imaging geometry have been presented elsewhere. Specifically, the authors are presenting data demonstrating the high linearity of CT numbers, the uniformity of axial reconstructions, and the high contrast spatial resolution of ten CBCT systems (1-2 mm) from two commercial vendors. They are also presenting data accumulated over the period of several months demonstrating the long-term stability of the flat-panel detector and of the distances measured on reconstructed volumetric images. Their tests demonstrate that each specific CBCT system has unique performance. In addition, scattered x rays are shown to influence the imaging performance in terms of spatial resolution, axial reconstruction uniformity, and the linearity of CT numbers.
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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 | 0.068 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".