Using the ACR CT accreditation phantom for routine image quality assurance on both CT and CBCT imaging systems in a radiotherapy environment
Bibliographic record
Abstract
Image-guided radiation therapy using cone-beam computed tomography (CBCT) is becoming routine practice in modern radiation therapy. The purpose of this work was to develop an imaging QA program for CT and CBCT units in our department, based on the American College of Radiology (ACR) CT accreditation phantom. The phantom has four testing modules, permitting one to test CT number accuracy, slice width, low contrast resolution, image uniformity, in-plane distance accuracy, and high-contrast resolution reproducibly with suggested window/levels for image analysis. Additional tests for contrast-to-noise ratio (CNR) and noise were added using the polyethylene and acrylic plugs. Baseline values were obtained from CT simulator images acquired on a Phillips Brilliance Big Bore CT simulator and CBCT images acquired on three Varian CBCTs for the imaging protocols most used clinically. Images were then acquired quarterly over a period of two years. Images were exported via DICOM and analyzed manually using OsiriX. Baseline values were used to ensure that image quality remained consistent quarterly, and baselines were reset at any major maintenance or recalibration. Analysis of CT simulator images showed that image quality was within ACR guidelines for all tested scanning protocols. All three CBCT systems were unable to distinguish the low-contrast resolution plugs and had the same high-contrast resolution over all imaging protocols. Analysis of CBCT results over time determined a range of values that could be used to establish quantitative tolerance levels for image quality deterioration. While appropriate for the helical CT, the ACR phantom and guidelines could be modified to be more useful in evaluating CBCT systems. In addition, the observed values for the CT simulator were well within ACR tolerances.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".