Automatic image guidance for prostate IMRT using low dose CBCT
Bibliographic record
Abstract
PURPOSE: Varian's On-Board Imager is a linac-integrated cone-beam CT (CBCT) system used at the authors' institution to acquire images prior to delivering each fraction of prostate intensity modulated radiotherapy. The images are used to determine a couch shift that realigns the tumor with the position obtained in the planning CT. However, this manual image-guided radiotherapy (IGRT) technique is operator dependent, time consuming, offers limited degrees of freedom, and requires significant imaging dose over the course of treatment. To overcome these problems, the authors propose two fully automatic IGRT techniques that require significantly less imaging dose. METHODS: Dose is reduced by lowering the x-ray tube mA s during CBCT acquisition at the cost of increasing image noise. In "forward" IGRT, the CBCT image is automatically registered to the planning CT to obtain the necessary couch shift. The "reverse" technique offers additional degrees of freedom as it involves nonrigid registration of the planning CT to the CBCT. Both techniques were evaluated using images of an anthropomorphic phantom with simulated motion and by retrospectively analyzing data from ten prostate cancer patients. RESULTS: IGRT error for the phantom data at 100% relative imaging dose was 8.2 +/- 3.7, 3.5 +/- 1.2,, and 2.1 +/- 0.6 mm for setup only, forward, and reverse techniques, respectively. For patient images acquired at 100% relative imaging dose, the errors were 5.4 +/- 1.7, 5.0 +/- 1.6, 5.0 +/- 2.0, and 4.0 +/- 1.6 mm for setup only, manual forward (performed clinically), automatic forward, and reverse IGRT, respectively. Furthermore, imaging dose could be reduced to 20% without a significant loss in image guidance accuracy. CONCLUSIONS: The presented image guidance methods are accurate while requiring only 20% of the standard imaging dose. The combination of low dose, automation, and accuracy enables frequent corrections during treatment, possibly leading to reduced margins and improved treatment outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".