Sci-Fri PM: Delivery - 12: Performance of Two Automated Image Guidance Techniques Employing Low Dose CBCT
Bibliographic record
Abstract
Image-guided radiotherapy (IGRT) is becoming the standard treatment for prostate cancer. One approach employs a linear accelerator-mounted, cone-beam computed tomography (CBCT) system to acquire 3D images at treatment. Typically, radiation therapists manually register such images to the planning CT to determine couch shifts, thereby ignoring anatomical rotations and deformations. Furthermore, CBCT systems deliver significant imaging doses over long fractionation schedules. Therefore, we continue developing image guidance (IG) techniques relying on automatic registration and low dose CBCT images. Our “global” method computes couch corrections while the “local” variant provides a deformable transformation that can be used to adapt the original plan. Previous validation with Varian's On-board Imager (OBI v1.3) showed that IG error is maintained despite reducing the mAs to 15% of the standard 1300. Recent improvements in OBI v1.4 result in similar image quality at 680 mAs (“pelvis” mode). Additionally, “pelvis spotlight” mode was introduced with additional lateral collimation and 720 mAs employed over 200 degrees. Retesting showed that global IG error is 3.5 ±1.0 mm irrespective of the OBI version down to 10% of the standard dose. Local IG required 20% of the standard dose to achieve 1.8 ± 0.6 mm accuracy with OBI v1.4 pelvis, while the 2.0 ± 0.5 mm error was maintained down to 15% with OBI v1.3 and v1.4 spotlight. In absolute terms, the dose savings achieved by our IG methods are cumulative with those offered by the upgrade. Our local IG technique has great potential to significantly improve the precision of radiation therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".