Sci-Thurs PM: Delivery-11: Image guidance for prostate IMRT using low dose cone beam CT
Bibliographic record
Abstract
Linac-mounted cone beam computed tomography (CBCT) using Varian's On Board Imager (OBI) currently delivers significant imaging dose and lacks automatic methods for clinical target volume (CTV) registration. In this work, we address these two issues to enable frequent treatment corrections during a course of prostate intensity modulated radiation therapy (IMRT). The process starts by acquiring a low dose (low mAs) CBCT image after patient setup. The image is then used in one of two automatic image guidance strategies. The "global" technique provides the couch corrections necessary to improve patient setup by registering the CBCT to the planning CT. The "local" method involves non-rigid registration of the planning CT to the CBCT followed by automatic treatment re-optimization using the deformed planning CT and contours. Thus, the global method attempts to correct patient setup to match the planned treatment, while the local method corrects the treatment to match the patient setup. Both techniques were evaluated using images of an anthropomorphic male pelvis phantom. Global image guidance resulted in a registration error of 3.6 ± 1.3 mm (imaging dose independent) and high treatment doses to the bladder and rectum for large magnitude motion. The local technique always resulted in clinically acceptable treatment doses due to a reduced registration error of 2.3 ± 0.8 mm, obtained at 15% of the OBI's default dose (125 kVp, 2 mAs per projection). These preliminary results show that our automatic local image guidance technique reduces imaging dose and is sufficiently accurate and robust for application in prostate IMRT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".