SU‐E‐J‐143: Respiratory‐Correlated Cone‐Beam CT for Potential Use in Measuring Lung Tumor Trajectory
Bibliographic record
Abstract
Purpose: Respiratory‐correlated (4D) CT scans produce a set of images corresponding to different phases of the breathing cycle. In lung cancer treatment, a potential application of 4D cone‐beam CT (CBCT) scans taken on the treatment unit is to measure the tumor trajectory before each treatment, to verify whether the motion remains stable over the course of treatment. This presentation describes techniques we are developing for tumor trajectory measurement with 4D CBCT on our Varian linacs. Methods: A CBCT scan of a phantom placed on a periodically moving platform is taken on a Varian iX unit. The Varian RPM system is used to record the “breathing” phase vs time, and this information is used to sort the projection images into phase bins. For each phase bin an image is reconstructed using conventional Feldkamp‐Davis‐Kress filtered backprojection. The “tumor” region is contoured manually on one phase image, and a deformable registration algorithm is used to calculate the shift in points on the regionˈs surface from this phase to each other phase. The centroid of these surface points is used as a measure of the tumor position. Results: The deformable registration algorithm succeeds on test data consisting of clinical 4DCT scans of lung cancer patients taken on a CT simulator. Tumor motions of a few mm are measured. Preliminary 4D CBCT phantom images taken at the normal gantry rotation speed of 6 degrees/second show significant streak artifacts since the angular spacing between projections is large (>10 degrees); this low image quality is not suitable for the registration algorithm. Conclusions: Deformable image registration between phases is a practical method to measure lung tumor trajectory. In a 4D CBCT scan, reducing the gantry rotation speed is necessary to improve image quality: we plan to investigate this on a Varian TrueBeam unit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".