Poster — Thur Eve — 23: Artificial Electron Disequilibrium Due to Inaccurate Cone‐Beam CT Data for Adaptive Lung Radiation Therapy
Bibliographic record
Abstract
Cone‐beam computed tomography (CBCT) is becoming a clinically useful imaging modality for image‐guided adaptive radiation therapy. The Varian On‐Board Imaging system (Varian Medical Systems Inc., Palo Alto California) uses a 125 kVp source, emitting a conical x‐ray beam, and a flat‐panel amorphous silicon detector. Unfortunately, CBCT images are prone to artifacts such as those caused by acceptance of x‐ray scatter from the patient at the detector plane, intra‐fraction motion, and x‐ray spectral “beam‐hardening”. Previous studies indicate that large dose inaccuracies occur when using CBCT lung images for adaptive dose computations, compared to other treatment sites with less tissue heterogeneity. We have compared dose distributions calculated using CBCT and 4‐dimensional (4D) time‐averaged CT (Philips Inc., Cleveland, OH) lung images of the same patient. Using 6MV fields, an under‐dosage of 55Gy was predicted for the CBCT planned target volume, compared to 60Gy predicted by the 4DCT based plan. CT number profiles from CBCT and 4DCT lung images revealed many undervalued pixels in the CBCT data, some corresponding to vacuum (−1000HU)! Monte Carlo simulations of dose deposition, using a water and lung slab geometry, were used to study the effects of ultra‐low density on the 3D dose distribution. It was found that a specific transition‐density induces lateral electron disequilibrium, and causes an undervaluation of dose in mid‐lung, along the central axis of the beam. Thus, CBCT images containing depressed CT number values in lung caused an artificial electron disequilibrium problem, which can be misinterpreted in adaptive treatment re‐planning.
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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 | 0.003 |
| 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 | 0.000 |
| 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 | 0.002 |
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".