X-ray scatter in quantitative megavoltage computed tomography: implications for adaptive radiation therapy
Bibliographic record
Abstract
The emergence of helical tomotherapy has provided a unique opportunity to combine aspects of diagnostic computed tomography and radiation treatment. Daily megavoltage computed tomography (MVCT) scans of a patient in the treatment position provide an ideal input for adaptive radiation therapy, whereby the quantitative CT knowledge of a patient from a treatment fraction combined with the knowledge of the therapy dose distribution can be used to alter and correct for the dose delivery in subsequent fractions. In order for adaptive radiotherapy to be successful, the quantitative information from the CT scan must be as accurate as possible in geometric and dosimetric information. One potential impediment to the accuracy of the CT data values is x-ray scatter. In our study, we quantify the magnitude of x-ray scatter in the tomotherapy (fan-beam) MVCT system, based on Monte Carlo simulations of the scatter-to-primary ratio (SPR) as a function of incident x-ray energy, fan-beam slice thickness, patient size, and air gap distance. Furthermore, based on these SPR values, the impact on CT number accuracy is shown, and the implications for adaptive radiotherapy (i.e. dose reconstruction) are discussed. Under conditions common to tomotherapy MVCT scanning, SPR values range from 0.02 to 0.16 (depending on the size of the phantom), and are generally lower than those encountered in diagnostic cone-beam CT and megavoltage portal imaging. These SPR values are sufficient enough to introduce CT number errors as high as 5 HU in soft-tissue and 100 HU in bone. The implication of this inaccuracy for adaptive radiotherapy would be to cause potential dose calculation errors during dose reconstruction and 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".