Study of novel techniques for verification imaging and patient dose reconstruction in external beam radiation therapy
Bibliographic record
Abstract
Treatment delivery verification is an essential step of radiotherapy. The purpose of this thesis is to develop new methods to improve the verification of photon and electron beam radiotherapy treatments. This is achieved through developing and testing (1) a way to acquire portal images during electron beam treatments, (2) a method to reconstruct the dose delivered to patients during photon beam treatments and (3) a technique to improve image quality in kilovoltage (kV) cone beam computed tomography (CBCT) by correcting for scattered radiation. The portal images were acquired using the Varian CL21EX linac and the Varian aS500 electronic portal imaging device (EPID). The EGSnrc code was used to model fully the CL21EX, the aS500 and the kV CBCT system. We demonstrate that portal images of electron beam treatments with adequate contrast and resolution can be produced using the bremsstrahlung photons portion of the electron beam. Monte Carlo (MC) calculations were used to characterize the bremsstrahlung photons and to obtain predicted images of various phantoms. The technique was applied on a head and neck patient. An algorithm to reconstruct the dose given to patients during photon beam radiotherapy was developed and validated. The algorithm uses portal images and MC simulations. The primary fluence at the detector is back-projected through the patient. CT geometry to obtain a reconstructed phase space file. The reconstructed phase space file is used to calculate the reconstructed dose to the patient using MC simulations. The reconstruction method was validated in homogeneous and heterogeneous phantoms for conventional and IMRT fields. The scattered radiation present in kV CBCT images was evaluated using MC simulations. Simulated predictions of the scatter distribution were subtracted from CBCT projection images prior to the reconstruction to improve the reconstructed image quality. Reducing the scattered radiation was found to improve contrast and reduce shading artifacts. MC simulations, in combination with experimental techniques, have been shown to be valuable tools in the development of treatment verification methods. The three novel methods presented in this thesis contribute to the improvement of radiotherapy treatment verification. They can potentially improve treatment outcome by ensuring a better target coverage.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".