Investigations in x-ray computed tomography polyacrylamide gel dosimetry
Bibliographic record
Abstract
Polyacrylamide gels (PAGs) are radiosensitive materials currently under development for use as three dimensional (3D) dosimeters in radiation therapy. Dose information is recorded in the gels and extracted through imaging. X-ray computed tomography (CT) has emerged as a promising gel imaging method due to a change in gel density that occurs upon irradiation. The accessibility of CT technology to cancer hospitals makes CT read-out clinically attractive, however the technique remains of limited clinical use due in part to poor dose resolution. This thesis investigates the use of CT for extracting dose information from PAG with an overall goal to improve achievable dose resolution. Thesis results are divided into three studies: a gel compositional study, a study of noise and dose resolution and a digital filtering study. The first study investigates the effects of gel composition on PAG CT dose response and the underlying density change. Systems for irradiating and imaging gels are designed and tested and dose response reproducibility is established. Results indicate dramatic variation in CT dose response sensitivity and range with gel composition. A model is developed to describe gel density change with dose, revealing two fundamental properties of the density to dose response: the density change that occurs per unit polymer yield is highest for gels with low and high concentrations of crosslinking molecule (%C) and the dose response sensitivity is linearly dependent on the total concentration of monomer in the gel. The second study investigates strategies for minimizing noise in x-ray CT polymer gel dosimetry and assesses system performance. Specifically, the effects of phantom design, scanning technique and image voxel size on image noise are investigated. This work leads to the establishment of a method of predicting image noise for any given CT imaging protocol. Image uniformity is also assessed, in the context of noise levels in gel dosimetry. The effect of scanning protocol on imaging time is established and the dose resolution achievable with an optimized system is calculated given voxel size and imaging time constraints. These results, when compared with published values for MRI and optical CT gel dosimetry indicate that CT dose resolution (e.g. 5%, 1 x 1 x 3 mm³ voxels), is still not at the level of the best MRI or optical CT techniques, however fast imaging times makes the rapid acquisition of volumetric data most feasible with x-ray CT. The third study investigates the potential of image filtering for improved dose resolution in CT gel dosimetry. CT image noise is characterized as Gaussian distributed and independent of signal strength and niters for reducing spatially invariant noise are investigated: mean, median, midpoint, adaptive mean, alpha-trimmed mean, sigma mean and a relatively new filter called SUSAN. The filters are tested on a CT image of a PAG irradiated with a clinically relevant dose distribution. Filter performance varies greatly in both achieved dose resolution and affects on the spatial distribution of dose. The ADAPTIVE and SUSAN filters provide the best overall performance, more than halving the dose resolution without significantly distorting the spatial distribution of dose. In summary, this thesis provides new insight into the fundamental nature of PAG density to dose response, develops strategies for minimizing image noise and quantifies system performance and demonstrates that digital image filtering is an effective tool to provide additional improvements to dose resolution.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".