Bibliographic record
Abstract
This dissertation examines the clinical feasibility of producing a new imaging procedure coined ‘Particle Neutron Gamma-X Detection (PNGXD)’. PNGXD is a newly proposed imaging concept developed specifically for the application of particle therapy. The premise is to take full advantage of the secondary neutrons produced from both the particle treatment unit and from within the patient by capturing them using a pre-administered gadolinium contrast agent (GDCA) located within the tumor volume [1]. In this thesis, PNGXD was investigated experimentally for proton therapy, a novel method to measure the neutron production was incorporated, and finally, gadolinium (Gd) neutron capture for 10 clinically viable charged particles was studied. In the first section, the first experimental measurements of Gd solution for five different configurations on a Mevion S250 passive double scattering treatment unit were performed. In the second section, a novel method for measuring slow neutrons using a commercial cadmium-telluride (CdTe) detector as an absolute thermal neutron measurement device on a proton therapy unit was investigated. In the third section, the potential to produce PNGXD as a result of secondary produced neutrons from 10 different charged particles all of which may prospectively be utilized for particle therapy was investigated. Lastly, in the final section applications and prospective future studies are discussed regarding the investigation of PNGXD.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".