Monte Carlo Simulation and Experimentation of Non-Collinear Gamma Ray Correlations as a Novel Medical Imaging Modality
Bibliographic record
Abstract
This thesis describes the simulation, experiment, and image analysis of a new modality of nuclear medical imaging. We propose the measurement of cascade gamma rays emitted by radioactive isotopes as an improvement over conventional Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT). This modality employs the coincidence detection of non-collinear cascade gamma rays as a means to perform event-by-event image reconstruction. This is unlike PET and SPECT, which require multiple events to locate the image point as they lack depth information of the photon trajectories. Our modality can be considered a marriage of PET and SPECT imaging as it employs coincidence techniques as in PET and collimation as in SPECT.\nFor the prototype experiment, we employed In-111 (2.8 days half-life). with a pair of high-intensity cascade gamma rays of energies Eγ1 = 171 keV and Eγ2 = 245 keV. We performed the measurement using the small-animal PET machine of the Institute of Physical and Chemical Research in Kobe, Japan (RIKEN-BDR). We employed Geant4 Application of Tomographic Emission (GATE) public domain software for the simulation and design of the collimators. We had them built at the Physics Machine Shop of the University of Saskatchewan.\nWe employed the cascade emissions of Ba-133 (half-life=10.5 years) for calibration purposes, procured the In-111 source from suppliers to RIKEN-BDR, and performed the measurements in the summer of 2022. We carried out the measurements for single and multiple point-like source arrangements. We have successfully performed the image reconstruction with the use of individual decay events in the coincidence detection.\nThe results are quite promising, as the image reconstruction and image resolutions are comparable to those of conventional PET images without resorting to involved statistical algorithms. The sources used were <5 MBq and measurements were taken for 2 to 15 hours. Thus, it seems this new modality offers promise of utility for molecular imaging applications. In conclusion, the thesis suggests the next steps in this direction.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".