Collimator design for gamma-ray cascade angular correlations in medical imaging
Bibliographic record
Abstract
Abstract In recent decades, Single Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have become workhorses of molecular imaging. The main drawback of these modalities is that they resort to statistical methods of image reconstruction, as the location of the individual nuclei emitting radiation is not accessible. To remedy this situation, we embarked on a project to introduce a new modality of nuclear medical imaging which exploits the non-collinear angular correlations of nuclear gamma-ray cascades subsequent to beta decays. This modality, if effective, determines the location of each decay nucleus. We have retrofitted the small animal PET assembly of RIKEN-Kobe (Japan) with tungsten+PLA (polylactic acid) collimators. The chosen material is inexpensive, amenable to three-dimensional (3D) printing, and has good photon attenuation properties. We included the collimator geometry in GATE (Geant4 Application for Tomographic Emission) simulations and developed algorithms for image reconstruction with medical isotope candidates viz., 111In and 43K. Our preliminary simulations show that data acquisition with a 2 MBq source for 900 s is sufficient to reproduce the source geometry with high resolution. This report summarizes our progress to date and the plans for the near future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".