Spatial resolution properties of digital autoradiography systems for pre-clinical alpha particle imaging (Conference Presentation)
Bibliographic record
Abstract
Attaching alpha-emitting radionuclides to cancer-targeting agents increases the anti-tumor effects of targeted cancer therapies. The success of alpha therapy for treating bone metastases has increased interest in using targeted alpha therapy (TAT) to treat a broad spectrum of metastatic cancers. Estimating radiation doses to targeted tumors, including small (<250 μm) clusters of cancer cells, and to non-targeted tissues is critical in the pre-clinical development of TATs. However, accurate quantification of heterogeneous distributions of alpha-emitters in small metastases is not possible with existing pre-clinical in-vivo imaging systems. Ex-vivo digital autoradiography using a scintillator in combination with an image intensifier and a charged coupled device (CCD) has gained interest for pre-clinical ex-vivo alpha particle imaging. We present a simulation-based analysis of the fundamental spatial resolution limits of digital autoradiography systems. Spatial resolution was quantified in terms of the modulation transfer function (MTF) and Wagner's equivalent aperture. We modeled systems operating in either particle-counting (PC) or energy-integrating (EI) mode using a cascaded systems approach that accounts for: 1) the stopping power of alpha particles; 2) the distance alpha particles travel within the scintillator; 3) optical blur, and; 4) binning in detector elements. We applied our analysis to imaging of astatine-211 using an LYSO scintillator with thickness ranging from 10 μm to 20 μm. Our analysis demonstrates that when these systems are operated in particle-counting mode with a centroid-calculation algorithm, the effective apertures of ~35 μm can be achieved, which suggests that digital autoradiography may enable quantifying the uptake of alpha emitters in tumors consisting of a few cancer cells. Future work will investigate the image noise and energy-resolution properties of digital autoradiography systems.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".