Simulation-based reconstruction of absolute activities from the<sup>99m</sup>Tc/<sup>111</sup>In dual-isotope SPECT/CT: phantom experiments and imaging of neuroendocrine tumors
Bibliographic record
Abstract
We investigate the quantitative accuracy of the reconstruction of absolute (99m)Tc and (111)In activities from (99m)Tc/(111)In dual-isotope SPECT studies. The separate reconstruction of two images is achieved by applying Monte Carlo simulation-based corrections for self-scatter and cross-talk between energy windows. For method evaluation, a series of (99m)Tc/(111)In physical phantom experiments was performed using a clinical SPECT/CT camera. The containers were filled with different ratios of (99m)Tc and (111)In activities to create cross-talk with varying severity levels. In addition, we illustrate the performance of our method by reconstructing images from four simultaneous (99m)Tc/(111)In SPECT/CT studies of neuroendocrine patients. Similarly to the phantom experiments, clinical cases provide examples with different severity of cross-talk. Phantom experiments showed that Monte Carlo simulation-based corrections improved both quantitative accuracy and visual properties of (99m)Tc and (111)In images. While the errors of absolute activities for both tracers in six containers ranged from 16% to 75% if no corrections for self-scatter and cross-talk were applied, these errors decreased to below 10% when images were reconstructed with the aforementioned corrections. These activities were measured using regions of interest larger than the true sizes of the containers in order to account for the spill-out effect. Analysis of patient studies confirmed that accurate simulation-based compensations improved resolution and contrast for both (99m)Tc and (111)In images.
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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) | 0.001 | 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".