<sup>177</sup>Lu SPECT imaging in the presence of <sup>90</sup>Y: does <sup>90</sup>Y degrade image quantification? a simulation study
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Bibliographic record
Abstract
Abstract This work aims to investigate the accuracy of quantitative SPECT imaging of 177 Lu in the presence of 90 Y, which occurs in dual-isotope radiopharmaceutical therapy (RPT) involving both isotopes. We used the GATE Monte Carlo simulation toolkit to conduct a phantom study, simulating spheres filled with 177 Lu and 90 Y placed in a cylindrical water phantom that was also filled with activity of both radionuclides. We simulated multiple phantom configurations and activity combinations by varying the location of the spheres, the concentrations of 177 Lu and 90 Y in the spheres, and the amount of background activity. We investigated two different scatter window widths to be used with triple energy window (TEW) scatter correction. We also created multiple realizations of each configuration to improve our assessment, leading to a total of 540 simulations. Each configuration was imaged using a simulated Siemens SPECT camera. The projections were reconstructed using the standard 3D OSEM algorithm, and errors associated with 177 Lu activity quantification and contrast-to-noise ratios (CNRs) were determined. In all configurations, the quantification error was within ± 6% of the no- 90 Y case, and we found that quantitative accuracy may slightly improve when 90 Y is present because of reduction of errors associated with TEW scatter correction. The CNRs were not significantly impacted by the presence of 90 Y, but they were increased when a wider scatter window width was used for TEW scatter correction. The width of the scatter windows made a small but statistically significant difference of 1%–2% on the recovered 177 Lu activity. Based on these results, we can conclude that activity quantification of 177 Lu and lesion detectability is not degraded by the presence of 90 Y.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it