<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
Bibliographic record
Abstract
Abstract This work aims to investigate the accuracy of quantitative SPECT imaging of 177Lu in the presence of 90Y, 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 177Lu and 90Y 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 177Lu and 90Y 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 177Lu activity quantification and contrast-to-noise ratios (CNRs) were determined. In all configurations, the quantification error was within ± 6% of the no-90Y case, and we found that quantitative accuracy may slightly improve when 90Y is present because of reduction of errors associated with TEW scatter correction. The CNRs were not significantly impacted by the presence of 90Y, 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 177Lu activity. Based on these results, we can conclude that activity quantification of 177Lu and lesion detectability is not degraded by the presence of 90Y.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".