Impact of dead time on quantitative 177Lu-SPECT (QSPECT) and kidney dosimetry during PRRT
Bibliographic record
Abstract
Abstract Background Dead time may affect the accuracy of quantitative SPECT (QPSECT), and thus of dosimetry. The aim of this study was to quantify the effect of dead time on 177Lu-QSPECT and renal dosimetry following peptide receptor radionuclide therapy (PRRT) of neuroendocrine tumours. Methods QSPECT/CT was performed on days 1 and 3 during 564 personalized 177Lu-octreotate cycles in 166 patients. The dead-time data for each scanning time point was compiled. The impact of not correcting QSPECT for the dead time was assessed for the kidney dosimetry. This was also estimated for empiric PRRT by simulating in our cohort a regime of 7.4 GBq/cycle. Results The probability to observe a larger dead time increased with the injected activity. A dead-time loss greater than 5% affected 14.4% and 5.7% of QSPECT scans performed at days 1 and 3, respectively. This resulted in renal absorbed dose estimates that would have been underestimated by more than 5% in 5.7% of cycles if no dead-time correction was applied, with a maximum underestimation of 22.1%. In the simulated empiric regime, this potential dose underestimation would have been limited to 6.2%. Conclusion Dead-time correction improves the accuracy of dosimetry in 177Lu radionuclide therapy and is warranted in personalized PRRT.
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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 | 0.003 | 0.007 |
| 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) | 0.001 | 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".