Impact of the dead-time correction method on quantitative 177Lu-SPECT (QSPECT) and dosimetry during radiopharmaceutical therapy
Bibliographic record
Abstract
BACKGROUND: Lu radiopharmaceutical therapy. We aimed to evaluate the impact of applying dead-time correction on the reconstructed SPECT image versus on the acquisition projections before reconstruction. METHODS: Lu peptide receptor radionuclide therapy were analysed. Dead time was determined based on the acquisition wide-spectrum count rate for each projection and averaged for the entire acquisition. Three dead-time correction methods (DTCMs) were used: the per-projection correction, where each projection was individually corrected before reconstruction (DTCM1, the standard of reference), and two per-volume methods using the average dead-time correction factor of the acquisition applied to all projections before reconstruction (DTCM2) or to the SPECT image after reconstruction (DTCM3). Relative differences in quantification were assessed for various volumes of interest (VOIs) on the phantom and patient SPECT images. In patients, the resulting dosimetry estimates for tissues of interest were also compared between DTCMs. RESULTS: Both per-volume DTCMs (DTCM2 and DTCM3) were found to be equivalent, with VOI count differences not exceeding 0.8%. When comparing the per-volume post-reconstruction DTCM3 versus the per-projection pre-reconstruction DTCM1, differences in VOI counts and absorbed dose estimates did not exceed 2%, with very few exceptions. The largest absorbed dose deviation was observed for a kidney at 3.5%. CONCLUSION: While per-projection dead-time correction appears ideal for QSPECT, post-reconstruction correction is an acceptable alternative that is more practical to implement in the clinics, and that results in minimal deviations in quantitative accuracy and dosimetry estimates, as compared to the per-projection correction.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".