Evaluation of dead-time corrections for post-radionuclide-therapy 177Lu quantitative imaging with low-energy high-resolution collimators
Bibliographic record
Abstract
OBJECTIVES: Dead-time (DT) effects rarely cause problems in diagnostic single-photon emission computed tomography (SPECT) studies; however, in post-radionuclide-therapy imaging, DT can be substantial. Therefore, corrections may be necessary if quantitative images are used in image-based dosimetry or for evaluation of therapy outcomes. This task is particularly challenging if low-energy collimators are used. Our goal was to design a simple method to determine the dead-time correction factor (DTCF) without the need for phantom experiments and complex calculations. METHODS: Planar and SPECT/CT scans of a water phantom containing a 70 ml bottle filled with lutetium-177 (Lu) were acquired over 60 days. Two small Lu markers were used in all scans. The DTCF based on the ratio of observed to true count rates measured over the entire spectrum and using photopeak primary photons only was estimated for phantom (DT present) and marker (no DT) scans. In addition, variations in counts in SPECT projections (potentially caused by varying bremsstrahlung and scatter) were investigated. RESULTS: For count rates that were about two-fold higher than typically seen in post-therapy Lu scans, the maximum DTCF reached a level of about 17%. The DTCF values determined directly from the phantom experiments using the total energy spectrum and photopeak counts only were equal to 13 and 16%, respectively. They were closely matched by those from the proposed marker-based method, which uses only two energy windows and measures photopeak primary photons (15-17%). CONCLUSION: A simple, marker-based method allowing for determination of the DTCF in high-activity Lu imaging studies has been proposed and validated using phantom experiments.
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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 | 0.008 |
| 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 | 0.000 |
| 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".