Practical considerations for establishing dead-time corrections in quantitative SPECT imaging
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Bibliographic record
Abstract
Quantitative SPECT studies require specific information about the equipment being used. Particularly in the context of therapeutic studies, the effect of dead-time can be significant and must be quantified. We explored different techniques for measuring the dead-time constant and applying dead-time corrections to the data. METHOD: The dead-time constant was measured on four similar SPECT/CT systems by following the response of the system to a uniform phantom initially containing 17 GBq of Lu-177 over a period of 23 days. It was then calculated using the two-source method with 1 332 MBq of Tc-99 m. The dead-time constant found was used to correct SPECT/CT phantom images either applying the correction by projection or globally on the image. RESULTS: Both methods of calculating the dead-time constant produced equivalent results. However, the dead-time constant varied by as much as 8% between machines of the same model and manufacturer. Correcting for dead-time by projection rather than globally produced slightly more precise results (0.94% error rather than 2.59% error). The benefit of this correction technique will be dependent on the level of asymmetry in the patient as well as the magnitude of the dead-time correction effect. CONCLUSION: quantification of the dead-time of a system can be performed quickly using the two-source method and any radioisotope. However, it is important to perform this measurement on every system being used. In vastly asymmetric images with high dead-time correction, correcting for dead-time by projection can be pertinent, increasing the precision of dosimetry calculations by several percent. However this additional gain may be within the error of SUV measurements for many clinical acquisitions.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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