Practical considerations for establishing dead-time corrections in quantitative SPECT imaging
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".