Quantitative SPECT (QSPECT) at high count rates with contemporary SPECT/CT systems
Bibliographic record
Abstract
Abstract Background Accurate QSPECT is crucial in dosimetry-based, personalized radiopharmaceutical therapy with 177Lu and other radionuclides. We compared the quantitative performance of three NaI(Tl)-crystal SPECT/CT systems equipped with low-energy high-resolution collimators from two vendors (Siemens Symbia T6; GE Discovery 670 and NM/CT 870 DR). Methods Using up to 14 GBq of 99mTc in planar mode, we determined the calibration factor and dead-time constant under the assumption that these systems have a paralyzable behaviour. We monitored their response when one or both detectors were activated. QSPECT capability was validated by SPECT/CT imaging of a customized NEMA phantom containing up to 17 GBq of 99mTc. Acquisitions were reconstructed with a third-party ordered subset expectation maximization algorithm. Results The Siemens system had a higher calibration factor (100.0 cps/MBq) and a lower dead-time constant (0.49 μs) than those from GE (75.4–87.5 cps/MBq; 1.74 μs). Activities of up to 3.3 vs. 2.3–2.7 GBq, respectively, were quantifiable by QSPECT before the observed count rate plateaued or decreased. When used in single-detector mode, the QSPECT capability of the former system increased to 5.1 GBq, whereas that of the latter two systems remained independent of the detectors activation mode. Conclusion Despite similar hardware, SPECT/CT systems’ response can significantly differ at high count rate, which impacts their QSPECT capability in a post-therapeutic setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".