The effect of object size on the sensitivity of single photon emission computed tomography: comparison of two CZT cardiac cameras and an Anger scintillation camera
Bibliographic record
Abstract
BACKGROUND: Heart sizes vary greatly across the spectrum of patients referred for myocardial perfusion imaging. We therefore performed a phantom study to explore under controlled circumstances how count rates change when different volumes containing the same amount of activity are scanned. Two dedicated cadmium-zinc-telluride cameras, the D-SPECT (Spectrum Dynamics, Caesarea, Israel) and Discovery 530c (D530c, GE Healthcare, Haifa, Israel), and the conventional SPECT Anger (A-SPECT, GE Healthcare, Haifa, Israel) camera are included in the study. METHODS: Different heart sizes were represented by syringes of various column heights mimicking a range of cardiac diameters. Syringes with fixed activity were scanned at five different volumes by successively adding non-radioactive water to the syringes. This procedure was repeated five times on each of the three cameras. Raw count rates were recorded for each scan to determine whether count rates changed with syringe column height. RESULTS: Using mixed-effect regression modeling, a linear relationship was found between count rate and water column height. For the D-SPECT, D530c, and A-SPECT, the changes in count rate for each centimeter increase in water column height were -1.75, +0.28, and -0.022 kilocounts per min per MBq, respectively (95% confidence intervals -1.89 to -1.61, 0.19 to 0.36, and -0.035 to -0.009); all effects are significantly different from each other and significantly different from zero. Average coefficients of variation were 0.080, 0.028, and 0.009. CONCLUSIONS: The D-SPECT demonstrated a significant progressive increase in count rate related to decreasing size of the imaged object. D530c count rate increased slightly with increasing column height. The Anger SPECT showed minimally increased count rates with decreasing column height, an order of magnitude smaller than the D-SPECT based on their relative coefficients of variation.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".