Analytically based photon scatter modeling for a multipinhole cardiac SPECT camera
Bibliographic record
Abstract
PURPOSE: Dedicated cardiac SPECT scanners have improved performance over standard gamma cameras allowing reductions in acquisition times and/or injected activity. One approach to improving performance has been to use pinhole collimators, but this can cause position-dependent variations in attenuation, sensitivity, and spatial resolution. CT attenuation correction (AC) and an accurate system model can compensate for many of these effects; however, scatter correction (SC) remains an outstanding issue. In addition, in cameras using cadmium-zinc-telluride-based detectors, a large portion of unscattered photons is detected with reduced energy (low-energy tail). Consequently, application of energy-based SC approaches in these cameras leads to a higher increase in noise than with standard cameras due to the subtraction of true counts detected in the low-energy tail. Model-based approaches with parallel-hole collimator systems accurately calculate scatter based on the physics of photon interactions in the patient and camera and generate lower-noise estimates of scatter than energy-based SC. In this study, the accuracy of a model-based SC method was assessed using physical phantom studies on the GE-Discovery NM530c and its performance was compared to a dual energy window (DEW)-SC method. METHODS: Tc-SPECT (140 ± 14 keV) were validated with point-source measurements and 15 anthropomorphic cardiac-torso phantom experiments and varying levels of extra-cardiac activity causing scatter inside the heart. The activity inserted into the myocardial compartment of the phantom was first measured using a dose calibrator. CT images were acquired on an Infinia Hawkeye (GE Healthcare) SPECT/CT and coregistered with emission data for AC. For comparison, DEW scatter projections (120 ± 6 keV ) were also extracted from the acquired list-mode SPECT data. Either APD or DEW scatter projections were subtracted from corresponding 140 keV measured projections and then reconstructed with AC (APD-SC and DEW-SC). Quantitative accuracy of the activity measured in the heart for the APD-SC and DEW-SC images was assessed against dose calibrator measurements. The difference between modeled and acquired projections was measured as the root-mean-squared-error (RMSE). APD-modeled projections for a clinical cardiac study were also evaluated. RESULTS: APD-modeled projections showed good agreement with SPECT measurements and had reduced noise compared to DEW scatter estimates. APD-SC reduced mean error in activity measurement compared to DEW-SC in images and the reduction was statistically significant where the scatter fraction (SF) was large (mean SF = 28.5%, T-test p = 0.007). APD-SC reduced measurement uncertainties as well; however, the difference was not found to be statistically significant (F-test p > 0.5). RMSE comparisons showed that elevated levels of scatter did not significantly contribute to a change in RMSE (p > 0.2). CONCLUSIONS: Model-based APD scatter estimation is feasible for dedicated cardiac SPECT scanners with pinhole collimators. APD-SC images performed better than DEW-SC images and improved the accuracy of activity measurement in high-scatter scenarios.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".