Feasibility of attenuation map alignment in pinhole cardiac SPECT using exponential data consistency conditions
Bibliographic record
Abstract
PURPOSE: Dedicated cardiac SPECT systems do not typically include an integrated CT scanner and thus attenuation correction requires registration of separately acquired transmission scans. Data consistency conditions are equations that express the redundancy between projections while taking into account the attenuation effects. This study assessed the feasibility of applying exponential data consistency conditions to rebinned pinhole projections for attenuation-map registration in pinhole cardiac SPECT. METHODS: Simulations of an anthropomorphic computer phantom with three different tracer activity distributions were performed with and without clinical levels of noise in the projection data. The first activity distribution contained activity only within the myocardium which satisfied the assumptions of the data consistency conditions. The other two distributions violated these assumptions by adding background activity and uptake in the liver. Simulations included acquisitions with 360, 31, and 9 pinhole projections and detector pixel sizes of 0.75 and 2.5 mm. A metric based on the average difference between pairs of exponential projections was used to evaluate registration accuracy. RESULTS: When activity is restricted to the myocardium, the registration error was 3.0 mm for 31 noisy pinhole projections with a detector size of 2.5 mm. When activity is added to the background and the liver, a correction for the extra-cardiac activity is needed but when applied, a registration error of 6.0 mm was achieved. CONCLUSION: These results suggest that it may be feasible to use exponential data consistency conditions to register pinhole cardiac SPECT and CT transmission data. Taxonomy: 8-6 (IM-SPECT/Registration).
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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 | 0.024 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".