Applying Exponential Data Consistency Conditions in SPECT with Multiple Activity Regions
Bibliographic record
Abstract
For conventional models of exponential SPECT (single photon emission computed tomography) projections, all the activity is required to be inside a convex region of homogeneous attenuation. In this work, we show that for certain applications, this restriction can be relaxed, while remaining mathematically correct. We point out that a subset of attenuated projections can be converted to exponential projections if different regions of activity can each be contained within different convex regions of homogeneous attenuation. Then, if the attenuated projection data have projection views where the different activity regions are separable, meaning there is no non-zero overlap in the two regions, the process of conversion to exponential projections can be applied separately to each. Thus, the combination by summation of each individual region converted into exponential projections gives the same result as converting those separable projections in the combined data set to exponential projections. Using an NCAT computer phantom, an activity distribution was set up simulating a heart with constant attenuation and a liver with constant attenuation, with no background activity, but with variable attenuation between the two regions due to the lungs. A set of attenuated parallel-hole projections were simulated including projection views where the heart activity sinogram and liver activity sinogram could be separated. Converting only the separable projection views to exponential projections, data consistency conditions on those exponential projections were applied to assess a data consistency-based attenuation map alignment method.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".