Comparison of two motion compensation models: Adding ordered subset into the mix
Bibliographic record
Abstract
The motion compensation model was introduced to improve statistical accuracy of iterative tomographic image reconstruction in the presence of periodic motion. However, this is achieved at the expense of substantial extra computational burden to handle all gated histograms simultaneously. An alternative model was proposed by re-interpreting the interdependence of the Poisson processes defining the projection data along the motion axis, thus allowing all gated histograms to be merged into a single one for reconstruction. It was demonstrated that much less computational resources were then required to converge using this Merged model, while achieving equivalent image quality. In this work, we investigate the impact of Ordered Subsets (OS) on the convergence rate and image quality of the Merged model compared to the Classical motion compensated model. Comparison of 1, 2, 4 and 8 OS was performed using simulated and experimental datasets. As expected, the gain in convergence rate was roughly proportional to the number of OS for both models and both datasets. Both models also converged to images that were equally well correlated to the ground truth. However, the Merged model was observed to retain its advantage in computational resources over the Classical model, which amounted to a three fold gain for our experimental dataset. In conclusion, the proposed Merged model is a promising approach for imaging applications involving periodic motion, such as cardiac or respiratory gated studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".