Correcting organ motion artifacts in x‐ray CT systems based on tracking of motion phase by the spatial overlap correlator. II. Experimental study
Bibliographic record
Abstract
This paper presents the experimental part of an investigation on tracking and eliminating organ motion artifacts in x-ray CT cardiac applications with emphasis on imaging coronary calcification. The system methodology consists of a software implementation of the spatial overlap correlator (SSOC) concept in x-ray CT scanners to track the net amplitude and phase of organ motion during the CT data acquisition process. A coherent sinogram synthesis (CSS) method is then used to identify the repeated phases of a periodic organ motion from the information provided by the SSOC process and hence synthesize a new sinogram with no motion effects. Since the SSOC scheme is capable of tracking cardiac motion, it identifies also the projection points associated with minimum amplitude cardiac motion effects. These points are used to identify a 180 degrees plus the fan angle sinogram for image reconstruction. This leads to a retrospective gating (RG) scheme that is based on the output of the SSOC process. Performance comparison of the proposed methodology with the retrospective ECG gating using real data sets with phantoms and human patients provides a performance assessment of the merits of the proposed methods. Real results demonstrate that the new methodology eliminates the requirement for ECG gating. Moreover, the CSS and the new RG methods do not require breath holding and they can be implemented in x-ray CT scanners to image coronary calcification and the heart's ventricles.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".