Rigid-body transformation of list-mode projection data for respiratory motion correction in cardiac PET
Why this work is in the frame
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Bibliographic record
Abstract
High-resolution cardiac PET imaging with emphasis on quantification would benefit from eliminating the problem of respiratory movement during data acquisition. Respiratory gating on the basis of list-mode data has been employed previously as one approach to reduce motion effects. However, it results in poor count statistics with degradation of image quality. This work reports on the implementation of a technique to correct for respiratory motion in the area of the heart at no extra cost for count statistics and with the potential to maintain ECG gating, based on rigid-body transformations on list-mode data event-by-event. A motion-corrected data set is obtained by assigning, after pre-correction for detector efficiency and photon attenuation, individual lines-of-response to new detector pairs with consideration of respiratory motion. Parameters of respiratory motion are obtained from a series of gated image sets by means of image registration. Respiration is recorded simultaneously with the list-mode data using an inductive respiration monitor with an elasticized belt at chest level. The accuracy of the technique was assessed with point-source data showing a good correlation between measured and true transformations. The technique was applied on phantom data with simulated respiratory motion, showing successful recovery of tracer distribution and contrast on the motion-corrected images, and on patient data with C15O and 18FDG. Quantitative assessment of preliminary C15O patient data showed improvement in the recovery coefficient at the centre of the left ventricle.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it