Motion correction of PET images using multiple acquisition frames
Bibliographic record
Abstract
Positron emission tomography (PET) is a relatively lengthy brain imaging method. Because it is difficult for the patient to stay still during the data acquisition, head motion during scans are a source of image degradation. To reduce the degradation, a simple data acquisition technique is described. This technique associates the incoming data with the real-space position of the head. It consists of constantly monitoring the head position during PET studies and comparing the head position to the initial head position associated with the current acquisition frame. Every time the maximum RMS displacement within the field of view (FOV) is larger than a specified threshold value, the data acquisition system starts acquiring the data into a new frame. A complete study is then acquired over several frames. The number of frames required depends on the motion of the head during the study and on the threshold value. At the end of the study all the acquired frames are reconstructed independently and each image is rotated and translated according to its associated initial head position. When these images are added together, it will produce a final image with fewer motion artefacts.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".