Performance assessment of motion correction for different distributions and count levels
Bibliographic record
Abstract
Motion correction methods are used to compensate for subject motion during a positron emission tomography (PET) scan, reducing the degradation in image quality and quantification and consequently improving the accuracy of data analysis. In this work a frame-by-frame realignment method applied to a [11C]raclopride dynamic brain scan has been assessed, where the scan has 26 frames of different count levels and a varying spatiotemporal distribution. The performance of the motion correction in its two modes (normal and enhanced) is assessed using list-mode bootstrapping giving insights into the precision (and not accuracy) of the motion correction with respect to both the count level and distribution of the radioligand. It is found that the precision of motion correction deteriorates with decreasing count level (as expected in short low-count frames) and varies according to the radial and axial position in the field of view (FOV). Also the precision is affected by the varying distribution of the radioligand in frames with similar count levels, i.e., the greater concentration of the radioactivity in the striatal regions (and lower radioactivity concentration in the cortical areas) decreases the precision. Of particular note is the observation that the precision is also considerably degraded when the enhanced mode is used-indicating that the improved motion correction accuracy comes at the expense of increased variability (hence the enhanced mode simply offers a different bias-variance trade-off). It becomes clear that although such frame-by-frame motion correction method does not require an external motion tracking device it is consequently affected by the count level in each frame, radioligand distribution, voxel position in the FOV and the mode of coregistration. Such assessment can be applied to any coregistration of either PET or SPECT images especially with low counts.
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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 | 0.004 | 0.019 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".