Metric for fast automated relative assessment of motion correction methods for dynamic PET imaging
Bibliographic record
Abstract
This work presents a metric for rapid assessment of motion correction quality to assist comparison between alternative motion correction methods for dynamic PET imaging with the high resolution research tomograph (HRRT). The designed metric allows automatic selection between motion correction methods without visual inspection and has been tested on simulated and real data. The metric relies on the sum of absolute voxel-by-voxel differences for consecutive frames. Noise in the reconstructed images can make it difficult to correctly distinguish between different motion correction methods and can significantly affect the numerical result of voxel-by-voxel differences for consecutive frames. To reduce the noise component, a low pass filter was applied by using an optimised Gaussian kernel (the optimisation was based on simulated data using a numerical brain phantom). Results from 26 real scans are reported. A newly improved motion correction is shown to perform better than the formerly used method. From the 26 cases considered, the proposed metric favoured use of the new motion correction for 23 cases, and only 3 cases were better under the old motion correction. The method presents a fast and effective way of comparing the relative efficacy of motion correction methods, without any need of a gold standard or reference image.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".