Free-breathing Motion Compensation Using Template Matching
Bibliographic record
Abstract
Modeling tracer kinetics from dynamic magnetic resonance imaging (dMRI) to understand microvascular characteristics typically requires acquisitions longer than 1 breath-hold. This has limited the application of dMRI in assessment of the upper abdomen. Here we present a template-based motion correction strategy for dMRI of liver metastases based on the correlation coefficient (CC), originally developed for tracking coronary arteries. This postprocessing method allows patient free breathing during sagittal dMRI acquisition and allows a more precise parametric mapping using tracer kinetic models. In a study of 6 subjects, a 64 x 64 template was accurately tracked retrospectively with mean CC = 0.72 +/- 0.07. Mean superior-inferior displacement tracked was 1.82 +/- 1.20 pixels, whereas mean anterior-posterior displacement was 7.72 +/- 4.58 pixels. Application of the CC method significantly improved the global fit (chi2) of a tracer kinetic model throughout tumor regions. Therefore, use of the CC postprocessing method for dMRI scans can improve the precision of dMRI tracer kinetic models.
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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 | 0.005 |
| 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) | 0.002 | 0.002 |
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".