Preterm neonatal diffusion processing using detection and replacement of outliers prior to resampling
Bibliographic record
Abstract
In diffusion weighted MRI, subject motion and brain pulsation lead both to signal drop-outs and image misalignment. Unsedated neonates, with their higher heart rate and propensity for motion are particularly prone to degraded scan quality that impairs diffusion tensor estimation. Retrospective registration and robust estimators are two methods that have previously been demonstrated to address motion and intensity outliers, respectively, in diffusion data. However, when taken together, the resampling of images to correct for misalignment can have the effect of averaging outlier voxels with uncorrupted voxels, thereby making outliers more difficult to detect. This article presents a method to remove outliers prior to resampling while taking misalignment into account so that this averaging of outliers with good data can be avoided. The proposed method is compared to other processing pipelines using simulations and data from unsedated preterm neonates. These results demonstrate advantages to the proposed method, particularly in subjects with high motion.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".