Diffusion MRI Analysis Techniques Inspired by the Preterm Infant Brain
Bibliographic record
Abstract
Diffusion MRI (dMRI) is a powerful imaging modality that allows us to non-invasively examine the organization and integrity of fibrous tissue, particularly the brain's white matter.The result of a dMRI scan is a 3D image where each voxel contains a model that describes the local diffusion pattern of water molecules.The complicated nature and high dimensionality of these voxel-wise models make dMRI analysis especially challenging.The challenges increase when we look at dMRI scans from infants born prematurely.The smaller brain size for these infants, and the still-emerging brain structures these infants possess, increase the challenges involved in processing, analyzing, and interpreting dMRI scans.This thesis introduces four computational contributions in the area of dMRI analysis that attempt to address challenges exacerbated when imaging the preterm infant brain.Specifically, these four contributions are: (a) the first information content estimators for unaltered dMRI data, including a mutual information estimator for image registration; (b) a novel dMRI segmentation algorithm based on a cross-sectional piecewise constant image model;(c) the first global, closed-form probabilistic tractography algorithm, one where tracts compete for space in the brain; and (d) STEAM: the first patient-specific statistical abnormality mapping technique.In these four contributions, we model the dMRI data more accurately in order to improve the accuracy of dMRI analysis techniques, particularly in the presence of the small brain sizes and still-emerging brain structures seen in preterm infant dMRI.We further make the source code for these contributions publicly available to aid in the reproducibility of our research.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".