A unified filtering method for estimating asymmetric orientation distribution functions
Bibliographic record
Abstract
Numerous filtering methods have been proposed for estimating asymmetric orientation distribution functions (ODFs) for diffusion magnetic resonance imaging (dMRI). It can be hard to make sense of all these different methods, which share similar features and result in similar outputs. In this work, we disentangle these many filtering methods proposed in the past and combine them into a novel, unified filtering equation. We also propose a self-supervised data-driven approach for calibrating the filtering parameter values. Our equation is implemented in an open-source GPU-accelerated python software to facilitate its integration into any existing dMRI processing pipeline. Our method is applied on multi-shell multi-tissue fiber ODFs from the Human Connectome Project dataset (1.25 mm3 native resolution) and on single-shell single-tissue fiber ODFs from the Bilingualism and the Brain dataset (2.0 mm3 isotropic resolution) to evaluate the occurence of asymmetric patterns on different spatial resolutions, representing cutting-edge and “clinical” research data. Asymmetry measures such as the asymmetric index (ASI) and our novel number of fiber directions (NuFiD) are then used to explain the behaviour of our method in these images. The contributions of this work are: (i) the disentanglement and unification of filtering methods for estimating asymmetric ODFs; (ii) a calibration method for automatically fixing the parameters governing the filtering; (iii) an open-source, efficient implementation of our unified filtering method for estimating asymmetric ODFs; (iv) a novel number of fiber directions (NuFiD) index for explaining asymmetric fiber configurations; and (v) a novel template of asymmetries, revealing that our filtering method estimates asymmetric configurations in at least 50% of the brain voxels (∼31% of the white matter and ∼63% of the gray matter).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".