Evaluating amplified magnetic resonance imaging as an input for computational fluid dynamics models of the cerebrospinal fluid
Bibliographic record
Abstract
Computational models that accurately capture cerebrospinal fluid (CSF) dynamics are valuable tools to study neurological disorders and optimize clinical treatments. While CSF dynamics interrelate with deformations of the ventricular volumes, these deformations have been simplified and even discarded in computational models because of the lack of detailed measurements. Amplified magnetic resonance imaging (aMRI) enables visualization of these complex deformations, but this technique has not been used for predicting CSF dynamics. To assess the feasibility of using aMRI as an input for computational fluid dynamics (CFD) models of the CSF, we deduced the amplified deformations of the cerebral ventricles from an aMRI dataset and imposed these deformations in our CFD model. Then, we compared the resulting CSF flow rates with those measured in vivo . The aMRI deformations yielded CSF flow following a pulsatile pattern in line with the flow measurements. The CSF flow rates were, however, subject to noise and increased. As a result, scaling of the deformations with a factor 1/8 was necessary to match the measured flow rates. This is the first application of aMRI for modelling CSF flow, and we demonstrate that incorporating non-uniform deformations can contribute to more detailed predictions and advance our understanding of ventricular CSF dynamics.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".