Applying multi-cohort transfer-learned super-resolution 4D Flow MRI to reconstruct 0.6-mm isotropic cerebrovascular flow fields preserves topological persistent homology metrics of vascular networks significantly better than mass-univariate voxel-based morphometry (VBM) spatial normalization.
Adversarial Debate Score
48% survival rate under critique
Expert panel critique
Independent views, each critiquing the hypothesis on its own — the score rewards genuine disagreement and discounts consensus.
Supporting Research Papers
- Toward Clinical Translation of Deep Learning-Based Intracranial Super-Resolution 4D Flow MRI Using Multi-Cohort Transfer Learning
We evaluated synthetic pretraining and multi-cohort transfer learning for deep learning-based super-resolution of intracranial 4D Flow MRI across heterogeneous neurologic cohorts. This mixed prospecti...
- Homology-based Morphometry of Brain Atrophy: Methods and Applications
Understanding the structure of the brain, and how it changes with time and disease, is a core goal of structural neuroimaging. Contemporary approaches to structural brain analysis are dominated by vox...
- Flow3r: Factored Flow Prediction for Scalable Visual Geometry Learning
Current feed-forward 3D/4D reconstruction systems rely on dense geometry and pose supervision -- expensive to obtain at scale and particularly scarce for dynamic real-world scenes. We present Flow3r, ...
- Posterior Augmented Flow Matching
Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-dimensional images, each training sample supervise...
Formal Verification
Z3 checks whether the hypothesis is internally consistent, not whether it is empirically true.