Skip to main content
Search

# Diffusion MRI

3 related articles

Subscribe to updates
  1. arXiv

    Brain age from white matter fiber orientation: 3.54-year mean error across 692 people, and predictions rotate with the input

    Brain age prediction no longer relies only on diffusion tensor scalar metrics: white matter fiber orientation distributions (WM-FODs) enter directly as spherical harmonic coefficients, processed by an SE(3) equivariant CNN that preserves their transformation properties under rotation. On 692 participants aged 36-90 from the Human Connectome Project Aging dataset, this combined framework outperformed the evaluated DTI-based models and conventional CNNs; averaging five cross-validation models gave a mean absolute error of 3.54 years and a Pearson correlation of 0.958. The advantage depended on combining WM-FODs with equivariant processing, and accuracy was highest at maximum spherical harmonic order Lmax = 2.

  2. A preoperative clue for pediatric Wilms tumor subtypes: D* separates stromal from epithelial, mixed and regressive slices

    Pediatric Wilms tumor is treated with preoperative chemotherapy under the SIOP-RTSG 2016 UMBRELLA protocol, and histological subtyping comes only after surgery, leaving no reliable preoperative marker. A retrospective study fitted intravoxel incoherent motion imaging with implicit neural representations and used patient-specific 3D-printed cutting guides to align MRI slices with histopathology slices, comparing parameters across subtypes in 94 slices from 30 lesions. Median D* was significantly higher in stromal than in epithelial, mixed and regressive slices, while D, f and ADC showed no significant differences between subtypes.

  3. arXiv

    Spinverse inverts face permeabilities through a differentiable Bloch-Torrey simulator to reconstruct diverse microstructural interfaces on synthetic meshes

    Spinverse presents a permeability-aware microstructure reconstruction method: on a fixed tetrahedral grid it treats each interior face permeability as a learnable parameter, optimizes face permeabilities by backpropagating a signal-matching loss through a fully differentiable Bloch-Torrey simulator, and recovers an interface by thresholding the learned permeability field; across a collection of synthetic voxel meshes it reconstructs diverse geometries and shows that sequence scheduling and regularization are critical to avoid outline-only solutions while improving boundary accuracy and structural validity.

You’re all caught up