Titlebook: Computational Diffusion MRI; 14th International W Muge Karaman,Remika Mito,Stefan Winzeck Conference proceedings 2023 The Editor(s) (if app

Allege

,A Unified Learning Model for?Estimating Fiber Orientation Distribution Functions on?Heterogeneous Mtion). However, a multi-stage learning strategy is typically required since the learning process relies on various middle representations, such as simple harmonic oscillator reconstruction (SHORE) representation. In this work, we present a unified dynamic network with a single-stage spherical convol

incubus

GRE

travail

Anisotropic Fanning Aware Low-Rank Tensor Approximation Based Tractography,nsidered tracts, our extended model significantly increases completeness of the reconstruction, at acceptable excess and additional computational cost. Its results are also more accurate than those from a simpler, isotropic fanning model that is based on Watson distributions.

amnesia

,Advanced Diffusion MRI Modeling Sheds Light on?FLAIR White Matter Hyperintensities in?an?Aging Cohoctometry approach, we focused on the FLAIR signal along the callosal pathways connecting the temporal lobes, demonstrating that posterior periventricular WMH are related to the loss of axonal tissue and intrusion of CSF into the white matter.

CUB

,Neural Spherical Harmonics for?Structurally Coherent Continuous Representation of?Diffusion MRI Sighile only using data from a single subject. Current methods model the dMRI signal in individual voxels, disregarding the intervoxel coherence that is present. We use a neural network to parameterize a spherical harmonics series (NeSH) to represent the dMRI signal of a single subject from the Human C

闡釋

,A Unified Learning Model for?Estimating Fiber Orientation Distribution Functions on?Heterogeneous Macquired in one or more shells. Recent developments in micro-structure imaging and multi-tissue decomposition have sparked renewed attention to the radial b-value dependence of the signal. Applications in tissue classification and micro-architecture estimation, therefore, require a signal representa

Magisterial

,Diffusion Phantom Study of?Fiber Crossings at?Varied Angles Reconstructed with?ODF-Fingerprinting,ng at narrow angles below .. ODF-Fingerprinting (ODF-FP) replaces the ODF maxima localization mechanism with pattern matching, allowing the use of all information stored in ODFs. In this work, we study the ability of ODF-FP to reconstruct fibers crossing at varied angles spanning .–. in physical dif

Ligament

,Improving Multi-Tensor Fitting with?Global Information from?Track Orientation Density Imaging,data, such as the multi-tensor model (MTM). This parameter is especially important when the goal is to provide bundle-specific tissue metrics. However, for MTM, statistical selection methods, such as the F-test, the Akaike and the Bayesian information criteria, tend to overestimate the number of ten

Sinus-Node

,BundleSeg: A Versatile, Reliable and?Reproducible Approach to?White Matter Bundle Segmentation,e registration procedure to a recently developed precise streamline search algorithm that enables efficient segmentation of streamlines without the need for tractogram clustering or simplifying assumptions. We show that BundleSeg achieves improved repeatability and reproducibility than state-of-the-