Connectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution. [PDF]
Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects extensive regions of the central nervous system. In this work, we evaluated the structural connectome of patients with PD, as mapped by diffusion-weighted MRI tractography ...
Kamagata K +12 more
europepmc +2 more sources
Diffusion MRI-driven tractography, a non-invasive technique that reveals how the brain is connected, is widely used in brain lateralization studies.
Yifei He, Yoonmi Hong, Ye Wu
doaj +4 more sources
White matter organization in developmental coordination disorder: A pilot study exploring the added value of constrained spherical deconvolution. [PDF]
Previous studies of white matter organization in sensorimotor tracts in developmental coordination disorder (DCD) have adopted diffusion tensor imaging (DTI), a method unable to reconcile pathways with ‘crossing fibres’. In response to limitations of the
Hyde C +7 more
europepmc +2 more sources
Improvement in White Matter Tract Reconstruction with Constrained Spherical Deconvolution and Track Density Mapping in Low Angular Resolution Data: A Pediatric Study and Literature Review. [PDF]
IntroductionDiffusion-weighted magnetic resonance imaging (DW-MRI) allows noninvasive investigation of brain structure in vivo. Diffusion tensor imaging (DTI) is a frequently used application of DW-MRI that assumes a single main diffusion direction per ...
Toselli B +7 more
europepmc +2 more sources
Deep Constrained Spherical Deconvolution for Robust Harmonization. [PDF]
Diffusion weighted magnetic resonance imaging (DW-MRI) captures tissue microarchitecture at millimeter scale. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, DW-MRI suffers from measurement variability (e.g., inter- and intra-site variability, hardware performance,
Yao T +11 more
europepmc +4 more sources
The Effect of Gradient Nonlinearities on Fiber Orientation Estimates From Spherical Deconvolution of Diffusion Magnetic Resonance Imaging Data [PDF]
Alberto De Luca +2 more
exaly +2 more sources
Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data. [PDF]
We present a rotation-equivariant unsupervised learning framework for the sparse deconvolution of non-negative scalar fields defined on the unit sphere. Spherical signals with multiple peaks naturally arise in Diffusion MRI (dMRI), where each voxel consists of one or more signal sources corresponding to anisotropic tissue structure such as white matter.
Elaldi A, Dey N, Kim H, Gerig G.
europepmc +5 more sources
Patient-specific structural connectomic differences of deep brain stimulation targets in treatment-resistant obsessive-compulsive disorder patients. [PDF]
Deep brain stimulation (DBS) is an established therapy for treatment-resistant obsessive-compulsive disorder (trOCD), yet clinical outcomes vary. This is partly due to differential cortico-striato-thalamo-cortical networks (CSTC) engagement across ...
Marquez-Franco R +9 more
europepmc +2 more sources
In this article, we consider how differing approaches that characterize biological microstructure with diffusion weighted magnetic resonance imaging intersect.
Matt G. Hall, Carson Ingo
doaj +1 more source
Deconvolution of spherical data corrupted with unknown noise
We consider the deconvolution problem for densities supported on a $(d-1)$-dimensional sphere with unknown center and unknown radius, in the situation where the distribution of the noise is unknown and without any other observations. We propose estimators of the radius, of the center, and of the density of the signal on the sphere that are proved ...
Capitao-Miniconi, Jérémie +1 more
openaire +4 more sources

