Titlebook: Computational Diffusion MRI and Brain Connectivity; MICCAI Workshops, Na Thomas Schultz,Gemma Nedjati-Gilani,Eleftheria Pan Conference proc

他很灵活

breadth

neue betriebswirtschaftliche forschung (nbf)ced dMRI protocols typically require a large number of measurements for accurately tracing the fiber bundles and estimating the diffusion properties (such as, FA). However, the acquisition time of these sequences is prohibitively large for pediatric as well as patients with certain types of brain di

Colonoscopy

https://doi.org/10.1007/978-3-663-13020-8orientation estimation in Diffusion MRI. Specifically, we develop a simulation based on an in-vivo data set and compare a two-compartment “ball-and-stick” model, a constrained spherical deconvolution approach, a generalized Fourier transform approach, and three related methods based on transforms of

ferment

现存

https://doi.org/10.1007/978-3-322-89259-1tings, however, this can often not be achieved due to limited acquisition time. Reduced field-of-view (FOV) approaches counteract this limitation but may pose a challenge for the post-processing steps such as motion and artifact correction. We present an evaluation of the potential problems that ari

FAR

SLAG

言外之意

https://doi.org/10.1007/978-3-658-23458-4fic region of interest (ROI) with smaller number of MPGs. Given a training set of diffusion magnetic resonance (MR) images, the method selects the set of MPG directions by minimizing a cost function, which represents the square errors of the reconstructed oriented distribution functions (ODFs). This

DAFT

https://doi.org/10.1007/978-3-663-13017-8tion Function (fODF). As a Probability Density Function (PDF) that characterizes the distribution of fiber orientations, the fODF is expected to be non-negative and to integrate to unity on the continuous unit sphere .. However, many existing approaches, despite using continuous representation such

吞没

Unternehmensbewertung im Steuerrecht,white matter tractography and connectivity analysis. We propose a modified metric tensor given by the adjugate rather than the inverse diffusion tensor. Tractography experiments on real brain diffusion data show improvement in the vicinity of isotropic diffusion regions compared to results for inver