@Article{NMTMA-6-1,
author = {},
title = {Numerical Schemes for Linear and Non-Linear Enhancement of DW-MRI},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2013},
volume = {6},
number = {1},
pages = {138--168},
abstract = {<p style="text-align: justify;">We consider the linear and non-linear enhancement of diffusion weighted magnetic resonance
 images (DW-MRI) to use contextual information in denoising and inferring fiber crossings.
 We describe the space of DW-MRI images in a moving frame of reference, attached to fiber
 fragments which allows for convection-diffusion along the fibers. Because of this approach,
 our method is naturally able to handle crossings in data. We will perform experiments showing
 the ability of the enhancement to infer information about crossing structures, even in diffusion
 tensor images (DTI) which are incapable of representing crossings themselves. We will present a
 novel non-linear enhancement technique which performs better than linear methods in areas around
 ventricles, thereby eliminating the need for additional preprocessing steps to segment out the
 ventricles. We pay special attention to the details of implementation of the various numerical schemes.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.2013.mssvm08},
url = {https://global-sci.com/article/90626/numerical-schemes-for-linear-and-non-linear-enhancement-of-dw-mri}
}