Publication
Title
Optimized workflow for diffusion kurtosis imaging of newborns
Author
Abstract
Diffusional kurtosis imaging (DKI) is a recently proposed extension of the conventional DTI model. It has been shown to offer more sensitive characterization of neural tissues than DTI. So far, DKI has only been applied to adult human and small animal studies, but not yet to human newborns. In this work, we present an optimized workflow for the acquisition and processing of DKI images of newborns. First, optimal set of diffusion weighting gradients for DKI studies of newborn subjects is proposed. Optimized gradients allow to estimate DKI parameters with the highest precision. Next, preprocessing and segmentation of the DKI data is considered, including motion correction, eddy currents suppression, intensity modulation and gradients reorientation. Finally, statistics of estimated diffusion and kurtosis parameters for different neonatal brain tissues are calculated.
Language
Dutch
Source (book)
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2011), Chicago, Illinois, USA, 30 March 2 April 2011
Publication
New York, N.Y. : IEEE, 2011
ISBN
978-1-4244-4127-3
Volume/pages
p. 922-926
ISI
000298849400213
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 02.08.2011
Last edited 12.05.2017
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