Title
Optimized workflow for diffusion kurtosis imaging of newborns Optimized workflow for diffusion kurtosis imaging of newborns
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
conferenceObject
Publication
New York, N.Y. :IEEE, [*]
Subject
Physics
Computer. Automation
Source (book)
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI 2011), Chicago, Illinois, USA, 30 March 2 April 2011
ISBN - Hoofdstuk
978-1-4244-4127-3
ISI
000298849400213
Carrier
E
Target language
Dutch (dut)
Affiliation
University of Antwerp
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.
E-info
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