Paper Info
Title
Improving Diffusion Tensor Estimation Using Adaptive And Optimized Filtering Based On Local Similarity
Abstract
The diffusion-weighted magnetic resonance imaging (DW-MRI) has been used to diagnose anomalies in human brain by describing the magnitude and directionality of water diffusion per voxel. Such information can be represented alternatively in diffusion tensor imaging (DT-MRI), yielding images of normal and abnormal white matter fiber structures, and maps of brain connectivity through fiber tracking. A DW-MRI study is usually characterized by a low signal to noise ratio, which may reflect in the poor estimation of DT-MRI. Filters based on local similarity have been receiving increasing attention, but they have been barely studied for DT-MRI. In this proposal we introduce adaptive and optimized filtering techniques based on local similarity for MRI to remove the biasing in both DW-MRI filtering and DT-MRI estimation, evidencing a better performance respect to classical filters and robust DT estimation algorithms. We estimate the DT-MRI extracting metrics computed from the DT to evaluate the filtering performance.
Year
DOI
Venue
2015
10.1007/978-3-319-19390-8_69
PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015)
Keywords
Field
DocType
Adaptive and optimized filtering, Diffusion tensor imaging, Diffusion-weighted magnetic resonance imaging
Voxel,Magnitude (mathematics),Diffusion MRI,Pattern recognition,Computer science,Diffusion-Weighted Magnetic Resonance Imaging,Signal-to-noise ratio,Filter (signal processing),Artificial intelligence,Magnetic resonance imaging
Conference
Volume
ISSN
Citations 
9117
0302-9743
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Andrés F. López-Lopera100.34
Mauricio A. Álvarez216523.80
Álvaro Á. Orozco31612.88