Paper Info
Title
Dictionary learning on the manifold of square root densities and application to reconstruction of diffusion propagator fields.
Abstract
In this paper, we present a novel dictionary learning framework for data lying on the manifold of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields given a multi-shell diffusion MRI data set. Unlike most of the existing dictionary learning algorithms which rely on the assumption that the data points are vectors in some Euclidean space, our dictionary learning algorithm is designed to incorporate the intrinsic geometric structure of manifolds and performs better than traditional dictionary learning approaches when applied to data lying on the manifold of square root densities. Non-negativity as well as smoothness across the whole field of the reconstructed DPs is guaranteed in our approach. We demonstrate the advantage of our approach by comparing it with an existing dictionary based reconstruction method on synthetic and real multi-shell MRI data.
Year
DOI
Venue
2013
10.1007/978-3-642-38868-2_52
IPMI
Keywords
Field
DocType
square root density,mri data,diffusion propagator field,existing dictionary,reconstruction method,real multi-shell,multi-shell diffusion,data point,diffusion propagator,novel dictionary,traditional dictionary,artificial intelligence,algorithms,magnetic resonance imaging,computer simulation
Data point,Computer vision,Dictionary learning,K-SVD,Computer science,Euclidean space,Propagator,Artificial intelligence,Square root,Smoothness,Manifold
Conference
Volume
ISSN
Citations 
23
1011-2499
2
PageRank 
References 
Authors
0.40
15
7
Name
Order
Citations
PageRank
Jiaqi Sun151.80
Yuchen Xie2573.08
Wenxing Ye3383.85
Jeffrey Ho42190101.78
Alireza Entezari530921.28
Stephen J. Blackband625132.60
B.C. Vemuri74208536.42