Paper Info
Title
Joint Estimation Of Spherical Harmonic Coefficients From Magnitude Diffusion-Weighted Images With Sparsity Constraints
Abstract
This paper presents a new method to jointly estimate the spherical harmonic coefficients for all the voxels from noisy magnitude diffusion-weighted images acquired in high angular resolution diffusion imaging. The proposed method uses a penalized maximum likelihood estimation formulation that integrates a noncentral chi distribution based noisy data model, a sparsity promoting penalty on the spherical harmonic coefficients and a joint sparse regularization on the diffusion-weighted image series. An efficient algorithm based on majorize-minimize and alternating direction method of multipliers is proposed to solve the resulting optimization problem. The performance of the proposed method has been evaluated using simulated and experimental data, which demonstrate the improvement over conventional methods in terms of estimation accuracy.
Year
Venue
Keywords
2015
2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)
spherical harmonic representation, penalized maximum likelihood estimation, noncentral chi distribution, sparse regularization, joint sparsity, majorize-minimize
Field
DocType
ISSN
Voxel,Data modeling,Noise measurement,Pattern recognition,Computer science,Signal-to-noise ratio,Spherical harmonics,Harmonic analysis,Regularization (mathematics),Artificial intelligence,Optimization problem
Conference
1945-7928
Citations 
PageRank 
References 
0
0.34
2
Authors
3
Name
Order
Citations
PageRank
Fan Lam1509.14
Bo Zhao2778.46
Zhi-Pei Liang352264.94