Paper Info
Title
Partially parallel MR image reconstruction using sensitivity encoding
Abstract
A new algorithm is presented for efficiently solving image reconstruction problems that arise in partially parallel magnetic resonance imaging. This algorithm minimizes an objective function of the form φ(Bu) + 1/2||FpSu - f||2, where φ is the regularization term which may be nonsmooth. In image reconstruction, the φ term corresponds to total variation smoothing and/or L1 regularization term. The least square term 1/2||FpSu - f||2 is the fidelity term. In our application, f represents undersampled data from a partially parallel imaging (PPI) system. The proposed algorithm is a generalization of the Bregman operator splitting algorithm with variable stepsize (BOSVS) in which the previous Barzilai-Borwein (BB) step is replaced by a cyclic BB (CBB) step, and an L1 term Ψ is added to the energy function. Experimental results on clinical partially parallel imaging data are given.
Year
DOI
Venue
2012
10.1109/ICIP.2012.6467300
Image Processing
Keywords
Field
DocType
biomedical MRI,image reconstruction,medical image processing,optimisation,smoothing methods,BOSVS,Bregman operator splitting algorithm with variable stepsize,L1 regularization term,PPI system,cyclic BB step,least square term,partially parallel MR image reconstruction problem,partially parallel imaging system,partially parallel magnetic resonance imaging,regularization term,total variation smoothing,Image reconstruction,magnetic resonance imaging,optimization,sensitivity encoding
Iterative reconstruction,Least squares,Operator splitting,Computer vision,Parallel magnetic resonance imaging,Parallel imaging,Regularization (mathematics),Smoothing,Artificial intelligence,Mathematics,Encoding (memory)
Conference
ISSN
ISBN
Citations 
1522-4880 E-ISBN : 978-1-4673-2532-5
978-1-4673-2532-5
0
PageRank 
References 
Authors
0.34
7
4
Name
Order
Citations
PageRank
Maryam Yashtini1173.13
William W. Hager21603214.67
Yunmei Chen363963.49
Xiaojing Ye416217.94