Paper Info
Title
A Large-Scale Optimization Method Using a Sparse Approximation of the Hessian for Magnetic Resonance Fingerprinting.
Abstract
Magnetic resonance fingerprinting (MRF) is a novel approach for quantitative imaging which enables the simultaneous determination of multiple tissue-related parameters within short acquisition times. The tissue-related parameters are usually estimated by template matching employing a large dictionary of test signals constructed on the basis of a physical model. We propose to analyze an MRF sequence by a least-squares approach and develop a large-scale optimization algorithm for this purpose. The algorithm is based on a nonmonotone trust region method and utilizes a sparse Jacobian and a sparse approximation of the Hessian. The algorithm is capable of identifying the tissue related parameters within reasonable calculation times. Simulation results are presented in which the proposed approach compares favorably with previously suggested template matching methods. Moreover, uncertainties for the estimates of the tissue-related parameters calculated on the basis of the approximate Hessian appear to provide a reasonable characterization of their accuracy.
Year
DOI
Venue
2017
10.1137/16M1095032
SIAM JOURNAL ON IMAGING SCIENCES
Keywords
Field
DocType
MRI,magnetic resonance fingerprinting,large-scale optimization,trust region method
Template matching,Trust region,Mathematical optimization,Jacobian matrix and determinant,Sparse approximation,Hessian matrix,Optimization algorithm,Quantitative imaging,Mathematics
Journal
Volume
Issue
ISSN
10
3
1936-4954
Citations 
PageRank 
References 
0
0.34
2
Authors
2
Name
Order
Citations
PageRank
G. Wubbeler112515.16
Clemens Elster29614.27