Abstract:
We develop a variational model and a faster and robust numerical algorithm for simultaneous sensitivity map estimation and image reconstruction in partially parallel MR i...View moreMetadata
Abstract:
We develop a variational model and a faster and robust numerical algorithm for simultaneous sensitivity map estimation and image reconstruction in partially parallel MR imaging with significantly under-sampled data. The proposed model uses a maximum likelihood approach to minimizing the residue of data fitting in the presence of independent Gaussian noise. The usage of maximum likelihood estimation dramatically reduces the sensitivity to the selection of model parameter, and increases the accuracy and robustness of the algorithm. Moreover, variable splitting based on the specific structure of the objective function, and alternating direction method of multipliers (ADMM) are used to accelerate the computation. The preliminary results indicate that the proposed method resulted in fast and robust reconstruction.
Published in: 2013 IEEE International Conference on Image Processing
Date of Conference: 15-18 September 2013
Date Added to IEEE Xplore: 13 February 2014
Electronic ISBN:978-1-4799-2341-0