Efficient Preconditioning in Joint Total Variation Regularized Parallel MRI Reconstruction
Parallel magnetic resonance imaging (pMRI) is a useful technique to aid clinical diagnosis. In this paper, we develop an accelerated algorithm for joint total variation (JTV) regularized calibrationless Parallel MR image reconstruction. The algorithm minimizes a linear combination of least squares data fitting term and the joint total variation regularization. This model has been demonstrated as a very powerful tool for parallel MRI reconstruction. The proposed algorithm is based on the iteratively reweighted least squares (IRLS) framework, which converges exponentially fast. It is further accelerated by preconditioned conjugate gradient method with a well-designed preconditioner. Numerous experiments demonstrate the superior performance of the proposed algorithm for parallel MRI reconstruction in terms of both accuracy and efficiency.
KeywordsPreconditioned Conjugate Gradient Preconditioned Conjugate Gradient Method Iteratively Reweighted Little Square Linear Convergence Rate Preconditioned Conjugate Gradient Iteration
Unable to display preview. Download preview PDF.
- 1.Chen, C., Huang, J., He, L., Li, H.: Preconditioning for accelerated iteratively reweighted least squares in structured sparsity reconstruction. In: CVPR 2014, pp. 2713–2720. IEEE (2014)Google Scholar
- 9.Lustig, M., Pauly, J.M.: SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn. Reson. Med. 64(2), 457–471 (2010)Google Scholar
- 12.Saad, Y.: Iterative methods for sparse linear systems. Siam (2003)Google Scholar
- 13.Sawyer, A.M., Lustig, M., Alley, M., Uecker, P., Virtue, P., Lai, P., Vasanawala, S., Healthcare, G.: Creation of fully sampled MR data repository for compressed sensing of the knee (2013)Google Scholar
- 14.Shewchuk, J.R.: An introduction to the conjugate gradient method without the agonizing pain (1994)Google Scholar