PSF Model-Based Reconstruction with Sparsity Constraints: Algorithm and Application to Real-Time Cardiac MRI

Citation:

B. Zhao, J. P. Haldar, and Z. P. Liang, “PSF Model-Based Reconstruction with Sparsity Constraints: Algorithm and Application to Real-Time Cardiac MRI,” 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 3390-3393, 2010.

Abstract:

The partially separable function (PSF) model has been successfully used to reconstruct cardiac MR images with high spatiotemporal resolution from sparsely sampled (k,t)-space data. However, the underlying model fitting problem is often ill-conditioned due to temporal undersampling, and image artifacts can result if reconstruction is based solely on the data consistency constraints. This paper proposes a new method to regularize the inverse problem using sparsity constraints. The method enables both partial separability (or low-rankness) and sparsity constraints to be used simultaneously for high-quality image reconstruction from undersampled (k,t)-space data. The proposed method is described and reconstruction results with cardiac imaging data are presented to illustrate its performance.

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Last updated on 05/18/2016