Further Development of Image Reconstruction from Highly Undersampled (k, t)-Space Data with Joint Partial Separability and Sparsity Constraints

Citation:

B. Zhao, J. P. Haldar, A. G. Christodoulou, and Z. P. Liang, “Further Development of Image Reconstruction from Highly Undersampled (k, t)-Space Data with Joint Partial Separability and Sparsity Constraints,” IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI). pp. 1593-1596, 2011.

Abstract:

Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying optimization problem efficiently. The proposed method has been validated using simulated cardiac imaging data, with the expected capability to reduce image artifacts and reconstruction noise.

Publisher's Version

Last updated on 05/18/2016