Novel Compressive Sensing MRI Methods with Combined Sparsifying Transforms Conference Paper uri icon

abstract

  • Compressive sensing (CS) is an emerging technique for fast MRI, which relies on the sparsity constraint of the underlying image to reduce the data acquisition requirement. Sparsifying transforms, such as total variation (TV), wavelet, curvelet, have been used in CS-MRI as regularization terms. Linear weighted summations of these regularization terms have also been used and tested. However, tuning the weights for individual terms is complicated and time-consuming. In this paper, a novel method that uses combined sparsifying transforms is proposed. This method applies transforms sequentially. It can avoid the artifacts associated with a single transform, as well as save the time of tuning the weights. Simulated results using in-vivo data show that the proposed method is efficient while providing similar or improved reconstruction quality. 2012 IEEE.

name of conference

  • Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics

published proceedings

  • Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics

author list (cited authors)

  • Dong, Y., & Ji, J.

citation count

  • 2

complete list of authors

  • Dong, Ying||Ji, Jim

publication date

  • January 2012