-
[show abstract]
[hide abstract]
ABSTRACT: Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) can significantly reduce imaging time in MRI, the former by utilizing multiple channel receivers and the latter by utilizing the sparsity of MR images in a transformed domain. In this work, pMRI and CS are integrated to take advantages of the sensitivity information from multiple coils and sparsity characteristics of MR images. Specifically, CS is used as a regularization method for the inverse problem raised by pMRI based on the L1 norm and a Total Variation (TV) term. We test the new method with a set of 8-channel, in-vivo brain MRI data at reduction factors from 2 to 8. Reconstruction results show that the proposed method outperforms several other regularized parallel MRI reconstruction such as the truncated Singular Value Decomposition (SVD) and Tikhonov regularization methods, in terms of residual artifacts and SNR, especially at reduction factors larger than 4.
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE; 09/2008
-
[show abstract]
[hide abstract]
ABSTRACT: Compressed sensing (CS) is a recently emerged technique for reconstructing signals from data sampled under the Nyquist rate. It takes advantage of the signal sparsity in a transformed domain to reconstruct high-resolution signals from reduced data. This paper presents a CS imaging method for dynamic magnetic resonance imaging. Specifically, a difference operator is applied to the temporal data frames to enhance the spatial signal sparsity for CS reconstruction. The new algorithm method was assessed using simulated and in-vivo dynamic imaging data. The result shows that the new method can obtain higher resolution than zero-padded Fourier reconstruction and the Keyhole method, and it results in reduced artifacts and noise than conventional CS reconstruction where no temporal information is used. It also shows that the new CS dynamic imaging method does not suffer substantial signal-to-noise loss.
Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on; 06/2008
-
[show abstract]
[hide abstract]
ABSTRACT: Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) can significantly reduce imaging time in MRI, the former by utilizing multiple channel receivers and the latter by utilizing the sparsity of MR images in a transformed domain. In this work, pMRI and CS are integrated to take advantages of the sensitivity information from multiple coils and sparsity characteristics of MR images. Specifically, CS is used as a regularization method for the inverse problem raised by pMRI based on the L1 norm and a Total Variation (TV) term. We test the new method with a set of 8-channel, in-vivo brain MRI data at reduction factors from 2 to 8. Reconstruction results show that the proposed method outperforms several other regularized parallel MRI reconstruction such as the truncated Singular Value Decomposition (SVD) and Tikhonov regularization methods, in terms of residual artifacts and SNR, especially at reduction factors larger than 4.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:1671-4.
-
[show abstract]
[hide abstract]
ABSTRACT: In this paper, a general expression of characteristic parameter for the classification of MPSK signals based on maximum likelihood criterion was presented. The computer simulations verify the effectiveness and correctness of the method.
Emerging Technologies: Frontiers of Mobile and Wireless Communication, 2004. Proceedings of the IEEE 6th Circuits and Systems Symposium on;
