Compressive Sensing Magnetic Resonance Imaging (CS-MRI) has been rapidly developed during last several years. To reconstruct an image from incomplete data using compressive sensing, the image has to be sparse or can be transformed to sparse representation. Gradient operators associated with total variation (TV) and discrete wavelet transform (DWT) are two commonly used sparsifying transforms in CS-MRI. Since the data acquired in MRI are complex, these transforms are usually applied to the real and the imaginary parts of the image independently. In this paper, we will explore the application of the complex wavelet transform (CWT) as a more effective sparsifying transform for CS-MRI. Specifically, dual-tree complex wavelet transform (DT-CWT), a CWT previously used for real or complex image compression, is integrated with compressive sensing reconstruction algorithm. We will test the new method using both simulated and in-vivo MRI data. The results will be compared with those of DWT and TV, which show that the new method can achieve better sparsity and reduced reconstruction errors in CS-MRI.

Date Published: 27 September 2011
PDF: 9 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81381I (27 September 2011); doi: 10.1117/12.892030

Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)