Abstract
Application of the compressed sensing (CS) in magnetic resonance imaging (MRI) significantly reduces the scan time as it is capable of recovering images at diagnostic resolution from a few random measurements in the k-space. In 2D multi-slice MRI, a strong inter-slice correlation exists which promotes further reduction in the scan time. In this paper, first, a fast interpolation technique is proposed to estimate missing samples in the k-space of some highly undersampled slices from neighboring low undersampled slices. Next, a novel wavelet tree sparsity based CS reconstruction method is developed for the reconstruction of images from raw 2D multi-slice data. The combination of fast interpolation and CS reconstruction gives accurate reconstruction of multi-slice MR images at significantly less computational time. Simulation results show that the proposed method gives better preservation of edges and other image details with less computational time than some of the recently developed fast CS reconstruction techniques.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
