Imperial College London > Talks@ee.imperial > COMMSP Seminar > Reconstruction of multi-channel modulated images by convex optimization with application to MRI

Reconstruction of multi-channel modulated images by convex optimization with application to MRI

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In some imaging applications, the image data are acquired through multiple sensing channels and the image reconstruction problem is to find a joint solution for the image and sensing channel functions. It is in general a nonlinear and nonconvex problem and parallel magnetic resonance imaging (pMRI) is a typical example of such problem. For the pMRI reconstruction problem, it is shown in this talk that, if only the magnitude image is reconstructed, there exists a convex solution space for the magnitude image and sensing channel modulated images. This solution space enables formulation of a regularized convex optimization problem and leads to a globally optimal and unique solution for the magnitude image reconstruction. The applications of the solution to in vivo MRI data sets result in superior reconstruction performance compared with other reconstruction algorithms.

Bio: Cishen Zhang received the B.Eng. degree in Computer Engineering from Tsinghua University, China and Ph.D. degree in Electrical Engineering from Newcastle University, Australia. He was with the Department of Electrical and Electronic Engineering at the University of Melbourne, Australia, as a Lecturer, Senior Lecturer, and Associate Professor and Reader in 1989-2002 and with the School of Electrical and Electronic Engineering and School of Chemical and Biomedical Engineering at Nanyang Technological University, Singapore, during 2002-2010. Since November 2010, he has been the Professor of Electrical and Electronic Engineering at Swinburne University of Technology in Melbourne, Australia. His research interests include control, signal processing and medical imaging.

This talk is part of the COMMSP Seminar series.

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