Model Based Image Reconstruction in MR imaging
Dr. Mathews Jacob, University Of Illinois at Urbana-Champaign, USA
About the speaker: Mathews Jacob was born on 16th of June 1975 at Kerala, India. He obtained his B.Tech in ECE from the National Institute of Technology, Calicut Kerala in 1996. For one year, he worked with Wipro R&D, Bangalore on hardware design. He received his M.E in signal processing from the Indian Institute of Science, Bangalore in 1999 and his Ph.D from the Biomedical Imaging Group at the Swiss Federal Institute of Technology in 2003 respectively. He is currently working as a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign . His research interests include sampling theory, steerable filters, shape extraction, image processing etc.
Dr. Mathews Jacob, University Of Illinois at Urbana-Champaign, USA
Seminar • 14 October 2004 • BM1.139
AbstractI will present a model-based approach to the reconstruction of Magnetic Resonance Spectroscopic Imaging (MRSI) data. MRSI is emerging as a powerful tool to understanding the functioning of the brain by studying the concentration of different metabolites. The main drawback of this technique is the long acquisition time, which is mainly due to the large number of samples to be acquired; this limits the applicability of the method for diagnosis. Most of the current approaches trade spatial resolution for spectral information to perform the imaging in a reasonable time. We present a new approach based using a deformable spatial compartmental model. The compartments themselves are derived from the segmentation of a high resolution anatomical image acquired prior to the spectroscopic scan. Since our model accounts for various non-idealities in the image formation like presence of magnetic inhomogeneities and difference in imaging protocols, our approach gives get a good fit to the low resolution data. We use an iterative optimization algorithm to derive the model parameters. I will also briefly touch upon a sampling problem in the context of parallel imaging. We derive an exact expression for the error in the reconstruction of parallel imaging data. We then proceed to minimize this error by properly choosing the optimal sampling locations in K-space using a greedy algorithm. I will also present some preliminary results.About the speaker: Mathews Jacob was born on 16th of June 1975 at Kerala, India. He obtained his B.Tech in ECE from the National Institute of Technology, Calicut Kerala in 1996. For one year, he worked with Wipro R&D, Bangalore on hardware design. He received his M.E in signal processing from the Indian Institute of Science, Bangalore in 1999 and his Ph.D from the Biomedical Imaging Group at the Swiss Federal Institute of Technology in 2003 respectively. He is currently working as a Beckman postdoctoral fellow at the University of Illinois at Urbana Champaign . His research interests include sampling theory, steerable filters, shape extraction, image processing etc.