TUT-4: A Compressed Tutorial on Rapid MRI: from Basic Principles to Parallel Imaging and Compressed Sensing
Date: Sunday Afternoon, October 12
Michael Lustig, Stanford University, John Pauly, Stanford University, and Philip Beatty, General Electric
Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality. Unlike Computed Tomography (CT), MRI does not use ionizing radiation. In addition, MRI provides a large number of flexible contrast parameters. These provide excellent soft tissue contrast. MRI can also be sensitized to many specific parameters. These include imaging brain oxygen saturation changes due to neuronal activity, measuring blood flow velocities, measuring temperature, and measuring the concentration of metabolites. MRI is also the only way to directly image diffusion of water molecules in vivo.
Since its invention more than 30 years ago, MRI has improved dramatically in imaging quality and imaging speed. This has revolutionized diagnostic medicine. Imaging speed is a major part of the revolution and is essential in many of the MRI applications. Improvements in MRI hardware and imaging techniques have enabled faster data collection, and hence faster imaging. However, we are currently at the point where fundamental physical and physiological effects limit our ability to simply encode data more quickly.
This fundamental limit has led many researches to look for methods to reduce the amount of acquired data without degrading image quality. Many of these methods seek to exploit redundancies in the MRI data. For example, using multiple receiver coils provides more useful data per MR acquisition (parallel imaging), requiring fewer acquisitions per scan. Redundancy can also be a known or modeled signal property such as spatial-temporal correlation or the sparsity and compressibility of the image (compressed sensing). The application of these methods leads to significant scan-time reduction, and clear benefits for patients and health care economics.
This tutorial will first review MR imaging from the basic principles of MR physics, signal generation, image formation, and simple image reconstruction. It will then go on to more advanced rapid imaging methods of echo-planar and spiral imaging. Finally it will cover the current state of the art techniques of parallel imaging and compressed sensing. Example applications include angiography, cardiac imaging, brain imaging and spectroscopic imaging.
Michael Lustig received his B.Sc from the Electrical Engineering Department, Technion-IIT, Haifa, Israel, in 2001. In 2008 he received his PhD from the Electrical Engineering Department, Stanford University, where he was working on the application of compressed sensing to rapid MRI. His current research interests include medical imaging reconstruction techniques, MR pulse sequence design, convex optimization and inverse problems.
John Pauly is an Associate Professor of Electrical Engineering Department at Stanford University. His main research interests are in magnetic resonance imaging (MRI), and the use of MRI for guiding minimally invasive interventional procedures. At Stanford he teaches classes in image reconstruction for medical imaging, and RF pulse design for MRI. He holds 42 U.S. patents, and has authored or coauthored 110 journal articles. He is a member of the IEEE, and is Associate Editor of IEEE Transactions on Medical Imaging.
Philip Beatty is a Development Scientist in the Applied Science Laboratory at General Electric. He is working on innovative reconstruction methods for magnetic resonance imaging (MRI) that enable fast, robust imaging that is less susceptible to motion and other confounding factors. Philip has a Ph.D. in Electrical Engineering from Stanford University.