CSCommunity: CS MRI tutorial in San Diego and Wikimization presentation.

I have asked Jon Dattorro to write a small presentation for this blog of Wikimization. Here it is:

Wikimization (http://wikimization.org) is a repository and resource for all things Optimization.

It is a wiki; meaning that anyone can edit and create pages. Pages can be protected against hacking by outsiders. So an author can protect their own work, if desired (No editing by adolescents is a guarantee.)

Wikimization is also a blog for mathematicians, scholars, students, and researchers.

Wikimization understands both LaTeX and HTML simultaneously. That is its greatest strength.

Multiple authors can edit their work simultaneously prior to publication.

Wherever there is Internet access, all the authors can see what the others are doing.

It eliminates email of Drafts and the consequent delays in collaboration from a distance.

Right now, Wikimization has become a popular place for biographies of people in Optimization.

Biographies are more narrative than factual since we try to capture the essence of an individual;

e.g., http://www.convexoptimization.com/wikimization/index.php/Osher

http://www.convexoptimization.com/wikimization/index.php/Bertsekas

http://www.convexoptimization.com/wikimization/index.php/Isaac_Newton

We also have a lot of videos by people in Optimization:

http://www.convexoptimization.com/wikimization/index.php/Optimization_Videos

Unlike Wikipedia, we allow speculation, research, and explicit authorship if desired.

The only requirement is that the material be somehow related to Optimization; SEO or mathematical.

We just got started a few months ago, so now is the time to get an account and request Sysop status. But anonymous entries are also allowed.

I hope that students will use it to work on their homework together, pose problems, and ask each other questions.

Professors can post solutions, scholars can post their open problems, definitions, slides from talks, their papers, etc.

Programmers can post their work in Courier font HTML; e.g.,http://www.convexoptimization.com/wikimization/index.php/Candes.m which is an applicaton of Convex Iteration to an example in Compressed Sensing given by Emmanuel Candes at a lecture on June 6, 2007:

http://www.convexoptimization.com/wikimization/index.php/Optimization_Videos

Wikimization was added to the Compressive Sensing 2.0 list. Thanks Jon !

Michael Lustig gives us a "heads up on an upcoming tutorial we are giving on compressed sensing in MRI for non-MRI folks. It is part of the weekend tutorials in ICIP'08 ( http://www.icip08.org/Tutorial_04.asp )" in San Diego on October 12th. The abstract of the tutorial reads:

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.

I added the date to the CS calendar.