Markov Random Fields (MRF's) are a very effective way to impose spatial smoothness in computer vision. I will describe an application of MRF's to a non-traditional but important problem in medical imaging: the reconstruction of MR images from raw fourier data. This can be formulated as a linear inverse problem, where the goal is to find a spatially smooth solution while permitting discontinuities. Although it is easy to apply MRF's for MR reconstruction, the resulting energy minimization problem poses some interesting challenges. It lies outside of the class of energy functions that can be straightforwardly minimized with graph cuts. I will show how graph cuts can nonetheless be adapted to solve this problem, and demonstrate some preliminary results that are extremely promising.

Joint work with Ashish Raj and Gurmeet Singh.

Ramin Zabih is an associate professor of Computer Science at Cornell University. His research interests focus on discrete optimization methods and their applications, especially in early vision and medical imaging. Since 2000 he has also held a joint appointment in the Radiology Department of Cornell's Weill Medical College. He is best known for his work on energy minimization via graph cuts, which is the basis for most of the top-performing stereo algorithms. Two of his papers on this topic received Best Paper awards at the European Conference on Computer Vision in 2002. He currently serves as an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, and is Program Co-chair of the 2007 IEEE International Conference on Computer Vision and Pattern Recognition.