Stephen J. Garnier,Griff L. Bilbro North Carolina State Univ. (United States) James W. Gault U.S. Army Research Office (United States) Wesley E. Snyder Bowman Gray School of Medicine/Wake Forest Univ. (United States) Youn-Sik Han Bowman Gray School of Medicine/Wake Forest Univ. (South Korea)
Our intent is to obtain images which most clearly differentiate soft tissue types in Magnetic Resonance Image data. We model the three unknown intrinsic parameter images and the data images as Markov random fields and compare maximum likelihood restorations with two maximum a posteriori (MAP) restorations. The mathematical model of the imaging process is strongly nonlinear in the region of interest, but does not appear to introduce local minima in the resulting constrained multidimensional optimization procedure. The application of non- quadratic prior probabilities however does require global optimization. We have developed a unique approach towards image restoration that produces images with significant improvements when compared to the original data. We have extended previous results that attempt to determine the intrinsic parameters from the MRI data, and have used these intrinsic parameter images to synthesize MR images. MR images with different TE and TR parameters do not require additional use of an MR scanner, since excellent synthetic MR images are obtained using the restored proton density and nuclear relaxation time images.