The effects of various basis image priors on MR Image MAP restoration

Abstract

We extend a previously reported technique for Magnetic Resonance Image (MRI) restoration, using a physical model (spin equation) and corresponding basis images. We determine the basis images (proton density and nuclear relaxation times) from the MRI data and use them to obtain excellent restorations.Magnetic Resonance Images depend nonlinearly on proton density,ρ, two nuclear relaxation times,T 1 andT 2, and two control parameters, TE and TR. We model images as Markov random fields and introduce four maximuma posteriori (MAP) restorations, nonlinear techniques using several different prior terms which reduce the correlated noise in the basis images, thereby reducing the noise in the restored MR images. The “product” and “sum” forms for basis (signal) and spatial correlations are discussed, compared and evaluated for various situations and features.