Compensation of Motion Artifacts in MRI via Graph-Based Optimization

In two-dimensional Fourier transform magnetic resonance imaging (2DFT-MRI), patient/object motion during the image acquisition results in ghosting and blurring. These motion artifacts are commonly considered as a major limitation in the MRI community. To correct these artifacts without resorting to additional navigator echoes, most existing methods perform image quality measure to estimate motion; but they may easily fail when the motion is large. Viewed as a blind image restoration problem where the motion point spread function (PSF) is unknown, state-of-the-art restoration algorithms can not be easily applied because they cannot handle a complex PSF kernel that has the same size as the image. To overcome these challenges, we propose a novel approach that exploits the image structure to segment the kernel into several fragments. Based on this kernel representation, determining a kernel fragment can be formulated as a binary optimization problem, where each binary variable represents whether a segment in MR signals is corrupted by a certain motion or not. We establish a graphical model for these variables and estimate the kernel by minimizing an energy functional associated with the model. Experimental results show that the proposed method can provide satisfactory compensation of motion artifacts even when large motions are involved in the MR images.