Correction of image distortion in echo planar image series using phase and intensity
Among all magnetic resonance imaging sequences, gradient-echo (GE) echo-planar imaging (EPI) has become the most common technique for the study of dynamic brain function and for other high-speed applications like cardiac imaging. However, the usefulness of GE-EPI is limited by the severe geometric and intensity distortion caused by inhomogeneity in the static magnetic field. Fluctuations in the magnetic field with time, induced by physiological processes and motion, makes dynamic image series suffer from dynamically varying image distortion. The focus of this work is the reduction of distortion in EPI series of the brain. It is well known that inhomogeneity in the static field can be determined using a “field-map”. In this work, we introduce an extension of the traditional field-map method, which we will call the “phase-map” method, to accomplish the calculation of inhomogeneity as it changes dynamically throughout the acquisition of a series of GE-EPI images. The calculation of this field and the correction of image distortion based on this field is made difficult by noise in the signal and motion of the anatomy. The major contribution of this work is the correction of image distortion in an EPI series in the face of this noise and motion. We develop a regularization method based on both the phase and the intensity of the image to reduce the estimation errors. The difficulty caused by motion arises from the fact that because of distortion the motion of the anatomy is different from the motion in the image. We study the methods of using registration to correct for dynamic distortion of EPI series in the presence of motion, and we suggest a strategy for motion compensation based on corrected EPIs. We incorporate a phase-gradient term into an optimization framework for correction of EPI series. We introduce an objective function that accounts not only for the standard intensity similarity but also for similarity to the gradient of the distortion field derived from the image phase. Experimental results are presented showing that our phase-map method is capable of estimating dynamic distortion-field caused by respiration and motion. A combination of phase and intensity information is shown to produce a distortion field that is more accurate than those produced by either method alone, thus making possible a more accurate correction of dynamic distortion and reduced spurious intensity variation in EPI series.