In many applications of MRI, motion is the most important factor limiting image quality. Recent advances in fast scanning, navigator echoes, and other techniques have addressed the issue of motion in many applications, but it remains a significant problem whenever there is a need for maximum image sharpness, imaging of moving structures, or if patients are predisposed to motion. It may well become the critical limitation in the increasingly important area of high-field, high-resolution imaging. The objective of this proposal is the further development, implementation, and validation of automatic methods (termed """"""""autocorrection"""""""") for the retrospective correction of motion artifacts in MRI. These methods, based on the iterative optimization of an image quality metric, can deduce and correct for patient motion during image acquisition from the raw data alone, with no need for special pulse sequences, motion tracking techniques, patient preparation, or specialized hardware. They have been shown to effectively reduce motion artifacts in certain types of images with no a priori knowledge of the patient motion. The research plan has two specific aims: (1) further development of autocorrection methodology, and (2) implementation and evaluation of autocorrection in specific clinical and research applications: joint and spine imaging, renal angiography, 3D head imaging, high-field inner ear imaging, and in vivo mouse MRI. These applications have high clinical and research importance, pose widely different technical requirements, and are representative of a wide range of possible future applications of autocorrection techniques. The overall significance of this work is that autocorrection techniques hold promise for wide applicability to the reduction of motion artifacts in many different kinds of MR acquisitions. The techniques proposed here are simple, flexible, and require only the raw data from the MR scanner. They are applicable to both conventional and fast scan applications, and are adaptable to some non-motion sources of artifacts as well. They represent perhaps the most flexible and universally applicable techniques for the correction of motion (and other) artifacts in MR imaging.
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