Motion is 1 extrinsic source for imaging artifacts in MRI that can strongly deteriorate image quality and, thus, impair diagnostic accuracy. In addition to involuntary physiological motion such as respiration and cardiac motion, intended and accidental patient movements can occur. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion‐free reacquisition can become time‐ and cost‐intensive. Numerous motion correction strategies have been proposed to reduce or prevent motion artifacts. These methods have in common that they need to be applied during the actual measurement procedure with a‐priori knowledge about the expected motion type and appearance. For retrospective motion correction and without the existence of any a‐priori knowledge, this problem is still challenging.
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Retrospective correction of motion‐affected MR images using deep learning frameworks
Semantic Scholar · Medicine · 2019
Abstract
Motion is 1 extrinsic source for imaging artifacts in MRI that can strongly deteriorate image quality and, thus, impair diagnostic accuracy. In addition to involuntary physiological motion such as respiration and cardiac motion, intended and accidental patient movements can occur. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion‐free reacquisition can become time‐ and cost‐intensive. Numerous motion correction strategies have been proposed to reduce or prevent motion artifacts. These methods have in common that they need to be applied during the actual measurement procedure with a‐priori knowledge about the expected motion type and appearance. For retrospective motion correction and without the existence of any a‐priori knowledge, this problem is still challenging.