Alexander Loktyushin

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PURPOSE Subject motion can severely degrade MR images. A retrospective motion correction algorithm, Gradient-based motion correction, which significantly reduces ghosting and blurring artifacts due to subject motion was proposed. The technique uses the raw data of standard imaging sequences; no sequence modifications or additional equipment such as tracking(More)
A considerable body of evidence derived from terror management theory indicates that the awareness of mortality represents a potent psychological threat engendering various forms of psychological defense. However, extant research has yet to examine the neurological correlates of cognitions about one's inevitable death. The present study thus investigated in(More)
trajectories (top) for translational (middle) and rotational motion (bottom). (b) Sagittal view from one subject before and after correction after performing translations in head-foot direction. FID navigator triggered acquisition of imaging navigators for retrospective head motion correction Maryna Babayeva, Alexander Loktyushin, Pavel Falkovskiy , Tobias(More)
PURPOSE Physiological nonrigid motion is inevitable when imaging, e.g., abdominal viscera, and can lead to serious deterioration of the image quality. Prospective techniques for motion correction can handle only special types of nonrigid motion, as they only allow global correction. Retrospective methods developed so far need guidance from navigator(More)
A challenging problem in image restoration is to recover an image with a blurry foreground. Such images can easily occur with modern cameras, when the auto-focus aims mistakenly at the background (which will appear sharp) instead of the foreground, where usually the object of interest is. In this paper we propose an automatic procedure that (i) estimates(More)
motion correction pipeline. 2334 Retrospective rigid motion correction of undersampled MRI data Alexander Loktyushin, Maryna Babayeva, Daniel Gallichan, Gunnar Krueger, Klaus Scheffler, and Tobias Kober Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany, Siemens ACIT CHUV Radiology, Siemens Healthcare IM BM PI, & Department(More)
Considerable practical interest exists in being able to automatically determine whether a recorded magnetic resonance image is affected by motion artifacts caused by patient movements during scanning. Existing approaches usually rely on the use of navigators or external sensors to detect and track patient motion during image acquisition. In this work, we(More)
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