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)
Exemplary FIDnav signal with extrapolated motion 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. Target Audience: MR engineers, physicists, and clinicians interested in motion correction. Introduction In-vivo(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)
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)
Introduction Subject head motion during long scans is one of the major sources of image artifacts in MR brain imaging. Although advances in parallel imaging techniques allow reducing the scan time significantly, it still remains in the order of minutes, increasing the probability of head motion to occur. Motion during the acquisition leads to inconsistent(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)
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