Alexander Loktyushin

Existential neuroscience: a functional magnetic resonance imaging investigation of neural responses to reminders of one’s mortality

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 ones inevitable death. The present study thus investigated in 17 male participants patterns of neural activation elicited by mortality threat. To induce mortality threat, participants answered questions arranged in trial blocks that referred to fear of death and dying. In the control condition participants answered questions about fear of dental pain. Neural responses to mortality threat were greater than to pain threat in right amygdala, left rostral anterior cingulate cortex, and right caudate nucleus. We discuss implications of these findings for stimulating further research into the neurological correlates of managing existential fear.

Blind retrospective motion correction of MR images.

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 devices are required. Rigid motion is assumed.

Blind multirigid retrospective motion correction of MR images

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 sequences or external sensors. We propose a fully retrospective nonrigid motion correction scheme that only needs raw data as an input.

Automatic foreground-background refocusing

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 the amount of out-of-focus blur, (ii) segments the image into foreground and background incorporating clues from the blurriness, (iii) recovers the sharp foreground, and finally (iv) blurs the background to refocus the scene. On several real photographs with blurry foreground and sharp background, we demonstrate the effectiveness and limitations of our method.

Automatic detection of motion artifacts in MR images using CNNS

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 present an algorithm based on convolutional neural networks that enables fully automated detection of motion artifacts in MR scans without special hardware requirements. The approach is data driven and uses the magnitude of MR images in the spatial domain as input. We evaluate the performance of our algorithm on both synthetic and real data and observe adequate performance in terms of accuracy and generalization to different types of data. Our proposed approach could potentially be used in clinical practice to tag an MR image as motion-free or motion-corrupted immediately after a scan is finished. This process would facilitate the acquisition of high-quality MR images that are often indispensable for accurate medical diagnosis.

Autofocusing-based phase correction

Phase artifacts due to B0 inhomogeneity can severely degrade the quality of MR images. The artifacts are particularly prominent in long‐TE scans and usually appear as ghosting and blur. We propose a retrospective phase correction method based on autofocusing. The proposed method uses raw data acquired with standard imaging sequences, and does not rely on navigators or external measures of field inhomogeneity.