Browse Topic: Magnetic resonance imaging (MRI)

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We are living in a digitally integrated and connected world. Evidenced by the use of smartphones, smartwatches, and other smart devices, there is no ending this trend. This holds true across many industries and applications, but is especially prevalent within medtech devices — a market that’s predicted to reach $432.6 billion by 2025.1
Development of a Subhuman Primate Brain Finite Element Model to Investigate Brain Injury Thresholds Induced by Head Rotation2019-22-00033/31/2020
An anatomically detailed rhesus monkey brain FE model was developed to simulate in vivo responses of the brain of sub-human primates subjected to rotational accelerations resulting in diffuse axonal injury (DAI). The material properties used in the monkey model are those in the GHBMC 50th percentile male head model (Global Human Body Model Consortium). The angular loading simulations consisted of coronal, oblique and sagittal plane rotations with the center of rotation in neck to duplicate experimental conditions. Maximum principal strain (MPS) and Cumulative strain damage measure (CSDM) were analyzed for various white matter structures such as the cerebrum subcortical white matter, corpus callosum and brainstem. The MPS in coronal rotation were 45% to 54% higher in the brainstem, 8% to 48% higher in the corpus callosum, 13% to 22% higher in the white matter when compared to those in oblique and sagittal rotations, suggesting that more severe DAI was expected from coronal and oblique rotations as compared to that from sagittal rotation. The level 1+ DAI was associated with 1.3 to 1.42 MPS and 50% CSDM (0.5) responses in the brainstem, corpus callosum and cerebral white matter. The mass scaling method, sometimes referred to as Holbourn's inverse 2/3 power law, used for development of human brain injury criterion was evaluated to understand the effect of geometrical and anatomical differences between human and animal head. Based on simulations conducted with the animal and human models in three different planes - sagittal, coronal and horizontal - the scaling from animal to human models are not supported due to lack of geometrical similitude between the animal and human brains. Thus, the scaling method used in the development of brain injury criterion for rotational acceleration/velocity is unreliable.
Arora, TusharZhang, LiyingPrasad, Priya
Self-Affinity of an Aircraft Pilot’s Gaze Direction as a Marker of Visual Tunneling2019-01-18529/16/2019
For the last few years, a great deal of interest has been paid to crew monitoring systems in order to address potential safety problems during a flight. They aim at detecting any degraded physiological and/or cognitive state of an aircraft pilot or crew, such as visual tunneling, also called inattentional blindness. Indeed, they might have a negative impact on the performance to pursue the mission with adequate flight safety levels. One of the usual approaches consists in using sensors to collect physiological signals which are then analyzed. Two main families exist to process the signals. The first one combines feature extraction and machine learning whereas the second is based on deep-learning approaches which may require a large amount of labeled data. In this work, we focused on the first family. In this case, various features can be deduced from the data by different approaches: spectrum analysis, a priori modeling and nonlinear dynamical system analysis techniques including the estimation of the self-affinity of the signals. In this paper, our purpose was to uncover whether the self-affinity of the pilot gaze direction can be related to his cognitive state. To this end, an experiment was carried out on thirteen subjects in a pilot activity representative environment based on a modified version of the software MATB-II. The scenarios were designed to elicit different levels of mental workload eventually associated to attentional tunneling. A database to train the machine learning step was first created by recording the gaze directions of the subjects with an eye-tracker. The self-affinities of these signals were extracted with the Detrended Fluctuation Analysis method. They constituted the inputs of the classifier. Then, other signals were analyzed and classified. Preliminary results showed promising abilities to detect visual tunneling episodes for different levels of mental workload.
Berthelot, BastienMazoyer, PatrickEgea, SarahAndré, Jean-MarcGrivel, ÉricLegrand, Pierrick
Magnetic resonance imaging (MRI) has evolved into one of the most powerful, non-invasive diagnostic imaging techniques in medicine and biomedical research. The superior resolution and in-depth anatomical details provided by MRI are essential for early diagnosis of many diseases. Chemical contrast agents (CAs) have been widely used for improving the sensitivity and diagnostic confidence in MRI.
Functional connections within the brain can be revealed through functional magnetic resonance imaging (fMRI), which shows simultaneous activations of blood flow in the brain during response tests. However, fMRI specialists currently do not have a tool for visualizing the complex data that comes from fMRI scans. They work with correlation matrices that table what functional region connections exist, but they have no corresponding visualization.
A new medical imaging method being developed at Rutgers University could help physicians detect cancer and other diseases earlier than before, speeding treatment and reducing the need for invasive, time-consuming biopsies.
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