196 research outputs found
Quantitative Susceptibility Mapping: Contrast Mechanisms and Clinical Applications.
Quantitative susceptibility mapping (QSM) is a recently developed MRI technique for quantifying the spatial distribution of magnetic susceptibility within biological tissues. It first uses the frequency shift in the MRI signal to map the magnetic field profile within the tissue. The resulting field map is then used to determine the spatial distribution of the underlying magnetic susceptibility by solving an inverse problem. The solution is achieved by deconvolving the field map with a dipole field, under the assumption that the magnetic field is a result of the superposition of the dipole fields generated by all voxels and that each voxel has its unique magnetic susceptibility. QSM provides improved contrast to noise ratio for certain tissues and structures compared to its magnitude counterpart. More importantly, magnetic susceptibility is a direct reflection of the molecular composition and cellular architecture of the tissue. Consequently, by quantifying magnetic susceptibility, QSM is becoming a quantitative imaging approach for characterizing normal and pathological tissue properties. This article reviews the mechanism generating susceptibility contrast within tissues and some associated applications
Learning-based Single-step Quantitative Susceptibility Mapping Reconstruction Without Brain Extraction
Quantitative susceptibility mapping (QSM) estimates the underlying tissue
magnetic susceptibility from MRI gradient-echo phase signal and typically
requires several processing steps. These steps involve phase unwrapping, brain
volume extraction, background phase removal and solving an ill-posed inverse
problem. The resulting susceptibility map is known to suffer from inaccuracy
near the edges of the brain tissues, in part due to imperfect brain extraction,
edge erosion of the brain tissue and the lack of phase measurement outside the
brain. This inaccuracy has thus hindered the application of QSM for measuring
the susceptibility of tissues near the brain edges, e.g., quantifying cortical
layers and generating superficial venography. To address these challenges, we
propose a learning-based QSM reconstruction method that directly estimates the
magnetic susceptibility from total phase images without the need for brain
extraction and background phase removal, referred to as autoQSM. The neural
network has a modified U-net structure and is trained using QSM maps computed
by a two-step QSM method. 209 healthy subjects with ages ranging from 11 to 82
years were employed for patch-wise network training. The network was validated
on data dissimilar to the training data, e.g. in vivo mouse brain data and
brains with lesions, which suggests that the network has generalized and
learned the underlying mathematical relationship between magnetic field
perturbation and magnetic susceptibility. AutoQSM was able to recover magnetic
susceptibility of anatomical structures near the edges of the brain including
the veins covering the cortical surface, spinal cord and nerve tracts near the
mouse brain boundaries. The advantages of high-quality maps, no need for brain
volume extraction and high reconstruction speed demonstrate its potential for
future applications.Comment: 26 page
Research and Design of Rootkit Detection Method
AbstractRootkit is one of the most important issues of network communication systems, which is related to the security and privacy of Internet users. Because of the existence of the back door of the operating system, a hacker can use rootkit to attack and invade other people's computers and thus he can capture passwords and message traffic to and from these computers easily. With the development of the rootkit technology, its applications are more and more extensive and it becomes increasingly difficult to detect it. In addition, for various reasons such as trade secrets, being difficult to be developed, and so on, the rootkit detection technology information and effective tools are still relatively scarce. In this paper, based on the in-depth analysis of the rootkit detection technology, a new kind of the rootkit detection structure is designed and a new method (software), X-Anti, is proposed. Test results show that software designed based on structure proposed is much more efficient than any other rootkit detection software
Post-processing Procedures for Passive GPS based Travel Survey
AbstractA challenge in posteriori data processing for passive GPS based travel survey, which constitute the heart of this paper, is to develop a series of methods to automatically restore the sequences of data points, both in space and time. It means the trips and activities occurred in the survey time should be identifiable chronologically and those identified by the program should respect this definition convention. Reference to the research results of our colleagues, and by combining the experiences of other French travel survey and personal mobility survey at Lille, a series of methods has been developed and put into application. The data outcome is ready for further applications
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Imaging the Centromedian Thalamic Nucleus Using Quantitative Susceptibility Mapping.
The centromedian (CM) nucleus is an intralaminar thalamic nucleus that is considered as a potentially effective target of deep brain stimulation (DBS) and ablative surgeries for the treatment of multiple neurological and psychiatric disorders. However, the structure of CM is invisible on the standard T1- and T2-weighted (T1w and T2w) magnetic resonance images, which hamper it as a direct DBS target for clinical applications. The purpose of the current study is to demonstrate the use of quantitative susceptibility mapping (QSM) technique to image the CM within the thalamic region. Twelve patients with Parkinson's disease, dystonia, or schizophrenia were included in this study. A 3D multi-echo gradient recalled echo (GRE) sequence was acquired together with T1w and T2w images on a 3-T MR scanner. The QSM image was reconstructed from the GRE phase data. Direct visual inspection of the CM was made on T1w, T2w, and QSM images. Furthermore, the contrast-to-noise ratios (CNRs) of the CM to the adjacent posterior part of thalamus on T1w, T2w, and QSM images were compared using the one-way analysis of variance (ANOVA) test. QSM dramatically improved the visualization of the CM nucleus. Clear delineation of CM compared to the surroundings was observed on QSM but not on T1w and T2w images. Statistical analysis showed that the CNR on QSM was significantly higher than those on T1w and T2w images. Taken together, our results indicate that QSM is a promising technique for improving the visualization of CM as a direct targeting for DBS surgery
Exact Solutions of Travelling Wave Model via Dynamical System Method
By using the method of dynamical system, the exact travelling wave solutions of the coupled nonlinear Schrödinger-Boussinesq equations are studied. Based on this method, the bounded exact travelling wave solutions are obtained which contain solitary wave solutions and periodic travelling wave solutions. The solitary wave solutions and periodic travelling wave solutions are expressed by the hyperbolic functions and the Jacobian elliptic functions, respectively. The results show that the presented findings improve the related previous conclusions. Furthermore, the numerical simulations of the solitary wave solutions and the periodic travelling wave solutions are given to show the correctness of our results
Homogeneous Carbon Nanotube/Carbon Composites Prepared by Catalyzed Carbonization Approach at Low Temperature
We synthesize carbon nanotube (CNT)/carbon composite using catalyzed carbonization of CNT/Epoxy Resin composite at a fairly low temperature of about 400∘C. The microstructure of the composite is characterized by scanning electron microscope (SEM), transmission electron microscope (TEM), and X-ray diffraction (XRD). The results indicate that CNTs and pyrolytic carbon blend well with each other. Pyrolytic carbon mainly stays in an amorphous state, with some of it forming crystalline structures. The catalyst has the effect of eliminating the interstices in the composites. Remarkable increases in thermal and electrical conductivity are also reported
Multivariate MR Biomarkers Better Predict Cognitive Dysfunction in Mouse Models of Alzheimers Disease
To understand multifactorial conditions such as Alzheimers disease (AD) we
need brain signatures that predict the impact of multiple pathologies and their
interactions. To help uncover the relationships between brain circuits and
cognitive markers we have used mouse models that represent, at least in part,
the complex interactions altered in AD. In particular, we aimed to understand
the relationship between vulnerable brain circuits and memory deficits measured
in the Morris water maze, and we tested several predictive modeling approaches.
We used in vivo manganese enhanced MRI voxel based analyses to reveal regional
differences in volume (morphometry), signal intensity (activity), and magnetic
susceptibility (iron deposition, demyelination). These regions included the
hippocampus, olfactory areas, entorhinal cortex and cerebellum. The image based
properties of these regions were used to predict spatial memory. We next used
eigenanatomy, which reduces dimensionality to produce sets of regions that
explain the variance in the data. For each imaging marker, eigenanatomy
revealed networks underpinning a range of cognitive functions including memory,
motor function, and associative learning. Finally, the integration of
multivariate markers in a supervised sparse canonical correlation approach
outperformed single predictor models and had significant correlates to spatial
memory. Among a priori selected regions, the fornix also provided good
predictors, raising the possibility of investigating how disease propagation
within brain networks leads to cognitive deterioration. Our results support
that modeling approaches integrating multivariate imaging markers provide
sensitive predictors of AD-like behaviors. Such strategies for mapping brain
circuits responsible for behaviors may help in the future predict disease
progression, or response to interventions.Comment: 23 pages, 3 Tables, 6 Figures; submitted for publicatio
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