73 research outputs found
Computational methods in electromagnetic biomedical inverse problems
This work concerns computational methods in electromagnetic biomedical inverse problems. The following biomedical imaging modalities are studied: electro/magnetoencephalography (EEG/MEG), electrical impedance tomography (EIT), and limited-angle computerized tomography (limited-angle CT). The use of a priori information about the unknown feature is necessary for finding an adequate answer to an inverse problem. Both classical regularization techniques and Bayesian methodology are applied to utilize the a priori knowledge. The inverse problems specifically considered in this work include determination of relatively small electric conductivity anomalies in EIT, dipole-like sources in EEG/MEG, and multiscale X-ray absorbing structures in limited-angle CT. Computational methods and techniques applied for solving these have been designed case-by-case. Results concern, among others, appropriate parametrization of inverse problems; two-stage reconstruction processes, in which a region of interest (ROI) is determined in the first stage and the actual reconstruction is found in the second stage; effective forward simulation through h- and p- versions of the finite element method (FEM); localization of dipole-like electric sources through hierarchical Bayesian models; implementation of the Kirsch factorization method for reconstruction of conductivity anomalies; as well as the use of a coarse-to-fine reconstruction strategy in linear inverse problems
Zeffiro user interface for electromagnetic brain imaging: a GPU accelerated FEM tool for forward and inverse computations in Matlab
This article introduces the Zeffiro interface (ZI) version 2.2 for brain
imaging. ZI aims to provide a simple, accessible and multimodal open source
platform for finite element method (FEM) based and graphics processing unit
(GPU) accelerated forward and inverse computations in the Matlab environment.
It allows one to (1) generate a given multi-compartment head model, (2) to
evaluate a lead field matrix as well as (3) to invert and analyze a given set
of measurements. GPU acceleration is applied in each of the processing stages
(1)-(3). In its current configuration, ZI includes forward solvers for
electro-/magnetoencephalography (EEG) and linearized electrical impedance
tomography (EIT) as well as a set of inverse solvers based on the hierarchical
Bayesian model (HBM). We report the results of EEG and EIT inversion tests
performed with real and synthetic data, respectively, and demonstrate
numerically how the inversion parameters affect the EEG inversion outcome in
HBM. The GPU acceleration was found to be essential in the generation of the FE
mesh and the LF matrix in order to achieve a reasonable computing time. The
code package can be extended in the future based on the directions given in
this article
Randomized Multiresolution Scanning in Focal and Fast E/MEG Sensing of Brain Activity with a Variable Depth
We focus on electromagnetoencephalography imaging of the neural activity and,
in particular, finding a robust estimate for the primary current distribution
via the hierarchical Bayesian model (HBM). Our aim is to develop a reasonably
fast maximum a posteriori (MAP) estimation technique which would be applicable
for both superficial and deep areas without specific a priori knowledge of the
number or location of the activity. To enable source distinguishability for any
depth, we introduce a randomized multiresolution scanning (RAMUS) approach in
which the MAP estimate of the brain activity is varied during the
reconstruction process. RAMUS aims to provide a robust and accurate imaging
outcome for the whole brain, while maintaining the computational cost on an
appropriate level. The inverse gamma (IG) distribution is applied as the
primary hyperprior in order to achieve an optimal performance for the deep part
of the brain. In this proof-of-the-concept study, we consider the detection of
simultaneous thalamic and somatosensory activity via numerically simulated data
modeling the 14-20 ms post-stimulus somatosensory evoked potential and field
response to electrical wrist stimulation. Both a spherical and realistic model
are utilized to analyze the source reconstruction discrepancies. In the
numerically examined case, RAMUS was observed to enhance the visibility of deep
components and also marginalizing the random effects of the discretization and
optimization without a remarkable computation cost. A robust and accurate MAP
estimate for the primary current density was obtained in both superficial and
deep parts of the brain.Comment: Brain Topogr (2020
Navier-Stokes Modelling of Non-Newtonian Blood Flow in Cerebral Arterial Circulation and its Dynamic Impact on Electrical Conductivity in a Realistic Multi-Compartment Head Model
Background and Objective: This study aims to evaluate the dynamic effect of
non-Newtonian cerebral arterial circulation on electrical conductivity
distribution (ECD) in a realistic multi-compartment head model. It addresses
the importance and challenges associated with electrophysiological modalities,
such as transcranial electrical stimulation, electro-magnetoencephalography,
and electrical impedance tomography. Factors such as electrical conductivity's
impact on forward modeling accuracy, complex vessel networks, data acquisition
limitations (especially in MRI), and blood flow phenomena are considered.
Methods: The Navier-Stokes equations (NSEs) govern the non-Newtonian flow model
used in this study. The solver comprises two stages: the first solves the
pressure field using a dynamical pressure-Poisson equation derived from NSEs,
and the second updates the velocity field using Leray regularization and the
pressure distribution from the first stage. The Carreau-Yasuda model
establishes the connection between blood velocity and viscosity. Blood
concentration in microvessels is approximated using Fick's law of diffusion,
and conductivity mapping is obtained via Archie's law. The head model used
corresponds to an open 7 Tesla MRI dataset, differentiating arterial vessels
from other structures. Results: The results suggest the establishment of a
dynamic model of cerebral blood flow for arterial and microcirculation. Blood
pressure and conductivity distributions are obtained through numerically
simulated pulse sequences, enabling approximation of blood concentration and
conductivity within the brain. Conclusions: This model provides an
approximation of dynamic blood flow and corresponding ECD in different brain
regions. The advantage lies in its applicability with limited a priori
information about blood flow and compatibility with arbitrary head models that
distinguish arteries.Comment: 13 pages; 8 figures; 2 tabl
Excessive load
Sometimes structural loads are excessively high, and a decision must be made if intervention, either removal of the excess load or strengthening of the structure, is needed. This issue was addressed previously in the retrofitting literature. However, equations for excess load calculation were not presented. This article includes equations based on the full probabilistic reliability model for the failure probability of excessive load of three materials: steel, timber, and concrete. Failure probabilities are given as a function of the load designed for full capacity according to the Eurocodes. Safe excessive loads, i.e., loads with a failure probability less than 1/1500, are given, too. The load combination is a critical issue in this study. There are many options for load combination, which vary regarding the dependent vs. independent load combination, dependent vs. independent reliability calculation, the reference time, and the reference reliability. The conclusion is that the loads should be combined dependently, reliability should be calculated dependently, the reference time is 50Â years, and the reliability is 50Â years. We stress that the reliability of steel structures is questionably low in the current Eurocodes.Peer reviewe
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