Solution of the Forward Problem in Magnetic-Field Tomography (MFT) Based on Magnetoencephalography (MEG)

Khan, Sharief; Aristovich, Kirill; Боровков, А. И.

doi:10.1109/tmag.2009.2012653

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…As examples, the above approach was successfully used for mathematical modelling, design and performance evaluation of primary sensors, actuators and devices which comprise measurement sub-systems [53][54][55][56][57][58][59].…”

Section: Physical Modelling Of Measurement Sub-systemsgeneric Approachmentioning

confidence: 99%

Mathematical modelling in the analysis and design of hard and soft measurement systems

Khan

Finkelstein

2013

Measurement

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Citation: Khan, S. and Finkelstein, L. (2013). Mathematical modelling in the analysis and design of hard and soft measurement systems. Measurement, 46(8), pp. 2936Measurement, 46(8), pp. -2941Measurement, 46(8), pp. . doi: 10.1016Measurement, 46(8), pp. /j.measurement.2013 This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent b s t r a c tThis paper briefly looks into the role and extent of mathematical modelling in the design and analysis of measurement systems, especially measurement sub-systems in the form of instruments and instrument elements. These fall within the classical domain of hard measurement (physical, strongly-defined measurement). It also examines the role and use of mathematical modelling in the area of soft measurement (non-physical, weakly defined measurement). These constitute two sub-sets of widely-defined measurement. Based on a number of examples it demonstrates the use of modern modelling techniques in the design and analysis of sub-systems in measurement technology. In doing so, it focuses on the scope and importance of physical modelling at a sub-system level, which ultimately contributes to modelling activities at a global systems level.

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Section: Physical Modelling Of Measurement Sub-systemsgeneric Approachmentioning

confidence: 99%

Mathematical modelling in the analysis and design of hard and soft measurement systems

Khan

Finkelstein

2013

Measurement

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“…The FE models used in this study are based on the methodologies and tools developed by the authors for building realistic models of the human brain [6,2]. Parametrical solid CAD spline models are used for further FE discretisation which incorporates geometrical accuracy limited only by existing MRI equipment.…”

Section: Model Descriptionmentioning

confidence: 99%

“…The current source is given in terms of interface/boundary conditions, which does not require special considerations for meshing regions in and around the current source [2]. A typical FE mesh is shown in Figure 4 together with the position In this case, the maximum value of the magnetic field flux density on the detection surface was taken as the solution parameter for testing modelling accuracy as a function of solution errors for successive solutions for various mesh densities.…”

Section: Quasistatic Magnetic Field Analysis Imentioning

confidence: 99%

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Investigation of optimal parameters for finite element solution of the forward problem in magnetic field tomography based on magnetoencephalography

Aristovich

Khan

Боровков

2011

J. Phys.: Conf. Ser.

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Abstract. This paper presents an investigation of optimal parameters for finite element (FE) solution of the forward problem in magnetic field tomography (MFT) brain imaging based on magnetoencephalography (MEG). It highlights detailed analyses of the main parameters involved and evaluates their optimal values for various cases of FE model solutions (e.g., steady-state, transient, etc.). In each case, a detail study of some of the main parameters and their effects on FE solution and its accuracy are carefully tested and evaluated. These parameters include: total number and size of 3D FE elements used, number and size of elements used in surface discretisation (of both white and grey matters of the brain), number and size of elements used for approximation of current sources, number of anisotropic properties used in steady-state and transient solutions, and the time steps used in transient analyses. The optimal values of these parameters in relation to solution accuracy and mesh convergence criteria have been found and presented.

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“…NURBS curves have been used as building blocks in CAD software for decades. Some works in optimizing the NURBS representation in certain inverse problems, including reverse engineering, fitting strategies, and recovering shapes from photographs, have been done in [1,4,12,14,18,26,32,31]. Other works in X-ray tomography that readily provide segmented images can be found in [19,20,15].…”

Section: Introductionmentioning

confidence: 99%

Shape recovery for sparse‐data tomography

Haario

Kallonen²,

Laine

et al. 2017

Math Methods in App Sciences

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A two-dimensional sparse-data tomographic problem is studied. The target is assumed to be a homogeneous object bounded by a smooth curve. A nonuniform rational basis splines (NURBS) curve is used as a computational representation of the boundary. This approach conveniently provides the result in a format readily compatible with computer-aided design software. However, the linear tomography task becomes a nonlinear inverse problem because of the NURBS-based parameterization. Therefore, Bayesian inversion with Markov chain Monte Carlo sampling is used for calculating an estimate of the NURBS control points. The reconstruction method is tested with both simulated data and measured X-ray projection data. The proposed method recovers the shape and the attenuation coefficient significantly better than the baseline algorithm (optimally thresholded total variation regularization), but at the cost of heavier computation.

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Solution of the Forward Problem in Magnetic-Field Tomography (MFT) Based on Magnetoencephalography (MEG)

Cited by 3 publications

References 6 publications

Mathematical modelling in the analysis and design of hard and soft measurement systems

Mathematical modelling in the analysis and design of hard and soft measurement systems

Investigation of optimal parameters for finite element solution of the forward problem in magnetic field tomography based on magnetoencephalography

Shape recovery for sparse‐data tomography

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