XIX Sympozjum Srodowiskowe PTZE - materialy.pdf
XIX Sympozjum Srodowiskowe PTZE - materialy.pdf XIX Sympozjum Srodowiskowe PTZE - materialy.pdf
XIX Sympozjum PTZE, Worliny 2009 Slices are connected to 3D volume, Fig.2-a. Volume is meshed by Delaney triangulation algorithm. Supported are first and second order elements. The achieved list of elements is imported in ANSYS, Fig.2-b. Tissue electromagnetic material properties are applied for every element in the list. a) (b) 4D models contain a time sequence of 3D models (Fig.3). All models in the sequence have common mesh which is deformed for each time step object shape. Fig. 3. 4D model time sequence Implementation 116 Fig. 2. Reconstructed volume The developed method and software tool was effectively applied for part of cardiac muscle reconstruction. Achieved models are suitable for electromagnetic field distribution calculations with FEM. References [1] R. Hartley. Projective Reconstruction and Invariants from Multiple Images. IEEE PAMI Vol. 16, No. 10, 1994, pp. 1036-1041. [2] T. McInerney, D. Terzopoulos. Deformable models in medical image analysis: a survey, Medical Image Analysis. 1996 [3] D. Pham, C. Xu, J. Prince, Current Methods in Medical Image Segmentation, Annu. Rev. Biomed. Eng. 2000. Vol. 2, 315–37 [4] I. Marinova, Modelling, Simulation and Visualization of Electromagnetic Interaction in Human Body, Ashikaga, Japan, June 2000 [5] I. Marinova, V. Mateev. Virtual Dynamic Visualization of Field Distributions in Human Body. International Symposium on Electrical Apparatus and Technologies – SIELA 2005, Proceedings, Vol. 2, 2-3 June 2005, Plovdiv, Bulgaria.
XIX Sympozjum PTZE, Worliny 2009 INVERSE APPROACH FOR RECONSTRUCTION OF CURRENT DENSITY VECTORS Iliana Marinova, Valentin Mateev Technical University of Sofia, Department of Electrical Apparatus, 1156 Sofia, 8 Kliment Ohridski St., Bulgaria, e-mail: iliana@tu-sofia.bg, vmateev@tu-sofia.bg Abstract In this paper we apply an inverse approach for 3D current sources reconstruction using measured magnetic field data. The reconstruction approach is based on the 3D Green’s function of Poisson and Helmholtz equations. The developed approach was effectively applied for current source distribution reconstruction of coil in linear nonmagnetic media. Introduction Current source distributions in biological structures are extremely important for medical diagnosis and therapy treatments in various applications. Magneto CardioGraphy (MCG) and Magneto EncephaloGraphy (MEG) process measured magnetic field data outside the human body, near the chest or head, for inside current imaging used for medical diagnoses. In magnetic stimulation therapy applications, current pulses are supplied to the coil to produce a strong magnetic field to stimulate nerve fibres. Magnetic stimulation occurs as result of the current flow and the accompanying electric field induced in the tissue by an externally applied magnetic field. Determination of magnetic field and current distributions in the tissue in order to generate prescribed stimulation effect is an inverse source problem. The current density distribution is basic part in coil design optimisation and electromagnetic systems syntheses. In this paper we apply an inverse approach for 3D current sources reconstruction using measured magnetic field data. The reconstruction approach uses 3D Green’s function. The magnetic fields are measured in a surface mesh over the tested object region. These data are used for field source reconstruction in inaccessible for direct measurements region. The developed reconstruction approach is effectively applied for current source distribution reconstruction of a circular coil in linear non-magnetic media. Inverse approach The magnetic field distribution can be described through the Green’s (1, 2) functions of Poisson(3) and Helmholtz(4) equations for magnetic vector potential(MVP) and complex MVP. [1, 4, 5] 117
- Page 66 and 67: XIX Sympozjum PTZE, Worliny 2009 Sp
- Page 68 and 69: XIX Sympozjum PTZE, Worliny 2009 gn
- Page 70 and 71: XIX Sympozjum PTZE, Worliny 2009 kt
- Page 72 and 73: XIX Sympozjum PTZE, Worliny 2009 gd
- Page 75 and 76: XIX Sympozjum PTZE, Worliny 2009 AN
- Page 77: Acknowledgement XIX Sympozjum PTZE,
- Page 80 and 81: XIX Sympozjum PTZE, Worliny 2009 ac
- Page 82 and 83: XIX Sympozjum PTZE, Worliny 2009 pl
- Page 84 and 85: XIX Sympozjum PTZE, Worliny 2009 Fi
- Page 86 and 87: XIX Sympozjum PTZE, Worliny 2009 Bi
- Page 88 and 89: XIX Sympozjum PTZE, Worliny 2009 Th
- Page 90 and 91: XIX Sympozjum PTZE, Worliny 2009 sp
- Page 92 and 93: XIX Sympozjum PTZE, Worliny 2009 hu
- Page 94 and 95: XIX Sympozjum PTZE, Worliny 2009 Fi
- Page 96 and 97: Conclusions XIX Sympozjum PTZE, Wor
- Page 98 and 99: XIX Sympozjum PTZE, Worliny 2009 Th
- Page 100 and 101: XIX Sympozjum PTZE, Worliny 2009 an
- Page 102 and 103: XIX Sympozjum PTZE, Worliny 2009 [3
- Page 104 and 105: H_zob 2. TFM geometry optimization
- Page 107 and 108: XIX Sympozjum PTZE, Worliny 2009 RE
- Page 109 and 110: XIX Sympozjum PTZE, Worliny 2009 BR
- Page 111 and 112: XIX Sympozjum PTZE, Worliny 2009 DI
- Page 113 and 114: XIX Sympozjum PTZE, Worliny 2009 NO
- Page 115: XIX Sympozjum PTZE, Worliny 2009 DY
- Page 119: References XIX Sympozjum PTZE, Worl
- Page 122 and 123: XIX Sympozjum PTZE, Worliny 2009 ty
- Page 125 and 126: XIX Sympozjum PTZE, Worliny 2009 WY
- Page 127: 1000 100 10 H-Field 3D [nT] E-Field
- Page 130 and 131: Rys. 1. Rozpatrywany model (rysunek
- Page 132 and 133: The usual stimulation is done by ma
- Page 134 and 135: Literatura XIX Sympozjum PTZE, Worl
- Page 136 and 137: XIX Sympozjum PTZE, Worliny 2009 80
- Page 138 and 139: XIX Sympozjum PTZE, Worliny 2009 Th
- Page 140 and 141: a) b) 45 40 35 30 25 20 15 10 5 5 1
- Page 142 and 143: XIX Sympozjum PTZE, Worliny 2009 Th
- Page 144 and 145: u1 i1 N1 u2 i2 N2 um i m Nm XIX Sym
- Page 147 and 148: XIX Sympozjum PTZE, Worliny 2009 A
- Page 149 and 150: Introduction XIX Sympozjum PTZE, Wo
- Page 151 and 152: XIX Sympozjum PTZE, Worliny 2009 IM
- Page 153 and 154: Introduction XIX Sympozjum PTZE, Wo
- Page 155 and 156: XIX Sympozjum PTZE, Worliny 2009 PE
- Page 157 and 158: XIX Sympozjum PTZE, Worliny 2009 WP
- Page 159: Tmax(K) 24 20 16 12 8 4 0 0.0004 0.
- Page 162 and 163: XIX Sympozjum PTZE, Worliny 2009 Ws
- Page 164 and 165: XIX Sympozjum PTZE, Worliny 2009 ce
<strong>XIX</strong> <strong>Sympozjum</strong> <strong>PTZE</strong>, Worliny 2009<br />
Slices are connected to 3D volume, Fig.2-a. Volume is meshed by Delaney triangulation<br />
algorithm. Supported are first and second order elements. The achieved list of elements is<br />
imported in ANSYS, Fig.2-b.<br />
Tissue electromagnetic material properties are applied for every element in the list.<br />
a) (b)<br />
4D models contain a time<br />
sequence of 3D models (Fig.3).<br />
All models in the sequence have<br />
common mesh which is deformed<br />
for each time step object shape.<br />
Fig. 3. 4D model time sequence<br />
Implementation<br />
116<br />
Fig. 2. Reconstructed volume<br />
The developed method and software tool was effectively applied for part of cardiac muscle<br />
reconstruction. Achieved models are suitable for electromagnetic field distribution<br />
calculations with FEM.<br />
References<br />
[1] R. Hartley. Projective Reconstruction and Invariants from Multiple Images. IEEE PAMI Vol.<br />
16, No. 10, 1994, pp. 1036-1041.<br />
[2] T. McInerney, D. Terzopoulos. Deformable models in medical image analysis: a survey,<br />
Medical Image Analysis. 1996<br />
[3] D. Pham, C. Xu, J. Prince, Current Methods in Medical Image Segmentation, Annu. Rev.<br />
Biomed. Eng. 2000. Vol. 2, 315–37<br />
[4] I. Marinova, Modelling, Simulation and Visualization of Electromagnetic Interaction in<br />
Human Body, Ashikaga, Japan, June 2000<br />
[5] I. Marinova, V. Mateev. Virtual Dynamic Visualization of Field Distributions in Human<br />
Body. International Symposium on Electrical Apparatus and Technologies – SIELA 2005,<br />
Proceedings, Vol. 2, 2-3 June 2005, Plovdiv, Bulgaria.