EIDORS: Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software

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Sensitivity of EIT and Reconstruction

Sensitivity of EIT in 2D

% Sensitivity map $Id: sensitivity_map01.m 2168 2010-04-05 13:12:44Z aadler $

imdl= mk_common_model('f2d1c',16); % 'j2d1c'
J= calc_jacobian(calc_jacobian_bkgnd(imdl));
img = mk_image(imdl, J(5,:)');

img.calc_colours.npoints= 256;
img.calc_slices.filter = conv2(ones(3),ones(3));
img.calc_colours.clim = .0004;
show_slices(img);
print_convert('sensitivity_map01a.png','-density 60');

img.calc_colours.cb_shrink_move = [0.5,0.8,0];
show_fem(img,[1,1]);
print_convert('sensitivity_map01b.png','-density 60');

Figure: Sensitivity field of 2D EIT to a single simulation ([1-2]) and measurement [7-8] pattern

Reconstruction from each component

% Sensitivity map $Id: sensitivity_map02.m 2169 2010-04-05 15:44:25Z aadler $

imdl= mk_common_model('a2c0',16); % Basic model - replace
imdl.fwd_model= ng_mk_cyl_models([2,1,.07],[16,1.0],[0.1]);
imdl.fwd_model.stimulation = mk_stim_patterns(16,1,[0,1],[0,1],{},1);
J= calc_jacobian(calc_jacobian_bkgnd(imdl));
img = mk_image(imdl, J(5,:)');

img.calc_colours.clim= 3e-6;

img.calc_colours.npoints= 256;
img.calc_slices.filter = conv2(ones(3),ones(3));
show_3d_slices(img,[0.7,1.0],[0.5],[]);
view(-70,22);
print_convert('sensitivity_map02a.png','-density 60');
view(-70,62);
print_convert('sensitivity_map02b.png','-density 60');

np = interp_mesh(img.fwd_model);
img.elem_data(np(:,3)>1.05)= 0;
show_fem(img);

% Show better
view(-70,52);
crop_model(gca, inline('-x/5 + z>1.05','x','y','z'))
print_convert('sensitivity_map02c.png','-density 60');

Figure: Sensitivity field of 3D EIT to a single simulation ([1-2]) and measurement [7-8] pattern