EIDORS: Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software

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Modelling EIT in a pig shaped thorax model

This example shows how EIDORS can by use Netgen to model the body shape of a pig and then use it to build a reconstruction algorithm and see the current flow in the body. For this exmample, you need at least Netgen version 4.9.13.

Here are some examples of the varity of models which can be generated using the function: ng_mk_extruded_model.

Load thorax shape and identify contours

This image is from a CT of a piglet with EIT electrodes, courtesy of Marc Bodenstein, Universität Mainz. Hand registered contours are available in this file [CT2.mat].

load CT2.mat
img = imread('pig-thorax.jpg');

hold on;
hh=plot(372-elec_pos(:,1),elec_pos(:,2), 'b.'); set(hh,'MarkerSize',20);
hold off

axis off; axis equal
print_convert pig_body01.jpg

% Shrink the model  for the next step
trunk = trunk*.01;
lung  = lung*.01; lung = flipud(lung(1:3:end,:)); % need counterclockwise shapes
elec_pos = elec_pos*.01;

Use Netgen to build a FEM model of the pig thorax

% Calculate electrode angles
pp= fourier_fit(trunk); sp = linspace(0,1,51);sp(end)=[]; centroid = mean(fourier_fit(pp, sp));
elec_pos = elec_pos - ones(size(elec_pos,1),1) * centroid;
electh= atan2(elec_pos(:,2),elec_pos(:,1))*180/pi; % electh=electh(1:3);

fmdl = ng_mk_extruded_model({2,{trunk, lung} ,[4,50],.1},[electh,1+0*electh],[0.1]);
[stim,meas_sel] = mk_stim_patterns(16,1,[0,1],[0,1],{'no_meas_current'}, 1);
fmdl.stimulation = stim;

fmdl.nodes = fmdl.nodes*diag([-1,-1,1]); % Flip x,y axis to match medical direction
img.elem_data(fmdl.mat_idx{2})= 0.3;

clf; show_fem(img); view(0,70);
print_convert pig_body02.jpg

Simulate Voltage Distribution

img_v = img;
% Stimulate between elecs 16 and 5 to get more interesting pattern
img_v.fwd_model.stimulation(1).stim_pattern = sparse([16;5],1,[1,-1],16,1);
img_v.fwd_solve.get_all_meas = 1;
vh = fwd_solve(img_v);

img_v = rmfield(img, 'elem_data');
img_v.node_data = vh.volt(:,1);
img_v.calc_colours.npoints = 128;
img_v.calc_colours.clim = 1.2;

show_slices(img_v,[inf,inf,0.8]); axis off; axis equal
print_convert pig_body03a.jpg
show_slices(img_v,[inf,inf,1.0]); axis off; axis equal
print_convert pig_body03b.jpg
show_slices(img_v,[inf,inf,1.2]); axis off; axis equal
print_convert pig_body03c.jpg

Left to Right Voltage distribution in slices at z= 0.8, z= 1.0, z= 1.2.

Current distribution

img_v = img;
img_v.fwd_model.mdl_slice_mapper.npx = 64;
img_v.fwd_model.mdl_slice_mapper.npy = 64;
img_v.fwd_model.mdl_slice_mapper.level = [inf,inf,0.8];
show_current(img_v, vh.volt(:,1));

axis equal; axis tight; print_convert pig_body04a.jpg

img_v.fwd_model.mdl_slice_mapper.level = [inf,inf,1.0];
show_current(img_v, vh.volt(:,1));

axis equal; axis tight; print_convert pig_body04b.jpg

Left to Right Current distribution in slices at z= 0.8, z= 1.0. Current looks larger at z= 0.8, because each slice is individually normalized to the maximum

Current distribution and streamlines

img_v.fwd_model.mdl_slice_mapper.npx = 1000;
img_v.fwd_model.mdl_slice_mapper.npy = 1000;
img_v.fwd_model.mdl_slice_mapper.level = [inf,inf,1.0];

% Calculate at high resolution
 q = show_current(img_v, vh.volt(:,1));

% Lower resolution to visualize
img_v.fwd_model.mdl_slice_mapper.npx = 64;
img_v.fwd_model.mdl_slice_mapper.npy = 64;
show_current(img_v, vh.volt(:,1));

sx =-centroid(1) - linspace(-1,1,15)';
sy =-centroid(2) + linspace(-1,1,15)';
hh=streamline(q.xp,q.yp, q.xc, q.yc,sx,sy);

axis equal; axis tight;  print_convert pig_body05a.jpg

Stream lines through z= 1.0.

Streamlines and the original image

img = imread('pig-thorax.jpg');

hh=streamline(q.xp,q.yp, q.xc, q.yc,sx,sy); set(hh,'Linewidth',2);
hh=streamline(q.xp,q.yp,-q.xc,-q.yc,sx,sy); set(hh,'Linewidth',2);

axis equal; axis tight; axis off; print_convert pig_body06a.jpg

Stream lines through z= 1.0.

2D FEM model for image reconstruction

fmdlr = ng_mk_extruded_model({0,trunk,[4,50],.1},[0,0],[0.1]);
fmdlr.nodes = fmdlr.nodes*diag([-1,-1]);

show_fem(fmdlr); view(0,90);
print_convert pig_body07.jpg

Simulated conductivity change and simulated voltages (with noise)

img = mk_image( fmdl, 1 );
img.elem_data( fmdl.mat_idx{2} ) = 0.3;
vh= fwd_solve(img);

% Put a ball in the object center
targ= mk_c2f_circ_mapping(fmdl, [-2.2;-1.2;1;0.3]);
img.elem_data = img.elem_data + targ*.5;

show_fem(img); view(0,90);
print_convert pig_body08a.jpg

vi = fwd_solve(img);
vi = add_noise( 3, vi, vh );
plot([vh.meas,  20*(vi.meas - vh.meas)]);
axis tight;
legend('meas ref','20x diff meas','Location','SouthWest');

print_convert pig_body08b.jpg

Left Simulated conductivity change region Right Simulated voltage signals

Reconstructions with simple and conforming models

Simple 2D circular model reconstruction
imdl = mk_common_model('c2c2',16);
imdl.fwd_model.electrode = imdl.fwd_model.electrode([1,16:-1:2]);
imdl.fwd_model = mdl_normalize(imdl.fwd_model, 1);
imr= inv_solve(imdl, vh, vi);
show_fem(imr); axis tight; axis off; axis equal

print_convert pig_body09a.jpg

Conforming model: 2D reconstructon with 3D forward model
cmdl.mk_coarse_fine_mapping.f2c_offset = [0,0,1];
cmdl.mk_coarse_fine_mapping.f2c_project = speye(3); % Scaling not required
cmdl.mk_coarse_fine_mapping.z_depth = 0.2;
c2f= mk_coarse_fine_mapping( fmdl, fmdlr);

imdl.name = 'CT pig 3D model';
imdl.fwd_model = fmdl;
imdl.rec_model = fmdlr;
imdl.fwd_model.coarse2fine = c2f;
imdl.jacobian_bkgnd.value = ones(size(fmdl.elems,1),1);
imdl.jacobian_bkgnd.value( fmdl.mat_idx{2} ) = 0.3;

imdl.fwd_model = mdl_normalize(imdl.fwd_model, 1);
imdl.hyperparameter.value = .03;
% Model background conductivity as lung
imr= inv_solve(imdl, vh, vi);

show_fem(imr); axis off ; axis tight

print_convert pig_body10a.jpg

Left Simple 2D circular model reconstruction Right 2D reconstructon with conforming 3D forward model

Last Modified: $Date: 2017-02-28 13:21:02 -0500 (Tue, 28 Feb 2017) $ by $Author: aadler $