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EIDORS: Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software

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One line starter program for EIDORS

To run this tutorial, you need to download and install EIDORS and then run this command in a matlab (or octave) session.
run /path/to/eidors3d/startup.m

Step 1: Get data

EIDORS provides lots of data to get you started. Here we choose a small file in the sample_data/ directory.

Type:

load montreal_data_1995

Step 2: Reconstruct and display image

show_slices(inv_solve(mk_common_model('d2c2',16),zc_h_demo4,zc_demo4)) 

Figure: Output image showing images of a non-conductive object moving across a saline tank.

One line starter program (explained in more detail)

Step 1: Get data

The key thing you need to know about your data are:
  • The medium shape and the electrode positions
    In this case, the measurements were made from a cylindrical tank with 16 electrodes in a plane.
  • The stimulation/measurement protocol
    In this case, the measurements were made using the adjacent stimulation and measurement (the Sheffield protocol)

Step 2a: Create an inverse model (imdl) from the template

imdl = mk_common_model('c2c2',16);
See the documentation for mk_common_model. It has lots of options. The function provides a circular model with adjacent stimulation patterns. If this is not what you want, it must be changed.

Step 2b: Create a forward model (fmdl) that matches the shape / electrodes

This step is not required, if mk_common_model provides you with the shape you need. Here, as an example, we create a circular tank, but also we can use many other functions.
n_rings = 12;
n_electrodes = 16;
three_d_layers = []; % no 3D
fmdl = mk_circ_tank( n_rings , three_d_layers, n_electrodes);
% then assign the fields in fmdl to imdl.fwd_model

Step 2c: Create a forward model (fmdl) that matches the stimulation / measurement protocol

Often the function mk_stim_patterns can do what you need; if not, you will need to:
options = {'no_meas_current','no_rotate_meas'};
[stim, meas_select] = mk_stim_patterns(16,1,'{ad}','{ad}',options,1);
imdl.fwd_model.stimulation = stim;
imdl.fwd_model.meas_select = meas_select;
If mk_stim_patterns doesn't provide what you need, then you will need to use a function like stim_meas_list.

Step 2d: Reconstruct the image (img) using inv_solve

data_homg = zc_h_demo4;
data_objs = zc_demo4; % from your file
img = inv_solve(imdl, data_homg, data_objs);

Step 2e: Display the image

show_slices(img);

Figure: Output image.

Last Modified: $Date: 2017-02-28 13:12:08 -0500 (Tue, 28 Feb 2017) $ by $Author: aadler $