# Tutorial 3 - EIT Algorithms¶

## Introduction:¶

Electrical Impedance Tomography results can be highly varied and depends on a few basic things like the number of electrodes you are using, the order of measurements(adjacent versus opposition method), how you baseline the data, and of course which algorithm you use.

## Step 1: Algorithm Overview¶

There are three algorithms in this project - Graz Consensus, Gauss-Newton or the Jacobian method, and Back Projection. Each has it’s own pros and cons and can be tuned. The Spectra kit is using a python port of EIDORS called pyEIT. This means that you can used most of EIDORS functionality in python.

For more detail on pyEIT start with the readme here - https://github.com/OpenEIT/pyEIT

For more detail on EIDORS and tutorials see - http://eidors3d.sourceforge.net/tutorial/tutorial.shtml

A couple of things this project would really benefit from if you felt like contributing are:

- a data file exchange format to import data from Spectra into EIDORS directly(should be doable but maintainer does not own Matlab currently to do this).
- A D-Bar algorithm implementation in python, potentially added to pyEIT. Here is a link to the matlab code and documentation for it - `<https://blog.fips.fi/tomography/eit/the-d-bar-method-for-electrical-impedance-tomography-simulated-data/ >`_