BIG > MRI Simulation and Reconstruction

MRI Simulation and Reconstruction

Matlab Framework for MRI Simulation and Reconstruction

Written by Matthieu Guerquin-Kern at the Biomedical Imaging Group (BIG), EPFL, Switzerland

scheme of the MRI measurement process and it mathematical formulation.

Outline

This package is a collection of Matlab functions that provides 1) analytical and rasterized multi-channel MRI simulations of realistic phantoms and 2) a collection of basic and state-of-the-art reconstruction methods including an efficient wavelet-based non-linear one. Demonstration and testing scripts are included. A detailed documentation is provided.

The analytical phantom simulation tools allow sound validations of reconstruction methods. The reconstruction framework is rather general and should be easy to adapt to any linear inverse problem. Wavelet transform and wavelet coefficients can be easily manipulated like Matlab's matrices and vectors.

References

M. Guerquin-Kern, L. Lejeune, K.P. Pruessmann, and M. Unser, "Realistic Analytical Phantoms for Parallel Magnetic Resonance Imaging," IEEE Transactions on Medical Imaging, vol. 31, no. 3, pp. 626-636, March 2012.
Webpage for the analytical MRI simulation code and phantom design tools.
PhD thesis chapter on the general principles of image reconstruction in MRI.
M. Guerquin-Kern, M. Haeberlin, K.P. Pruessmann, and M. Unser, "A Fast Wavelet-Based Reconstruction Method for Magnetic Resonance Imaging," IEEE Transactions on Medical Imaging, vol. 30, no. 9, pp. 1649-1660, September 2011.

Software

Requirements

Some functions have been written in C++. Most of them have fall-back counterparts written in Matlab language. The package was developed and tested under Linux and MacOSX platforms with Matlab R2011b. Partial testing demonstrated that the code is mainly compatible with Octave 3.2.

If you have a Unix-like operating system on a multi-core computer, you can use the POSIX-thread library to parallelize the computations. To do so, 1) make sure this library is installed on your system such that Matlab's mex can find it; 2) uncomment line 30 in 'myerfzparts_c.cpp' (from the error you got it looks like you have it uncommented)
If you do not care much about calculation speed or if you use windows, or if things just do not work, just comment line 30 in 'myerfzparts_c.cpp'.

Download and install

Download the code here [128 KB].

Add the folder and subfolders in your matlab path. Some C++ sources are distributed with the code. To compile them as MEX binaries, simply run 'make' in matlab prompt (make sure that you have a compiler installed using the mex setup).

You can start with the script DemoSimuAndRecon.m and continue with PerformTests.m.

Please note that the MRI scanner data mentioned in the documentation is not shared publicly. Without these data, the two functions DemoBrainEPI.m and DemoBrainSpiral.m will not work.

Conditions of use

You are free to use this software for research purposes, but you should not redistribute it without our consent. It would also be courteous for you to include the aforementioned citations and acknowledgment whenever you present or publish results that are based on it. EPFL makes no warranties of any kind on this software and shall in no event be liable for damages of any kind in connection with the use and exploitation of this technology.

Documentation

Download the documentation here [PDF, 223 KB].

Related Works

See this link for more details on the simulation part.

Acknowledgements

MEX files for wavelet transform by Cedric Vonesch, 2008. Publicly distributed.
MEX files for determining the points inside a polygon by Bruno Luong, 2010. Publicly distributed.
Matlab code for n-dimensional "n permute k" problem by Matt Fig, 2009. Publicly distributed.
Matlab functions for the computation of MRI data with Bezier-defined phantoms by Laurent Lejeune under the author's supervision, 2010. See here.
Marcel Leutenegger's instructions, publicly distributed, have been followed in the C++/MEX multi-threaded code that computes the error function of a complex variable.
The matlab and MEX files for gridding and NUFT computation have been coded by the author during spring 2012, inspired by L. Greengard and J.-Y. Lee (in Accelerating the nonuniform fast Fourier transform, SIAM Review, vol. 46, no. 3, pp. 443-454, 2004) and a partial implementation that is publicly distributed by Jeff Fessler.