flyingdorothia / processmri

ProcessMRI

Process of MRI images including:

• Rician denoising by non-local means (Wiest-Daesslé et al., 2008)
• Temporal phase correction of complex data with multiple echoes (Bjarnason et al., 2013)
• n-exponential fitting on multiple echoes for density and T2* estimation

For methods involving multi-echo data, it is assumed the echoes are represented along the last dimension of the image of size n.

For temporal phase correction, the real and imaginary parts are assumed to be mixed in the last dimension of the image, that is to say the "echo"-dimension of size 2*n. The first n images are the real images, and the last n images are the complex images for the n echoes.

The mono-exponential fitting can be performed by linear regression on the logarithm of the data, or through non-negative least squares of a n-exponential function (see scipy.optimize.curve_fit).

Installation

cd ProcessMRI
git submodule init
git submodule update
pip install -r requirements.txt

Usage

python main.py

A graphical user interface should open.

1. File/Open: Open a Bruker directory or a NifTi MRI image. If this operation was successful, this
2. Process/...": choose the method to apply. For each method, various parameters can be chosen, as well as the output directory. The filename is determined as : input_name + method_name.nii.

References

Wiest-Daesslé, N., Prima, S., Coupé, P., Morrissey, S.P., Barillot, C., 2008. Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI, in: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (Eds.), Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 171–179. https://doi.org/10.1007/978-3-540-85990-1_21

Bjarnason, T.A., Laule, C., Bluman, J., Kozlowski, P., 2013. Temporal phase correction of multiple echo T2 magnetic resonance images. Journal of Magnetic Resonance 231, 22–31. https://doi.org/10.1016/j.jmr.2013.02.019