hdocmsu/mripy

A python based MRI reconstruction toolbox with compressed sensing, parallel imaging and machine-learning functions

mripy

A python based MRI reconstruction toolbox with compressed sensing, parallel imaging and machine-learning functions

Key functions

• "bloch_sim/" contains functions for MRI sequence simulation, these functions are designed for MR fingerprinting experiment
• "fft/" this is a wrap of FFT functions, i.e. cuFFT, FFTW, and NUFFT, implemented for both CPU and GPU
• "pics/" contains optimization algorithms, such as ADMM, conjugate gradient, gradient descent, for MRI compressed sensing and parallel imaging reconstructions, as well as operators such as total variation, Hankel matrix, coil sensitivity
• "neural_network/" contains a wrap of tensorflow functions for creating and testing neural_network, and zoo/ contains examples for full connection net, CNN, Unet, and FCN.
• "test/" contains testing code for above functions and something I am working on right now, e.g. MRI PICS reconstruction, IDEAL + CS reconstruction, FC or CNN for MRF quantification, Unet for creating mask on medical images

Examples

• Figure 1 MRIPY toolbox contains three major blocks: synthetic MRI, iterative/non-iterative reconstruction, and machine learning interface.

* Figure 2 An illustration of MR fingerprinting (MRF) simulation in conjunction with a trained neural network for MRF time course parameter prediction. Random flip angle (FA) and repetition time (TR) were shown on top. * Figure 3 An example of parallel imaging (PI) and compressed sensing (CS) reconstruction (PICS) with alterative direction multiplier method (ADMM) and wavelet L1 or total variation minimization regularization (TV). Raw MRI data is from website (http://people.eecs.berkeley.edu/~mlustig/CS.html).