PyHySCO 0.0.4

PyHySCO

This is a package for Echo-Planar MRI susceptibility artifact correction implemented in PyTorch.

Installation

From PyPI:

pip install PyHySCO

Python package dependencies (automatically installed by pip) are listed in requirements.txt. It is suggested to run the python file tests/test_all.py to ensure all tests are passing and the code is setup properly.

Usage

Command Line Correction

The program can be run directly from a terminal or command line by using the python command to run the file pyhysco.py or if installed using pip the command pyhysco. Supplying the following required parameters:

• file_1: file path of first image (stored as nii.gz) with phase encoding direction opposite of file_2
• file_2: file path of second image (stored as nii.gz) with phase encoding direction opposite of file_1
• ped: phase-encoding dimension (1, 2, or 3)

Use the help flag (--help) to see optional parameters available.

Minimal Usage:

pyhysco --file_1 <image1> --file_2 <image2> --ped <phase encoding direction>

Example:

pyhysco --file_1 image1.nii.gz --file_2 image2.nii.gz --ped 1 --output_dir results/ --max_iter 25

Write a Correction Script

A user-written script can be used to call the methods of the program.

Example:

from EPI_MRI.EPIMRIDistortionCorrection import *
from optimization.GaussNewton import *
import torch

device = 'cuda:0' if torch.cuda.is_available() else 'cpu'

# load the image and domain information
# change this function call to be the filepath for your data
data = DataObject('../data/156334_v.nii.gz', '../data/156334_-v.nii.gz', 1, device=device,dtype=torch.float32)

loss_func = EPIMRIDistortionCorrection(data, 300, 1e-4, regularizer=myLaplacian3D, PC = JacobiCG)
# initialize the field map
B0 = loss_func.initialize(blur_result=True)
# set-up the optimizer
# change path to be where you want logfile and corrected images to be stored
opt = GaussNewton(loss_func, max_iter=500, verbose=True, path='results/gnpcg-Jac/')
# optimize!
opt.run_correction(B0)
# save field map and corrected images
opt.apply_correction()
# see plot of corrected images
opt.visualize()

Examples and Further Documentation

There are a set of examples in the examples directory. Full API documentation is in the docs directory. See also Instructions.md for an overview of the correction process.