pygrappa 0.3.7

GeneRalized Autocalibrating Partially Parallel Acquisitions.

Installation

pip install pygrappa

Usage

The function is called with undersampled k-space data and calibration data (usually a fully sampled portion of the center of k-space). The unsampled points in k-space should be exactly 0:

from pygrappa import grappa

sx, sy, ncoils = kspace.shape[:]
cx, cy, ncoils = calib.shape[:]
res = grappa(kspace, calib, kernel_size=(5, 5), coil_axis=-1)
sx, sy, ncoils = res.shape[:]

If calibration data is in the k-space data, simply extract it:

from pygrappa import grappa

sx, sy, ncoils = kspace.shape[:] # center 20 lines are ACS
ctr, pd = int(sy/2), 10
calib = kspace[:, ctr-pd:ctr+pad, :].copy()
res = grappa(kspace, calib, kernel_size=(5, 5), coil_axis=-1)
sx, sy, ncoils = res.shape[:]

Also see the examples module.

A very similar experimental GRAPPA implementation with the same interface can be called:

from pygrappa import cgrappa
res = cgrappa(kspace, calib, kernel_size=(5, 5), coil_axis=-1)

This function uses much of the same code as the Python grappa() implementation, but has certain parts written in C++ and all compiled using Cython. It runs about twice as fast but is considered experimental.

About

GRAPPA is a popular parallel imaging reconstruction algorithm. Unfortunately there aren’t a lot of easy to use Python implementations available, so I decided to release this simple one.