Sep 02, 2022
Version 2
Processing of Radial K-space DW-MRI Data V.2
- Miguelrj 1,
- Hee Kwon Song1,
- Stephen Pickup1,
- Mamtaaryagupta 1,
- Rong Zhou1
- 1University of Pennsylvania, Department of Radiology
Protocol Citation: Miguelrj , Hee Kwon Song, Stephen Pickup, Mamtaaryagupta , Rong Zhou 2022. Processing of Radial K-space DW-MRI Data. protocols.io https://dx.doi.org/10.17504/protocols.io.j8nlkkwj1l5r/v2Version created by Miguel Romanello Joaquim
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: September 02, 2022
Last Modified: September 02, 2022
Protocol Integer ID: 69520
Keywords: mri data, mri data the protocol, reconstruction of diffusion, kurtosis index, diffusion, metrics of adc, space data
Abstract
The protocol includes reconstruction of diffusion weighted images from radial k-space data and using a 3-parameter fit to derive metrics of ADC and kurtosis index.
Image reconstruction
Radially acquired diffusion-weighted images (DWIs) are reconstructed using the following steps:
- Apply zero-order phase correction to each radial spoke using the average phase offset at the center slice of the lowest b-value image
- Zerofill k-space by a factor of 2 to double field of view
- Multiply signal of each point by its respective area on a Voronoi diagram of the points (including added zerofill points) in k-space
- Re-grid each radially defined point to its nearest Cartesian coordinate using its Kaiser-Besel index
- Apply Fourier transform to now Cartesian-defined k-space
To use this, place both DWI_shell.ipynb and DWI_processing_functions.py in the same directory (your working directory). The readme.txt file outlines specific file formats, function inputs, and outputs.
Quickly, once all files are formatted according to readme.txt, the first cell defines all image and reconstruction parameters:
############## Parameters to change #####################
input_file = 'fid' # k-space data
# Acquisition data
xres_ro = 128 # total readout points stored
views = 403 # number of radial views
# angl = (math.sqrt(5)-1)/2 * PI # golden angle in radians
angl = 2*PI/views # used by Steve Pickup
# Image data
xres = 96 # actual number of points collected
yres = 96
slices = 16
bvalues = 5
b_array = np.array([10,535,1070,1479,2141]) # b-values
Each cell should then be run consecutively.