March 2017 - justinblaber.org

In the previous article, I discussed the general processing flow for DWMRI data. In this article, I’ll go into more detail on the preprocessing (and Quality Assurance) of diffusion data, and we’ll do it with Docker and Singularity!

Tools and files used in this article:

Some things: the documentation on topup and eddy is very good and anyone can invest the time and get them both working. Instead, I’ve opted to simply dockerize/singularize the entire process into a pipeline so that you can get it up and running quickly. The pipeline is called “dtiQA” and uses topup/eddy to preprocess the data and then runs a QA using DTI-related statistics. The version of FSL used in the container is 5.0.10 and has the 5.0.11 eddy patch.

scans.zip contains four DWMRI scans acquired in this order:

1000 b-value x 32 gradient directions (1000_32_1)
1000 b-value x 6 gradient directions (1000_6_rev)
2000 b-value x 60 gradient directions (2000_60)
1000 b-value x 32 gradient directions (1000_32_2)

Continue reading “DWMRI Preprocessing (and Quality Assurance) with Docker and Singularity!”

This post serves as a brief introduction to DWMRI processing.

Tools and files used in this article:

dwmri.dcm is a 32 direction, 1000 b-value DWMRI dicom file, dcm2niix is a commonly used DICOM to NIFTI converter, nifti_utils is a library to work with NIFTIs in MATLAB, dwmri_visualizer is a visualization tool for diffusion images in MATLAB, and FSL is a powerful and commonly used medical image processing library.

A very common pipeline for dwmri processing is:

$1.\ ACQUISITION$
$\downarrow$
$2.\ DICOM$
$\downarrow$
$3.\ NIFTI,\ BVEC,\ BVAL$
$\downarrow$
$4.\ PREPROCESSING$
$\downarrow$
$5.\ PROCESSING$