Welcome!#
Implementing a head-motion correction algorithm for diffusion MRI in Python, using DIPY and NiTransforms#
Summary. This tutorial walks attendees through the development of one fundamental step in the pre-processing of diffusion MRI data using a community-driven approach and relying on existing tools. The tutorial justifies the NiPreps approach to pre-processing, describing how the framework attempts to enhance or extend the MRI scanner to produce “analysis-grade” data. This is important because data produced by the scanner is typically not digestible for statistical analyses directly.
Researchers resort to either:
modifying their experimental design so that it matches the requirements of large-scale studies that have made all of their software tools publicly available
creating custom pre-processing pipelines tailored to each particular study
This tutorial has been designed to engage signal processing engineers and imaging researchers in the NiPreps community, demonstrating how to fill in the gaps of their pre-processing needs regardless of their field.
Objectives
Get a tour of the NiPreps framework
Learn how to contribute to “open source” software
Understand the basics of dMRI data and pre-processing
Discover how to integrate some of the tools in the NiPreps framework