This lesson is still being designed and assembled (Pre-Alpha version)

Lesson Title

This Data Carpentry lesson aims to introduce learners to the analysis of diffusion Magnetic Resonance Imaging (dMRI) data using primarily Python. Its target audience includes researchers willing to discover the dMRI field, as well as under-graduate or graduate students willing to broaden their skills from related fields, such as neuroscience, computational neuroscience, or medical image analysis.

Prerequisites for instructors

This lesson assumes that the instructors have solid knowledge about diffusion MRI, scientific programming tools used throughout the lesson (see the BIDS-dMRI Setup page), and teaching experience. If you are teaching this lesson in a workshop, please see the Instructor notes.

Prerequisites for learners

This lesson assumes that the learners have a basic understanding of how a signal or image is represented digitally, an entry-level knowledge about aspects linked to medical image analysis, and a minimal set of computing and programming skills to install the required tools and to follow the lesson (see the BIDS-dMRI Setup page. This lesson requires learners to have completed the Introduction to MRI and BIDS carpentry corresponding to the Neuroimaging Curriculum.


Setup Download files required for the lesson
00:00 1. Introduction to Diffusion How is dMRI data represented
What is diffusion weighting
00:25 2. Preprocessing dMRI data What are the standard preprocessing steps?
How do we register with an anatomical image?
00:55 3. Diffusion Tensor Imaging (DTI) What is diffusion tensor imaging?
What metrics can be derived from DTI?
01:30 4. Local reconstruction - Constrained Spherical Deconvolution (CSD) What is Constrained Spherical Deconvolution (CSD)?
What does CSD offer compared to DTI?
02:05 5. Tractography - Probabilistic Why do we need tractography algorithms beyond the deterministic ones?
How is probabilistic tractography different from deterministic tractography?
02:40 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.