Introduction to dMRI

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.

Schedule

		Setup	Download files required for the lesson
Introduction to Diffusion MRI data	00:00	1 Introduction to Diffusion MRI data	How is dMRI data represented? What is diffusion weighting?
Preprocessing dMRI data	00:00	2 Preprocessing dMRI data	What are the standard preprocessing steps? How do we register with an anatomical image?
Local fiber orientation reconstruction	00:00	3 Local fiber orientation reconstruction	What information can dMRI provide at the voxel level?
	02:20	3.1 Diffusion Tensor Imaging (DTI)	What is diffusion tensor imaging? What metrics can be derived from DTI?
	02:55	3.2 Constrained Spherical Deconvolution (CSD)	What is Constrained Spherical Deconvolution (CSD)? What does CSD offer compared to DTI?
Tractography	00:00	4 Tractography	What information can dMRI provide at the long range level?
	02:20	4.1 Local tractography	What input data does a local tractography method require? Which steps does a local tractography method follow?
	02:50	4.2 Deterministic tractography	What computations does a deterministic tractography require? How can we visualize the streamlines generated by a tractography method?
	03:20	4.3 Probabilistic tractography	Why do we need tractography algorithms beyond the deterministic ones? How is probabilistic tractography different from deterministic tractography?
	03:55	Finish

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