In two previous blog posts (Diffusion without equations Parts I and II), we described the basics of diffusion MRI qualitatively. We have now developed a larger course for MR scientists interested in an introduction to diffusion MRI. The course consists of six lectures (plus one bonus lecture) taken from various educational sessions held at ISMRM meetings around the world. The course was put together in collaboration with the ISMRM Education Committee, and you can watch it in its entirety below.
Diffusion MRI spans a great amount of territory, from large-scale neuronal connectivity (tractography) to detailed description of neuronal microstructure, tumor microstructure, and more. This course covers the methodology used for these applications, as well as acquisition and pre-processing of diffusion MRI data.
Historically, the development of the methodology used for tractography and microstructure has been somewhat separate, and at the most recent ISMRM annual meeting in Honolulu, the diffusion study group was described as an uneasy marriage between tractography and microstructure. We would contend that it is a marriage that keeps getting better and better, with the spouses increasingly combining their strengths to perform analysis such as microstructure informed tractography. Let us propose that Els Fieremans, Karla Miller, Alexander Leemans, Maxime Descoteaux, Ileana Jelescu, and Daniel Alexander, together with many other contributors to this field, have been valuable marriage counselors. Let’s hear what they have to say!
Do you think you already know this stuff? Then try the interactive quiz below! The questions also serve as course annotations, so casual readers can find out which topics are addressed in the course. Clicking on the question will take you to the exact video location where the topic is discussed. Or you can just try to answer without watching the video, and see if you got it right.
Els Fieremans – Introduction to Diffusion MRI1. In biological tissue, for a very short time scale (a few ms), the diffusion of the water molecules can be described as:
2. Which of the following statements is true about the diffusion of water molecules in the brain, over a timescale of roughly 50 ms?
3.Which parameter should be changed to increase the diffusion length/displacement being probed in a diffusion MRI experiment?
4. How will the log of the normalized diffusion weighted signal change if the diffusion sensitizing gradient strength is doubled?
5. What is the relationship between the b-value and the resolution of the displacement distribution estimated with q-space techniques?
Karla Miller – What is Your Sequence?1. What is the key effect that creates diffusion weighted MR contrast?
3. How can one significantly reduce the scan time in diffusion imaging without altering the contrast?
4. How does the time between the second and the third 90° radiofrequency (RF) pulses in a stimulated echo sequence affect the diffusion time dependent contrast?
5. Why is the steady state free precession (SSFP) sequence good for diffusion imaging of tissues with short T2?
Alexander Leemans – Analysis Pipelines for Diffusion MRI Data: From Voxels to Connectomes1. How is the signal intensity drift in dMRI data best corrected?
2. When correcting for subject motion in diffusion MRI data, which processing step should be included to prevent a bias?
3. How does the size of a tract passing close to the grey matter or the ventricles determine the extent of the confound caused by partial voluming, for statistics computed for the entire tract volume?
Maxime Descoteaux – Diffusion Tractography: Principles & Methods1. How often does more than one fiber orientation occupy a white matter voxel in a diffusion MRI acquisition with 2 mm isotropic voxel size?
4. Which structures will be over-represented if the tractography is seeded from every voxel in the brain white matter?
Ileana Jelescu – Diffusion Analysis: Tissue & Signal Models1. Which of the following is a main advantage of the tissue models over the signal models?
2. What is the maximum b-value that is suitable for use in signal models derived from the second order Taylor expansion of bD in healthy brain?
3. Which of the following could be a consequence of fixed parallel diffusivity parameters in a multi-compartment model?
Daniel Alexander – Diffusion Modelling: Signal Models vs. Biophysical Models1. Which of the following metrics, derived from the reconstructed diffusion propagator, would be indicative of anisotropy?
3. Which of the following parameters are estimated by the AxCaliber model, but not by the Neurite Orientation Dispersion and Density Imaging (NODDI) model?
4. What assumption is made by some diffusion models to aid in distinguishing the intra-axonal from the extra-axonal space?
Now that you are done, we would love to get your feedback! Feel free to discuss the current questions in the comments section, or even better, contribute new ones! It doesn’t matter whether you are an expert or a novice, your knowledge (or lack thereof) could contribute to a fun experiment in crowdsourcing MR educational content. Our aim is to explore new ways to educate MR scientists. If successful, this kind of course structure may be considered for CME credit in the future.
P.S. For a historical perspective on the field, we highly recommend the interview that Derek Jones conducted with John Tanner for last year’s edition of the MRM Highlights magazine.