Diffusion imaging is a non-invasive imaging technique which provides fascinating and valuable insights into the anatomy of the human brain. It has established a broad spectrum of clinically useful research studies and applications that focus on initiating therapies to ensure the best possible development, on monitoring the progression of diseases, and on planning neurosurgical interventions.
This half-day course will provide attendees an overview on diffusion imaging techniques. Basic concepts of the diffusion-weighted MRI acquisition are given, followed by a comparison of different spherical diffusion functions which are used to represent the fiber direction of the underlying tissue. Fiber tracking, one of the most popular applications of diffusion imaging, will be explained with a focus on its reliability to reconstruct crossing, kissing, merging and fanning axonal fibers. Further parts of this course will explain quantitative approaches and advanced visualization techniques like fiber clustering, glyph rendering and illustrative visualization. Throughout this course, we pay attention to the validation by hardware phantoms and software phantoms and thoroughly discuss related literature on the inherent uncertainty of diffusion imaging. Clinical impact and challenges are explained, and a review will be made of existing publicly-available packages, focusing on topics such as usability, speed, and extensibility.
This lecture-style course has been designed to complement SC1065 Exploring Brain Connectivity in-vivo: from Theory to Practice . Attendees will benefit maximally by taking both courses.
be familiar with acquisition protocols of diffusion-weighted magnetic resonance imaging
differentiate between various spherical diffusion functions including the orientation distribution function (ODF), the fiber orientation density (FOD), and the persistent angular structure PAS
distinguish between deterministic and probabilistic fiber tracking and explain their advantages and disadvantages
classify fiber clustering techniques with respect to their used similarity measures
justify quantification techniques for special clinical questions
be familiar with advanced visualization techniques like glyph rendering and illustrative visualization
identify the aspects which introduce uncertainty to the analysis and visualization of diffusion-weighted images
design software and hardware phantoms for validating your own MR sequences or algorithms
The course is intended to everyone interested in learning concepts, algorithms and techniques of diffusion imaging. Advanced researches can profit from the strong focus on uncertainty and clinical applications.