Modeling Fiber Orientations Using Diffusion MRI

Abstract Diffusion MRI can probe neural fiber orientations through an in vivo measurement of water diffusion on a subvoxel scale, ultimately enabling tracking structural connections in the brain. This chapter introduces the principles behind resolving fiber orientations in diffusion MRI and provides an overview of the models and techniques that have advanced this field over the years. We will first introduce the concept of q → -space as the fundamental descriptor of the diffusion propagator. Subsequently, we will discuss the main developments toward practical, scan-time-efficient fiber orientation modeling, starting with diffusion tensor imaging and moving on to higher-order representations of increasingly complex fiber topologies. We will particularly discuss spherical deconvolution as the most widely adopted approach to modeling fiber orientation distributions. Finally, we will point out the major differences and similarities between the various techniques, distinguishing discrete versus continuous representations and model-based versus data-driven methods.