Recent advances in diffusion MRI modeling: Angular and radial reconstruction

Abstract

Recent advances in diffusion magnetic resonance image (dMRI) modeling have led to the development of several state of the art methods for reconstructing the diffusion signal. These methods allow for distinct features to be computed, which in turn reflect properties of fibrous tissue in the brain and in other organs. A practical consideration is that to choose among these approaches requires very specialized knowledge. In order to bridge the gap between theory and practice in dMRI reconstruction and analysis we present a detailed review of the dMRI modeling literature. We place an emphasis on the mathematical and algorithmic underpinnings of the subject, categorizing existing methods according to how they treat the angular and radial sampling of the diffusion signal. We describe the features that can be computed with each method and discuss its advantages and limitations. We also provide a detailed bibliography to guide the reader.

Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis

“…Recent advances in dMRI acquisition protocols have lead to significant improvements 5 in signal reconstruction [2,3,4], driving the development of novel tools for processing and interpreting dMRI data. Among the many applications using dMRI data, the quantitative characterization of white matter geometry and its genetic basis [5,6,7] is an important step in the study of the human brain, essential to understanding the mechanisms of neurological function and disease [8,9,10,11].…”

Integral curves from noisy diffusion MRI data with closed-form uncertainty estimates

“…Some examples of these representations include diffusion tensors that are higher than second order, spherical deconvolution models, and spherical wavelet models; see Assemlal et al (2011). To our knowledge, none of these models have been studied from the standpoint of theoretically rigorous statistical error analysis.…”

Whole Brain Networks for Treatment for Epilepsy

“…A Bayesian approach by Friman et al [18] considered conditional probabilities, but with more specific conditions associated with the trajectory of each track, particularly as influenced by boundaries A number of issues remain to be resolved. Optimal calculation of the local probability may depend on the choice of dODF or fODF, of which there are many types [19]. Other numerical solutions may yield even greater performance benefits.…”