Optimal Experimental Design for Biophysical Modelling in Multidimensional Diffusion MRI

Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue, the research community has developed the so-called Standard Model (SM) that has been widely used. However, in clinically applicable acquisition protocols, the inverse problem that recovers the SM parameters from a set of MR diffusion measurements using pairs of short pulsed field gradients was shown to be ill-posed. Multidimensional dMRI was shown to solve this problem by combining linear and planar tensor encoding data. Given sufficient measurements, multiple choices of b-tensor sets provide enough information to estimate all SM parameters. However, in the presence of noise, some sets will provide better results. In this work, we develop a framework for optimal experimental design of multidimensional dMRI sequences applicable to the SM. This framework is based on maximising the determinant of the Fisher information matrix, which is averaged over the full SM parameter space. This averaging provides a fairly objective information metric tailored for the expected signal but that only depends on the acquisition configuration. The optimisation of this metric can be further restricted to any subclass of desirable design constraints like, for instance, hardware-specific constraints. In this work, we compute the optimal acquisitions over the set of all b-tensors with fixed eigenvectors

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works

Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI

Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data

Length of intact plasma membrane determines the diffusion properties of cellular water

Molecular diffusion in a boundary-free medium depends only on the molecular size, the temperature, and medium viscosity. However, the critical determinant of the molecular diffusion property in inhomogeneous biological tissues has not been identified. Here, using an in vitro system and a highresolution MR imaging technique, we show that the length of the intact plasma membrane is a major determinant of water diffusion in a controlled cellular environment and that the cell perimeter length (CPL) is sufficient to estimate the apparent diffusion coefficient (ADC) of water in any cellular environment in our experimental system (ADC = ?0.21 × CPL + 1.10). We used this finding to furtherexplain the different diffusion kinetics of cells that are dying via apoptotic or non-apoptotic cell death pathways exhibiting characteristic changes in size, nuclear and cytoplasmic architectures, and membrane integrity. These results suggest that the ADC value can be used as a potential biomarker for cell death

Microstructure Imaging by Diffusion MRI

Diffusion weighted imaging provides a unique tool to interrogate the microstructure of living tissue without the need for an invasive procedure. For timescales applicable in a clinical MRI setting, the diffusion process is sensitive to the structural configuration of tissue on the micrometer scale, i.e., the size of cells. Features such as the packing density of the cell matrix affect the overall rate of diffusion, and anisotropic structures impose barriers on the diffusion such that they appear to move faster or slower along certain directions, for example, along and across axonal bundles, respectively. In this chapter we survey the foundation for the diffusion MRI contrast, and discuss how it assumes features depending on the tissue microstructure and give examples of how these can be quantified. Due to their prevalence in neuroimaging, we focus on diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). To emphasize that diffusion MRI is evolving rapidly, we also look ahead to more advanced methods for analysis and alternatives to the conventional experimental design