Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats.

Madelaine Daianu,Terrence C Town,Tara M Weitz,Russell E Jacobs,Paul M Thompson

doi:10.1371/journal.pone.0145205

Abstract

Diffusion weighted imaging (DWI) is widely used to study microstructural characteristics of the brain. Diffusion tensor imaging (DTI) and high-angular resolution imaging (HARDI) are frequently used in radiology and neuroscience research but can be limited in describing the signal behavior in composite nerve fiber structures. Here, we developed and assessed the benefit of a comprehensive diffusion encoding scheme, known as hybrid diffusion imaging (HYDI), composed of 300 DWI volumes acquired at 7-Tesla with diffusion weightings at b = 1000, 3000, 4000, 8000 and 12000 s/mm2 and applied it in transgenic Alzheimer rats (line TgF344-AD) that model the full clinico-pathological spectrum of the human disease. We studied and visualized the effects of the multiple concentric “shells” when computing three distinct anisotropy maps–fractional anisotropy (FA), generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA). We tested the added value of the multi-shell q-space sampling scheme, when reconstructing neural pathways using mathematical frameworks from DTI and q-ball imaging (QBI). We show a range of properties of HYDI, including lower apparent anisotropy when using high b-value shells in DTI-based reconstructions, and increases in apparent anisotropy in QBI-based reconstructions. Regardless of the reconstruction scheme, HYDI improves FA-, GFA- and NQA-aided tractography. HYDI may be valuable in human connectome projects and clinical research, as well as magnetic resonance research in experimental animals.

Highlights

Diffusion-weighted imaging (DWI) is a powerful and widely used tool to study water diffusion in the brain
fractional anisotropy (FA), generalized fractional anisotropy (GFA) and normalized quantitative anisotropy (NQA) metrics can be affected by the diffusion sampling scheme and the b-value [32] (Fig 1), which sensitizes the signal to different aspects of water diffusion
The standard diffusion tensor imaging (DTI) metrics–axial diffusivity (AX), radial diffusivity (RD) and mean diffusivity (MD)–which are involved in computing FA, significantly decreased with increasing number of q-sampling shells across the white matter (FDR critical P

Summary

Introduction

Diffusion-weighted imaging (DWI) is a powerful and widely used tool to study water diffusion in the brain. Statistical measures of local diffusion can be estimated from a minimum of six independent diffusion-sensitized images, and one non-diffusion weighted image ( known as the b0 image) These images are used to model diffusion anisotropy in diffusion tensor imaging (DTI)–the first proposed approach designed to estimate a 3x3 diffusion tensor [1], or the covariance matrix of a 3-dimensional Gaussian distribution. With DTI it is hard to model partial volume effects–where white matter, gray matter and cerebrospinal fluid may all contribute to diffusion in the same voxel [2] [3] In these cases, the DTI model will fail to reconstruct neuronal structures [3] [4] and more complex mathematical frameworks are needed, as well as more sophisticated acquisition protocols

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Conclusion