Fusion in diffusion MRI for improved fibre orientation estimation: An application to the 3T and 7T data of the Human Connectome Project

Stamatios N Sotiropoulos,Moisés Hernández-Fernández,An T Vu,Jesper L Andersson,Steen Moeller,Essa Yacoub,Christophe Lenglet,Kamil Ugurbil,Timothy E.J Behrens,Saad Jbabdi

doi:10.1016/j.neuroimage.2016.04.014

Abstract

Determining the acquisition parameters in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. Images of lower resolution have less spatial specificity but higher signal to noise ratio (SNR). At the same time higher angular contrast, important for resolving complex fibre patterns, also yields lower SNR. Considering these trade-offs, the Human Connectome Project (HCP) acquires high quality dMRI data for the same subjects at different field strengths (3T and 7T), which are publically released. Due to differences in the signal behavior and in the underlying scanner hardware, the HCP 3T and 7T data have complementary features in k- and q-space. The 3T dMRI has higher angular contrast and resolution, while the 7T dMRI has higher spatial resolution. Given the availability of these datasets, we explore the idea of fusing them together with the aim of combining their benefits. We extend a previously proposed data-fusion framework and apply it to integrate both datasets from the same subject into a single joint analysis. We use a generative model for performing parametric spherical deconvolution and estimate fibre orientations by simultaneously using data acquired under different protocols. We illustrate unique features from each dataset and how they are retained after fusion. We further show that this allows us to complement benefits and improve brain connectivity analysis compared to analyzing each of the datasets individually.

Highlights

Determining the optimal acquisition protocol in diffusion magnetic resonance imaging is governed by a series of trade-offs
We explore whether these trade-offs can be tackled by combining high spatial resolution data with data of higher angular resolution and contrast
We present an approach for estimating the fibre orientation density functions by simultaneously analysing two datasets of different spatial resolutions and angular contrasts

Summary

Introduction

Determining the optimal acquisition protocol in diffusion magnetic resonance imaging (dMRI) is governed by a series of trade-offs. An increase in the spatial resolution of the acquisition yields lower signal to noise ratio (SNR). An increase in the angular contrast of the images (i.e. higher b value) reduces SNR. High spatial resolution reduces partial volume effects and allows exquisite tissue details to be revealed, as has been shown from postmortem acquisitions (Roebroeck et al, 2008; Miller et al, 2011; Leuze et al, 2014), specialised sequences (Heidemann et al, 2012) or in-vivo acquisitions using bespoke scanners (McNab et al, 2013; Sotiropoulos et al, 2013c). High SNR and/or angular contrast are beneficial for accurate and precise estimation of tissue microstructure properties from the dMRI signal, e.g. High SNR and/or angular contrast are beneficial for accurate and precise estimation of tissue microstructure properties from the dMRI signal, e.g. (Tournier et al, 2004; Behrens et al, 2007; Descoteaux et al, 2009; Sotiropoulos et al, 2012; Zhang et al, 2012)

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