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

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)396-409
Number of pages14
JournalNeuroImage
Volume134
DOIs
StatePublished - Jul 1 2016

Bibliographical note

Funding Information:
We acknowledge support from the Human Connectome Project (1U54MH091657-01), from the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research, Biotechnology Research Center (BTRC) grant (P41 EB015894) from NIBIB, NIH, and NINDS Institutional Center Core Grants to Support Neuroscience Research (P30NS076408). Also funding from the UK EPSRC (EP/L023067/1) and UK MRC (MR/L009013/1). Computations were performed using the facilities of the Washington University Center for High Performance Computing, which were partially provided through grant NCRR 1S10RR022984-01A1. Members of the WU-Minn HCP Consortium are listed at http://www.humanconnectome.org/about/hcp-investigators.html and http://www.humanconnectome.org/about/hcp-colleagues.html. Visualisations were performed using the Connectome Workbench http://www.humanconnectome.org/ software/connectome-workbench.html. Data used correspond to the following HCP subject IDs: 102311, 108323, 109123, 131217, 158035, 200614, 352738, 573249, 770352, 910241.

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