  The benefits, opportunities and growing availability of ultra-high field
magnetic resonance imaging (MRI) for humans have prompted an expansion in
research and development efforts towards increasingly more advanced
high-resolution imaging techniques. To maximize their effectiveness, these
efforts need to be supported by powerful computational simulation platforms
that can adequately reproduce the biophysical characteristics of MRI, with high
spatial resolution. In this work, we have sought to address this need by
developing a novel digital phantom with realistic anatomical detail up to
100-um resolution, including multiple MRI properties that affect image
generation. This phantom, termed BigBrain-MR, was generated from the publicly
available BigBrain histological dataset and lower-resolution in-vivo 7T-MRI
data, using a newly-developed image processing framework that allows mapping
the general properties of the latter into the fine anatomical scale of the
former. Overall, the mapping framework was found to be effective and robust,
yielding a diverse range of realistic "in-vivo-like" MRI contrasts and maps at
100-um resolution. BigBrain-MR was then tested in three different imaging
applications (motion effects and interpolation, super-resolution imaging, and
parallel imaging reconstruction) to investigate its properties, value and
validity as a simulation platform. The results consistently showed that
BigBrain-MR can closely approximate the behavior of real in-vivo data, more
realistically and with more extensive features than a more classic option such
as the Shepp-Logan phantom. This novel phantom is therefore deemed a favorable
choice to support methodological development in brain MRI, and has been made
freely available to the community.
