Magnetic resonance resonance (MRI) is a widely used modality to obtain in vivo tissue information.
Clinical applications are near countless, and almost all body parts can be examined using an MR
scanner. As the method is non invasive, does not use ionizing radiation and provides excellent soft
tissue contrast, it also appears as an excellent tool for neuroscience research. The major drawback
of MRI remains the relatively long acquisition times, of the order of several minutes. During the
measurement, the subject must stay still and avoid moving at all costs, as otherwise image artefacts
will appear and potentially render the acquired data (partially) unusable. As higher image resolution
imply longer acquisition time, probing finer anatomical details imply ultimately requires dealing with
said motion. While some research goes in the way of reducing the acquisition time, it necessarily
comes at the price of lower sensitivity and hence inherently diminishes the achievable gain for high-
resolution imaging as the signal is weaker to start with.
In this work, the focus is to try and compensate for motion during brain imaging using a navigator
method. This amounts to measure not only the desired image, but also other MR based information,
called navigator, at regular intervals during the scan. A modeling step then establishes a link between
the navigators samples and the head position change. Incorporating the motion information into the
main image reconstruction framework helps to retrospectively reduce the impact of said motion and
the associated incoherences which would appear during the standard reconstruction. Brain imaging is
probably the easiest case of motion correction in MRI, as the motion can readily be well approximated
as rigid.
The navigator methods developed and investigated in this work, called FatNavs, are based on the
fat signal, which in head imaging is very sparse in space and therefore can be imaged rapidly. They
also present the advantage of reduced impact on the main image water signal.
Several implementation strategies were tested as, due to the versatility of MRI, all image contrasts
cannot be ideally navigated using a single general implementation. Applications to inversion recovery
based sequences (MP2RAGE) used a well separated navigator and image acquisition scheme. This
method being routinely acquired, comparison to Moir Ì e Phase Tracking, the current gold standard
for motion tracking and correction, was also performed in collaboration with Hendrik Mattern from
the Magdeburg University.
For gradient-echo imaging sequences (GRE), on which time-of-flight angiography and susceptibil-
ity induced contrasts are based, both separate and mixed acquisition schemes were tested. Further-
more, for imaging protocols using long echo time, the fluctuation of the magnetic field during the scan
can also induce severe artefacts. Therefore, extension of the FatNavs to a dual-echo field-mapping
version was also explored.
Finally, combination of FatNavs with FID navigators, which lack spatial information but have
much higher temporal resolution, was investigated for both motion and field fluctuation retrospective
correction.