Pieter F Buur1, Wietske van der Zwaag1, José P Marques2, and Daniel Gallichan3
1Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, 2Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands, 3Centre d'Imagerie BioMédicale (CIBM), EPFL Lausanne, Lausanne, Switzerland
Synopsis
Motion
correction using interleaved fat navigators is a promising approach for
high-resolution brain imaging at 7 Tesla. We have implemented a 3D-EPI fat
navigator to reduce acquisition time and thereby minimize overhead in sequences
with little or no dead time. The efficacy of motion-induced artefact removal
using the fat navigators is demonstrated for 0.6 mm isotropic inversion-prepared
(MPRAGE) and 0.5 mm isotropic non-prepared 3D TFE (GRE) protocols.Purpose
To minimize the acquisition time of
fat-only image navigators in order to allow motion correction of high-resolution
anatomical images with reduced scanning overhead.
The gain in signal-to-noise
ratio at ultra-high field MRI enables very high resolution structural and functional imaging of the human
brain. At these resolutions (~0.5mm and smaller), even minimal head movement,
which is unavoidable especially in older and clinical populations, can cause
significant artefacts and blurring that obscure the fine detail in the images.
Recently, a navigator-based approach employing fat-selective excitation was
introduced1 that allows for estimation and retrospective correction
of submillimeter head motion. One drawback of the method is the relatively long
acquisition time of the navigator. The current implementation builds on the previous
method by using an undersampled 3D-EPI readout similar to2, to
drastically shorten the acquisition time of the navigator, thereby allowing for
a more flexible use of the fat navigators and reduced overhead in sequences
without any (or with reduced) dead time such as gradient echo (GRE) sequences.
Methods
Data were collected
on a 7T MRI scanner (Philips Healthcare, Cleveland, Ohio, USA) with a
32-channel head coil (Nova Medical, Wilmington, Massachusetts, USA). Fat
navigators with a 3D-EPI readout were implemented as follows: fat-selective
excitation using a 121-binomial pulse scheme, FA 1°, TR/TE 17/5.6 ms, 2 mm
isotropic voxels, sagittal slice orientation, FOV 240x240x160 mm (FHxAPxRL),
SENSE factor 4x2 (APxRL) and partial Fourier factor
0.75 in both PE directions, resulting in a volume TR of 450 ms. Navigators were
inserted into
(1) a 0.6 mm isotropic inversion-prepared 3D TFE (MPRAGE) and
(2)
a 0.5 mm isotropic (non-prepared, GRE) TFE sequence using the Multiple Instantaneous Switchable Scans (MISS) functionality available on the Philips platform. Imaging
parameters for
(1): TR/TE/TI 6.2/2.3/1300 ms, FA 7°, FOV 195x224x156
mm, SENSE 2x2, 128 TFE shots, TFE interval 4500 ms, including the navigator
(Figure 1). Imaging parameters for
(2): TR/TE 19/15 ms, flip angle 7°, FOV 195x224x156
mm, SENSE 2x2, 156 TFE shots, TFE interval 4500 ms, including the navigator. In
both sequences, navigators were acquired every TFE shot (i.e. k-plane)
resulting in a total acquisition time of 10 and 11 minutes, respectively. For
the non-prepared TFE, the shot length was chosen such that the scanning
overhead due to the navigator acquisition was only 10%.
Three healthy volunteers were scanned, and instructed to gradually move their
head over the course of the acquisition. Motion parameters were estimated from
the fat navigator images using SPM8 and subsequently used to correct the raw
k-space data as previously described
1. Data shown has been
bias-field corrected.
Results
Figure 1
shows the pulse sequence diagram for the interleaved sequence as well as an
example of a 3D-EPI fat navigator image. Note that a fast navigator is
especially important for the non-prepared TFE. Figure 2 shows the motion
parameters and corresponding uncorrected and corrected T
1-weighted images for three
subjects. Figure 3 shows the motion parameters for the non-prepared acquisitions, as
well as the corresponding image with and without motion correction for two
subjects. Zoomed areas of the main panel show the correction in more detail. Although
subjects were explicitly asked to move, displacements were similar to those seen in naive
subjects (1-2 mm overall, 1-6 degrees rotation). The corrected images clearly
show the beneficial effect of incorporating the fat navigator-estimated motion
parameters into the image reconstruction for both sequences. Motion effects are
especially prominent in the uncorrected images at boundaries, near vessels and
at the cortical surface.
Discussion
Fat
navigators form a versatile, cost-effective and easily implemented alternative
to more elaborate motion correction methods relying on external hardware
3.
The sequence
interleaving capabilities of the MISS functionality very straightforwardly
allow the acquisition of fat-images interspersed with any host sequence. Here,
we have chosen to demonstrate the effectiveness of the 3D-EPI based fat images
in the T
1w-TFE/MPRAGE and TFE/GRE sequences. Incorporation in other
3D sequences such as MP2RAGE or TSE sequences would also be possible.
Judging from the increased image quality of the motion corrected data, 3D-EPI
based fat navigators clearly show potential for reliably detecting motion. The
shorter readout of the 3D-EPI allows the acquisition of a fat-navigator image
in 450ms, compared to the 1.2s used previously for a GRE-readout at the same
nominal resolution
1, leading to acceptable overhead in continuous acquisitions such as the
TFE and TSE.
Conclusion
We
successfully implemented a fat navigator scheme on a Philips platform, whereby
the use of a 3D-EPI
readout allows shorter fat navigator acquisition times. Successful correction
of 1-5 mm motion was achieved in two different image types, namely T
1w-TFE
and non-prepared TFE.
Acknowledgements
No acknowledgement found.References
1. Gallichan, D., Marques, J. P. and Gruetter, R. (2015), Retrospective
correction of involuntary microscopic head movement using highly
accelerated fat image navigators (3D FatNavs) at 7T. Magn Reson Med.
doi: 10.1002/mrm.25670
2. Mårtensson M, Engström M, Avventi E, et al. (2015). 3D
FatNav: Prospective Motion Correction for Clinical Brain Imaging. ISMRM 2015, #0816.
3. Maclaren, J., Herbst, M., Speck, O. and Zaitsev, M. (2013), Prospective
motion correction in brain imaging: A review. Magn Reson Med,
69: 621–636.