Malte Hoffmann1 and Stephen J Sawiak1,2
1Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom, 2Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, United Kingdom
Synopsis
Motion corruption leads to major artefacts in fMRI which are damaging in
studies. Most processing pipelines employ linear registration of the brain
volume at each time point. As slices are acquired individually, however, it is
possible for each slice to require a different transformation. Here, we compared
the efficacy of linear volume registration, independent slice-based
registration and volume non-linear registration for retrospective correction of
fMRI acquisitions using data from forty non-cooperative patients with
substantial motion.Purpose
Motion artefacts severely compromise fMRI
analysis and most processing pipelines include a linear volume registration
step. Since the 2D EPI data are acquired in slices, can correcting each slice
within a volume individually lead to better motion correction? We compare the
effects of slice-based registration and non-linear registration between volumes
for each frame to assess whether these methods are useful. We did this in a set
of particularly motion-prone patients with varying degrees of disorders of
consciousness whose motion patterns have been previously studied.
1Methods
Patients with various degrees of disorders
of consciousness (n=40) were scanned on a Siemens (Erlangen, Germany) 3T
system. A total of 59 EPI time series with 300 frames each was acquired (TR/TE 2000/30ms, voxel size 3×3mm2, matrix size
64×64, 32 3mm slices with 0.75mm gap). All patients gave informed consent, and
the study was approved by the local research ethics committee.
Slice-based registration
The slices of each volume of a time series were
registered individually within the first volume of the series. Initially, each
slice was compared to the corresponding slice of the reference volume. The
latter was subsequently moved within the reference volume to find an optimum
rigid-body transformation. Optimisation was performed minimising the sum of
squared slice differences (SSD) with a six-dimensional gradient descent method.
We evaluated the accuracy of the registration by
corrupting a low-motion volume with synthetic motion. Extreme motion parameters
were drawn from a normal distribution with 1mm/° standard deviation (STD) for
each of the three translations and rotations respectively. Each slice of the
corrupted volume was generated by applying the resulting transform to the
original voxel coordinates of the slice, and interpolating at the new location.
Data quality was evaluated with three different
measures: (1) We determined the total SSD between all volumes of a time series and
the first. (2) The overlap of CSF with CSF of the first volume was computed by
thresholding each image at 60% of the maximum intensity within the time series
and counting matching voxels. (3) We evaluated temporal standard deviation
(tSTD) by averaging the STD of each voxel time series in a dataset.
We compared the performance of volumetric image
registration with spm_realign2, slice-based motion correction and
non-linear registration with FNIRT3. All volumes were registered
with the first of each time series.
Results
Figure 1 shows a low-motion dataset before and
after simulation of between-slice motion. Despite injected motion exceeding five
times the motion detected in the worst dataset, slice-based registration
successfully restored the dataset (see Figure
1C). The deviation between simulated and detected motion parameters is
given in Table 1.
Figure 2B shows the typical slice displacement
from a patient dataset, while the slice alignment in the absence of motion is illustrated
in Figure 2A. Quality measures for
the different registration methods are compared in Table 2. Both slice-based and non-linear correction improved data
quality across all categories.
Discussion
Both slice-based and non-linear
registration had a significant (p < 0.05, Student’s t-test) improvement over
volume methods to tSTD. There was no significant different in the tSTDs between
these methods. Motion that occurs during the
acquisition of single image slices in EPI (typical acquisition times of ~50ms)
is a second-order effect that is often neglected in fMRI analysis pipelines. Here,
we showed that data quality can be improved substantially over volume
registration if a slice-based method is used. While registration of individual
slices models the acquisition scheme, it does not take other second-order
effects such as spin history and coil sensitivity profiles into account. This
is an explanation why the more general non-linear registration achieves further
improvements.
Conclusion
If prospective motion correction is not
an option, retrospective non-linear registration of each volume of a time
series can be used to significantly improve data quality metrics. The
similarity in improvement seen between slice-based and non-linear registration
techniques suggests that the majority of non-linear deformations are the result
of inter-slice motion. This being the case, incorporating slice-based
transforms into the regularisation of the non-linear transforms may be an
important contribution to avoid overfitting in non-linear image registration of
fMRI time series.
Acknowledgements
The authors would like to thank the Cambridge Research into Impaired
Consciousness Group for sharing data.References
1. Hoffmann M et al. A survey of patient
motion in disorders of consciousness and optimization of its retrospective
correction. Magn Reson Imaging 2015;33(3):346-50.
2. Friston KJ et al. Spatial
registration and normalization of images. Hum Brain Map 1995;2:165-189.
3. Jenkinson M et al. FSL. NeuroImage
2012;62(2):782-90.