David Bancelin1, Pedro Lima Cardoso1, Beata Bachrata1,2, Andreas Ehrmann1, Siegfried Trattnig1,2, and Simon D. Robinson1,3,4
1High-Field MR Centre, Medical University of Vienna, Vienna, Austria, 2Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria, 3Department of Neurology, Medical University of Graz, Graz, Austria, 4Centre for Advanced Imaging, University of Queensland, Queensland, Australia
Synopsis
Respiratory and cardiac data are generally used to properly account for the presence of physiological
noise in fMRI data. Pneumatic belts can be unreliable, but we show that EPI phase data can be used to
generate a reliable respiratory time series from which regressors are used in a GLM procedure to correct
magnitude data. The efficacy of our method is compared with respect to (i) uncorrected magnitude data and
(ii) magnitude data corrected using respiratory belt-derived regressors.
Introduction
The proximity of the brainstem
to the fourth ventricle and arteries generates physiological noise (respiration
and cardiac fluctuations) in BOLD fMRI data due to changes in B0 and
flow effects. These reduce BOLD sensitivity
to activation in the small grey matter nuclei in that region. To remove these
perturbations, respiratory and cardiac time series are generally derived from
measurements with peripheral devices (respiratory bellows and pulse oximeters)
and then regressed out using e.g. RETROICOR(1),
a process that is generally effective in reducing signal variance in
the brainstem, as shown by Harvey et al(2)
.
As respiratory measurements
can be unreliable, we attempt to derive respiratory time series from phase data
from the fMRI time series. Indeed, it has been shown by Zahneisen et al(3) that
phase variations in the brainstem are closely related to
the respiration cycle.
In this work, we propose to derive a respiratory signal
from phase data in order to derive slice-wise respiratory regressors for
correcting magnitude data. We compare our results with those obtained with respiratory belt-derived regressors
from the PhysIO Toolbox(4).Methods
Three healthy volunteers
were scanned with a Siemens PRISMA 3T using a multiband EPI sequence(5) (TE/TR
= 28/1000 ms, 128x128 matrix, 1.7x1.7x2.0 mm voxel size, 12 sagittal
slices, 300 volumes, multiband factor 2). Parallel measurements were made
with a respiratory belt.
The phase data were combined using the Virtual Receiver Coil method(6),
unwrapped with SEGUE(7) and filtered
in the range of typical respiration frequencies 0.08-0.5 Hz.
After averaging the phase in
each slice, phase variations were removed from the averaged phase $$$\phi_{j,t}$$$ in
slice j at each time point t, such that: $$$\phi_{j,t} = \phi_{j,t} - \left (\phi_{j,\scriptscriptstyle{1}} - \phi_{i,\scriptscriptstyle{1}} \right)$$$ where the index i refers
to a reference slice.
The slice TR-sampled respiratory time series was then
obtained by reordering each slice in a single scalar time series according to the slice timing acquisition. This was
used to calculate a scalar then a slice-wise respiratory phase.
To correct the magnitude data using the GLM, we considered a similar
respiratory Fourier order expansion of four (as suggested by Harvey et al(2)) to
generate slice-wise cosines and sines respiratory regressors and model the
respiratory noise with RETROICOR.
To evaluate the efficacy of our method, we generated temporal SNR (tSNR) maps in the middle slice from the magnitude data corrected with respiratory regressors from phase data and belt measurements.
In order to represent the magnitude of the respiratory correction for each voxel, we calculated the square root of the sum of the squares of the t-scores of all respiratory cosines and sines Fourier coefficients.
All results were
compared with those obtained via the respiratory belt
measurements.Results
Figure 1 shows, for each subject, a comparison between (a)
the variance normalized phase-derived (blue continuous line) and belt-derived (red dashed line) respiration time series zoomed on the first 60 s of the
experiment (left columns), and (b) the associated power spectra (right columns).
Both correlation values in time series and power spectra show that our model is
in a good agreement with the measurements.
A similar comparison between the
slice TR-sampled respiration phase obtained using the respiratory belt and
phase-derived signal are displayed on the left column in Fig. 2 where the phase-derived time series correlates well
with the one from the respiratory belt, with a correlation above 0.5 for each
subject. The two first regressors (right columns) are also in a good agreement, with a mean
correlation (averaged over the 12 slices) above 0.4.
Figure 3 compares the gain in tSNR with our method. Not only results are in a good
agreement with the tSNR obtained with the respiratory belt, but they even
exhibit a slight gain: up to 4% more variance reduction in the brainstem area
(subject 3).
We display in Fig. 4 spatial t-maps in the middle slice (t>2.3) of the respiratory regressors
from phase data (left columns) and from the respiratory belt (middle columns),
and a gain map (in percent) between both (right columns). Although the area in
the spinal cord exhibits negative gains, our
method can reproduce t-maps from the measurements and enable t-scores
improvement of voxels located in the pons and midbrain. Discussion
Our study demonstrates that
it is possible to extract a reliable respiratory signal by means of the phase
data. The approach we propose to removing slice dependency in the phase allows
the derivation of signals which are sampled at the slice TR rather than the
volume TR, offering the possibility to identify
cardiac-related fluctuations, which are generally subcritically sampled. As
seen in Fig. 3, using 4D phase data seems more effective
in reducing signal variance because our respiratory regressors are directly derived from voxels carrying respiratory
information. Our method has the advantage of not depending on respiratory
measurements, which require additional patient preparation to set up, are
fragile and not always trivial to correlate with the fMRI time series. Conclusion
We have successfully
demonstrated in this study that not only respiratory noise correction can be
achieved without performing parallel measurements by using phase data, but also
that variance in voxels can be better reduced. Acknowledgements
This study was funded by the Austrian Science Fund project FWF31452. SDR is supported by Marie Sklodowska-Curie Action MS-fMRI-QSM 794298.References
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