David Bancelin^{1}, Pedro Lima Cardoso^{1}, Beata Bachrata^{1,2}, Andreas Ehrmann^{1}, Siegfried Trattnig^{1,2}, and Simon D. Robinson^{1,3,4}

^{1}High-Field MR Centre, Medical University of Vienna, Vienna, Austria, ^{2}Christian Doppler Laboratory for Clinical Molecular MR Imaging, Vienna, Austria, ^{3}Department of Neurology, Medical University of Graz, Graz, Austria, ^{4}Centre for Advanced Imaging, University of Queensland, Queensland, Australia

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.

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

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

The phase data were combined using the Virtual Receiver Coil

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

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.

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.

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Figure 1: Left columns: comparison, for each subject, between the slice TR-sampled respiratory time series derived from phase data (blue continuous line) and the respiratory belt (dashed red line). Right columns: comparison between the power spectrum of each respiratory time series. Both graphs show a high correlation.

Figure 2: Left columns: correlation between the slice TR-sampled scalar respiratory phase time series (similar
colour code as in Fig. 1). Right columns: mean correlation over the 12 slices between each cosine (blue filled circles)
and sine (red open triangle) regressors (up to the Fourier order expansion 4) obtained either from phase data or the
respiratory belt.

Figure 3: Gain in tSNR (in percent) compared to the respiratory belt-derived regressors where a non-negligible gain
of 4% in the brainstem can be observed for subject 3

Figure 4: Spatial t-maps (t>2.3) of respiratory regressors derived with our method (left columns) and with the respiratory belt (middle columns). Both maps agree in the location of respiration-corrected voxels in the brainstem. Apart
in the spinal cord, the right columns show in addition positive gains in t-scores in the upper part of the brainstem with
our method compared to the measurements.