Exploring T2*-Dependence in Physiological Brain Pulsations using Multi-Echo MR-Encephalography
Poster No:
1541
Submission Type:
Abstract Submission
Authors:
Antonia Barghoorn1, Niels Schwaderlapp1, Jürgen Hennig1, Maxim Zaitsev1
Institutions:
1Division of Medical Physics, University Medical Center Freiburg, University of Freiburg, Freiburg, Germany
First Author:
Antonia Barghoorn
Division of Medical Physics, University Medical Center Freiburg, University of Freiburg
Freiburg, Germany
Division of Medical Physics, University Medical Center Freiburg, University of Freiburg
Freiburg, Germany
Co-Author(s):
Niels Schwaderlapp
Division of Medical Physics, University Medical Center Freiburg, University of Freiburg
Freiburg, Germany
Division of Medical Physics, University Medical Center Freiburg, University of Freiburg
Freiburg, Germany
Jürgen Hennig
Division of Medical Physics, University Medical Center Freiburg, University of Freiburg
Freiburg, Germany
Division of Medical Physics, University Medical Center Freiburg, University of Freiburg
Freiburg, Germany
Maxim Zaitsev
Division of Medical Physics, University Medical Center Freiburg, University of Freiburg
Freiburg, Germany
Division of Medical Physics, University Medical Center Freiburg, University of Freiburg
Freiburg, Germany
Introduction:
Magnetic resonance encephalography (MREG) enables whole-brain imaging at a temporal resolution of 100 ms and thus extends the observable frequency range of brain dynamics to include physiological processes such as cardiac pulsations and respiration (Hennig et al., 2021). At the lower temporal resolutions used in conventional fMRI, these higher-frequency signals are aliased into lower frequency bands. This aliasing complicates the differentiation between neuronal BOLD fluctuations from physiological noise, in particular for single-echo acquisitions (Kundu et al., 2017).
To extend the capabilities of MREG for investigating physiological brain activity, an interleaved multi-echo MREG sequence featuring two alternating echo times (TE1 = 15 ms and TE2 = 36 ms) was implemented in this study. At a TR of 200 ms, Multi-Echo MREG was used to investigate the T2*-dependence of the signal in frequency bands associated with respiratory and cardiac activity during resting-state acquisitions.
To extend the capabilities of MREG for investigating physiological brain activity, an interleaved multi-echo MREG sequence featuring two alternating echo times (TE1 = 15 ms and TE2 = 36 ms) was implemented in this study. At a TR of 200 ms, Multi-Echo MREG was used to investigate the T2*-dependence of the signal in frequency bands associated with respiratory and cardiac activity during resting-state acquisitions.
Methods:
Spherical stack of spirals trajectories were used for both echo times in ME-MREG (Assländer et al., 2013). An asymmetric trajectory design was implemented to reduce the TE for the first echo which results in a highly undersampled first half of the k-space (Barghoorn et al., 2024). The second half is sampled more densely to compensate and align with the 75 ms duration of the symmetrical trajectory used for TE2 = 36 ms.
Resting-state data were acquired from 4 volunteers during an 8-minute scan on a 3 T Siemens Prisma Scanner (Erlangen, Germany). MREG parameters were TR =200 ms, matrix size = 192x192x150, and spatial resolution = 3x3x3 mm³. Echo times alternated between TE1 =15 ms and TE2 = 36 ms with each acquisition lasting 100 ms. Additional data were acquired with 2D multi-slice, multi-gradient echo sequence (TR = 1000 ms, TE = 2.46/4.92 ms) for regularized image reconstruction using sensitivity encoding in MATLAB (Natick, Massachusetts, USA). A Fourier power spectrum analysis was conducted on the reconstructed datasets from TE1 and TE2 to characterize physiological noise. For each voxel, the Fourier Power spectrum was calculated and filtered to isolate the frequency bands corresponding to the heartbeat (approx. 0.8 Hz to 1.2 Hz) and respiration (approx. 0.1 Hz to 0.4 Hz). Spectral maps were computed by summing the squared amplitudes of the Fourier component for each voxel in the respective frequency range.
Resting-state data were acquired from 4 volunteers during an 8-minute scan on a 3 T Siemens Prisma Scanner (Erlangen, Germany). MREG parameters were TR =200 ms, matrix size = 192x192x150, and spatial resolution = 3x3x3 mm³. Echo times alternated between TE1 =15 ms and TE2 = 36 ms with each acquisition lasting 100 ms. Additional data were acquired with 2D multi-slice, multi-gradient echo sequence (TR = 1000 ms, TE = 2.46/4.92 ms) for regularized image reconstruction using sensitivity encoding in MATLAB (Natick, Massachusetts, USA). A Fourier power spectrum analysis was conducted on the reconstructed datasets from TE1 and TE2 to characterize physiological noise. For each voxel, the Fourier Power spectrum was calculated and filtered to isolate the frequency bands corresponding to the heartbeat (approx. 0.8 Hz to 1.2 Hz) and respiration (approx. 0.1 Hz to 0.4 Hz). Spectral maps were computed by summing the squared amplitudes of the Fourier component for each voxel in the respective frequency range.
Results:
As seen in Figure 1, the highest cardiac spectral power is concentrated in large blood vessels. Here, the power in the second echo time was consistently higher than in the first echo time across all volunteers. A maximum intensity projection along the z-axis revealed an angiographic-like represenation of the vascular network in the brain. In contrast, respiratory spectral power was higher in the first echo which suggests less T2*-dependence and is likely linked to motion (Figure 2).
Conclusions:
The observed effects in the cardiac frequency range are likely due to subvoxel phase incoherence caused by the varying blood velocities in each voxel (Kaandorp et al., 1999). Since most blood vessel diameters are smaller than the used voxel size, changes in blood velocity during the cardiac cycle lead to periodic phase loss. This is a T2* effect that becomes more pronounced at longer echo times. Conversely, respiratory effects were associated with periodic motion rather than phase incoherence (Mckeown et al., 1998). By employing two echo times, ME-MREG can help to separate these distinct physiological processes and may be used to characterize how different fluid compartments (e.g., blood vessels, interstitial spaces, and CSF) contribute to brain pulsations. This could provide further insights into their role in driving the glymphatic system (Kiviniemi et al., 2016).
Modeling and Analysis Methods:
Methods Development 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Cerebral Blood Flow
FUNCTIONAL MRI
MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Barghoorn, A., Hennig, J., Schwaderlapp, N., & Zaitsev, M. (2024). Multi-Echo MR-Encephaolgraphy for ultra-fast resting-state fMRI. Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB). https://www.esmrmb2024.org/abstracts-form/posters-e/abstract-data/ad3e9e752be9bffe4091eb60be91cd5d
Hennig, J., Kiviniemi, V., Riemenschneider, B., Barghoorn, A., Akin, B., Wang, F., & LeVan, P. (2021). 15 Years MR-encephalography. Magma (New York, N.Y.), 34(1), 85–108. https://doi.org/10.1007/s10334-020-00891-z
Kaandorp, D. W., Kopinga, K., & Wijn, P. F. F. (1999). Separation of haemodynamic flow waves measured by MR into forward and backward propagating components. Physiological Measurement, 20(2), 187. https://doi.org/10.1088/0967-3334/20/2/308
Kiviniemi, V., Wang, X., Korhonen, V., Keinänen, T., Tuovinen, T., Autio, J., LeVan, P., Keilholz, S., Zang, Y.-F., Hennig, J., & Nedergaard, M. (2016). Ultra-fast magnetic resonance encephalography of physiological brain activity – Glymphatic pulsation mechanisms? Journal of Cerebral Blood Flow & Metabolism, 36(6), 1033–1045. https://doi.org/10.1177/0271678X15622047
Kundu, P., Voon, V., Balchandani, P., Lombardo, M. V., Poser, B. A., & Bandettini, P. A. (2017). Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals. NeuroImage, 154, 59–80. https://doi.org/10.1016/j.neuroimage.2017.03.033
Mckeown, M. J., Makeig, S., Brown, G. G., Jung, T., Kindermann, S. S., Bell, A. J., & Sejnowski, T. J. (1998). Analysis of fMRI data by blind separation into independent spatial components. Human Brain Mapping, 6(3), 160–188. https://doi.org/10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1