Open-Source Vendor-Independent MR-Encephalography for fMRI at High Temporal Resolutions
Poster No:
1540
Submission Type:
Abstract Submission
Authors:
Antonia Barghoorn1, Jürgen Hennig1, Niels Schwaderlapp1, Niklas Wehkamp1, 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):
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
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
Niklas Wehkamp
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 to detect resting-state functional connectivity and physiological brain activity at a frequency of up to 10 Hz (Zahneisen et al., 2011). The traditional implementation using Siemens IDEA requires vendor-specific programming that hinders the reproducibility across different MRI systems. In contrast, the open-source Pulseq framework provides a platform-independent sequence definition that simplifies the development, application and sharing of the MREG sequence (Chen et al., n.d.; Layton et al., 2017). In this study, the temporal signal-to-noise-ratio (tSNR) and activation during a visual stimulation paradigm were compared for MREG implementations using Siemens and Pulseq to test the feasibility of a harmonized acquisition protocol across different imaging platforms.
Methods:
MREG employs a spherical stack of spirals trajectory (Assländer et al., 2013) that was designed in MATLAB (Natick, Massachusetts, USA) and subsequently converted into an arbitrary gradient waveform using the Pulseq MATLAB toolbox. Data were acquired using Pulseq MREG and Siemens MREG for three healthy volunteers at 3 T (Siemens Prismafit, Erlangen, Germany). Measurement parameters were TR=100 ms, TE=36 ms, matrix size = 192x192x150, spatial resolution = 3x3x3 mm³, and excitation flip angle=25°. Additional data were acquired from a 2D multi-slice multi gradient echo sequence (TR=1000 ms, TE=2.46/4.92 ms) to perform regularized image reconstruction using sensitivity encoding. Source code for the Pulseq sequence and the reconstruction toolbox is open-source and freely available (https://github.com/UKLFR-MR/mreg_recon_tool_pulseq). Visual stimulation consisted of four cycles of 13 s of a flickering checkerboard followed by 18 s of rest. Activation maps were computed using FMRISTAT (Worsley et al., 2002) after performing motion correction, highpass-filtering above 0.01 Hz, and smoothing with a Gaussian kernel of 6 mm using FSL (Jenkinson et al., 2012). Temporal SNR maps were derived by dividing each voxel's mean signal intensity over time by its temporal standard deviation during rest.
Results:
As illustrated in Figure 1, in vivo MREG images acquired with both the Pulseq and Siemens implementations exhibited comparable overall image quality characteristic of MREG. Across the whole brain in the three volunteers, temporal signal-to-noise ratio (tSNR) values were highly similar with mean values of 70.5±6.3 for Pulseq and 68.3±6.8 for Siemens. Functional activation analysis (Figure 2) demonstrated that both acquisition methods produced comparable activation patterns. At a statistical threshold of t = 7 (p < 0.001, uncorrected), the mean number of activated voxels was slightly higher for Pulseq (n = 1242) than for Siemens (n = 1067). The percent signal change time courses were nearly identical for both acquisition methods, as seen in Figure 2 for one exemplary volunteer. Both Pulseq and Siemens showed BOLD signal increases of approximately 2% during periods of stimulation with small differences in the amplitude and shape of the response.
Conclusions:
These results demonstrate that the open-source Pulseq implementation of MREG can achieve the image quality, tSNR, and functional activation patterns comparable to those of the established Siemens sequence. The very similar percent signal changes and BOLD responses indicate that brain activity can be reliably sampled using Pulseq MREG. These findings support the feasibility of a harmonized MREG acquisition protocol for different imaging platforms and provide the opportunity for more reproducible and accessible neuroimaging studies.
Modeling and Analysis Methods:
Methods Development 1
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Keywords:
Development
FUNCTIONAL MRI
Open-Source Code
Open-Source Software
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Chen, Q., Wehkamp, N., Wan, C., Hucker, P., & Zaitsev, M. (n.d.). Automated, Open-Source, Vendor-Independent Quality Assurance Protocol Based on the Pulseq Framework. Submitted to MAGMA on 27 November 2024 (in Review).
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Layton, K. J., Kroboth, S., Jia, F., Littin, S., Yu, H., Leupold, J., Nielsen, J.-F., Stöcker, T., & Zaitsev, M. (2017). Pulseq: A rapid and hardware-independent pulse sequence prototyping framework. Magnetic Resonance in Medicine, 77(4), 1544–1552. https://doi.org/10.1002/mrm.26235
Worsley, K. J., Liao, C. H., Aston, J., Petre, V., Duncan, G. H., Morales, F., & Evans, A. C. (2002). A General Statistical Analysis for fMRI Data. NeuroImage, 15(1), 1–15. https://doi.org/10.1006/nimg.2001.0933
Zahneisen, B., Grotz, T., Lee, K. J., Ohlendorf, S., Reisert, M., Zaitsev, M., & Hennig, J. (2011). Three-dimensional MR-encephalography: Fast volumetric brain imaging using rosette trajectories. Magnetic Resonance in Medicine, 65(5), 1260–1268.