7 Tesla Real-time fMRI using a real-time distortion correction algorithm

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Tuesday, June 27, 2017: 12:45 PM  - 2:45 PM 

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Abstract Submission 

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Monday, June 26 & Tuesday, June 27 


Johan van der Meer1,2, Lydia Hellrung3, Myung-Ho In4, Florian Götting5, Viola Borchardt5, Harald Möller6, Martin Walter5


1QIMR Berghofer Medical Research Institute, Brisbane, Australia, 2Clinical Affective Neuroimaging Laboratory, Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany, 3Nuclear Magnetic Resonance Unit, Max Planck institute for human cognitive and brain sciences, Dresden, Germany, 4Department of Biomedical Magnetic Resonance, Otto-von-Guericke university, Magdeburg, Germany, 5Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany, 6Nuclear Magnetic Resonance Unit, Max Planck institute for human cognitive and brain sciences, Leipzig, Germany

First Author:

Johan van der Meer    -  Lecture Information | Contact Me
QIMR Berghofer Medical Research Institute|Clinical Affective Neuroimaging Laboratory, Department of Behavioral Neurology, Leibniz Institute for Neurobiology
Brisbane, Australia|Magdeburg, Germany


Even though the higher spatial resolution in 7T imaging is a boon for real-time fMRI BOLD imaging, there are several practical concerns which need to be addressed before any real-time BOLD study can successfully performed. A primary concern for obtaining a neurofeedback signal is image quality: For Real-Time fMRI feedback, it is often desired to pick a mask region (generated either automatically from an atlas, or hand-drawn using an anatomical scan) of the same subject in order to determine a brain region used for extracting neurofeedback signals [1]. At 3 Tesla, because distortions are usually limited to shifts along 2 or 3 voxels and localized only in certain regions, it is relatively safe to use anatomically pre-generated mask regions of interest without correcting for EPI distortions. However, at 7 Tesla, image distortions are usually larger (with shifts of 20-30 voxels), and distributed along the entire brain. This has negative consequences for the BOLD signal which is used in a NF experiment. Therefore, a distortion correction step is essential to be able to use any pre-generated masks. In this work we present improvements in image quality due to an on-line distortion correction [2]. With respect to signal dropout, this effect is also more pronounced at 7T. The signal dropout depends on imaging slab orientation and also on phase encoding direction [3]. In this work we present the effect of slice encoding direction on the location of the regions affected by signal distortion. In order to assess this effect, the signal must first be corrected for signal distortions.


We scanned one subject using a echo-planar imaging (EPI) scanning sequence covering a part of the brain including the amygdalae and cingulated cortex, with a resolution of 1.4x1.4x1.8 mm^3, 31 imaging slices, and a TR of 2000 msec. In order to assess the advantageous effects of distortion correction for real-time fMRI imaging, we segmented the amygdalae from a T1 anatomical scan of the same subject and compare how the imaging mask corresponds to both distorted and undistorted images. Furthermore, we used four different imaging runs – one with a phase-encoding direction of AP (anterior-posterior), one with PA, one with RL (right-left), and the last one with LR. All images were exported in real-time to an external (Real-Time) computer for further analysis. In addition, we will compare the NF signal from the Amygdala from a group of 34 subjects between distortion-corrected and non-corrected scans.


Figure 1 shows the difference between distortion un-corrected and distortion-corrected images. The most pronounced effect of image distortion can be spotted in frontal regions: in the region near the Aygdalae, there is also a distortion (folding) effect. Figure 2 shows distortion-corrected images, for the different phase-encoding directions of the EPI sequence. Since distortions are corrected for, it is possible to truly assess image signal dropout. The direction of the phase-encoding direction has a clear effect on which regions are affected by (total) signal dropout.
Supporting Image: figure1.png
   ·Figure 1. EPI images that are acquired in real-time. Left: Uncorrected, Right: Corrected for distortions.
Supporting Image: distortions.png
   ·Figure 2. EPI images acquired and distortion-corrected in real-time, for the different phase-encoding directions of the imaging slices.


Image distortion correction is essential in real-time fMRI studies performed at a 7 Tesla scanner. Once available, the available increased resolution achievable would be an asset in the further development of real-time fMRI and its application for Rt-fMRI for brain-computer interfaces and research on psychiatric disorders.

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1. Hollman, M., et al. A new concept of a unified parameter management, experiment control, and data analysis in fMRI: application to real-time fMRI at 3T and 7T. J Neurosci Methods. 2008 Oct 30;175(1):154-62
2. In, Myung-Ho, Highly accelerated PSF-mapping for EPI distortion correction with improved fidelity. MAGMA. 2012 Jun;25(3):183-92
3. Deichmann, R., Schwarzbauer, C. & Turner, R. Optimisation of the 3D MDEFT sequence for anatomical brain imaging: technical implications at 1.5 and 3 T. Neuroimage 21, 757–767 (2004).
4. Hollman, M., (2011), 'Predicting Decisions in Human Social Interactions Using Real-Time fMRI and Pattern Classification', PLoS ONE, 6(10), e25304
5. In, Myung-Ho (2012), 'Highly accelerated PSF-mapping for EPI distortion correction with improved fidelity. MAGMA. Jun;25(3):183-92
6. Weiskopf (2007). 'Optimized EPI for fMRI studies of the orbitofrontal cortex: compensation of susceptibility-induced gradients in the readout direction', MAGMA 20, 39–49.