7 Tesla Real-time fMRI using a real-time distortion correction algorithm
2017-06-27

Introduction: 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. Methods: 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. Results: 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. Conclusions: 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.