2334
Susceptibility distortion correction for fMRI1Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Synopsis
Multiband echo planar imaging (EPI) offers increased temporospatial resolution and statistical power for functional magnetic resonance imaging (fMRI) but the higher spatial resolution comes at the cost of higher susceptibility-related spatial distortions. In diffusion MRI (dMRI), studies have shown that distortion correction is better when using blip-reversed EPI data (known under the term blip-up/down images) as compared to the standard fieldmap approach. This has motivated fMRI studies to acquire their data with blip-up/down directions and to use these to reduce susceptibility distortion. Here, we qualitatively illustrate why this can lead to erroneous results and quantify this error across 10 subjects.
Introduction
Multiband echo planar imaging (MB-EPI) is a recently developed protocol used in functional magnetic resonance imaging (fMRI)1 that offers increased temporal/spatial resolution with increased statistical power2,3 but the higher spatial resolution comes at a price of considerable spatial distortion in the phase-encoding direction caused by susceptibility-related field inhomogeneities. With the rising popularity of multiband imaging methods and increasingly short TRs, susceptibility-induced distortions (SD) are becoming a common issue for fMRI that still needs to be addressed. Diffusion MRI (dMRI) studies suggest that SD is better corrected when using two images with reversed phase-encoding directions (blip-up/down images) compared to acquiring a field map4,5, as is standard for fMRI. However, when extending the blip-up/down approach to fMRI by acquiring one additional gradient-echo (GE) EPI scan in a phase-encoding direction opposite to the standard functional images and combining the data to correct SD in the GE/fMRI data, an error can be introduced as illustrated in Figs.2 and 3. Since, this problem has frequently been reappearing in prominent neuroimaging software forums (e.g. FSL or SPM), we decided to quantify the induced error when using GE-EPI as compared to SE-EPI for blip-up/down correction.
Methods
Pairs of SE- and GE-EPIs with opposite phase-encoding directions were acquired on a Siemens Magnetom Prisma 3T scanner from 10 healthy participants (aged 19–29 years, 5 females), with a multiband acceleration factor of 4 and no in-plane acceleration, resulting in substantial distortions. Phase was encoded anterior-to-posterior. Slice resolution was isotropic 2-mm in-plane. GE-EPIs had TE=40ms, TR=1.54s, and flip angle=60$$$^\circ$$$. For each phase-encoding direction, ten volumes of GE-EPIs were acquired and averaged before analyses in order to smooth over signal dropouts. SE-EPIs had TE=200ms, TR=5s, flip angle=90$$$^\circ$$$, and refocus flip angle=180$$$^\circ$$$. Single volumes of SE-EPIs were acquired. T1-weighted MPRAGE structural images were also acquired with isotropic 1-mm voxels in-plane; no slice gap in coronal slices, TE=2.98ms, TR=2.3s, and flip angle=9$$$^\circ$$$. We estimated the field inhomogeneities using two open-source tools: the Computational Morphometry Toolkit6 and HySCOv.27. HySCO is part of the ACID toolbox integrated into the commonly used fMRI analysis software SPM8. CMTK uses nonrigid transformations generated by multilevel B-splines, whereas HySCO adopts a diffeomorphic transformation algorithm. To normalize image intensity fluctuations due to signal drop-outs in GE-EPIs, the averaged GE-EPIs were first bias-corrected before estimating the deformation field using HySCO or CMTK.Corrections were performed on the SE-EPIs, rather than the GE-EPIs, in order to evaluate corrections unconfounded by signal dropouts. To facilitate evaluation, the average of the corrected/unwarped blip-up/down of SE-EPIs were coregistered to the individual’s MPRAGE, and the transformation applied to all other images. Ideally, corrections should result in the blip-up/down SE-EPIs being more similar to each other and to the MPRAGE, here used as the anatomical reference. We quantified goodness in terms of the differences in the root mean squares (ΔRMS) of image intensity (1) between the corrected blip-up/down SE-EPIs, and (2) the corrected EPIs vs. MPRAGE, where the lower the ΔRMS, the more similar the images are and hence the better the correction. We also statistically tested ΔRMS differences between SE-EPI- vs. GE-EPI-based corrections with paired Wilcoxon signed rank tests, where α<0.017 indicates significant differences, corresponding to a (Bonferonni) corrected threshold of α<0.05.Results
Blip-up/down SE-corrected EPIs visually differed less from the MPRAGE (Figs.2-3). The lower ΔRMS between blip-up/down and MPRAGE when corrected using SE-EPIs vs. GE-EPIs (dashed blue/dotted green lines, respectively, in Fig.4) confirmed this, though statistically there were no differences in images corrected by CMTK. GE-corrected pairs were overall less different than SE-corrected pairs. ΔRMS between blip-up/down SE-EPIs were also more similar for SE-corrected images than GE-corrected images for HySCO but not for CMTK (see Fig.4 for details).Discussion
Individual visual inspection of air-tissue boundaries and between tissue
types of corrected SE-EPIs compared to MPRAGE as an anatomical
reference showed that field inhomogeneities estimated from
blip-up/blip-down SE-EPI pairs lead to better spatial correction overall
than when estimated from GE-EPIs. This is confirmed when quantitatively
comparing the intensity differences (ΔRMS) between corrected
blip-up and blip-down images and between corrected images and MPRAGE. Conclusion
In order to correct for susceptibility-induced distortion correction in fMRI (GE-EPI), a pair of additional SE-EPIs should be acquired with the same sequence parameters in opposite phase-encoding directions. The additional ca. 1-minute scan time results in better SD corrections.Acknowledgements
This research was partially funded by the German Research Foundation (DFG-Antrag SO 952/8-1). SM received funding from the European Union’s Horizon 2020 research and 654 innovation program under the Marie Sklodowska-Curie grant agreement No 658589, and from the BMBF (01EW1711A and B) in the framework of 656 ERA-NET NEURON.References
1Feinberg DA, Setsompop, K. Ultra-fast MRI of the human brain with simultaneousmulti-slice imaging. J. Mar. Res. 2013; 229: 90–100.
2Chen L, Vu AT, Xu J, Moeller S, Ugurbil K, Yacoub E, Feinberg D. Evaluationof highly accelerated simultaneous multi-slice EPI for fMRI. NeuroImage. 2015; 104: 452–459.
3Todd N, Moeller S, Auerbach EJ, Yacoub E, Flandin G, Weiskopf N. Evaluationof 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts. NeuroImage. 2016; 124: 32–42.
4Esteban O, Daducci A, Caruyer E, O'Brien K, Ledesma-Carbayo MJ, Bach-Cuadra M, Santos A. Simulation-based evaluation of susceptibility distortion correction methods in diffusion MRI for connectivity analysis. In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), April 29 2014-May 2 2014. 2014; 738–741.
5Graham MS, Drobnjak I, Jenkinson M, and Zhang H. Quantitative assessment ofthe susceptibility artefact and its interaction with motion in diffusion MRI. PLoS ONE1. 2017; 2:e0185647.
6Rohlfing T. Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE Trans Med Imaging. 2012; 31: 153–163.
7Ruthotto L, Kugel H, Olesch J, Fischer B, Modersitzki J, Burger M, Wolters CH. Diffeomorphic susceptibility artefact correction of diffusion-weighted magnetic resonanceimages. Phys Med Biol. 2012; 57: 5715–5731.
8Friston KJ, Ashburner JT, Kiebel S, Nichols T, Penny WD, eds. Statistical Parametric Mapping: The Analysis of Functional Brain Images. Oxford: Elsevier; 2007.
Figures
