Improved cortical boundary registration for locally distorted fMRI scans

English version

Autoři: Tim van Mourik ^aff001; Peter J. Koopmans ^aff002; David G. Norris ^aff001
Působiště autorů: Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands ^aff001; Erwin L. Hahn Institute for Magnetic Resonance Imaging, Essen, Germany ^aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0223440

Souhrn

With continuing advances in MRI techniques and the emergence of higher static field strengths, submillimetre spatial resolution is now possible in human functional imaging experiments. This has opened up the way for more specific types of analysis, for example investigation of the cortical layers of the brain. With this increased specificity, it is important to correct for the geometrical distortions that are inherent to echo planar imaging (EPI). Inconveniently, higher field strength also increases these distortions. The resulting displacements can easily amount to several millimetres and as such pose a serious problem for laminar analysis. We here present a method, Recursive Boundary Registration (RBR), that corrects distortions between an anatomical and an EPI volume. By recursively applying Boundary Based Registration (BBR) on progressively smaller subregions of the brain we generate an accurate whole-brain registration, based on the grey-white matter contrast. Explicit care is taken that the deformation does not break the topology of the cortical surface, which is an important requirement for several of the most common subsequent steps in laminar analysis. We show that RBR obtains submillimetre accuracy with respect to a manually distorted gold standard, and apply it to a set of human in vivo scans to show a clear increase in spacial specificity. RBR further automates the process of non-linear distortion correction. This is an important step towards routine human laminar fMRI for large field of view acquisitions. We provide the code for the RBR algorithm, as well as a variety of functions to better investigate registration performance in a public GitHub repository, https://github.com/TimVanMourik/OpenFmriAnalysis, under the GPL 3.0 license.

Klíčová slova:

Algorithms – Optimization – Functional magnetic resonance imaging – Neuroimaging – Central nervous system – Data acquisition – Deformation – Echo planar imaging

Zdroje

1. Dumoulin SO, Fracasso A, van der Zwaag W, Siero JCW, Petridou N. Ultra-high field MRI: Advancing systems neuroscience towards mesoscopic human brain function. NeuroImage. 2017;https://doi.org/10.1016/j.neuroimage.2017.01.028.

2. Trampel R, Bazin PL, Pine K, Weiskopf N. In-vivo magnetic resonance imaging (MRI) of laminae in the human cortex. NeuroImage. 2017;https://doi.org/10.1016/j.neuroimage.2017.09.037.

3. Koopmans PJ, Barth M, Orzada S, Norris DG. Multi-echo fMRI of the cortical laminae in humans at 7 T. Neuroimage. 2011;56(3):1276–1285. doi: 10.1016/j.neuroimage.2011.02.042 21338697

4. Muckli L, De Martino F, Vizioli L, Petro L, Smith F, Ugurbil K, et al. Contextual Feedback to Superficial Layers of V1. Current Biology. 2015;25(20):2690–2695. doi: 10.1016/j.cub.2015.08.057 26441356

5. Kok P, Bains L, van Mourik T, Norris D, de Lange F. Selective Activation of the Deep Layers of the Human Primary Visual Cortex by Top-Down Feedback. Current Biology. 2016;26(3):371–376. doi: 10.1016/j.cub.2015.12.038 26832438

6. Felleman DJ, Van Essen DC. Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex. 1991;1(1):1–47.1822724

7. Self MW, van Kerkoerle T, Goebel R, Roelfsema PR. Benchmarking laminar fMRI: Neuronal spiking and synaptic activity during top-down and bottom-up processing in the different layers of cortex. NeuroImage. 2017;https://doi.org/10.1016/j.neuroimage.2017.06.045.28648888

8. Zilles K. Cortex. The human nervous system; 1990.

9. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of Americal. 2000;97(20):11050–11055. doi: 10.1073/pnas.200033797

10. Poser BA, Setsompop K. Pulse sequences and parallel imaging for high spatiotemporal resolution MRI at ultra-high field. NeuroImage. 2017;https://doi.org/10.1016/j.neuroimage.2017.04.006.

11. Mansfield P. Multi-planar image formation using NMR spin echoes. Journal of Physics C: Solid State Physics. 1977;10(3):L55. doi: 10.1088/0022-3719/10/3/004

12. Schmitt F, Stehling MK, Turner R. Echo-Planar Imaging, Theory, Technique and Application. Springer; 1998.

13. Dale AM, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage. 1999;9(2):179—194. http://dx.doi.org/10.1006/nimg.1998.0395.

14. Bazin PL, Weiss M, Dinse J, Schäfer A, Trampel R, Turner R. A computational framework for ultra-high resolution cortical segmentation at 7Tesla. Neuroimage. 2014;93:201–209. doi: 10.1016/j.neuroimage.2013.03.077 23623972

15. Goebel R. BrainVoyager—Past, present, future. NeuroImage. 2012;62(2):748—756. https://doi.org/10.1016/j.neuroimage.2012.01.083.

16. Ress D, Glover GH, Liu J, Wandell B. Laminar profiles of functional activity in the human brain. NeuroImage. 2007;34(1):74—84. http://dx.doi.org/10.1016/j.neuroimage.2006.08.020.17011213

17. Koopmans PJ, Barth M, Norris DG. Layer-specific BOLD activation in human V1. Human Brain Mapping. 2010;31(9):1297–1304. doi: 10.1002/hbm.20936 20082333

18. Kashyap S, Ivanov D, Havlicek M, Poser BA, Uludag K. Impact of acquisition and analysis strategies on cortical depth-dependent fMRI. NeuroImage. 2017;https://doi.org/10.1016/j.neuroimage.2017.05.022.

19. Huber L, Handwerker DA, Jangraw DC, Chen G, Hall A, Stüber C, et al. High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1. Neuron. 2017;96(6):1253–1263.e7. doi: 10.1016/j.neuron.2017.11.005 29224727

20. Scheeringa, Koopmans, van Mourik, Norris, Jensen. The relationship between oscillatory EEG activity and the laminar-specific BOLD signal. PNAS. 2016. doi: 10.1073/pnas.1522577113 27247416

21. Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 field variations. Magnetic Resonance in Medicine. 1995;34(1):65–73. doi: 10.1002/mrm.1910340111 7674900

22. Togo H, Rokicki J, Yoshinaga K, Hisatsune T, Matsuda H, Haga N, et al. Effects of Field-Map Distortion Correction on Resting State Functional Connectivity MRI. Frontiers in Neuroscience. 2017;11:656. doi: 10.3389/fnins.2017.00656 29249930

23. Dymerska B, Poser BA, Barth M, Trattnig S, Robinson SD. A method for the dynamic correction of B0-related distortions in single-echo EPI at 7T. NeuroImage. 2018;168:321–331. doi: 10.1016/j.neuroimage.2016.07.009 27397624

24. Esteban O, Zosso D, Daducci A, Bach-Cuadra M, Ledesma-Carbayo MJ, Thiran J-P, Santos A. Surface-driven registration method for the structure-informed segmentation of diffusion MR images. NeuroImage. 2016;139:450–461. doi: 10.1016/j.neuroimage.2016.05.011

25. Cox RW. AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and Biomedical Research. 1996;29(3):162—173. https://doi.org/10.1006/cbmr.1996.0014.8812068

26. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. NeuroImage. 2009;48(1):63—72. http://dx.doi.org/10.1016/j.neuroimage.2009.06.060.19573611

27. Waehnert MD, Dinse J, Weiss M, Streicher MN, Waehnert P, Geyer S, et al. Anatomically motivated modeling of cortical laminae. Neuroimage. 2014;93 Pt 2:210–220. doi: 10.1016/j.neuroimage.2013.03.078 23603284

28. Leprince Y, Poupon F, Delzescaux T, Hasboun D, Poupon C, Rivière D. Combined Laplacian-equivolumic model for studying cortical lamination with ultra high field MRI (7 T). In: Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on; 2015. p. 580–583.

29. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Medical Image Analysis. 2001;5(2):143—156. http://dx.doi.org/10.1016/S1361-8415(01)00036-6.11516708

30. Collins DL, Holmes CJ, Peters TM, Evans AC. Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping. 1995;3(3):190–208. doi: 10.1002/hbm.460030304

31. Sethian JA. Level Set Methods and Fast Marching Methods. Cambridge University Press; 1999.

32. Delaunay B. Sur la sphere vide. Izv Akad Nauk SSSR, Otdelenie Matematicheskii i Estestvennyka Nauk. 1934;7 (793-800):1–2.

33. Haase A, Frahm J, Matthaei D, Hanicke W, Merboldt KD. FLASH imaging. Rapid NMR imaging using low flip-angle pulses. Journal of Magnetic Resonance (1969). 1986;67(2):258—266. https://doi.org/10.1016/0022-2364(86)90433-6.

34. Andersson JLR, Hutton C, Ashburner J, Turner R, Friston K. Modeling Geometric Deformations in {EPI} Time Series. NeuroImage. 2001;13(5):903—919. http://dx.doi.org/10.1006/nimg.2001.0746.

35. Poser BA, Koopmans PJ, Witzel T, Wald LL, Barth M. Three dimensional echo-planar imaging at 7 Tesla. Neuroimage. 2010;51(1):261–266. doi: 10.1016/j.neuroimage.2010.01.108 20139009

36. Marques JP, Kober T, Krueger G, van der Zwaag W, Van de Moortele PF, Gruetter R. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage. 2010;49(2):1271–1281. doi: 10.1016/j.neuroimage.2009.10.002 19819338

37. van Mourik T, Snoek L, Knapen T, Norris D. Porcupine: a visual pipeline tool for neuroimaging analysis. bioRxiv. 2017.

38. Avants B, Tustison N, Song G. Advanced Normalization Tools (ANTS); 2011.

39. Saad ZS, Glen DR, Chen G, Beauchamp MS, Desai R, Cox RW. A new method for improving functional-to-structural {MRI} alignment using local Pearson correlation. NeuroImage. 2009;44(3):839—848. http://dx.doi.org/10.1016/j.neuroimage.2008.09.037.

Improved cortical boundary registration for locally distorted fMRI scans

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