High-Resolution Numerical Simulation of Respiration-Induced Dynamic B0 Shift in the Head in High-Field MRI

  • Lee, So-Hee (Center for Neuroscience Imaging Research, Institute for Basic Science (IBS)) ;
  • Barg, Ji-Seong (Center for Neuroscience Imaging Research, Institute for Basic Science (IBS)) ;
  • Yeo, Seok-Jin (Center for Neuroscience Imaging Research, Institute for Basic Science (IBS)) ;
  • Lee, Seung-Kyun (Center for Neuroscience Imaging Research, Institute for Basic Science (IBS))
  • Received : 2018.08.29
  • Accepted : 2019.02.01
  • Published : 2019.03.29


Purpose: To demonstrate the high-resolution numerical simulation of the respiration-induced dynamic $B_0$ shift in the head using generalized susceptibility voxel convolution (gSVC). Materials and Methods: Previous dynamic $B_0$ simulation research has been limited to low-resolution numerical models due to the large computational demands of conventional Fourier-based $B_0$ calculation methods. Here, we show that a recently-proposed gSVC method can simulate dynamic $B_0$ maps from a realistic breathing human body model with high spatiotemporal resolution in a time-efficient manner. For a human body model, we used the Extended Cardiac And Torso (XCAT) phantom originally developed for computed tomography. The spatial resolution (voxel size) was kept isotropic and varied from 1 to 10 mm. We calculated $B_0$ maps in the brain of the model at 10 equally spaced points in a respiration cycle and analyzed the spatial gradients of each of them. The results were compared with experimental measurements in the literature. Results: The simulation predicted a maximum temporal variation of the $B_0$ shift in the brain of about 7 Hz at 7T. The magnitudes of the respiration-induced $B_0$ gradient in the x (right/left), y (anterior/posterior), and z (head/feet) directions determined by volumetric linear fitting, were < 0.01 Hz/cm, 0.18 Hz/cm, and 0.26 Hz/cm, respectively. These compared favorably with previous reports. We found that simulation voxel sizes greater than 5 mm can produce unreliable results. Conclusion: We have presented an efficient simulation framework for respiration-induced $B_0$ variation in the head. The method can be used to predict $B_0$ shifts with high spatiotemporal resolution under different breathing conditions and aid in the design of dynamic $B_0$ compensation strategies.


Supported by : Institute for Basic Science of the Republic of Korea


  1. Koch KM, Rothman DL, de Graaf RA. Optimization of static magnetic field homogeneity in the human and animal brain in vivo. Prog Nucl Magn Reson Spectrosc 2009;54:69-96
  2. Kim PK, Lim JW, Ahn CB. Higher order shimming for ultrafast spiral-scan imaging at 3 tesla MRI system. J Korean Soc Magn Reson Med 2007;11:95-102
  3. Foerster BU, Tomasi D, Caparelli EC. Magnetic field shift due to mechanical vibration in functional magnetic resonance imaging. Magn Reson Med 2005;54:1261-1267
  4. Hutton C, Andersson J, Deichmann R, Weiskopf N. Phase informed model for motion and susceptibility. Hum Brain Mapp 2013;34:3086-3100
  5. Bouwman JG, Bakker CJ. Alias subtraction more efficient than conventional zero-padding in the Fourier-based calculation of the susceptibility induced perturbation of the magnetic field in MR. Magn Reson Med 2012;68:621-630
  6. Zahneisen B, Asslander J, LeVan P, et al. Quantification and correction of respiration induced dynamic field map changes in fMRI using 3D single shot techniques. Magn Reson Med 2014;71:1093-1102
  7. Zeller M, Kraus P, Muller A, Bley TA, Kostler H. Respiration impacts phase difference-based field maps in echo planar imaging. Magn Reson Med 2014;72:446-451
  8. Van de Moortele PF, Pfeuffer J, Glover GH, Ugurbil K, Hu X. Respiration-induced $B_0$ fluctuations and their spatial distribution in the human brain at 7 Tesla. Magn Reson Med 2002;47:888-895
  9. Vannesjo SJ, Wilm BJ, Duerst Y, et al. Retrospective correction of physiological field fluctuations in high-field brain MRI using concurrent field monitoring. Magn Reson Med 2015;73:1833-1843
  10. Meineke J, Nielsen T. Data-driven correction of $B_0$-off-resonance fluctuations in gradient-echo MRI. In Proceedings of the 26th Annual Meeting of ISMRM. Paris, France 2018:1172
  11. Marques JP, Bowtell R. Application of a Fourier-based method for rapid calculation of field inhomogeneity due to spatial variation of magnetic susceptibility. Concepts Magn Reson Part B 2005;25B:65-78
  12. Lee SK, Hwang SH, Barg JS, Yeo SJ. Rapid, theoretically artifact-free calculation of static magnetic field induced by voxelated susceptibility distribution in an arbitrary volume of interest. Magn Reson Med 2018;80:2109-2121
  13. Segars WP, Mahesh M, Beck TJ, Frey EC, Tsui BM. Realistic CT simulation using the 4D XCAT phantom. Med Phys 2008;35:3800-3808
  14. Silva-Rodriguez J, Tsoumpas C, Dominguez-Prado I, Pardo-Montero J, Ruibal A, Aguiar P. Impact and correction of the bladder uptake on 18 F-FCH PET quantification: a simulation study using the XCAT2 phantom. Phys Med Biol 2016;61:758-773
  15. Koybasi O, Mishra P, St James S, Lewis JH, Seco J. Simulation of dosimetric consequences of 4D-CT-based motion margin estimation for proton radiotherapy using patient tumor motion data. Phys Med Biol 2014;59:853-867
  16. Lowther N, Ipsen S, Marsh S, Blanck O, Keall P. Investigation of the XCAT phantom as a validation tool in cardiac MRI tracking algorithms. Phys Med 2018;45:44-51
  17. Paganelli C, Summers P, Gianoli C, Bellomi M, Baroni G, Riboldi M. A tool for validating MRI-guided strategies: a digital breathing CT/MRI phantom of the abdominal site. Med Biol Eng Comput 2017;55:2001-2014
  18. Dewal RP, Yang QX. Volume of interest-based fourier transform method for calculation of static magnetic field maps from susceptibility distributions. Magn Reson Med 2016;75:2473-2480
  19. Raj D, Paley DP, Anderson AW, Kennan RP, Gore JC. A model for susceptibility artefacts from respiration in functional echo-planar magnetic resonance imaging. Phys Med Biol 2000;45:3809-3820
  20. Lee SK, Barg JS, Yeo SJ. Respiration-induced dynamic $B_0$ shifts in the head: numerical simulation based on generalized susceptibility voxel convolution (gSVC). The 6th International Congress on Magnetic Resonance Imaging (ICMRI). Seoul, Korea 2018