Investig Magn Reson Imaging.  2019 Mar;23(1):38-45. 10.13104/imri.2019.23.1.38.

High-Resolution Numerical Simulation of Respiration-Induced Dynamic Bâ‚€ Shift in the Head in High-Field MRI

Affiliations
  • 1Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea. seungkyun@skku.edu
  • 2Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea.

Abstract

PURPOSE
To demonstrate the high-resolution numerical simulation of the respiration-induced dynamic B0 shift in the head using generalized susceptibility voxel convolution (gSVC).
MATERIALS AND METHODS
Previous dynamic B0 simulation research has been limited to low-resolution numerical models due to the large computational demands of conventional Fourier-based B0 calculation methods. Here, we show that a recently-proposed gSVC method can simulate dynamic B0 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 B0 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 B0 shift in the brain of about 7 Hz at 7T. The magnitudes of the respiration-induced B0 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 B0 variation in the head. The method can be used to predict B0 shifts with high spatiotemporal resolution under different breathing conditions and aid in the design of dynamic B0 compensation strategies.

Keyword

Dynamic Bâ‚€ shift; Dynamic shim; Respiration; Susceptibility; Brain; 7T

MeSH Terms

Brain
Compensation and Redress
Head*
Human Body
Magnetic Resonance Imaging*
Methods
Respiration
Torso

Figure

  • Fig. 1. Sagittal cross sections of the brain mask (dotted line) and the susceptibility model at voxel sizes of 0.1 cm (a) and 0.7 cm (b). Tick mark labels indicate voxels. For each voxel size, frames 1 (left) and 5 (right) are displayed out of the 10 frames in the breathing cycle simulated by the 4D XCAT model. Color bar at the bottom indicates susceptibility in ppm (0 = air and lung, −8.5 = blood, −9 = other tissue, −11 = bone).

  • Fig. 2. Midsagittal view of the respiration-induced B0 shift in the brain at 7T at 10 time points in a breathing cycle. Time order is from left-to-right. The B0 calculation algorithm gSVC was used. (a) and (b) correspond to voxel sizes of 0.1 cm and 0.7 cm, respectively. Note significant differences between B0 shifts computed at 0.7 cm vs. 0.1 cm resolution.

  • Fig. 3. Time dependence of B0 shift statistics in the brain during breathing computed at 0.1 cm voxel size. Maximum B0 shift of 7.2 Hz is observed at 2 s (5th frame).

  • Fig. 4. Mean B0 shift gradients in the brain at the 5th frame (fully-inhaled), computed at 0.1 cm voxel resolution, along the x (left-to-right, a), y (posterior-to-anterior, b), z (foot-to-head, c) directions. The blue traces correspond to B0 values averaged over slices normal to each direction. The straight lines indicate linear fitting to the data.

  • Fig. 5. Plot of B0 shift gradients as a function of time at 0.1 cm voxel resolution. The gradient values were obtained by volumetric linear fitting at each time point. The maximum gradient amplitudes in the y, z directions are observed at 2 s (5th frame). Gradient in the x-direction shows little change.

  • Fig. 6. Plot of B0 shift gradients at frame 5 relative to frame 1 as a function of the voxel size. The gradient values become increasingly more erratic as the voxel size increases, indicating reduced reliability at large voxel sizes (larger than about 0.5 cm).


Reference

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