WE-G-217A-07: Increased MR Spatial Accuracy with Improved Gradient Nonlinearity and Magnet Inhomogeneity Correction.

PURPOSE

To develop improved distortion correction of MR images based on higher degree spherical harmonic models of the gradient system and the main magnetic field.

METHODS

The induced field gradient along all three axes can be modeled by first order spherical harmonics. These models provide a true encoding of the physical location of a spin to the frequency at which it is detected. Currently on many commercial systems, only the lower 5 degrees of the model are used for gradient nonlinearity correction. While this provides sufficient accuracy for diagnostic imaging, the gradient nonlinearity correction was extended to include all first order harmonics up to the 9th degree to improve the spatial accuracy of the images. Using zeroth degree spherical harmonics up to the 20th order, a model of the main magnetic field was also incorporated into the correction algorithm. Shifts caused by field inhomogeneity were calculated using knowledge of the receiver bandwidth, frequency encode direction, and the magnetic field at any given point. These corrections were applied to images of a 50 cm diameter phantom, acquired with an extended FOV 3D FGRE sequence. Any improvements in spatial accuracy were measured in the resulting images.

RESULTS

Visual improvements in spatial accuracy were observed with both corrections. With standard gradient nonlinearity correction, edges of the phantom were distorted in a wave-like fashion. With accurate models, almost all of the errors at the edges of the phantom were corrected when both gradient and field homogeneity corrections were applied.

CONCLUSION

With accurate models of the gradient and magnetic field, the two greatest system-induced spatial errors can be corrected. This improved spatial accuracy enables the use of widebore MR scanners for therapy planning on large FOV images and guidance of percutaneous devices. Further applications include extended FOV imaging for combined PET-MR systems. All authors are employed by General Electric Healthcare.