Image-based gradient non-linearity characterization to determine higher-order spherical harmonic coefficients for improved spatial position accuracy in magnetic resonance imaging.

PURPOSE Spatial position accuracy in magnetic resonance imaging (MRI) is an important concern for a variety of applications, including radiation therapy planning, surgical planning, and longitudinal studies of morphologic changes to study neurodegenerative diseases. Spatial accuracy is strongly influenced by gradient linearity. This work presents a method for characterizing the gradient non-linearity fields on a per-system basis, and using this information to provide improved and higher-order (9th vs. 5th) spherical harmonic coefficients for better spatial accuracy in MRI. METHODS A large fiducial phantom containing 5229 water-filled spheres in a grid pattern is scanned with the MR system, and the positions all the fiducials are measured and compared to the corresponding ground truth fiducial positions as reported from a computed tomography (CT) scan of the object. Systematic errors from off-resonance (i.e., B0) effects are minimized with the use of increased receiver bandwidth (±125kHz) and two acquisitions with reversed readout gradient polarity. The spherical harmonic coefficients are estimated using an iterative process, and can be subsequently used to correct for gradient non-linearity. Test-retest stability was assessed with five repeated measurements on a single scanner, and cross-scanner variation on four different, identically-configured 3T wide-bore systems. RESULTS A decrease in the root-mean-square error (RMSE) over a 50cm diameter spherical volume from 1.80mm to 0.77mm is reported here in the case of replacing the vendor's standard 5th order spherical harmonic coefficients with custom fitted 9th order coefficients, and from 1.5mm to 1mm by extending custom fitted 5th order correction to the 9th order. Minimum RMSE varied between scanners, but was stable with repeated measurements in the same scanner. CONCLUSIONS The results suggest that the proposed methods may be used on a per-system basis to more accurately calibrate MR gradient non-linearity coefficients when compared to vendor standard corrections.

[1]  J. Michael Fitzpatrick,et al.  A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities , 1992, IEEE Trans. Medical Imaging.

[2]  J. Challis.  A procedure for determining rigid body transformation parameters. , 1995, Journal of biomechanics.

[3]  Christopher J. Hardy,et al.  Improved correction for gradient nonlinearity effects in diffusion‐weighted imaging , 2013, Journal of magnetic resonance imaging : JMRI.

[4]  D. Jaffray,et al.  Harmonic analysis for the characterization and correction of geometric distortion in MRI. , 2014, Medical physics.

[5]  Yue Cao,et al.  Phantom-based characterization of distortion on a magnetic resonance imaging simulator for radiation oncology , 2016, Physics in medicine and biology.

[6]  Joshua Kim,et al.  Technical Note: Characterization and correction of gradient nonlinearity induced distortion on a 1.0 T open bore MR-SIM. , 2015, Medical physics.

[7]  K Hwang,et al.  WE-G-217A-06: Spatial Accuracy Quantification of an MR System. , 2012, Medical physics.

[8]  Mayer,et al.  Using the Hough transform for HOLZ line identification in convergent beam electron diffraction , 1999, Journal of microscopy.

[9]  Anders M. Dale,et al.  Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data , 2006, NeuroImage.

[10]  Yunhong Shu,et al.  Partial fourier and parallel MR image reconstruction with integrated gradient nonlinearity correction , 2016, Magnetic resonance in medicine.

[11]  M. Bernstein,et al.  MRI in radiation oncology: Underserved needs , 2016, Magnetic resonance in medicine.

[12]  Luke Xie,et al.  Magnetic Resonance Histology of Age-Related Nephropathy in the Sprague Dawley Rat , 2012, Toxicologic pathology.

[13]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[14]  Huawei Zhao,et al.  Use of spherical harmonic deconvolution methods to compensate for nonlinear gradient effects on MRI images , 2004, Magnetic resonance in medicine.

[15]  B. Erickson,et al.  Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning. , 2014, Medical physics.

[16]  R. Turner,et al.  Passive screening of switched magnetic field gradients , 1986 .

[17]  H. Quick,et al.  Field of view extension and truncation correction for MR-based human attenuation correction in simultaneous MR/PET imaging. , 2014, Medical physics.

[18]  W. J. Lorenz,et al.  Correction of spatial distortion in magnetic resonance angiography for radiosurgical treatment planning of cerebral arteriovenous malformations. , 1992, Magnetic Resonance Imaging.

[19]  Yunhong Shu,et al.  NonCartesian MR image reconstruction with integrated gradient nonlinearity correction. , 2015, Medical physics.

[20]  Norbert Schuff,et al.  Measurement of MRI scanner performance with the ADNI phantom. , 2009, Medical physics.

[21]  Leonard Wee,et al.  Feasibility of MRI-only treatment planning for proton therapy in brain and prostate cancers: Dose calculation accuracy in substitute CT images. , 2016, Medical physics.

[22]  Christian P Karger,et al.  Accuracy of device-specific 2D and 3D image distortion correction algorithms for magnetic resonance imaging of the head provided by a manufacturer , 2006, Physics in medicine and biology.

[23]  J Yang,et al.  Investigation of MR image distortion for radiotherapy treatment planning of prostate cancer , 2005, Physics in medicine and biology.

[24]  Matt A. Bernstein,et al.  Peripheral nerve stimulation characteristics of an asymmetric head‐only gradient coil compatible with a high‐channel‐count receiver array , 2016, Magnetic resonance in medicine.

[25]  J Balter,et al.  Patient-induced susceptibility effect on geometric distortion of clinical brain MRI for radiation treatment planning on a 3T scanner , 2013, Physics in medicine and biology.

[26]  Mary Feng,et al.  Quantitative characterizations of ultrashort echo (UTE) images for supporting air–bone separation in the head , 2015, Physics in medicine and biology.

[27]  Yunhong Shu,et al.  Integrated image reconstruction and gradient nonlinearity correction , 2015, Magnetic resonance in medicine.