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TH‐CD‐207‐11: Gradient Nonlinearity Calibration and Correction for Head‐Only Asymmetric Gradient System
Author(s) -
Tao S,
Trzasko J,
Shu Y,
Weavers P,
Huston J,
Lee S,
Mathieu J,
Foo T,
Bernstein M
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4926269
Subject(s) - imaging phantom , fiducial marker , distortion (music) , nonlinear system , nonlinear distortion , sagittal plane , calibration , physics , computer science , optics , artificial intelligence , medicine , amplifier , optoelectronics , cmos , quantum mechanics , radiology
Purpose: Due to engineering limitations, the gradient fields in clinical MRI inevitably contain high‐order, nonlinear components. The presence of gradient nonlinearity (GNL) causes image geometrical distortion. Standard correction methods are based on parameterization of the simulated gradient fields with spherical harmonic polynomials. Conventional whole‐body gradient systems typically employ symmetric designs, and the GNL for such systems usually contains only odd‐order terms (up to 5th‐order). Recently, a high‐performance, head‐only gradient system was developed. Due to asymmetric design, this new system exhibits more complex GNL profiles. Here, we demonstrate measurement‐based high‐order(N>5) GNL‐correction on this new system using a fiducial phantom and iterative model‐fitting procedure. Methods: The Alzheimer's Disease Neuroimaging Initiative (ADNI) phantom was scanned with a 3D IR‐FSPGR sequence (sagittal acquisition plane, Nx=Ny=256, Nz=196, x= y=1.05mm, z=1.3mm, BW=125kHz) on the head‐only gradient system operating at 80mT/m, 500T/m/s. This phantom contains 160 fiducial spheres (diameter=1.0 or 1.5cm) that are distributed within a diameter=20cm spherical shell. The spatial positions of the fiducials were then measured from the distorted images and fit using a spherical harmonic polynomial model and iterative fitting procedure. The model order was increased from 1 to 10 step‐by‐step to test the effects of including high‐order terms (N>5). Then, the coefficients were used to correct distortion. The residual root‐mean‐square‐error (RMSE) was calculated from fiducial positions tracked from GNL‐corrected images. Results: The RMSE analysis shows that GNL‐distortion is reduced from 3.93 to 0.39mm by using correction terms up to 7th‐order including both even‐and odd‐order terms. The addition of higher orders (N>7) provides negligible benefit. Conclusion: The GNL of a high‐performance, head‐only gradient system was successfully measured. The use of up to 7th‐order correction terms was found to be sufficient for correcting GNL‐distortion in a typical brain scan. These techniques could also improve geometric accuracy for whole‐body MRI systems used for radiation therapy planning.