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뇌과학이미징연구단
뇌과학 이미징 연구단
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Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system

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Title
Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system
Author(s)
S Tao; J D Trzasko; J L Gunter; P T Weavers; Y Shu; J Huston III; Seung-Kyun Lee; E T Tan; M A Bernstein
Publication Date
2017-01
Journal
PHYSICS IN MEDICINE AND BIOLOGY, v.62, no.2, pp.N18 - N31
Publisher
IOP PUBLISHING LTD
Abstract
Due to engineering limitations, the spatial encoding gradient fields in conventional magnetic resonance imaging cannot be perfectly linear and always contain higher-order, nonlinear components. If ignored during image reconstruction, gradient nonlinearity (GNL) manifests as image geometric distortion. Given an estimate of the GNL field, this distortion can be corrected to a degree proportional to the accuracy of the field estimate. The GNL of a gradient system is typically characterized using a spherical harmonic polynomial model with model coefficients obtained from electromagnetic simulation. Conventional whole-body gradient systems are symmetric in design; typically, only odd-order terms up to the 5th-order are required for GNL modeling. Recently, a high-performance, asymmetric gradient system was developed, which exhibits more complex GNL that requires higher-order terms including both odd-and even-orders for accurate modeling. This work characterizes the GNL of this system using an iterative calibration method and a fiducial phantom used in ADNI (Alzheimer's Disease Neuroimaging Initiative). The phantom was scanned at different locations inside the 26 cm diameter-spherical-volume of this gradient, and the positions of fiducials in the phantom were estimated. An iterative calibration procedure was utilized to identify the model coefficients that minimize the mean-squared-error between the true fiducial positions and the positions estimated from images corrected using these coefficients. To examine the effect of higher-order and even-order terms, this calibration was performed using spherical harmonic polynomial of different orders up to the 10th-order including even- and odd-order terms, or odd-order only. The results showed that the model coefficients of this gradient can be successfully estimated. The residual root-mean-squared-error after correction using up to the 10th-order coefficients was reduced to 0.36 mm, yielding spatial accuracy comparable to conventional whole-body gradients. The even-order terms were necessary for accurate GNL modeling. In addition, the calibrated coefficients improved image geometric accuracy compared with the simulation-based coefficients. © 2016 Institute of Physics and Engineering in Medicine Printed in the UK
URI
https://pr.ibs.re.kr/handle/8788114/3773
ISSN
0031-9155
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > Journal Papers (저널논문)
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