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Abstract #2035

Practical Correction of Gradient Nonlinearities in Diffusion-Weighted Imaging

Praitayini Kanakaraj1, Leon Y Cai2, Francois Rheault3, Baxter P Rogers4, Adam Anderson2,4, Kurt G Schilling4, and Bennett A Landman1,2,4,5
1Department of Computer Science, Vanderbilt University, Nashville, TN, United States, 2Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, 3Department of Computer Science, Université de Sherbrooke, Sherbrooke, QC, Canada, 4Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, 5Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States

Synopsis

Keywords: Data Processing, Diffusion/other diffusion imaging techniquesGradient nonlinearity correction is well-established but not straightforward to implement in existing diffusion software packages due to it producing gradient tables that vary by voxel. We propose a simple, practical approach that approximates full correction by: (1) scaling the diffusion signal and (2) resampling the gradient orientations. Our approach results in uniform gradients across the corrected image and provides the key advantage of seamless integration into current diffusion pipelines. The proposed method resulted in negligible differences in multi- compartment indices from the standard voxel-wise empirical correction.

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