Prospective gradient nonlinearity correction for diffusion MRI: uncover lost sensitivity to non-Gaussian diffusion and tissue microstructure

Motivation: Time-dependent diffusion MRI, which is sensitive to non-Gaussian diffusion, reveals tissue microstructures and has been shown to improve cancer imaging and neuroimaging. However, gradient nonlinearity results in subject position-dependent bias for non-Gaussian diffusion characterization. Correction methods are needed. Goal(s): To reduce the effect of gradient nonlinearity on 2D time-dependent diffusion MRI. Approach: Slice-by-slice scaling of diffusion encoding gradients was applied to compensate for gradient nonlinearity. Results: Uncorrected $$$\frac{ADC(60Hz)}{ADC(0Hz)}$$$ of a non-Gaussian diffusion phantom showed errors in off-center slices, where the actual diffusion gradient amplitude was reduced compared to prescribed values. The errors were reduced by prospectively increasing the prescribed diffusion gradient amplitude. Impact: MR physicists, neuroimaging scientists, and radiologists, who are interested in microstructure imaging by probing time-dependent, non-Gaussian diffusion, will benefit from increased robustness to gradient nonlinearity and subject position, especially when using high-performance gradient systems that may have increased nonlinearity.