Understanding aliasing effects and their removal in SPEN MRI: A k-space perspective
Sijie Zhong, Minjia Chen, Xiaokang Wei, Ke Dai, Hao Chen, Lucio Frydman, Zhiyong Zhang
Abstract
Purpose
To characterize the mechanism of formation and the removal of aliasing artifacts and edge ghosts in spatiotemporally encoded (SPEN) MRI within a k-space theoretical framework.
Methods
SPEN’s quadratic phase modulation can be described in k-space by a convolution matrix whose coefficients derive from Fourier relations. This k-space model allows us to pose SPEN’s reconstruction as a deconvolution process from which aliasing and edge ghost artifacts can be quantified by estimating the difference between a full sampling and reconstructions resulting from undersampled SPEN data.
Results
Aliasing artifacts in SPEN MRI reconstructions can be traced to image contributions corresponding to high-frequency k-space signals. The k-space picture provides the spatial displacements, phase offsets, and linear amplitude modulations associated to these artifacts, as well as routes to removing these from the reconstruction results. These new ways to estimate the artifact priors were applied to reduce SPEN reconstruction artifacts on simulated, phantom, and human brain MRI data.
Conclusion
A k-space description of SPEN’s reconstruction helps to better understand the signal characteristics of this MRI technique, and to improve the quality of its resulting images.