Artificial Neural Network for Suppression of Banding Artifacts in Balanced Steady-State Free Precession MRI

Abstract

This study is the first attempt for a learning-based algorithm to be applied to banding artifact suppression in balanced steady-state free precession (bSSFP). We trained multilayer perceptron (MLP) models with two or four phase‑cycling datasets and banding-free datasets as inputs and outputs, respectively. We demonstrated that MLP was superior to existing methods in terms of banding artifact suppression and SNR efficiency, which was clearer in two phase‑cycling datasets. Furthermore, MLP was widely applicable to various image sets, irrespective of scan parameters, body organs, and field strengths. The learning-based approach is promising for banding artifact suppression of bSSFP.