Motion compensated multi-contrast MRI using deep factor model

Recent quantitative parameter mapping methods including MR fingerprinting collect a time series of images that capture the evolution of magnetization. The focus of this work is to introduce a novel approach termed as deep factor model, which offers an efficient representation of the multi-contrast image time series. The higher efficiency of the representation enables the acquisition of the images in a highly undersampled fashion, which translates to reduced scan time in 3D high-resolution multi-contrast applications. The approach integrates motion estimation and compensation, making the approach robust to subject motion during the scan.