Motion correction for native myocardial T1 mapping using self-supervised deep learning registration with contrast separation

In myocardial T-1 mapping, undesirable motion poses significant challenges because uncorrected motion can affect T-1 estimation accuracy and cause incorrect diagnosis. In this study, we propose and evaluate a motion correction method for myocardial T-1 mapping using self-supervised deep learning based registration with contrast separation (SDRAP). A sparse coding based method was first proposed to separate the contrast component from T-1-weighted (T1w) images. Then, a self-supervised deep neural network with cross-correlation (SDRAP-CC) or mutual information as the registration similarity measurement was developed to register contrast separated images, after which signal fitting was performed on the motion corrected T1w images to generate motion corrected T-1 maps. The registration network was trained and tested in 80 healthy volunteers with images acquired using the modified Look-Locker inversion recovery (MOLLI) sequence. The proposed SDRAP was compared with the free form deformation (FFD) registration method regarding (1) Dice similarity coefficient (DSC) and mean boundary error (MBE) of myocardium contours, (2) T-1 value and standard deviation (SD) of T-1 fitting, (3) subjective evaluation score for overall image quality and motion artifact level, and (4) computation time. Results showed that SDRAP-CC achieved the highest DSC of 85.0 +/- 3.9% and the lowest MBE of 0.92 +/- 0.25 mm among the methods compared. Additionally, SDRAP-CC performed the best by resulting in lower SD value (28.1 +/- 17.6 ms) and higher subjective image quality scores (3.30 +/- 0.79 for overall quality and 3.53 +/- 0.68 for motion artifact) evaluated by a cardiologist. The proposed SDRAP took only 0.52 s to register one slice of MOLLI images, achieving about sevenfold acceleration over FFD (3.7 s/slice).