StyleReg - Style Transfer as a Preprocess Step for Myocardial T1 Mapping

Diffuse myocardial diseases can be diagnosed using T1 mapping technique based on T1 relaxation times from MRI data. The T1 relaxation parameter is acquired through pixel-wise fitting of the MRI signal. Hence, pixels misalignment resulted by cardiac motion leads to an inaccurate T1-mapping. Therefore, registration is needed. However, due to the intensity differences between the different time-points, recent unsupervised deep-learning approaches based on minimizing the mean-squared-error (MSE) between the images cannot be utilized directly. To overcome this challenge, we propose a new double-stage method, in which a style-transfer is used to harmonize the signal intensities over time, followed by an unsupervised deep-learning based minimization of the MSE between the images. We evaluated our approach on a publicly available cardiac T1 mapping database of 210 subjects. Our approach achieved the best median model-fitting R^2 compared to baseline methods (0.9794, vs. 0.9651/0.9744/0.9756) and T1 values which are much closer to the the expected myocardial T1 value. Furthermore, both metrics have less variability compared to the other methods.