Deep-learning-based Group-wise Motion Correction for Myocardial T1 Mapping

Diffuse myocardial diseases can be diagnosed using T1 mapping technique. The T1 relaxation parameter is computed through the pixel-wise model fitting. Hence, pixel misalignment resulted by cardiac motion leads to an inaccurate T1 mapping. Therefore, registration is needed. However, standard registration methods are computationally expensive. To overcome this challenge, we propose a new deep-learning-based group-wise registration approach that register all the different time points simultaneously. Our approach achieved the best median model-fitting R2 compared to baseline methods (0.9846, vs. 0.9651/0.9744/0.9756), and achieve reasonably close T1 value to the expected myocardial T1 value