Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method

PURPOSE: Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and compare two fully automated motion tracking methods for the steady state free precession (SSFP) cine magnetic resonance imaging (MRI), and explore their use in real clinical scenario with different patient groups. METHODS: We proposed two automated cardiac motion tracking method: (a) a traditional registration‐based method, named full cardiac cycle registration, which simultaneously tracks all cine frames within a full cardiac cycle by joint registration of all frames; and (b) a modern convolutional neural network (CNN)‐based method, named Groupwise MotionNet, which enhances the temporal coherence by fusing motion along a continuous time scale. Both methods were evaluated on the healthy volunteer data from the MICCAI 2011 STACOM Challenge, as well as on patient data including hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI). RESULTS: The full cardiac cycle registration method achieved an average end‐point error (EPE) 2.89 ± 1.57 mm for cardiac motion tracking, with computation time of around 9 min per short‐axis cine MRI (size 128 × 128, 30 cardiac phases). In comparison, the Groupwise MotionNet achieved an average EPE of 0.94 ± 1.59 mm, taking < 1 s for a full cardiac phases. Further experiments showed that registration method had stable performance, independent of patient cohort and MRI machine, while the CNN‐based method relied on the training data to deliver consistently accurate results. CONCLUSION: Both registration‐based and CNN‐based method can track the cardiac motion from SSFP cine MRI in a fully automated manner, while taking temporal coherence into account. The registration method is generic, robust, but relatively slow; the CNN‐based method trained with heterogeneous data was able to achieve high tracking accuracy with real‐time performance.