Recovering Cardiac Electrical Activity from Medical Image Sequence: A Model-Based Approach

Because of the intrinsic physiological coupling between the motion and the electrical activity of human heart and available higher resolution imaging sequences, we believe that image-derived cardiac kinematic measurement should be able to reflect patient-specific propagation of cardiac transmembrane potential (TMP). Therefore, in this paper we developed a model-based filter framework, which can recover cardiac electrical activity from MR image sequences. In this particular implementation, the cardiac electro-mechanical coupling process will be properly modelled over a meshfree particle representation of cardiac volume and its fiber structure, and then a model-based unscented Kalman filter (UKF) will be created to incorporate an electro-mechanical coupling model into the state space equation to estimate cardiac electrical activity from MR image sequences. At the end, we not only investigate the performance of our algorithm through two synthetic motion data sets, which are generated by healthy and diseased propagation patterns in an authentical cardiac geometry respectively, but also show the potential usage of our algorithm in clinical diagnosis through a test of one clinical MR image sequence.

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