Rationale and objectives: The aim of the study is to build cardiac wall motion models to characterize mechanical dyssynchrony and predict pacing sites for the left ventricle of the heart in cardiac resynchronization therapy (CRT).
Materials and methods: Cardiac magnetic resonance imaging data from 20 patients are used, in which half have heart failure problems. We propose two spatio-temporal ventricular motion models to analyze the mechanical dyssynchrony of heart: radial motion series and wall motion series (a time series of radial length or wall thickness change). The hierarchical agglomerative clustering technique is applied to the motion series to find candidate pacing sites. All experiments are performed separately on each ventricular motion model to facilitate performance comparison among models.
Results: The experimental results demonstrate that the proposed methods perform as well as we expect. Our techniques not only effectively generate the candidate pacing sites list that can help guide CRT, but also derive clustering results that can distinguish the heart conditions between patients and normals perfectly to help medical diagnosis and prognosis. After comparing the results between two different ventricular motion models, the wall motion series model shows a better performance.
Conclusion: In a traditional CRT device deployment, pacing sites are selected without efficient prediction, which runs the risk of suboptimal benefits. Our techniques can extract useful wall motion information from ventricular mechanical dyssynchrony and identify the candidate pacing sites with maximum contraction delay to assist pacemaker implantation in CRT.