Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Nora Eccles Cardiovascular Research and Training Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Biomedical Engineering, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States.
Cardiovascular Bioengineering Imaging (CBM) Lab at the Massachusetts General Hospital, Boston, MA, United States.
Comput Biol Med. 2022 Mar;142:105174. doi: 10.1016/j.compbiomed.2021.105174. Epub 2022 Jan 20.
Electrocardiographic imaging (ECGI) is a noninvasive technique to assess the bioelectric activity of the heart which has been applied to aid in clinical diagnosis and management of cardiac dysfunction. ECGI is built on mathematical models that take into account several patient specific factors including the position of the heart within the torso. Errors in the localization of the heart within the torso, as might arise due to natural changes in heart position from respiration or changes in body position, contribute to errors in ECGI reconstructions of the cardiac activity, thereby reducing the clinical utility of ECGI. In this study we present a novel method for the reconstruction of cardiac geometry utilizing noninvasively acquired body surface potential measurements. Our geometric correction method simultaneously estimates the cardiac position over a series of heartbeats by leveraging an iterative approach which alternates between estimating the cardiac bioelectric source across all heartbeats and then estimating cardiac positions for each heartbeat. We demonstrate that our geometric correction method is able to reduce geometric error and improve ECGI accuracy in a wide range of testing scenarios. We examine the performance of our geometric correction method using different activation sequences, ranges of cardiac motion, and body surface electrode configurations. We find that after geometric correction resulting ECGI solution accuracy is improved and variability of the ECGI solutions between heartbeats is substantially reduced.
心电图成像是一种非侵入性技术，用于评估心脏的生物电活动，已被应用于辅助临床诊断和心脏功能障碍的管理。心电图成像是基于数学模型的，这些模型考虑了几个患者特定的因素，包括心脏在躯干内的位置。由于呼吸或体位变化引起的心脏位置的自然变化而导致的心脏在躯干内的位置误差，会导致心脏活动的心电图成像重建误差，从而降低心电图成像的临床实用性。在这项研究中，我们提出了一种利用非侵入式体表电位测量来重建心脏几何结构的新方法。我们的几何校正方法通过利用一种迭代方法，在所有心跳之间交替估计心脏生物电源，然后为每个心跳估计心脏位置，从而在一系列心跳中同时估计心脏位置。我们证明，我们的几何校正方法能够减少几何误差并提高心电图成像在广泛的测试场景中的准确性。我们使用不同的激活序列、心脏运动范围和体表电极配置来检查我们的几何校正方法的性能。我们发现，经过几何校正后，心电图成像的解算精度得到了提高，并且心跳之间的心电图成像解算的可变性大大降低。
