光学相干层析成像
烧蚀
医学
射频消融术
心内注射
心房颤动
体内
心脏消融
心脏成像
病变
导管消融
放射科
生物医学工程
内科学
病理
生物技术
生物
作者
Zhensheng Huang,Xiaowei Zhao,Ohad Ziv,Kenneth R. Laurita,Andrew M. Rollins,Christine P. Hendon
摘要
Radiofrequency ablation (RFA) is a minimally invasive procedure that is commonly used for the treatment of atrial fibrillation. However, it is associated with a significant risk of arrhythmia recurrence and complications owing to the lack of direct visualization of cardiac substrates and real-time feedback on ablation lesion transmurality. Within this manuscript, we present an automated deep learning framework for in vivo intracardiac optical coherence tomography (OCT) analysis of swine left atria. Our model can accurately identify cardiac substrates, monitor catheter-tissue contact stability, and assess lesion transmurality on both OCT intensity and polarization-sensitive OCT data. To the best of our knowledge, we have developed the first automatic framework for in vivo cardiac OCT analysis, which holds promise for real-time monitoring and guidance of cardiac RFA therapy..
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