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使用近红外光谱法进行心脏心内膜左心房基质和病变深度测绘。

Cardiac endocardial left atrial substrate and lesion depth mapping using near-infrared spectroscopy.

作者信息

Park Soo Young, Yang Haiqiu, Marboe Charles, Ziv Ohad, Laurita Kenneth, Rollins Andrew, Saluja Deepak, Hendon Christine P

机构信息

Department of Electrical Engineering, Columbia University, New York, USA.

Department of Cell Biology and Pathology, Columbia University Irving Medical Center, New York, USA.

出版信息

Biomed Opt Express. 2022 Mar 2;13(4):1801-1819. doi: 10.1364/BOE.451547. eCollection 2022 Apr 1.

Abstract

Atrial fibrillation (AF) is a rapid irregular electrical activity in the upper chamber and the most common sustained cardiac arrhythmia. Many patients require radiofrequency ablation (RFA) therapy to restore sinus rhythm. Pulmonary vein isolation requires distinguishing normal atrial wall from the pulmonary vein tissue, and atrial substrate ablation requires differentiating scar tissue, fibrosis, and adipose tissue. However, current anatomical mapping methods for strategically locating ablation sites by identifying structural substrates in real-time are limited. An intraoperative tool that accurately provides detailed structural information and classifies endocardial substrates could help improve RF guidance during RF ablation therapy. In this work, we propose a 7F NIRS integrated ablation catheter and demonstrate endocardial mapping on swine (n = 12) and human (n = 5) left atrium (LA). First, pulmonary vein (PV) sleeve, fibrosis and ablation lesions were identified with NIRS-derived contrast indices. Based on these key spectral features, classification algorithms identified endocardial substrates with high accuracy (<11% error). Then, a predictive model for lesion depth was evaluated on classified lesions. Model predictions correlated well with histological measurements of lesion dimensions (R = 0.984). Classified endocardial substrates and lesion depth were represented in 2D spatial maps. These results suggest NIRS integrated mapping catheters can serve as a complementary tool to the current electroanatomical mapping system to improve treatment efficacy.

摘要

心房颤动(AF)是上腔室快速且不规则的电活动,是最常见的持续性心律失常。许多患者需要射频消融(RFA)治疗来恢复窦性心律。肺静脉隔离需要区分正常心房壁与肺静脉组织,心房基质消融需要区分瘢痕组织、纤维化和脂肪组织。然而,目前通过实时识别结构基质来战略性定位消融部位的解剖标测方法有限。一种能准确提供详细结构信息并对心内膜基质进行分类的术中工具,有助于改善射频消融治疗期间的射频引导。在这项工作中,我们提出了一种集成近红外光谱(NIRS)的7F消融导管,并在猪(n = 12)和人(n = 5)的左心房(LA)上进行了心内膜标测。首先,利用NIRS衍生的对比指数识别肺静脉(PV)袖套、纤维化和消融灶。基于这些关键光谱特征,分类算法能高精度(误差<11%)识别心内膜基质。然后,在分类后的病灶上评估了病灶深度的预测模型。模型预测与病灶尺寸的组织学测量结果相关性良好(R = 0.984)。分类后的心内膜基质和病灶深度呈现在二维空间图中。这些结果表明,集成NIRS的标测导管可作为当前电解剖标测系统的补充工具,以提高治疗效果。

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