Park Soo Young, Singh-Moon Rajinder P, Wan Elaine Y, Hendon Christine P
Department of Electrical Engineering, Columbia University, 500 W 120th Street, New York, NY, 10027, USA.
Department of Medicine, Division of Cardiology, Columbia University Medical Center, 630 W 168th Street, New York, NY, 10032, USA.
Biomed Opt Express. 2019 May 16;10(6):2829-2846. doi: 10.1364/BOE.10.002829. eCollection 2019 Jun 1.
Atrial fibrillation (Afib) can lead to life threatening conditions such as heart failure and stroke. During Afib treatment, clinicians aim to repress unusual electrical activity by electrically isolating the pulmonary veins (PV) from the left atrium (LA) using radiofrequency ablation. However, current clinical tools are limited in reliably assessing transmurality of the ablation lesions and detecting the presence of gaps within ablation lines, which can warrant repeat procedures. In this study, we developed an endoscopic multispectral reflectance imaging (eMSI) system for enhanced discrimination of tissue treatment at the PV junction. The system enables direct visualization of cardiac lesions through an endoscope at acquisition rates up to 25 Hz. Five narrowband, high-power LEDs were used to illuminate the sample (450, 530, 625, 810 and 940nm) and combinatory parameters were calculated based on their relative reflectance. A stitching algorithm was employed to generate large field-of-view, multispectral mosaics of the ablated PV junction from individual eMSI images. A total of 79 lesions from 15 swine hearts were imaged, . Statistical analysis of the acquired five spectral data sets and ratiometric maps revealed significant differences between transmural lesions, non-transmural lesions around the venoatrial junctions, unablated posterior wall of left atrium tissue, and pulmonary vein (p < 0.0001). A pixel-based quadratic discriminant analysis classifier was applied to distinguish four tissue types: PV, untreated LA, non-transmural and transmural lesions. We demonstrate tissue type classification accuracies of 80.2% and 92.1% for non-transmural and transmural lesions, and 95.0% and 92.8% for PV and untreated LA sites, respectively. These findings showcase the potential of eMSI for lesion validation and may help to improve AFib treatment efficacy.
心房颤动(房颤)可导致心力衰竭和中风等危及生命的状况。在房颤治疗期间,临床医生旨在通过使用射频消融术将肺静脉(PV)与左心房(LA)进行电隔离,从而抑制异常电活动。然而,当前的临床工具在可靠评估消融灶的透壁性以及检测消融线内间隙的存在方面存在局限性,这可能需要重复手术。在本研究中,我们开发了一种内镜多光谱反射成像(eMSI)系统,用于增强对PV交界处组织治疗的辨别能力。该系统能够通过内窥镜以高达25Hz的采集速率直接观察心脏病变。使用五个窄带、高功率发光二极管对样本进行照明(450、530、625、810和940nm),并根据它们的相对反射率计算组合参数。采用拼接算法从单个eMSI图像生成消融PV交界处的大视野多光谱镶嵌图。对15头猪心脏的79个病变进行了成像。对获取的五个光谱数据集和比率图进行统计分析,结果显示透壁病变、静脉心房交界处周围的非透壁病变、未消融的左心房后壁组织以及肺静脉之间存在显著差异(p<0.0001)。应用基于像素的二次判别分析分类器来区分四种组织类型:PV、未处理的LA、非透壁和透壁病变。我们分别展示了非透壁和透壁病变的组织类型分类准确率为80.2%和92.1%,PV和未处理的LA部位的分类准确率分别为95.0%和92.8%。这些发现展示了eMSI在病变验证方面的潜力,并可能有助于提高房颤治疗效果。