Department of Robotics Engineering, Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
Electrophysiology Clinical Research and Innovations, The Texas Heart Institute, 6770 Bertner Ave, Houston, TX, 77030, USA.
Sci Rep. 2024 Aug 21;14(1):19370. doi: 10.1038/s41598-024-68046-x.
Atrial fibrillation (A-fib) is the most common type of heart arrhythmia, typically treated with radiofrequency catheter ablation to isolate the heart from abnormal electrical signals. Monitoring the formation of ablation-induced lesions is crucial for preventing recurrences and complications arising from excessive or insufficient ablation. Existing imaging modalities lack real-time feedback, and their intraoperative usage is in its early stages. A critical need exists for an imaging-based lesion indexing (LSI) method that directly reflects tissue necrosis formation. Previous studies have indicated that spectroscopic photoacoustic (sPA) imaging can differentiate ablated tissues from their non-ablated counterparts based on PA spectrum variation. In this paper, we introduce a method for detecting ablation lesion boundaries using sPA imaging. This approach utilizes ablation LSI, which quantifies the ratio between the signal from ablated tissue and the total tissue signal. We enhance boundary detection accuracy by adapting a regression model-based compensation. Additionally, the method was cross-validated with clinically used intraoperative monitoring parameters. The proposed method was validated with ex vivo porcine cardiac tissues with necrotic lesions created by different ablation durations. The PA-measured lesion size was compared with gross pathology. Statistical analysis demonstrates a strong correlation (R > 0.90) between the PA-detected lesion size and gross pathology. The PA-detected lesion size also exhibits a moderate to strong correlation (R > 0.75) with local impedance changes recorded during procedures. These results suggest that the introduced PA imaging-based LSI has great potential to be incorporated into the clinical workflow, guiding ablation procedures intraoperatively.
心房颤动(房颤)是最常见的心律失常类型,通常采用射频导管消融术来隔离心脏以避免异常电信号。监测消融诱导损伤的形成对于预防复发和因消融过度或不足引起的并发症至关重要。现有的成像方式缺乏实时反馈,其术中应用尚处于早期阶段。因此,迫切需要一种基于成像的消融损伤指数(LSI)方法,该方法能够直接反映组织坏死的形成。先前的研究表明,光谱光声(sPA)成像可以根据 PA 光谱变化来区分消融组织与其非消融组织。在本文中,我们提出了一种使用 sPA 成像检测消融损伤边界的方法。该方法利用消融 LSI,该指数量化了消融组织与总组织信号之间的信号比。我们通过采用基于回归模型的补偿来提高边界检测的准确性。此外,该方法还与临床中使用的术中监测参数进行了交叉验证。该方法在不同消融时间产生的坏死病变的离体猪心组织上进行了验证。用 PA 测量的病变大小与大体病理进行了比较。统计分析表明,PA 检测到的病变大小与大体病理之间具有很强的相关性(R > 0.90)。PA 检测到的病变大小与术中记录的局部阻抗变化也具有中度至强相关性(R > 0.75)。这些结果表明,所提出的基于 PA 成像的 LSI 具有很大的潜力被整合到临床工作流程中,以指导术中消融程序。