Fast personalized electrophysiological models fromcomputed tomography images for ventricular tachycardia ablation planning
Fast personalized electrophysiological models fromcomputed tomography images for ventricular tachycardia ablation planning
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DOI：
10.1093/europace/euy228
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发表时间：
2018-11-01
期刊：
影响因子：
6.1
通讯作者：
Sermesant, Maxime
中科院分区：
文献类型：
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作者：
Cedilnik, Nicolas;Duchateau, Josselin;Sermesant, Maxime
患者特定心脏计算机模型的临床应用需要快速而强大的处理管道，这些管道可以无缝集成到临床工作流程中。我们的目标是建立这样一个管道，从计算机断层扫描（CT）图像的个性化心脏电生理（EP）模型。模拟输出可能是有用的背景下，梗死后室性心动过速（VT）射频消融（RFA）规划术前的目标prediction.Methods和结果-模型个性化的支持是从CT图像获得的患者特定的虚拟三维心脏。在这里，疤痕被识别为自动计算的厚度图上的心肌壁变薄。然后，我们使用Eikonal模型的波前传播速度降低在受损地区。基于图像的血管增强算法可以自动识别VT峡部。个性化模型用于虚拟起搏。我们获得了一个非常快的管道，可以在几分钟内进行模拟。从半自动图像分割阶段开始，它是完全自动化的。计算时间范围与虚拟起搏工具的构造兼容。在这个工具中，起始点和一个可选的方向块可以交互选择。定向块是对组织不应性建模的简单方法。将输出激动标测图与术前采集的EP数据进行比较。我们表明，这个框架允许再现记录的折返性VT激活patterns.Conclusion我们的模拟框架有一个应用程序在VT射频消融干预规划。它可以用来指导电生理探索，甚至术前预测消融目标。这可以缩短干预时间并提高成功率。
Aims Clinical application of patient-specific cardiac computer models requires fast and robust processing pipelines that can be seamlessly integrated into clinical workflows. We aim at building such a pipeline from computed tomography (CT) images to personalized cardiac electrophysiology (EP) model. The simulation output could be useful in the context of post-infarct ventricular tachycardia (VT) radiofrequency ablation (RFA) planning for pre-operative targets prediction.Methods and results The support for model personalization is a patient-specific virtual three-dimensional heart obtained from CT images. Here, the scar is identified as thinning of the myocardial wall on automatically computed thickness maps. We then use an Eikonal model of wave front propagation with reduced velocity in the damaged areas. An image-based vessel enhancement algorithm can automatically identify VT isthmuses. The personalized model is used for virtual pacing. We obtained a very fast pipeline that enables simulations in only a few minutes. It is fully automated starting from the semi-automated image segmentation phase. The computational time frame is compatible with the construction of a virtual pacing tool. In this tool, onset points and an optional directional block could be interactively selected. The directional block is a simple way to model tissue refractoriness. Output activation maps are compared with EP data acquired pre-operatively. We show that this framework allows the reproduction of recorded re-entrant VT activation patterns.Conclusion Our simulation framework has an application in VT RFA intervention planning. It could be used to guide EP explorations and even predict ablation targets pre-operatively. This could reduce intervention duration and improve success rate.