Intraprocedure Model-Guided Electrophysiology

心房颤动（AF）和室性心动过速（VT）影响着美国数百万患者。 这些心律失常可以用导管消融术治愈，但心律失常经常复发，这些复发性心律失常 通常是由于不完全消融造成的可逆传导阻滞。无法确认 在所需位置完全消融的病变是大于40%的复发的主要因素， 消融后VT和消融后AF复发率大于30%。此外，不可能与 目前的技术足以预测室速通过疤痕的途径，这是消融的目标。 该项目的总体目标是将联合收割机高分辨率磁共振成像（MRI）与 有限的侵入性标测，快速计算建模，以预测心律失常电路和目标， 消融术该目标包括使用该技术在手术期间更新消融靶点， 识别和消融任何剩余的致肿瘤基质。 我们假设，计算建模，优化高分辨率MRI，和有限的侵入性 标测，可以（1）帮助预测心律失常电路的位置（2）帮助预测关键的心律失常电路的位置。 消融目标，和（3）帮助评估消融的完整性。一旦得到验证， 这些能力可以帮助显著改善复杂消融的结果，成为消融的一部分， 这是未来的方法，并成为改善其他干预措施成果的平台。 我们已经开发出改进的高分辨率成像方法，可以准确区分 梗死瘢痕和边缘区与正常组织的距离。这种高分辨率成像还可以允许检测 传导通道可能存在于其他致密的疤痕中，并且可能是某些疤痕的关键部分。 VT电路。我们也在寻求有限的侵入性标测作为检测晚电位存在的一种手段 以帮助检测和/或验证可能难以识别的导电通道 目前的MRI方法。我们将进一步改进高分辨率成像，用于计算模型的输入， 沿着来自侵入性标测的传导通道的检测和/或确认，将预测 心律失常电路的位置，并允许快速和准确地确定最佳目标的消融。在 此外，由于模型可以在接近真实的时间内运行，并且由于我们可以执行术中MRI，因此我们将 还研究了使用计算模型预测何时需要完成额外的消融 所有致瘤基质消融。最后，我们开发了成像方法， 从完全消融（坏死）组织中消融不完全（可逆性损伤）组织。如果切除一些 如果在手术过程中发现病变不完整，则可以进行额外的消融以完成消融。 消融，并可能大大减少心律失常复发。这个项目是一个合作， 约翰霍普金斯大学（高分辨率MRI，侵入性标测）和西门子（计算建模）。

Atrial fibrillation (AF) and ventricular tachycardia (VT) affect millions of patients in the United States. These arrhythmias can be cured with catheter ablation, but the arrhythmias often recur, and these recurrences are generally due to reversible conduction block from incomplete ablation. The inability to confirm the presence of completely ablated lesions in the desired locations is the major factor in the greater than 40% recurrence of VT after ablation, and the greater than 30 % recurrence of AF after ablation. In addition, it is not possible with current technology to adequately predict the pathways of VT through scar, which are the targets for ablation. The overall goal of this project is to combine high resolution Magnetic Resonance Imaging (MRI) and limited invasive mapping, with fast computational modeling, to predict arrhythmia circuits and targets for ablation. This goal includes using this technology to update ablation targets during a procedure to allow for identification and ablation of any remaining arrhythmogenic substrate as ablation is proceeding. We hypothesize that computational modeling, optimized with high-resolution MRI, and limited invasive mapping, can (1) aid in predicting the locations of arrhythmia circuits (2) aid in predicting the locations of critical ablation targets, and (3) aid in assessing the completeness of ablation. Once validated, these enhanced capabilities could help to dramatically improve the outcomes from complex ablations, become part of ablation methods of the future, and become a platform for improving outcomes from other interventions. We have already developed improved high resolution imaging methods that allow accurate differentiation of infarct scar and border zone from normal tissue. This high resolution imaging may also allow for detection of conducting channels that may be present in otherwise dense scar, and which may be a critical part of some VT circuits. We are also pursuing limited invasive mapping as a means to detect the presence of late potentials in scar to aid in the detection and/or verification of conducting channels, which may be difficult to identify with current MRI methods. We will further improve high resolution imaging for input for a computational model that along with the detection and/or confirmation of conduction channels from invasive mapping, will predict arrhythmia circuit locations, and allow the fast and accurate determination of optimal targets for ablation. In addition, since the model can be run in near real time, and since we can perform intra-procedure MRI, we will also study the use of the computational model for predicting when additional ablation is needed to complete the ablation of all arrhythmogenic substrate. Finally, we have developed imaging methods that differentiate incompletely ablated (reversibly damaged) tissue from completely ablated (necrotic) tissue. If ablation of some lesions is found to be incomplete during a procedure, additional ablation can be performed to complete the ablation, and likely substantially reduce arrhythmia recurrences. This project is a collaboration between the Johns Hopkins University (High Resolution MRI, invasive mapping), and Siemens (computational modeling).