Artificial Intelligence 3D Biventricular Scar Modelling to Guide Precision VT Ablation

心脏性猝死(SCD)是由心率改变引起的突然的、意外的死亡，占所有心脏死亡的一半。75%的SCD与先前的心脏病发作有关，心脏病发作时，供应心脏血液的冠状动脉受阻，导致脑室(心脏底部的两个泵房)形成疤痕组织。这种疤痕通常是异质性的，死亡和活细胞的复杂混合会导致异常和危险的心律失常(室性心律失常-VAS)。VAS可分为室性心动过速(VT)和室颤(VF)。VT/VF的高危患者会被提供一种特殊的设备，称为植入式心律转复除颤器(ICD)，它可以通过静脉插入心脏。ICD已被证明对预后有好处，因为它们提供了治疗，例如，如果患者经历VA，通过小而强大的电击将心率恢复到正常。虽然ICD是救命的，但它们不能阻止VAS的发生，这意味着设备反复或甚至是一次痛苦的电击可能会给患者带来巨大的心理和社会压力。需要同时使用药物来减少或阻止VAS的发生，但这些药物可能无效或患者耐受性差。在这种情况下，VT或VF的消融可以通过放置在患者心脏内的导管通过放置在患者心脏内的导管到达心脏肌肉(心内膜)或通过他们的胸壁到达心脏外部(心外膜)，通过燃烧嵌入到VAS的疤痕组织中的异常活细胞来完成。可以使用临床电生理(EP)实验室中的3D标测系统来创建脑室的几何形状，在其上可以使用诸如组织电压之类的参数来显示来自患者心脏的数据，从而允许显示疤痕的位置和负责VT/VF的疤痕中活细胞的潜在区域。这有助于将操作员引导到需要消融的区域。然而，SCAR是一个复杂的3D结构，不出所料，VT消融过程往往具有挑战性和长时间(有时长达8小时)，两年内复发率高达40%-50%。为了改善VT消融的手术和患者预后，全球都在努力改善SCAR的可视化，我们对SCAR结构的复杂性及其如何导致VT的理解，并利用这些信息来指导VT消融。心脏MRI扫描(CMR)能够识别脑室瘢痕，并已成为瘢痕评估的基石，在犬研究和临床人类研究中，瘢痕与组织学检查显示出良好的相关性。提高CMR方案的分辨率和对ICD患者进行CMR仍然是两个重大挑战。我们诺丁汉大学医院已经克服了这两个问题，现在正在对疤痕消退良好的ICD患者进行CMR研究。利用这一能力和先进的人工智能知识和技术，我们相信有可能提高我们对VAS的机械理解，以解释为什么一些但不是所有的SCAR患者会经历VA。开发一个显示关键疤痕特征的AI 3D疤痕模型也可以帮助指导精确的VT消融。目的：本研究的总体目标是开发一种全自动的人工智能(AI)3D计算双室瘢痕模型，该模型使用晚期Gd增强(LGE)心脏磁共振成像(CMR)来描述左心室瘢痕。这一目标将通过以下目标实现：1.模型开发2.可行性测试3.该模型的临床验证

Sudden cardiac death (SCD) is sudden, unexpected death caused by a change in heart rhythm and is responsible for half of all cardiac deaths. 75% of SCDs are related to a previous heart attack which occurs when a coronary artery supplying blood to the heart becomes blocked leading to scar tissue in the ventricles (the 2 bottom pumping chambers of the heart). This scar is often heterogeneous with a complex mix of dead and living cells causing abnormal and dangerous heart rhythms (ventricular arrhythmias-VAs). VAs can be divided into ventricular tachycardia (VT) & ventricular fibrillation (VF). Patients at high risk of VT/VF are offered a special device called an implantable cardioverter-defibrillator (ICD) which is inserted through the veins into the heart. ICDs have proven prognostic benefit as they deliver treatment eg a small, but powerful shock to revert the heart rhythm back to normal if a patient experiences a VA. Although ICDs are life-saving they not prevent VAs from occurring which means recurrent, or even a single, painful shock from the device can lead to significant psychological and social stress to the patient. Concurrent use of medications is required to reduce or stop VAs from occurring however they can be ineffective or poorly tolerated by patients. In this scenario ablation of VT or VF can be performed by burning the abnormal living cells embedded in scar tissue responsible for VAs via catheters either placed inside the patient's heart to reach the inside of the heart muscle (endocardium) or through their chest wall to reach the outside of the heart (epicardium). A geometry of the ventricle can be created using 3D mapping systems in the clinical electrophysiology (EP) lab onto which data from the patient's heart can be displayed using parameters such as the voltage of the tissue thus allowing the display of the location of the scar and potential areas of living cells in the scar responsible for the VT/VF. This helps guide the operator to the regions which require ablation. However scar is a complex 3D architecture and unsurprisingly VT ablation procedures are often challenging and long (sometimes up to 8 hours) with recurrence rates of up to 40-50% at 2 years. In order to improve procedural and patient outcomes from VT ablation there is a global effort to improve the visualisation of scar, our understanding of the complexity of scar architecture and how it causes VT and to use this information to guide VT ablation. Cardiac MRI scans (CMR) are able to identify ventricular scar and have become the cornerstone for scar assessment with excellent correlation of scar shown with histological examination in canine studies as well as in clinical human studies. Improving the resolution of CMR protocols and performing CMRs in patients with ICDs remain two significant challenges. We at Nottingham University Hospitals have overcome both of these and are now performing CMR studies in ICD patients with excellent scar resolution. Leveraging this capability with advancing knowledge and techniques in artificial intelligence we believe it is possible to improve our mechanistic understanding of VAs to explain why some, but not all patients with scar experience VA. Development of an AI 3D scar model which displays critical scar features can also help guide precision VT ablation. Aims:The overall aim of the study is to develop a fully automated artificial intelligence (AI) 3D computational biventricular scar model using Late Gadolinium Enhancement (LGE) cardiac magnetic resonance imaging (cMR) for descriptive representation of left ventricular scar. The aim will be addressed through the following objectives: 1. Model development 2. Feasibility testing3. Clinical Validation of the model