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

人工智能 3D 双心室疤痕建模指导精确 VT 消融

• 批准号: MR/V037595/1
• 负责人: Shahnaz Jamil-Copley
• 金额: $ 32.49万
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV037595%2F1
• 关键词: Artificial; Intelligence; 3D; Biventricular; Scar

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

心源性猝死（SCD）是由心律变化引起的突然、意外死亡，占所有心源性死亡的一半。75%的SCD与先前的心脏病发作有关，当向心脏供血的冠状动脉阻塞时发生心脏病发作，导致心室（心脏的两个底部泵室）中的瘢痕组织。这种疤痕通常是异质性的，具有死细胞和活细胞的复杂混合物，导致异常和危险的心律（室性心律失常-VA）。VA可分为室性心动过速（VT）和心室颤动（VF）。为VT/VF高危患者提供了一种称为植入式心律转复除颤器（ICD）的特殊设备，该设备通过静脉插入心脏。ICD已被证明具有预后益处，因为它们提供治疗，例如，如果患者发生VA，则通过小但强大的电击将心律恢复正常。虽然ICD可以挽救生命，但它们不能防止VA的发生，这意味着该设备的复发性，甚至是单一的疼痛性电击可能会给患者带来重大的心理和社会压力。需要同时使用药物来减少或阻止VA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的消融可以通过燃烧嵌入导致VA的瘢痕组织中的异常活细胞来进行，经由放置在患者心脏内的导管到达心肌内部（内膜）或穿过胸壁到达心脏外部（心外膜）。心室的几何形状可以使用临床电生理学（EP）实验室中的3D标测系统来创建，来自患者心脏的数据可以使用诸如组织的电压的参数显示在该3D标测系统上，从而允许显示疤痕的位置和疤痕中负责VT/VF的活细胞的潜在区域。这有助于将操作者引导到需要消融的区域。然而，瘢痕是一种复杂的3D结构，并且不出意料的是，室性心动过速消融术通常具有挑战性且时间长（有时长达8小时），2年时复发率高达40-50%。为了改善室性心动过速消融术的手术和患者结局，全球都在努力改善疤痕的可视化，我们对疤痕结构复杂性及其如何导致室性心动过速的理解，并使用这些信息指导室性心动过速消融术。心脏MRI扫描（CMR）能够识别心室瘢痕，并已成为瘢痕评估的基石，在犬研究和临床人体研究中，瘢痕与组织学检查具有良好的相关性。提高CMR协议的分辨率和在植入ICD的患者中进行CMR仍然是两个重大挑战。我们在诺丁汉大学医院已经克服了这两个问题，现在正在ICD患者中进行CMR研究，疤痕解决效果非常好。利用这种能力与先进的人工智能知识和技术，我们相信有可能提高我们对VA的机械理解，以解释为什么一些但不是所有的瘢痕患者都会经历VA。显示关键疤痕特征的AI 3D疤痕模型的开发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人工智能（AI）3D计算双心室瘢痕模型，使用晚期钆增强（LGE）心脏磁共振成像（cMR）描述左心室瘢痕。该目标将通过以下目标来实现：1。模型开发2.可行性测试3.模型的临床验证

项目成果

159 Comparing image quality and reporting times for scar identification between 2d and 3d sequences in cardiac magnetic resonance imaging

159 比较心脏磁共振成像中 2d 和 3d 序列之间疤痕识别的图像质量和报告时间

• DOI: 10.1136/heartjnl-2022-bcs.159
• 发表时间: 2022
• 作者: Jathanna N

15 Scar Radiomic Feature Associations with Clinical Endpoints in Ischaemic Heart Disease

15 疤痕放射学特征与缺血性心脏病临床终点的关联

• DOI: 10.1136/heartjnl-2022-bscmr.15
• 发表时间: 2023
• 作者: Jathanna N

Translating 3D Whole Heart LGE to Clinical Practice; Early Feasibility Results in a Tertiary UK Centre

将 3D 全心 LGE 转化为临床实践；

• DOI: 10.58530/2022/1118
• 作者: Jathanna N

Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging-A systematic review.

• DOI: 10.1016/j.cvdhj.2021.11.005
• 发表时间: 2021-12
• 期刊: Cardiovascular digital health journal
• 作者: Jathanna N;Podlasek A;Sokol A;Auer D;Chen X;Jamil-Copley S
• 通讯作者: Jamil-Copley S

The Nottingham Ischaemic Cardiovascular Magnetic Resonance resource (NotIs CMR): a prospective paired clinical and imaging scar database-protocol.

• DOI: 10.1186/s12968-023-00978-1
• 发表时间: 2023-11-27
• 期刊: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance