Hidden haemodynamics: A Physics-InfOrmed, real-time recoNstruction framEwork for haEmodynamic virtual pRototyping and clinical support (PIONEER)

隐藏的血液动力学：用于血液动力学虚拟原型和临床支持的物理信息实时重建框架 (PIONEER)

• 批准号: EP/W00481X/1
• 负责人: Stavroula Balabani
• 金额: $ 38.55万
• 依托单位: University College London
• 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: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW00481X%2F1
• 关键词: Hidden; haemodynamics; Physics; InfOrmed; real

Personalising care, i.e. tailoring therapeutic recommendations to people's individual health needs, has always been a clinicians' goal throughout the history of medicine. But never before has it been possible to design interventions and to predict how our bodies will respond to those. New possibilities are now emerging as we bring together novel approaches, such as state-of-the-art imaging and modelling and simulation. The NHS Long Term Plan identifies cardiovascular disease as a clinical priority and the single biggest condition where lives can be saved by the NHS over the next 10 years. There are currently over 43000 often life-saving vascular interventions p/year in England alone, predicted to increase due to an ageing population and rise in co-morbidities. Many of these interventions require surgery and/or permanent and personalised vascular implants. Vascular surgeons rely on superb skill and flair to perform some of the most complex (and life-critical) interventions; patients, on the other hand, rely on these interventions being safe or high-performing, for a lifetime. But how do we know that this will be the case? That these interventions are optimal? Getting the right intervention (often, surgical) to the right patient, at the right time, i.e. precision vascular surgery, has until now, been an unachievable goal. To realise this goal, we require transformative engineering technologies, fundamentally different from those used today. For the vascular surgery of the future to become a reality, we need pioneering work able to predict the future outcome of an individualised vascular intervention with an acceptable level of realism, fast enough to allow the exploration of multiple possibilities in short periods of time, and trustworthy enough such that they elicit trust and confidence from clinical practitioners. Blood flow (haemodynamics) plays a pivotal role in the initiation and progression of most vascular conditions and the clinical outcomes of interventions. However, hemodynamic information is not readily available in routine clinical practice -despite advances in medical imaging- where a variety of imaging modalities are used routinely. More crucially, imaging data can only give us information about the present, not the future; they cannot tell us what the outcome of any given -often personalised- intervention will be. Here is a case where engineering tools can make a real difference by providing blood flow information for vascular diseases, that cannot be measured in vivo and more importantly, by creating computer models of potential interventions, and their outcomes. By fusing computational blood flow models and imaging data we can make a real breakthrough in clinical pre-operative planning and personalise treatment.In PIONEER we plan to develop the most sophisticated, physics-driven computational tools that will extract, in real-time, accurate unsteady and three-dimensional hemodynamic information (velocity and pressure) from routinely used vascular imaging data. This information will be used for haemodynamic virtual prototyping of personalised cardiovascular interventions and tailoring of cardiovascular devices. The work will enable a fundamental step forward towards precision vascular surgery and will provide expert support for vascular surgeons in their decision-making process, leading to a dramatic improvement in the management of individual patients' risk. To catalyse this vision, we will work synergistically with three top hospitals in the country (Royal Free Hospital, Barts Hospital and GOSH), two patient groups (AVM Butterfly Charity and Aortic Awareness UK) and a leading medical device company, Terumo Aortic. Together, we will firstly create a proof of concept that will pave the way to introduce our ground-breaking technology in clinical and manufacturing workflows.

个性化护理，即根据人们的个人健康需求定制治疗建议，一直是临床医生在医学史上的目标。但以前从来没有可能设计干预措施，并预测我们的身体将如何应对这些措施。随着我们将最先进的成像、建模和模拟等新方法结合在一起，新的可能性正在出现。NHS长期计划将心血管疾病确定为临床优先事项，也是NHS在未来10年内可以挽救生命的最大疾病。目前，仅在英格兰每年就有超过43000例通常挽救生命的血管介入治疗，预计由于人口老龄化和合并症的增加而增加。这些干预措施中的许多需要手术和/或永久性和个性化的血管植入物。血管外科医生依靠高超的技能和天赋来执行一些最复杂（和生命关键）的干预措施;另一方面，患者一生都依赖于这些干预措施的安全性或高性能。但我们怎么知道情况会是这样呢？这些干预措施是最佳的吗？到目前为止，在正确的时间为正确的患者进行正确的干预（通常是手术），即精确的血管手术，一直是一个无法实现的目标。为了实现这一目标，我们需要变革性的工程技术，这些技术与今天使用的技术有着根本的不同。为了使未来的血管外科手术成为现实，我们需要开创性的工作，能够预测个性化血管介入的未来结果，具有可接受的现实水平，足够快，可以在短时间内探索多种可能性，并且足够值得信赖，以便获得临床医生的信任和信心。血流（血液动力学）在大多数血管疾病的发生和进展以及干预的临床结局中起着关键作用。然而，血流动力学信息在常规临床实践中并不容易获得-尽管在医学成像方面取得了进展-其中常规使用各种成像模式。更关键的是，成像数据只能给我们提供关于现在的信息，而不是未来的信息;它们不能告诉我们任何给定的（通常是个性化的）干预的结果。这里有一个例子，工程工具可以通过提供血管疾病的血流信息来产生真实的影响，这些信息不能在体内测量，更重要的是，通过创建潜在干预及其结果的计算机模型。通过融合计算血流模型和成像数据，我们可以在临床术前规划和个性化治疗方面取得真实的突破。在PIONEER中，我们计划开发最复杂的物理驱动计算工具，从常规使用的血管成像数据中实时提取准确的非稳态和三维血流动力学信息（速度和压力）。这些信息将用于个性化心血管介入的血液动力学虚拟原型设计和心血管器械的定制。这项工作将使精确血管手术向前迈出一大步，并将为血管外科医生的决策过程提供专家支持，从而大大改善对个体患者风险的管理。为了实现这一愿景，我们将与该国三家顶级医院（皇家免费医院、Barts医院和GOSH）、两个患者团体（AVM蝴蝶慈善机构和英国主动脉意识）以及一家领先的医疗器械公司Terumo Aortic协同合作。我们将首先创建概念验证，为在临床和制造工作流程中引入我们的突破性技术铺平道路。

项目成果

The influence of minor aortic branches in Type-B Aortic Dissection: patient-specific flow simulations informed by 4D-Flow MRI

主动脉小分支对 B 型主动脉夹层的影响：4D 流 MRI 提供的患者特异性血流模拟

• DOI: 10.21203/rs.3.rs-2210252/v1
• 发表时间: 2022
• 作者: Stokes C

Towards Reduced Order Models via Robust Proper Orthogonal Decomposition to capture personalised aortic haemodynamics

• DOI: 10.1101/2023.01.21.524933
• 发表时间: 2023-01
• 期刊: Journal of biomechanics
• 影响因子: 2.4
• 作者: Chotirawee Chatpattanasiri;G. Franzetti;M. Bonfanti;V. Díaz-Zuccarini;S. Balabani

Decomposition of power number in a stirred tank and real time reconstruction of 3D large-scale flow structures from sparse pressure measurements

搅拌罐中功率数的分解以及稀疏压力测量的 3D 大规模流动结构的实时重建

• DOI: 10.1016/j.ces.2023.118881
• 发表时间: 2023
• 期刊: Chemical Engineering Science
• 影响因子: 4.7
• 作者: Mikhaylov K

Experimental evaluation of the patient-specific haemodynamics of an aortic dissection model using particle image velocimetry.

• DOI: 10.1016/j.jbiomech.2022.110963
• 发表时间: 2022-03
• 期刊: JOURNAL OF BIOMECHANICS
• 影响因子: 2.4
• 作者: Franzetti, Gaia;Bonfanti, Mirko;Homer-Vanniasinkam, Shervanthi;Diaz-Zuccarini, Vanessa;Balabani, Stavroula
• 通讯作者: Balabani, Stavroula

Three-dimensional characterisation of macro-instabilities in a turbulent stirred tank flow and reconstruction from sparse measurements using machine learning methods

湍流搅拌罐流中宏观不稳定性的三维表征以及使用机器学习方法从稀疏测量中重建

• DOI: 10.1016/j.cherd.2023.06.044
• 发表时间: 2023
• 期刊: Chemical Engineering Research and Design
• 影响因子: 3.9
• 作者: Mikhaylov K