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，即为人们的个人健康需求量身定制治疗建议，一直是临床医生在整个医学史上的目标。但是，从来没有做出设计干预措施并预测我们的身体将如何应对这些干预措施。随着我们将新颖的方法汇总在一起，例如最先进的成像，建模和仿真，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，并且是最大的疾病，在未来10年内，NHS可以挽救生命。目前，仅英格兰就有超过43000种经常挽救生命的血管干预措施，预计由于人口老龄化和合并症的增长而预计会增加。这些干预措施中有许多需要手术和/或永久性和个性化的血管植入物。血管外科医生依靠出色的技能和才能来执行一些最复杂（和生命中的）干预措施。另一方面，患者依靠这些干预措施是安全的或表现高的一生。但是，我们怎么知道情况将是这种情况？这些干预措施是最佳的吗？在正确的时间（即精密血管手术）到现在为止，要对正确的患者进行正确的干预（通常是手术），这是一个无法实现的目标。为了实现这一目标，我们需要变革性工程技术，与当今使用的技术根本不同。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，能够以可接受的现实主义水平来预测个性化的血管干预的未来结果，足以允许在短时间内探索多种可能性，并足够值得信赖，以使他们能够从临床实践者那里获得信心和信心。血流（血流动力学）在大多数血管条件和干预措施的临床结果的起始和进展中起关键作用。但是，在常规临床实践中不容易获得血液​​动力学信息 - 尽管医学成像中的进展是限制的，在这种情况下，通常使用各种成像方式。更重要的是，成像数据只能为我们提供有关当前的信息，而不是未来。他们无法告诉我们任何经常给定的个人化干预措施的结果是什么。在这种情况下，工程工具可以通过为血管疾病提供血流信息来产生真正的差异，而血管疾病无法在体内，更重要的是，通过创建潜在干预的计算机模型及其结果来衡量。通过融合计算血流模型和成像数据，我们可以在临床前计划和个性化治疗方面取得真正的突破。在先驱者中，我们计划开发最复杂的物理驱动的计算工具，这些工具将实时，准确，准确的无稳定和三维血液动力学信息（VELOCITY和压力）从常规使用的血流图像中提取。此信息将用于个性化心血管干预措施的血液动力学虚拟原型制作和心血管设备的裁缝。这项工作将使迈向精确血管手术的基本一步，并将在其决策过程中为血管外科医生提供专家支持，从而极大地改善了个人患者风险的管理。为了促进这一愿景，我们将与该国的三家顶级医院（皇家免费医院，巴特医院和天哪），两个患者组（AVM蝴蝶慈善机构和主动脉意识英国）和领先的医疗设备公司Terumo主动脉协同工作。首先，我们将共同创建概念证明，为介绍我们在临床和制造工作流程中的开创性技术铺平道路。

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

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

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

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