Enabling efficient and certifiable solutions in diagnostic biomechanics by rephrasing model-based inverse problems.

通过重新表述基于模型的逆问题，在诊断生物力学中实现高效且可认证的解决方案。

• 批准号: 499746055
• 负责人: Professor Phaedon-Stelios Koutsourelakis, Ph.D.
• 金额: --
• 依托单位: Professorship for Data-driven Materials Modeling
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/499746055?language=en
• 关键词: Enabling; efficient; certifiable; solutions; diagnostic

The prodigious advances of recent years in artificial intelligence and machine learning have unavoidably impacted the biomedical sector and have fueled a novel, data-centric paradigm for personalized medicine. Nevertheless andin biomechanics in particular, data alone, cannot lead to satisfactory answers unless the rich and plentiful domain knowledge that is available in the form of physics-based models is incorporated. Consider for example elastography which represents the primary application domain of this proposal and employs imaging data collected during the deformation of tissue in order to produce a diagnosis. This is achieved by using a physics-based model which enables the non-invasiveness of the procedure that is advantageous in terms of ease, cost and reducing the risk of complications to the patient. Furthermore, rather than purely data-based techniques which rely on the raw, imaging and can detect perhaps variations in tissue density, the identification of mechanical properties can lead to earlier and more accurate diagnosis and ultimately enable to patient-specific treatment strategies. The fusion of data with continuum-mechanics' models poses several challenges and the present project aims to make novel contributions along the following three fronts: uncertainty quantification, model errors and computational efficiency. Uncertainty plays a central role in all data-centric problems and it is imperative that it is quantified. The obvious source of uncertainty is the data itself which are invariably noisy, frequently scarce or incomplete. Another source of uncertainty that has been largely ignored in pertinent literature is the one stemming from the model itself. While the models employed encapsulate knowledge accumulated over decades, they are imperfect, idealizations of the physical reality. Even though it is always possible to fit model parameters to data, if the model is incorrect, it can lead to incorrect diagnosis and prognosis. The third challenge that we intend to address pertains to the computational complexity of solving model-based, inverse problems. Practically all pertinent numerical techniques rely on the availability of a well-posed, forward model which needs to be solved millions of times and each of these solves might require several hours on multiple CPUs or bespoke hardware. The project proposes a novel Bayesian framework which overcomes the limitations of traditional, black-box-based formulations. The governing equations are treated as data sources (virtual observables) which are adaptively and hierarchically mined in a self-supervised fashion. In addition, we propose a physically interpretable framework that distinguishes between reliable and unreliable governing equations and can provide quantitative indicators of model errors over the problem domain.Finally, we propose algorithmic advances that address computational scalability issues and draw nearer to real-time, diagnostic capabilities.

近年来，人工智能和机器学习的巨大进步已经对生物医学领域产生了不可忽视的影响，并为个性化医疗提供了一种新的、以数据为中心的范式。然而，尤其是在生物力学中，只有数据，不能得到令人满意的答案，除非将丰富的领域知识以基于物理的模型的形式结合起来。考虑例如弹性成像，其代表该提议的主要应用领域并且采用在组织变形期间收集的成像数据以便产生诊断。这是通过使用基于物理的模型来实现的，该模型能够实现手术的非侵入性，这在容易性、成本和降低患者并发症的风险方面是有利的。此外，与依赖于原始成像并且可以检测组织密度的可能变化的纯粹基于数据的技术不同，机械特性的识别可以导致更早和更准确的诊断，并最终实现患者特异性治疗策略。数据与连续力学模型的融合带来了一些挑战，本项目旨在沿着以下三个方面做出新的贡献：不确定性量化，模型误差和计算效率。不确定性在所有以数据为中心的问题中起着核心作用，因此必须对其进行量化。不确定性的明显来源是数据本身，这些数据总是嘈杂的，经常是稀缺的或不完整的。另一个在相关文献中基本上被忽视的不确定性来源是模型本身。虽然所采用的模型封装了几十年来积累的知识，但它们是不完美的，是物理现实的理想化。尽管总是可以将模型参数拟合到数据，但如果模型不正确，则可能导致错误的诊断和预后。我们打算解决的第三个挑战涉及解决基于模型的逆问题的计算复杂性。实际上，所有相关的数值技术都依赖于一个适定的正演模型的可用性，该模型需要求解数百万次，并且这些求解中的每一次都可能需要在多个CPU或定制硬件上花费数小时。该项目提出了一种新的贝叶斯框架，克服了传统的黑盒公式的局限性。 控制方程被视为数据源（虚拟可观），这是自适应和分层挖掘的自我监督的方式。此外，我们提出了一个物理上可解释的框架，区分可靠和不可靠的控制方程，并可以提供定量指标的模型错误的问题domain.Finally，我们提出了算法的进步，解决计算的可扩展性问题，并提请更接近实时，诊断能力。