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A novel computing framework to automatically process cardiac valve image data and predict treatment outcomes

一种新颖的计算框架，可自动处理心脏瓣膜图像数据并预测治疗结果

基本信息

批准号：
10162650
负责人：
RUDOLPH L GLEASON
金额：
$ 38.44万
依托单位：
GEORGIA INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10162650
关键词：
3-Dimensional Adverse event Algorithms Anatomy Area Artificial Intelligence Attention Biomechanics Biomedical Computing Clinical Clinical Engineering Complex Computer Analysis Computer Models Computer Simulation Consumption Coronary Occlusions Data Data Analyses Data Set Development Device Designs Device or Instrument Development Devices Dictionary Disease Elements Evaluation Extravasation Feedback Finite Element Analysis Generations Geometry Goals Guidelines Heart Valves Hospitals Hour Human Image Intervention Laboratories Language Learning Left ventricular structure Machine Learning Manuals Methods Mitral Valve Modeling Outcome Output Patient-Focused Outcomes Patients Performance Plant Roots Postoperative Period Problem Sets Procedures Process Property Research Personnel Response Elements Running Rupture Sampling Shapes Statistical Data Interpretation Stents Structure Techniques Testing Thinness Time Tissue Model Training Translations Treatment outcome Uncertainty Variant X-Ray Computed Tomography algorithm training aortic valve aortic valve replacement ascending aorta base calcification clinical application clinical imaging clinical practice clinically translatable deep learning deep neural network heart function heart imaging imaging modality improved innovation machine learning algorithm models and simulation novel patient population personalized approach population based prevent reconstruction research clinical testing simulation speech recognition time resolved data two-dimensional virtual clinical trial virtual patient

项目摘要

PROJECT SUMMARY There is a massive amount of clinical three-dimensional (3D) cardiac image data available today in numerous hospitals, but such data has been considerably underutilized in both clinical and engineering analyses of cardiac function. These 3D data offers unique and valuable information, allowing researchers to develop innovative, personalized approaches to treat diseases. Furthermore, using these 3D datasets as input to computational models can facilitate a population-based analysis that can be used to quantify uncertainty in treatment procedures, and can be utilized for virtual clinical trials for innovative device development. However, there are several critical technical bottlenecks preventing simulation-based clinical evaluation a reality: 1) difficulty in automatic 3D reconstruction of thin complex structures such as heart valve leaflets from clinical images, 2) computational models are constructed without mesh correspondence, which makes it challenging to run batch simulations and conduct large patient population data analyses due to inconsistencies in model setups, and 3) computing time is long, which inhibits prompt feedback for clinical use. A potential paradigm-changing solution to the challenges is to incorporate machine learning algorithms to expedite the geometry reconstruction and computational analysis procedures. Therefore, the objective of this proposal is to develop a novel computing framework, using advanced tissue modeling and machine learning techniques, to automatically process pre-operative clinical image data and predict post-operative clinical outcomes. Transcatheter aortic valve replacement (TAVR) intervention will serve as a testbed for the modeling methods. In Aim 1, we will develop novel shape dictionary learning (SDL) based methods for automatic reconstruction of TAVR patient aortic valves. Through the modeling process, mesh correspondence will be established across the patient geometric models. The distribution and variation of TAVR patient geometries will be described by statistical shape models (SSMs). In Aim 2, population-based FE analysis of the TAVR procedure will be conducted on thousands of virtual patient models generated by the SSMs (Aim 1). A deep neural network (DNN) will be developed and trained to learn the relationship between the TAVR FE inputs and outputs. Successful completion of this study will result in a ML-FE surrogate for TAVR analysis, combining the automated TAVR patient geometry reconstruction algorithms and the trained DNN, to provide fast TAVR biomechanics analysis without extensive re-computing of the model. Furthermore, the algorithms developed in this study can be generalized for other applications and devices.

项目摘要目前，在医学领域存在大量可用的临床三维（3D）心脏图像数据。许多医院，但这些数据在临床和工程分析中都没有得到充分利用心脏功能。这些3D数据提供了独特而有价值的信息，使研究人员能够开发创新、个性化的疾病治疗方法。此外，使用这些3D数据集作为输入，计算模型可以促进基于人口的分析，该分析可以用于量化治疗程序，并可用于虚拟临床试验的创新设备开发。然而，在这方面，有几个关键的技术瓶颈阻碍了基于模拟的临床评价成为现实：1）在临床上难以自动3D重建薄的复杂结构，如心脏瓣膜小叶图像，2）计算模型的构建没有网格对应，这使得它具有挑战性，由于模型设置的不一致性，运行批量模拟并进行大型患者群体数据分析， 3）计算时间长，抑制了临床使用的及时反馈。应对这些挑战的一个潜在的改变范式的解决方案是结合机器学习算法以加快几何重建和计算分析过程。因此，这一目标一项提议是开发一种新的计算框架，使用先进的组织建模和机器学习技术，以自动处理术前临床图像数据并预测术后临床结果。经导管主动脉瓣置换术（TAVR）介入将作为建模的试验平台方法.在目标1中，我们将开发新的基于形状字典学习（SDL）的自动识别方法。 TAVR患者主动脉瓣重建。在建模过程中，网格对应关系将在患者几何模型中建立。TAVR患者几何形状的分布和变化将统计形状模型（Statistical Shape Models，SSM）。在目标2中，TAVR手术的基于人群的FE分析将在SSM生成的数千个虚拟患者模型上进行（目标1）。深度神经网络 (DNN)将开发和培训，以了解TAVR FE输入和输出之间的关系。本研究的成功完成将产生TAVR分析的ML-FE替代品，结合自动化 TAVR患者几何结构重建算法和经过训练的DNN，以提供快速TAVR生物力学无需对模型进行大量的重新计算。此外，在这项研究中开发的算法可以可以推广到其他应用和设备。