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:9973167
- 负责人:
- 金额:$ 38.36万
- 依托单位:
- 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 2022-05-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAdverse eventAlgorithmsAnatomyAreaArtificial IntelligenceAttentionBiomechanicsBiomedical ComputingClinicalClinical EngineeringComplexComputer AnalysisComputer ModelsComputer SimulationConsumptionCoronary OcclusionsDataData AnalysesData SetDevelopmentDevice DesignsDevice or Instrument DevelopmentDevicesDictionaryDiseaseElementsEvaluationExtravasationFeedbackFinite Element AnalysisGenerationsGeometryGoalsGuidelinesHeart ValvesHospitalsHourHumanImageInterventionLaboratoriesLanguageLearningLeft ventricular structureMachine LearningManualsMethodsMitral ValveModelingOutcomeOutputPatient-Focused OutcomesPatientsPerformancePlant RootsPostoperative PeriodProblem SetsProceduresProcessPropertyResearch PersonnelResponse ElementsRunningRuptureSamplingShapesStatistical Data InterpretationStentsStructureTechniquesTestingThinnessTimeTissue ModelTrainingTranslationsTreatment outcomeUncertaintyVariantX-Ray Computed Tomographyalgorithm trainingaortic valveaortic valve replacementascending aortabasecalcificationclinical applicationclinical imagingclinical practiceclinically translatabledeep learningdeep neural networkheart functionheart imagingimaging modalityimprovedinnovationmachine learning algorithmmodels and simulationnovelpatient populationpersonalized approachpopulation basedpreventreconstructionresearch clinical testingsimulationspeech recognitiontime resolved datatwo-dimensionalvirtualvirtual clinical trial
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.
项目总结
今天有大量的临床三维(3D)心脏图像数据,可在
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
心脏功能的变化。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出
创新的、个性化的疾病治疗方法。此外,使用这些3D数据集作为输入以
计算模型可以促进基于总体的分析,这种分析可以用来量化
治疗程序,并可用于创新设备开发的虚拟临床试验。然而,
有几个关键的技术瓶颈阻碍了基于模拟的临床评估的实现:1)
临床心脏瓣膜等薄壁复杂结构的自动三维重建困难
图像,2)计算模型是在没有网格对应的情况下构建的,这使得对
由于模型设置中的不一致,运行批量模拟并进行大量患者总体数据分析,
3)计算时间较长,不利于临床应用。
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程序。因此,这样做的目的是
建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
用于自动处理术前临床图像数据和预测术后临床的技术
结果。经导管主动脉瓣置换术(TAVR)干预将作为建模的试验床
方法:研究方法。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患者主动脉瓣重建术通过建模过程,将网格之间的对应关系
建立了患者的几何模型。TAVR患者几何构型的分布和变异
可以用统计形状模型(SSM)来描述。在目标2中,TAVR程序的基于总体的有限元分析
将在SSMS(目标1)生成的数千个虚拟患者模型上进行。一种深度神经网络
将开发和培训(DNN),以了解TAVR FE输入和输出之间的关系。
这项研究的成功完成将导致ML-FE替代TAVR分析,结合自动化
TAVR患者几何重建算法和训练的DNN,提供快速TAVR生物力学
无需对模型进行大量重新计算即可进行分析。此外,本研究中开发的算法可以
可推广到其他应用和设备。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Wei Sun其他文献
Wei Sun的其他文献
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