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Automated data curation to ensure model credibility in the Vascular Model Repository

自动数据管理以确保血管模型存储库中模型的可信度

基本信息

批准号：
10175029
负责人：
Alison L Marsden
金额：
$ 33.03万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-12 至 2023-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10175029
关键词：
3-Dimensional Adoption Anatomic Models Anatomy Basic Science Blood Vessels Blood flow Cardiac Cardiovascular Diseases Cardiovascular Models Cardiovascular system Clinical Clinical Data Clinical Sciences Collaborations Communities Complex Computer software Data Data Science Data Set Databases Diagnosis Disease Electrophysiology (science)Ensure Feedback Fostering Funding Goals High Performance Computing Image Image Analysis Incentives Intake Intervention Joints Laws Machine Learning Manuals Maps Mechanics Medical Device Designs Medical Imaging Methods Modeling Musculoskeletal Operative Surgical Procedures Patient risk Patients Physics Physiological Process Publications Radiology Specialty Recording of previous events Reproducibility Research Resolution Resources Risk Assessment Running Science Software Engineering Source Code Supervision System Techniques Time Triage Uncertainty United States National Institutes of Health automated analysis base clinical application clinical care computing resources data curation data format data repository data resource deep learning experience gigabyte imaging Segmentation innovation large scale data models and simulation novel online repository open data open source repository respiratory shape analysis simulation software development stem success supercomputer supervised learning three-dimensional modeling treatment planning trustworthiness unsupervised learning web portal

项目摘要

Three-dimensional anatomic modeling and simulation (3D M&S) in cardiovascular (CV) disease have become a crucial component of treatment planning, medical device design, diagnosis, and FDA approval. Comprehensive, curated 3-D M&S databases are critical to enable grand challenges, and to advance model reduction, shape analysis, and deep learning for clinical application. However, large-scale open data curation involving 3-D M&S present unique challenges; simulations are data intensive, physics-based models are increasingly complex and highly resolved, heterogeneous solvers and data formats are employed by the community, and simulations require significant high-performance computing resources. Manually curating a large open-data repository, while ensuring the contents are verified and credible, is therefore intractable. We aim to overcome these challenges by developing broadly applicable automated curation data science to ensure model credibility and accuracy in 3-D M&S, leveraging our team’s expertise in CV simulation, uncertainty quantification, imaging science, and our existing open data and open source projects. Our team has extensive experience developing and curating open data and software resources. In 2013, we launched the Vascular Model Repository (VMR), providing 120 publicly-available datasets, including medical image data, anatomic vascular models, and blood flow simulation results, spanning numerous vascular anatomies and diseases. The VMR is compatible with SimVascular, the only fully open source platform providing state-of-the-art image-based blood flow modeling and analysis capability to the CV simulation community. We propose that novel curation science will enable the VMR to rapidly intake new data while automatically assessing model credibility, creating a unique resource to foster rigor and reproducibility in the CV disease community with broad application in 3D M&S. To accomplish these goals, we propose three specific aims: 1) Develop and validate automated curation methods to assess credibility of anatomic patient-specific models built from medical image data, 2) Develop and validate automated curation methods to assess credibility of 3D blood flow simulation results, 3) Disseminate the data curation suite and expanded VMR. The proposed research is significant and innovative because it will 1) enable rapid expansion of the repository by limiting curator intervention during data intake, leveraging compatibility with SimVascular, 2) increase model credibility in the CV simulation community, 3) apply novel supervised and unsupervised approaches to evaluate anatomic model fidelity, 4) leverage reduced order models for rapid assessment of complex 3D data. This project assembles a unique team of experts in cardiovascular simulation, the developers of SimVascular and creator of the VMR, a professional software engineer, and radiology technologists. We will build upon our successful track record of launching and supporting open source and open data resources to ensure success. Data curation science for 3D M&S will have direct and broad impacts in other physiologic systems and to ultimately impact clinical care in cardiovascular disease.

心血管（CV）疾病的三维解剖建模与仿真（3D M&S）已成为一种新的研究方向。治疗计划、医疗器械设计、诊断和FDA批准的关键组成部分。全面，精心策划的三维M&S数据库对于应对重大挑战、推进模型简化、塑造分析和深度学习用于临床应用。然而，涉及3-D M&S的大规模开放数据策展提出了独特的挑战;模拟是数据密集型的，基于物理的模型越来越复杂，社区采用了高分辨率、异构的求解器和数据格式，需要大量的高性能计算资源。手动管理大型开放数据存储库，因此，确保内容得到核实和可信是一项棘手的任务。我们的目标是克服这些挑战通过开发广泛适用的自动化策展数据科学，以确保模型的可信度，利用我们团队在CV模拟、不确定性量化成像科学，以及我们现有的开放数据和开源项目。我们的团队拥有丰富的经验开发和管理开放数据和软件资源。2013年，我们推出了血管模型库， (VMR)，提供120个公开可用的数据集，包括医学图像数据、解剖血管模型，以及血流模拟结果，涵盖众多血管解剖结构和疾病。VMR兼容 SimVascular是唯一一个提供最先进的基于图像的血流建模的完全开源平台和分析能力的CV模拟社区。我们建议，新的策展科学将使 VMR快速吸收新数据，同时自动评估模型可信度，创建一个独特的资源，在CV疾病社区中培养严谨性和可重复性，并在3D M&S中广泛应用。完成为了实现这些目标，我们提出了三个具体目标：1）开发和验证自动化策展方法来评估从医学图像数据建立的解剖患者特定模型的可信度，2）开发和验证自动化评估3D血流模拟结果可信度的管理方法，3）传播数据管理套件扩展VMR。这项研究具有重大意义和创新性，因为它将1）使快速通过在数据获取期间限制管理员干预来扩展存储库， SimVascular，2）增加CV模拟社区中的模型可信度，3）应用新的监督和评估解剖模型保真度的无监督方法，4）利用降阶模型进行快速复杂的3D数据。该项目汇集了一个独特的心血管模拟专家团队， SimVascular的开发人员和VMR的创建者，专业软件工程师和放射学技术专家。我们将建立在我们成功的启动和支持开源和开放的记录，数据资源，以确保成功。3D M&S的数据管理科学将对其他领域产生直接和广泛的影响。生理系统，并最终影响心血管疾病的临床护理。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

svMorph: Interactive Geometry-Editing Tools for Virtual Patient-Specific Vascular Anatomies

DOI：
10.1115/1.4056055
发表时间：
2023-03-01
期刊：
JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME
影响因子：
1.7
作者：
Pham，Jonathan;Wyetzner，Sofia;Marsden，Alison L. L.
通讯作者：
Marsden，Alison L. L.

Branched Latent Neural Maps

分支潜在神经图

DOI：
10.1016/j.cma.2023.116499
发表时间：
2024
期刊：
Computer Methods in Applied Mechanics and Engineering
影响因子：
7.2
作者：
Salvador, Matteo;Marsden, Alison Lesley
通讯作者：
Marsden, Alison Lesley

Automated generation of 0D and 1D reduced-order models of patient-specific blood flow.

DOI：
10.1002/cnm.3639
发表时间：
2022-10
期刊：
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING
影响因子：
2.1
作者：
Pfaller, Martin R.;Pham, Jonathan;Verma, Aekaansh;Pegolotti, Luca;Wilson, Nathan M.;Parker, David W.;Yang, Weiguang;Marsden, Alison L.
通讯作者：
Marsden, Alison L.

Hemodynamic effects of entry and exit tear size in aortic dissection evaluated with in vitro magnetic resonance imaging and fluid-structure interaction simulation.

DOI：
10.1038/s41598-023-49942-0
发表时间：
2023-12-18
期刊：
Scientific reports
影响因子：
4.6
作者：
通讯作者：

Computational simulation-derived hemodynamic and biomechanical properties of the pulmonary arterial tree early in the course of ventricular septal defects.

DOI：
10.1007/s10237-021-01519-4
发表时间：
2021-12
期刊：
BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
影响因子：
3.5
作者：
Dong, Melody L.;Lan, Ingrid S.;Yang, Weiguang;Rabinovitch, Marlene;Feinstein, Jeffrey A.;Marsden, Alison L.
通讯作者：
Marsden, Alison L.