AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures

AitF：协作研究：3D/4D 心脏图像的拓扑算法：理解复杂和动态结构

• 批准号: 1733798
• 负责人: Yusu Wang
• 金额: $ 27.3万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733798&HistoricalAwards=false
• 关键词: AitF; Collaborative; Research; Topological; Algorithms

The interiors of ventricles of a human heart are spanned by a fine net of muscle fibers that are difficult to resolve, even in high resolution CT images. An accurate account of these structures, however, could improve diagnosis of cardiac disease, evaluation of cardiac function, assessment of stroke risk, and simulation of cardiac blood flow. Topology is the branch of abstract mathematics that deals with connections; this project uses the theory of persistent homology to identify crucial topological handles that can be useful for accurate reconstruction and analysis of the complex cardiac dynamics from these CT images. The outcome of the project will not only advance our understanding of cardiac function, but also generate novel computational topology methods that are more efficient and effective for practical applications. This project not only bridges the gap between the theory of computational topology and the practical problem of cardiac image analysis, but also trains the next generation of researchers and educators to do so by a carefully integrated education plan. The PIs will engage undergraduate students, high school students, women and other underrepresented students in their proposed research.The goal of this project is to develop a topological approach to unveil the intrinsic structures from complex and dynamic 3D/4D cardiac data, and furthermore, to provide principled tools to quantitatively analyze these structures. The PIs will create new computational topology methodologies and algorithms to extract rich information from the intrinsic structure of cardiac data. They will develop novel methodologies to extract localized topological features and to track them based on their spatial and temporal coherence. They also plan to design new algorithms to untangle ambiguous and uncertain situations for tracking structures through time sequence data. The resulting techniques and software will be validated on cardiac CT data to produce quantitative assessments of accuracy and to characterize the advantages and limitations of these approaches. Domain experts will validate the quality of the approaches via scientific hypotheses and data exploration. The methods to be developed are general and will impact other scientific fields where intrinsic complex and dynamic structures exist.

即使在高分辨率的CT图像中，人类心脏的脑室内部也被一张由肌肉纤维组成的精细网络所覆盖，这些纤维很难分辨。然而，对这些结构的准确描述可以改善心脏病的诊断、心功能的评估、中风风险的评估和心脏血流的模拟。拓扑学是处理联系的抽象数学的一个分支；这个项目使用持久同调理论来识别关键的拓扑句柄，这些拓扑句柄可以用于从这些CT图像中准确地重建和分析复杂的心脏动力学。该项目的成果不仅将促进我们对心脏功能的理解，而且还将产生更高效、更有效的实际应用的新的计算拓扑方法。这个项目不仅在计算拓扑学理论和心脏图像分析的实际问题之间架起了一座桥梁，而且还通过精心整合的教育计划培训了下一代研究人员和教育工作者。PIS将吸引本科生、高中生、女性和其他代表性不足的学生参与他们拟议的研究。该项目的目标是开发一种拓扑方法，从复杂和动态的3D/4D心脏数据中揭示内在结构，并进一步提供原则性工具来定量分析这些结构。PI将创建新的计算拓扑方法和算法，以从心脏数据的内在结构中提取丰富的信息。他们将开发新的方法来提取局部拓扑特征，并基于它们的空间和时间一致性来跟踪它们。他们还计划设计新的算法，通过时间序列数据来解开模糊和不确定的情况，以跟踪结构。由此产生的技术和软件将在心脏CT数据上进行验证，以产生准确性的定量评估，并表征这些方法的优势和局限性。领域专家将通过科学假设和数据探索来验证方法的质量。要开发的方法是一般性的，并将影响存在内在复杂和动态结构的其他科学领域。

项目成果

Topology-Aware Segmentation Using Discrete Morse Theory

• 发表时间: 2021-03
• 期刊: ArXiv
• 作者: Xiaoling Hu;Yusu Wang;Fuxin Li;D. Samaras;Chao Chen

Persistence Enhanced Graph Neural Network

• 发表时间: 2020-06
• 作者: Qi Zhao;Ze Ye;Chao Chen;Yusu Wang

Heuristic Search for Homology Localization Problem and Its Application in Cardiac Trabeculae Reconstruction

同源定位问题的启发式搜索及其在心脏小梁重建中的应用

• DOI: 10.24963/ijcai.2019/182
• 发表时间: 2019
• 期刊: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
• 作者: Zhang, Xudong;Wu, Pengxiang;Yuan, Changhe;Wang, Yusu;Metaxas, Dimitris;Chen, Chao
• 通讯作者: Chen, Chao

A Topological Regularizer for Classifiers via Persistent Homology

• 发表时间: 2018-06
• 作者: Chao Chen;Xiuyan Ni;Qinxun Bai;Yusu Wang