CRII: ACI: 4D Dynamic Anisotropic Meshing and Applications

CRII：ACI：4D 动态各向异性网格划分和应用

• 批准号: 1657364
• 负责人: Zichun Zhong
• 金额: $ 17.5万
• 依托单位: Wayne 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: 2017-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657364&HistoricalAwards=false
• 关键词: CRII; ACI; 4D; Dynamic; Anisotropic

There is an emerging need in healthcare, transportation, and simulation areas to realistically reconstruct, visualize, and capture space-time (4D) models/images (dynamic objects) from a complicated scenario in real-time. For example, high-fidelity dynamically modeling and visualizing 4D deformable shapes and variations of organs and their surrounding tissues in real-time become important for building an effective 4D model planning/capturing for radiation therapy (e.g., 4D-Doctor system). It requires dynamic anisotropic modeling and multi-modality imaging techniques for accurate registration, segmentation, and visualization. The goal of this project is to develop a tool for efficiently computing high-quality 4D dynamic anisotropic meshing models for complicated 4D objects with features and details in the large-scale volume image data. This project involves several disciplines, such as geometric modeling, computer graphics, and medical image processing, and has a potential to provide a high-quality platform for both interdisciplinary research and education integration. This project will aim to enrich the computer science and engineering curriculum at Wayne State University in both the undergraduate and graduate levels. Therefore, this research aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity and welfare.A theoretical and computational framework for 4D dynamic anisotropic meshing with high quality and efficiency is essential for tackling challenges in efficiently representing and capturing the 4D data. The key strength of this project focuses on the transformative research ideas and approaches in particle-based approach for Riemannian metric mesh modeling, serving as a foundation for geometry-guided 3D/4D imaging informatics. The proposed exploratory research activities will address the following major themes and objectives: (1) to develop a novel particle-based method for high-quality 3D and 4D anisotropic tetrahedral meshing; (2) to evaluate the mesh quality and apply the proposed theoretical meshing approaches in applications to medical imaging, and develop a testbed system to evaluate its capability and potential in 4D-Doctor system, including 4D image registration and segmentation. The unified theoretical particle-based meshing framework, integrating Gaussian energy, dynamic Riemannian metrics, and high-dimensional embedding theory, can enable efficient generation of dynamic anisotropic meshes from a brand new perspective. This research initiative is innovative as it will establish a novel geometric modeling framework supporting 3D/4D imaging informatics.

医疗保健、交通和模拟领域日益需要从复杂的场景中实时真实地重建、可视化和捕获时空 (4D) 模型/图像（动态对象）。例如，实时高保真动态建模和可视化器官及其周围组织的 4D 可变形形状和变化对于构建有效的放射治疗 4D 模型规划/捕获（例如 4D-Doctor 系统）变得非常重要。它需要动态各向异性建模和多模态成像技术来实现精确配准、分割和可视化。该项目的目标是开发一种工具，用于针对具有大规模体积图像数据特征和细节的复杂 4D 对象，高效计算高质量 4D 动态各向异性网格模型。该项目涉及几何建模、计算机图形学、医学图像处理等多个学科，有潜力为跨学科研究和教育融合提供优质平台。该项目旨在丰富韦恩州立大学本科生和研究生的计算机科学和工程课程。因此，这项研究符合 NSF 促进科学进步、促进国民健康、繁荣和福利的使命。高质量、高效率的 4D 动态各向异性网格划分理论和计算框架对于应对 ef&#64257 中的挑战至关重要；科学地表示和捕获 4D 数据。该项目的关键优势在于基于粒子的黎曼度量网格建模方法的变革性研究思想和方法，作为几何引导的 3D/4D 成像信息学的基础。拟议的探索性研究活动将解决以下主要主题和目标：（1）开发一种新颖的基于粒子的高质量3D和4D各向异性四面体网格划分方法； (2) 评估网格质量并将所提出的理论网格方法应用于医学成像，并开发一个测试平台系统来评估其在 4D-Doctor 系统中的能力和潜力，包括 4D 图像配准和分割。基于统一理论的粒子网格划分框架，集成高斯能量、动态黎曼度量和高维嵌入理论，可以从全新的角度高效生成动态各向异性网格。这项研究举措具有创新性，因为它将建立一个支持 3D/4D 成像信息学的新颖几何建模框架。

项目成果

Field-Aligned and Lattice-Guided Tetrahedral Meshing

场对齐和晶格引导的四面体网格划分

• 发表时间: 2018-01
• 期刊: Computer graphics forum
• 影响因子: 2.5
• 作者: Ni, Saifeng;Zhong, Zichun;Huang, Jin;Wang, Wenping;Guo, Xiaohu
• 通讯作者: Guo, Xiaohu

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

VC-Net：用于高度稀疏和噪声图像数据的分割和可视化的深度体合成网络

• DOI: 10.1109/tvcg.2020.3030374
• 发表时间: 2020-09-14
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Yifan Wang;Guoli Yan;Haikuan Zhu;S. Buch;Ying Wang;E. Haacke;Jing Hua;Z. Zhong
• 通讯作者: Z. Zhong

Surface Reconstruction by Parallel and Unified Particle-Based Resampling from Point Clouds

通过基于点云的并行和统一粒子重采样进行表面重建

• 发表时间: 2019-01
• 期刊: Computer aided geometric design
• 影响因子: 1.5
• 作者: Zhong, Sikai;Zhong, Zichun;Hua, Jing
• 通讯作者: Hua, Jing

JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation

JointVesselNet：用于 3D 脑血管分割的联合体积投影卷积嵌入网络

• DOI: 10.1007/978-3-030-59725-2_11
• 发表时间: 2020-10-04
• 作者: Yifan Wang;Guoli Yan;Haikuan Zhu;S. Buch;Ying Wang;E. Haacke;Jing Hua;Z. Zhong
• 通讯作者: Z. Zhong

Computing a High-Dimensional Euclidean Embedding from an Arbitrary Smooth Riemannian Metric

从任意平滑黎曼度量计算高维欧几里得嵌入

• 发表时间: 2018-01
• 期刊: ACM transactions on graphics
• 影响因子: 6.2
• 作者: Zhong, Zichun;Wang, Wenping;Lévy, Bruno;Hua, Jing;Guo, Xiaohu
• 通讯作者: Guo, Xiaohu