CHS: Small: High-Dimensional Euclidean Embedding for 4D Volumetric Shape with Multi-Tensor Fields

CHS：小型：具有多张量场的 4D 体积形状的高维欧几里得嵌入

基本信息

批准号：
1816511
负责人：
Zichun Zhong
金额：
$ 50万
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：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816511&HistoricalAwards=false
关键词：
CHS Small Dimensional Euclidean Embedding

项目摘要

The overall objective of this research is to develop a rigorous computing system to make the internal workings of the human body easier to understand and analyze. Many complex real-world 4D (space-time) dynamic objects have both heterogenous and anisotropic (unequal along different axes) properties, which can often be captured by multi-modality imaging devices (e.g., 4D-CT/MRI/Ultrasound/DTI), and there is a pressing need to model and analyze these objects. For example, in cardiology, high-fidelity modeling and processing of 4D deformable volumes of cardiac organs and tissues with complex properties, shape geometry, motion and deformation at different phases of the cardiac cycle in real-time becomes important for building an effective and unified tool which doctors can then use to accurately visualize, track, and diagnose. Similar applications also exist in lung cancer treatment, prostate cancer treatment, and so on. This project will also provide several educational activities for undergraduate and graduate students, as well as outreach to local middle school students. This project centers around a high-d Euclidean geometric embedding framework that integrates Riemannian metric, tensor field, and Nash embedding theory, making it possible to effectively and efficiently represent and process the 4D Riemannian volumetric shapes from a new perspective. The computational realization of the high-d embedding will transform a 3D/4D shape with arbitrary metric tensor fields obtained from 3D/4D heterogenous data feature/property space into a novel high-d shape isometric space which preserves all intrinsic geometric characteristics as well as integrating other multi-modality properties. The generalized geometric embedding space through the unified Riemannian metric tensor fields allows formal and diverse study of geometry scalability and variability in shape optimization, processing and measurement involved in data informatics. In the high-d embedding space, complicated Riemannian metric computations in optimization, reconstruction, comparison and analysis will be replaced with simple and efficient Euclidean computations under the isotropic metric. Through the validation of the framework using 4D shape-tensor reconstruction and analysis, it will be possible to offer medical imaging and biomedicine communities an accurate, robust, and rigorous approach for geometric reasoning and quantitative assessment of multi-heterogenous features and properties across different objects.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项研究的总体目的是开发一种严格的计算系统，以使人体的内部运作易于理解和分析。许多复杂的现实世界4D（时空）动态物体具有异质和各向异性（沿不同轴沿不同轴的不等式）特性，通常可以通过多模式成像设备（例如4D-CT/MRI/MRI/ULTRASOLSOUND/DTI）捕获，并且需要建模和分析这些对象。例如，在心脏病学中，具有复杂特性的心脏器官和组织的高保真建模和处理具有复杂特性，形状几何形状，运动和变形在心脏周期的不同阶段的实时阶段变得很重要，对于建立有效和统一的工具很重要，这些工具可以准确地可视化，跟踪，跟踪，跟踪，跟踪和诊断。在肺癌治疗，前列腺癌治疗等中也存在类似的应用。该项目还将为本科生和研究生提供几项教育活动，并向当地的中学生提供宣传。该项目围绕着一个高清欧几里得几何嵌入框架，该框架整合了Riemannian指标，张量场和NASH嵌入理论，从而使您可以从新的角度有效地有效地代表和处理4D Riemannian体积形状。高-D嵌入的计算实现将转变为3D/4D形状，该形状具有从3D/4D异源数据特征/属性空间获得的任意度量张量字段，成为一个新型的高D形状等轴测空间，该空间保留了所有内在的几何学特性以及整合其他多模型属性。通过统一的riemannian度量张量场的广义几何嵌入空间可以正式和多样化的数据可扩展性和形状优化，处理和测量中涉及数据信息学的变异性。在高-D嵌入空间中，在优化，重建，比较和分析中，复杂的Riemannian度量计算将被各向同性度量指标下的简单有效的欧几里得计算所取代。通过使用4D形状调整器的重建和分析验证框架，将有可能为医学成像和生物医学群落提供一种准确，健壮，严格的方法，用于几何推理和多态性特征和属性的多态特征和属性，以反映NSF的智力依据，以表现出NSF的合法传统和构建，以表现出良好的依据。标准。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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

DOI：
10.1007/978-3-030-59725-2_11
发表时间：
2020-10
期刊：
影响因子：
0
作者：
Yifan Wang-;Guoli Yan;Haikuan Zhu;S. Buch;Ying Wang;E. Haacke;Jing Hua;Z. Zhong
通讯作者：
Yifan Wang-;Guoli Yan;Haikuan Zhu;S. Buch;Ying Wang;E. Haacke;Jing Hua;Z. Zhong

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

DOI：
10.1109/tvcg.2020.3030374
发表时间：
2021-02-01
期刊：
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
影响因子：
5.2
作者：
Wang, Yifan;Yan, Guoli;Zhong, Zichun
通讯作者：
Zhong, Zichun

A-CNN: Annularly Convolutional Neural Networks on Point Clouds

DOI：
10.1109/cvpr.2019.00760
发表时间：
2019-01-01
期刊：
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
影响因子：
0
作者：
Komarichev, Artem;Zhong, Zichun;Hua, Jing
通讯作者：
Hua, Jing

TCB-spline-based Image Vectorization