Collaborative Research ITR/NGS: An Integrated Simulation Environment for High-Resolution Computational Methods in Electromagnetics with Biomedical Applications
合作研究 ITR/NGS:电磁学与生物医学应用高分辨率计算方法的集成仿真环境
基本信息
- 批准号:0324957
- 负责人:
- 金额:$ 28万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2004
- 资助国家:美国
- 起止时间:2004-01-15 至 2007-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Current technologies for radiationbased treatment of cancerous tumors rely almost exclusively on diagnosticimages such as MRI and PET scans to enable a careful targeting of multiple high-intensity beams.However, the very complex nature of the penetration of radiation energy into the biological tissue makes such targeting difficult and error-prone, possibly even prohibiting radiative treatment due to the risk of damaging essential tissue in close proximity to the cancerous areas. Such issues are naturally of particular concern in relation to treatment of brain cancer.This project will conduct research on the development of a simulation environment which eventually will provide a virtual patient-specific model of the area of interest and an ability to accurately and efficiently model wave-propagation within such an environment, with the potential to ultimately provide the radiation specialist with an online tool for fine tuning the targeting of radiation energy and, at a future stage, perhaps even model the impact of the energy deposition and heat release on the tissue.The project will develop an environment comprising of (a) the cleaning and segmentation of MRI data, including data with noise sensitivity, (b) extraction of material data and construction of a patient-specific volume model of the target of interest, (c) the generation of high-order, curvilinear, finite elements grids, (d) full as well as reduced order modeling of the penetration/refraction of electromagnetic energy into the volume model, and (e) visualization and extraction of physiological data of interest. These different elements will be integrated into a flexible, stand-alone environment and will, as part of the development, be tested extensively on phantom data as well as real MRI data, possibly with added artificial noise to explore robustness.The key developments will include new image segmentation and cleaning algorithms, improved material models, the development of efficient high-order accurate computational schemes for wave-propagation, efficient methods for domain truncation, and tools for visualization and data extraction. These are all problems of generic importance with potential for impact well beyond the particular application being considered.
目前用于基于辐射治疗癌性肿瘤的技术几乎完全依赖于诸如MRI和PET扫描等诊断图,以仔细靶向多个高强度光束。但是,辐射能量渗透到生物组织中的渗透能量非常复杂的性质,即靶向困难和差异性的辐射治疗,可能会因癌症而造成癌症的风险,从而使辐射性治疗造成癌症的危险。此类问题自然是与脑癌治疗有关的特别关注。该项目将对开发模拟环境进行研究,最终将提供一种虚拟患者特定的感兴趣领域的模型,并具有在这种环境中准确有效地对波浪进行准确有效地模型的能力,并有可能最终在网上发行能量的能量和范围的范围,甚至可以在线启动范围,甚至可以在线启动范围,甚至可以在线启动范围。 tissue.The project will develop an environment comprising of (a) the cleaning and segmentation of MRI data, including data with noise sensitivity, (b) extraction of material data and construction of a patient-specific volume model of the target of interest, (c) the generation of high-order, curvilinear, finite elements grids, (d) full as well as reduced order modeling of the penetration/refraction of electromagnetic energy into the volume model, and (e)可视化和提取感兴趣的生理数据。 These different elements will be integrated into a flexible, stand-alone environment and will, as part of the development, be tested extensively on phantom data as well as real MRI data, possibly with added artificial noise to explore robustness.The key developments will include new image segmentation and cleaning algorithms, improved material models, the development of efficient high-order accurate computational schemes for wave-propagation, efficient methods for domain truncation,以及可视化和数据提取的工具。这些都是通用重要性的问题,具有影响远远超出所考虑的特定应用的潜力。
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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Anne Gelb其他文献
Empirical Bayesian Inference Using a Support Informed Prior
使用支持知情先验的经验贝叶斯推理
- DOI:
10.1137/21m140794x - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Jiahui Zhang;Anne Gelb;Theresa Scarnati - 通讯作者:
Theresa Scarnati
Edge detection from truncated Fourier data using spectral mollifiers
使用光谱缓和器从截断的傅立叶数据中进行边缘检测
- DOI:
10.1007/s10444-011-9258-4 - 发表时间:
2011 - 期刊:
- 影响因子:1.7
- 作者:
D. Cochran;Anne Gelb;Yang Wang - 通讯作者:
Yang Wang
A High-Dimensional Inverse Frame Operator Approximation Technique
一种高维逆框算子逼近技术
- DOI:
10.1137/15m1047593 - 发表时间:
2016 - 期刊:
- 影响因子:0
- 作者:
Guohui Song;Jacqueline Davis;Anne Gelb - 通讯作者:
Anne Gelb
A High Order Method for Determining the Edges in the Gradient of a Function
确定函数梯度边的高阶方法
- DOI:
- 发表时间:
2009 - 期刊:
- 影响因子:0
- 作者:
R. Saxena;Anne Gelb;H. Mittelmann - 通讯作者:
H. Mittelmann
Learning the Dynamics for Unknown Hyperbolic Conservation Laws Using Deep Neural Networks
使用深度神经网络学习未知双曲守恒定律的动力学
- DOI:
10.1137/22m1537333 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Zhen Chen;Anne Gelb;Yoonsang Lee - 通讯作者:
Yoonsang Lee
Anne Gelb的其他文献
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{{ truncateString('Anne Gelb', 18)}}的其他基金
Conference: North American High Order Methods Con (NAHOMCon)
会议:北美高阶方法大会 (NAHOMCon)
- 批准号:
2333724 - 财政年份:2024
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
Collaborative Research: Accurate, Efficient and Robust Computational Algorithms for Detecting Changes in a Scene Given Indirect Data
协作研究:准确、高效和稳健的计算算法,用于检测给定间接数据的场景变化
- 批准号:
1912685 - 财政年份:2019
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
Collaborative Research: An Integrated Approach to Convex Optimization Algorithms
协作研究:凸优化算法的集成方法
- 批准号:
1732434 - 财政年份:2016
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
Collaborative Research: An Integrated Approach to Convex Optimization Algorithms
协作研究:凸优化算法的集成方法
- 批准号:
1521600 - 财政年份:2015
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
Novel Numerical Approximation Techniques for Non-Standard Sampling Regimes
非标准采样制度的新颖数值逼近技术
- 批准号:
1216559 - 财政年份:2012
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
Southwest Conference on Integrated Mathematical Methods in Medical Imaging; February 2010; Tempe, Arizona
西南医学影像综合数学方法会议;
- 批准号:
0944521 - 财政年份:2009
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
FRG: Collaborative Research: Integrated Mathematical Methods in Medical Imaging
FRG:合作研究:医学成像中的综合数学方法
- 批准号:
0652833 - 财政年份:2007
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
High Order Reconstruction Using Spectral Methods
使用谱方法进行高阶重建
- 批准号:
0510813 - 财政年份:2005
- 资助金额:
$ 28万 - 项目类别:
Standard Grant
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