A Simulation Framework for X-Ray Phase-Contrast Imaging

X 射线相衬成像仿真框架

基本信息

  • 批准号:
    10289325
  • 负责人:
  • 金额:
    $ 17.93万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-09-01 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

X-ray phase-contrast imaging (XPCI) can dramatically improve soft tissue contrast in X-ray medical imaging. Despite worldwide efforts to develop novel XPCI systems, we do not yet have a numerical framework to rigorously predict the performance of a clinical XPCI system at a human scale. In this study, we propose to develop such a simulation framework to accomplish this using the breakthroughs in two main components: a realistic human-scaled numerical phantom for XPCI, and a wave optics-based simulator for accurately propagating X-ray wave through a realistic human numerical phantom. For the numerical phantom, the biggest challenges are to incorporate various organs with multi- scale structures in a human-size phantom and to define material properties for human body parts under normal and a variety of pathophysiological conditions. To address these issues, we will extend the XCAT phantom, a human phantom that is widely used in medical imaging simulation, to incorporate sub-organ structures and tissue textures, and to assign appropriate material properties to various tissues for XPCI simulation. For the simulator, we have recently demonstrated the possibility of XPCI simulation at a human scale by applying a wave optics-based imaging model to XCAT phantom. In the proposed project, we will develop a general-purpose XPCI simulator that can be used with variety of geometries, X-ray optics, and tissue models. Using the data we have already acquired on a synchrotron-based, high-performance XPCI setup, we will validate the simulator so that its predictions match experimentally acquired data. Finally, we will distribute the program with a graphical user interface in order that the users can easily simulate their own XPCI systems. Source codes and a detailed user manual will be distributed as well.
X射线相衬成像(XPCI)可以显著提高X射线软组织对比度 医学成像。尽管全世界都在努力开发新的xpci系统,但我们还没有 一种用于严格预测临床xPCI系统性能的数值框架 人体尺度。在本研究中,我们建议开发这样一个仿真框架来完成 这利用了两个主要组成部分的突破:一个逼真的人类尺度的数值 XPCIPhantom和基于波动光学的精确传播X射线波模拟器 通过一个逼真的人类数字幻影。 对于数字体模来说,最大的挑战是将各种器官与多个器官结合在一起。 人体尺寸体模中的比例结构和定义人体的材料属性 各部位处于正常和各种病理生理条件下。为了解决这些问题,我们 将扩展xCAT体模,这是一种在医学成像中广泛使用的人体体模 模拟,结合亚器官结构和组织纹理,并指定适当的 用于XPCI模拟的各种组织的材料属性。 对于模拟器,我们最近演示了在人类身上进行xPCI模拟的可能性 通过将基于波动光学的成像模型应用于xCAT体模来进行缩放。在建议的 项目,我们将开发一个通用的XPCI模拟器,可以用于各种 几何学、X射线光学和组织模型。使用我们已经在 基于同步加速器的高性能XPCI设置,我们将验证模拟器,以便其 预测与实验获得的数据相匹配。最后,我们将使用一个 图形用户界面,以便用户可以方便地模拟自己的XPCI系统。 源代码和详细的用户手册也将分发。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Synergistic Role of Quantitative Diffusion Magnetic Resonance Imaging and Structural Magnetic Resonance Imaging in Predicting Outcomes After Traumatic Brain Injury.
  • DOI:
    10.1097/rct.0000000000001284
  • 发表时间:
    2022-03-01
  • 期刊:
  • 影响因子:
    1.3
  • 作者:
    Avesta A;Yendiki A;Perlbarg V;Velly L;Khalilzadeh O;Puybasset L;Galanaud D;Gupta R
  • 通讯作者:
    Gupta R
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Rajiv Gupta其他文献

Rajiv Gupta的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 17.93万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了