Cardiac CT: Advanced Architectures and Algorithms
心脏 CT:先进架构和算法
基本信息
- 批准号:7792699
- 负责人:
- 金额:$ 62.49万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2010
- 资助国家:美国
- 起止时间:2010-02-01 至 2013-01-31
- 项目状态:已结题
- 来源:
- 关键词:AffectAlgorithmsAmericanAnatomyArchitectureArtsCardiacCardiovascular DiseasesClinicalClinics and HospitalsDataDevelopmentDiagnosticDoseElectron Beam TomographyElementsEngineeringEnsureEvaluationFunctional ImagingGenerationsGoalsHealth Care CostsHeartImageKnowledgeLow Dose RadiationMeasurementMeasuresMorbidity - disease rateMorphologic artifactsMotionNoiseOutcomePatientsPerformancePhaseProtocols documentationPublishingRadiationRadonResearchResearch PersonnelResolutionRetinal ConeRotationSamplingScanningSchemeSourceSpeedSpottingsStagingSystemSystems AnalysisTechnologyTestingTheoretical StudiesTherapeuticTomography, Computed, ScannersVirginiaWeightX-Ray Computed Tomographybasedesigndetectorheart motionimprovedindexinginnovationmortalitynext generationnovelprototypepublic health relevancequantumreconstructionresearch studysimulationsocial
项目摘要
DESCRIPTION (provided by applicant): Cardiovascular diseases are pervasive with high mortality and morbidity at tremendous social and healthcare costs. There are urgent needs for significantly higher fidelity cardiac CT with substantially lower radiation dose, which is currently not possible because of technical limitations. Although cardiac CT technology has improved significantly from 16 to 320 detector rows and from single to dual source, there remain technical challenges in terms of temporal resolution, spatial resolution, radiation dose, and so on. Based on an ideal academic-industrial partnership between Virginia Tech and the GE Global Research Center (GEGR), we are motivated to advance the state-of-the-art in cardiac CT dramatically and define the next generation cardiac CT system. The overall goal of this project is to develop novel cardiac CT architectures and the associated reconstruction algorithms, and define the next-generation cardiac CT system. The specific aims are to (1) design, analyze and compare novel cardiac CT architectures with novel sources and scanning trajectories; (2) develop analytic and iterative cardiac CT reconstruction algorithms for ROI-oriented scanning and dynamic imaging for the proposed cardiac CT architectures; and (3) evaluate and validate the proposed architectures and algorithms in theoretical studies, numerical simulations, phantom experiments and observer studies. On completion of this project, we will have singled out the most promising cardiac CT architecture and algorithms to achieve 16cm coverage, 50ms temporal resolution, 20lp/cm spatial resolution, 10HU noise level, and 5mSv effective dose simultaneously for the entire examination, with detailed specifications and performance evaluation, setting the stage for prototyping a next-generation cardiac CT system in a Phase-II project. This project will make a quantum leap in cardiac CT, in the sense that our proposed cardiac CT technology will enable significantly better diagnostic performance and bring major therapeutic benefits that affect 61.8 million Americans.
PUBLIC HEALTH RELEVANCE: Cardiovascular diseases are pervasive with high mortality and morbidity at tremendous social and healthcare costs. Cardiac CT technology needs major improvements to capture a fast beating heart with better image clarity at lower radiation dose. The overall goal of this project is to develop the next generation cardiac CT architecture and algorithms for significantly superior diagnostic performance and therapeutic outcomes that affect 61.8 million Americans.
描述(由申请人提供):心血管疾病普遍存在,死亡率和发病率高,社会和医疗保健成本巨大。迫切需要具有显着更高保真度和更低辐射剂量的心脏CT,但由于技术限制,目前还不可能实现。尽管心脏CT技术已经从16排到320排,从单源到双源有了显著的改进,但在时间分辨率、空间分辨率、辐射剂量等方面仍然存在技术挑战。基于弗吉尼亚理工大学和GE全球研究中心(GEGR)之间理想的学术-工业合作伙伴关系,我们致力于推动心脏CT技术的发展,并定义下一代心脏CT系统。本项目的总体目标是开发新的心脏CT架构和相关的重建算法,并定义下一代心脏CT系统。具体目标是:(1)设计、分析和比较具有新颖源和扫描轨迹的新颖心脏CT架构;(2)针对所提出的心脏CT架构开发用于ROI导向扫描和动态成像的分析和迭代心脏CT重建算法;以及(3)在理论研究、数值模拟幻影实验和观察者研究。在该项目完成后,我们将挑选出最有前途的心脏CT架构和算法,以实现16 cm的覆盖范围,50 ms的时间分辨率,20 lp/cm的空间分辨率,10 HU的噪声水平,并同时为整个检查5 mSv的有效剂量,详细的规格和性能评估,为第二阶段项目中的下一代心脏CT系统的原型设计奠定了基础。该项目将在心脏CT领域实现飞跃,因为我们提出的心脏CT技术将显著提高诊断性能,并带来影响6180万美国人的重大治疗益处。
公共卫生相关性:心血管疾病普遍存在,死亡率和发病率高,社会和医疗保健成本巨大。心脏CT技术需要进行重大改进,以在较低辐射剂量下捕获快速跳动的心脏,并获得更好的图像清晰度。该项目的总体目标是开发下一代心脏CT架构和算法,以实现显著优越的上级诊断性能和治疗结果,影响6180万美国人。
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(1)
数据更新时间:{{ journalArticles.updateTime }}
{{
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 }}
Bruno De Man其他文献
Bruno De Man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 }}
{{ truncateString('Bruno De Man', 18)}}的其他基金
Constrained Disentanglement (CODE) Network for CT Metal Artifact Reduction in Radiation Therapy
用于减少放射治疗中 CT 金属伪影的约束解缠结 (CODE) 网络
- 批准号:
10184493 - 财政年份:2021
- 资助金额:
$ 62.49万 - 项目类别:
Deviceless and Autonomous Prospective Cardiac CT Triggering
无设备和自主前瞻性心脏 CT 触发
- 批准号:
10452540 - 财政年份:2020
- 资助金额:
$ 62.49万 - 项目类别:
Deviceless and Autonomous Prospective Cardiac CT Triggering
无设备和自主前瞻性心脏 CT 触发
- 批准号:
10674706 - 财政年份:2020
- 资助金额:
$ 62.49万 - 项目类别:
Deviceless and Autonomous Prospective Cardiac CT Triggering
无设备和自主前瞻性心脏 CT 触发
- 批准号:
10029731 - 财政年份:2020
- 资助金额:
$ 62.49万 - 项目类别:
Deviceless and Autonomous Prospective Cardiac CT Triggering
无设备和自主前瞻性心脏 CT 触发
- 批准号:
10227088 - 财政年份:2020
- 资助金额:
$ 62.49万 - 项目类别:
Open-access X-ray and CT simulation toolkit for research in cancer imaging and dosimetry
用于癌症成像和剂量测定研究的开放式 X 射线和 CT 模拟工具包
- 批准号:
9913492 - 财政年份:2019
- 资助金额:
$ 62.49万 - 项目类别:
Cardiac CT: Advanced Architectures and Algorithms
心脏 CT:先进架构和算法
- 批准号:
8210901 - 财政年份:2010
- 资助金额:
$ 62.49万 - 项目类别:
Cardiac CT: Advanced Architectures and Algorithms
心脏 CT:先进架构和算法
- 批准号:
8706645 - 财政年份:2010
- 资助金额:
$ 62.49万 - 项目类别:
Cardiac CT: Advanced Architectures and Algorithms
心脏 CT:先进架构和算法
- 批准号:
8014879 - 财政年份:2010
- 资助金额:
$ 62.49万 - 项目类别:
相似海外基金
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:
$ 62.49万 - 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:
$ 62.49万 - 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:
$ 62.49万 - 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:
$ 62.49万 - 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:
$ 62.49万 - 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:
$ 62.49万 - 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:
$ 62.49万 - 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:
$ 62.49万 - 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:
$ 62.49万 - 项目类别:
Continuing Grant
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:
$ 62.49万 - 项目类别:
Research Grant