Deviceless and Autonomous Prospective Cardiac CT Triggering
无设备和自主前瞻性心脏 CT 触发
基本信息
- 批准号:10029731
- 负责人:
- 金额:$ 106.06万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-08-01 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:AlgorithmsAnatomyAngiographyBolus InfusionCaliforniaCardiacCause of DeathClinicalContrast MediaCoronaryCoronary heart diseaseDataData AnalysesDiagnosisDiagnosticElectrocardiogramEnsureFeasibility StudiesFinancial compensationGoalsHeartHospitalsImageInstitutional Review BoardsIodineMeasurementMorphologyMotionMyocardialOutcomePatientsPerformancePerfusionPhasePhysicsPhysiologic pulsePreparationProspective StudiesProtocols documentationRadiation Dose UnitResearchRoentgen RaysRotationScanningSystemTechniquesTechnologyTherapeutic InterventionTimeTrainingTranslatingTubeUniversitiesWomanX-Ray Computed Tomographyalgorithm developmentbasecontrast enhanceddeep learningdeep learning algorithmexperienceheart imagingimage reconstructionimaging modalityinnovationmennon-invasive imagingprospectivereconstructionstandard of caretemporal measurementtime interval
项目摘要
PROJECT SUMMARY/ABSTRACT
Coronary heart disease (CHD) is the leading cause of death worldwide. An estimated 3.8 million men and 3.4
million women die each year from CHD. Cardiac CT is a safe, accurate, non-invasive imaging modality used for
diagnosing CHD and for planning therapeutic interventions. Cardiac CT exams are still challenging to perform
due to the beating heart and the need to carefully time the scan based on cardiac phase and based on when the
peak iodine contrast enhancement is reached. The overall exam duration and the complexity of performing these
exams (contrasted with limited reimbursement levels) have limited patient access to cardiac CT to academic
hospitals and specialized cardiac imaging centers. As compared to other CT exams, cardiac CT exams require
additional patient preparation time, additional CT scans to track the bolus, and additional contrast agent to avoid
missing the peak enhancement.
The goal of this project is to develop a smart cardiac CT scanner that autonomously determines the optimal
scan time interval without ECG, traditional bolus tracking or timing bolus. Initial results show that it is possible
to extract cardiac gating information from a few CT projection measurements prior to the diagnostic CT scan,
without reconstruction. This is made possible by an innovative combination of fast X-ray tube pulsing and deep
learning raw data analysis. This project builds on GE Research's experience with cardiac CT technologies, deep
learning algorithms and X-ray tube physics, as well as the strong clinical cardiac CT expertise at the University
of California San Diego.
The outcome of this project will be a clinical feasibility study of the autonomous triggering approach, which
has the potential to simplify and increase patient access to cardiac CT, while reducing exam time, reducing con-
trast agent volume, and ensuring robust image quality.
项目概要/摘要
冠心病(CHD)是全世界死亡的主要原因。估计有 380 万男性和 3.4
每年有数百万妇女死于冠心病。心脏CT是一种安全、准确、无创的成像方式,用于
诊断 CHD 并规划治疗干预措施。心脏 CT 检查仍然具有挑战性
由于心脏跳动,并且需要根据心脏相位和心脏何时开始仔细计算扫描时间
达到碘对比度增强峰值。总体考试持续时间和执行这些考试的复杂性
检查(与有限的报销水平相比)限制了患者获得心脏 CT 的学术机会
医院和专门的心脏成像中心。与其他CT检查相比,心脏CT检查需要
额外的患者准备时间、追踪推注的额外 CT 扫描以及避免使用的额外造影剂
缺少峰值增强。
该项目的目标是开发一款智能心脏 CT 扫描仪,可自主确定最佳心脏 CT 扫描仪
扫描时间间隔无需心电图、传统推注跟踪或定时推注。初步结果表明这是可能的
在诊断 CT 扫描之前从一些 CT 投影测量中提取心脏门控信息,
无需重建。这是通过快速 X 射线管脉冲和深度扫描的创新组合来实现的。
学习原始数据分析。该项目以 GE Research 在心脏 CT 技术方面的经验为基础,深入
学习算法和 X 射线管物理,以及大学强大的临床心脏 CT 专业知识
加利福尼亚州圣地亚哥。
该项目的成果将是自主触发方法的临床可行性研究,该方法
有潜力简化并增加患者接受心脏 CT 的机会,同时减少检查时间、减少费用
造影剂体积,并确保稳定的图像质量。
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)
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:
$ 106.06万 - 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:
$ 106.06万 - 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:
$ 106.06万 - 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:
$ 106.06万 - 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:
$ 106.06万 - 项目类别:
Cardiac CT: Advanced Architectures and Algorithms
心脏 CT:先进架构和算法
- 批准号:
7792699 - 财政年份:2010
- 资助金额:
$ 106.06万 - 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:
$ 106.06万 - 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:
$ 106.06万 - 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:
$ 106.06万 - 项目类别:
相似海外基金
Linking Epidermis and Mesophyll Signalling. Anatomy and Impact in Photosynthesis.
连接表皮和叶肉信号传导。
- 批准号:
EP/Z000882/1 - 财政年份:2024
- 资助金额:
$ 106.06万 - 项目类别:
Fellowship
Digging Deeper with AI: Canada-UK-US Partnership for Next-generation Plant Root Anatomy Segmentation
利用人工智能进行更深入的挖掘:加拿大、英国、美国合作开发下一代植物根部解剖分割
- 批准号:
BB/Y513908/1 - 财政年份:2024
- 资助金额:
$ 106.06万 - 项目类别:
Research Grant
Simultaneous development of direct-view and video laryngoscopes based on the anatomy and physiology of the newborn
根据新生儿解剖生理同步开发直视喉镜和视频喉镜
- 批准号:
23K11917 - 财政年份:2023
- 资助金额:
$ 106.06万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Genetics of Extreme Phenotypes of OSA and Associated Upper Airway Anatomy
OSA 极端表型的遗传学及相关上呼吸道解剖学
- 批准号:
10555809 - 财政年份:2023
- 资助金额:
$ 106.06万 - 项目类别:
computational models and analysis of the retinal anatomy and potentially physiology
视网膜解剖学和潜在生理学的计算模型和分析
- 批准号:
2825967 - 财政年份:2023
- 资助金额:
$ 106.06万 - 项目类别:
Studentship
Computational comparative anatomy: Translating between species in neuroscience
计算比较解剖学:神经科学中物种之间的翻译
- 批准号:
BB/X013227/1 - 财政年份:2023
- 资助金额:
$ 106.06万 - 项目类别:
Research Grant
Doctoral Dissertation Research: Social and ecological influences on brain anatomy
博士论文研究:社会和生态对大脑解剖学的影响
- 批准号:
2235348 - 财政年份:2023
- 资助金额:
$ 106.06万 - 项目类别:
Standard Grant
Development of a novel visualization, labeling, communication and tracking engine for human anatomy.
开发一种新颖的人体解剖学可视化、标签、通信和跟踪引擎。
- 批准号:
10761060 - 财政年份:2023
- 资助金额:
$ 106.06万 - 项目类别:
Understanding the functional anatomy of nociceptive spinal output neurons
了解伤害性脊髓输出神经元的功能解剖结构
- 批准号:
10751126 - 财政年份:2023
- 资助金额:
$ 106.06万 - 项目类别:
Anatomy and functions of LTP interactomes and their relationship to small RNA signals in systemic acquired resistance
LTP相互作用组的解剖和功能及其与系统获得性耐药中小RNA信号的关系
- 批准号:
BB/X013049/1 - 财政年份:2023
- 资助金额:
$ 106.06万 - 项目类别:
Research Grant