Quantitative Cardiac PET/CT Imaging

定量心脏 PET/CT 成像

基本信息

  • 批准号:
    7185215
  • 负责人:
  • 金额:
    $ 13.51万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2007
  • 资助国家:
    美国
  • 起止时间:
    2007-02-01 至 2012-01-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Positron emission tomography (PET) combined with computed tomography (CT) in an integrated PET/CT scanner offers a single-study, noninvasive technique for the diagnosis of coronary artery disease. PET/CT cardiac scans can provide complementary functional and anatomic assessments: PET can quantitate myocardial perfusion and metabolism offering insight into small vessel disease and tissue viability; while contrast enhanced CT angiography provides information on coronary anatomy and atherosclerotic burden. PET offers the potential for truly quantitative measurements, but this quantitation is confounded by (1) misaligned CT-based attenuation correction (CTAC) factors and (2) the limited sensitivity of PET imaging. Respiratory motion, cardiac motion, and/or patient movement cause misalignment between the CTAC image and the PET image. To reduce this misalignment, Aim 1 develops and evaluates CT acquisition protocols tailored for attenuation correction in cardiac PET imaging to improve qualitative and quantitative accuracy. The limited sensitivity of PET imaging results in noisy dynamic studies and requires image acquisitions over multiple respiratory and cardiac cycles. Aim 2 investigates methods to reduce these degradations with reconstruction methods tailored for myocardial blood flow estimation in cardiac PET imaging. This research plan complements a comprehensive training plan to facilitate Dr. Alessio's development as a bioimaging researcher and junior faculty member in the Department of Radiology at the University of Washington. Dr. Alessio's previous research has focused on statistical image processing and tomographic reconstruction. The proposed training plan including mentoring, biomedical coursework, and scholarly activities will allow him to transition into a biomedical role and solve pressing clinical problems in cardiac imaging. The mentors for this proposal represent several decades of experience - James Caldwell and James Bassingthwaighte for cardiovascular function and modeling and Thomas Lewellen and Paul Kinahan for nuclear medicine physics, image generation, and clinical protocol optimization. The University of Washington, with its internationally recognized programs in cardiovascular bioengineering, nuclear cardiology, and diagnostic physics, is an ideal environment for the development of an independent research career combining state of the art imaging techniques with clinical needs for the assessment of coronary disease. This development will occur while contributing solutions to what are widely recognized as the most important challenges in cardiac PET/CT imaging.
描述(由申请人提供):集成 PET/CT 扫描仪中的正电子发射断层扫描 (PET) 与计算机断层扫描 (CT) 相结合,提供了一种用于诊断冠状动脉疾病的单项研究、无创技术。 PET/CT 心脏扫描可以提供补充的功能和解剖评估:PET 可以定量心肌灌注和代谢,从而深入了解小血管疾病和组织活力;而对比增强 CT 血管造影可提供有关冠状动脉解剖和动脉粥样硬化负担的信息。 PET 提供了真正定量测量的潜力,但这种定量受到以下因素的影响:(1) 未对准的基于 CT 的衰减校正 (CTAC) 因素和 (2) PET 成像的有限灵敏度。呼吸运动、心脏运动和/或患者运动导致 CTAC 图像和 PET 图像之间未对准。为了减少这种错位,Aim 1 开发并评估了专为心脏 PET 成像中的衰减校正而定制的 CT 采集协议,以提高定性和定量的准确性。 PET 成像的有限灵敏度会导致动态研究存在噪音,并且需要在多个呼吸和心动周期内采集图像。目标 2 研究通过为心脏 PET 成像中心肌血流估计量身定制的重建方法来减少这些退化的方法。该研究计划是对综合培训计划的补充,以促进 Alessio 博士作为华盛顿大学放射科生物成像研究员和初级教员的发展。 Alessio 博士之前的研究重点是统计图像处理和断层扫描重建。拟议的培训计划包括指导、生物医学课程和学术活动,将使他能够过渡到生物医学角色并解决心脏成像领域紧迫的临床问题。该提案的导师代表了数十年的经验 - James Caldwell 和 James Bassingthwaighte 负责心血管功能和建模,Thomas Lewellen 和 Paul Kinahan 负责核医学物理、图像生成和临床方案优化。华盛顿大学在心血管生物工程、核心脏病学和诊断物理学方面拥有国际公认的课程,为将最先进的成像技术与评估冠状动脉疾病的临床需求相结合的独立研究事业的发展提供了理想的环境。这一发展将同时为被广泛认为是心脏 PET/CT 成像中最重要的挑战提供解决方案。

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 }}

Adam M Alessio其他文献

Adam M Alessio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('Adam M Alessio', 18)}}的其他基金

Development of Artificial Intelligence (AI) based algorithms to classify the Pneumoconioses
开发基于人工智能 (AI) 的算法来对尘肺病进行分类
  • 批准号:
    10709621
  • 财政年份:
    2022
  • 资助金额:
    $ 13.51万
  • 项目类别:
Development of Artificial Intelligence (AI) based algorithms to classify the Pneumoconioses
开发基于人工智能(AI)的算法来对尘肺病进行分类
  • 批准号:
    10428946
  • 财政年份:
    2022
  • 资助金额:
    $ 13.51万
  • 项目类别:
Automatic Rib Fracture Detection in Pediatric Radiography to Identify Non-Accidental Trauma
儿科放射线照相中的自动肋骨骨折检测以识别非意外创伤
  • 批准号:
    9976563
  • 财政年份:
    2019
  • 资助金额:
    $ 13.51万
  • 项目类别:
IEEE Medical Imaging Conference
IEEE 医学影像会议
  • 批准号:
    8910150
  • 财政年份:
    2015
  • 资助金额:
    $ 13.51万
  • 项目类别:
Low-dose Myocardial Perfusion Imaging by CT
CT 低剂量心肌灌注成像
  • 批准号:
    9039123
  • 财政年份:
    2012
  • 资助金额:
    $ 13.51万
  • 项目类别:
Low-dose Myocardial Perfusion Imaging by CT
CT 低剂量心肌灌注成像
  • 批准号:
    8650918
  • 财政年份:
    2012
  • 资助金额:
    $ 13.51万
  • 项目类别:
Low-dose Myocardial Perfusion Imaging by CT
CT 低剂量心肌灌注成像
  • 批准号:
    8460469
  • 财政年份:
    2012
  • 资助金额:
    $ 13.51万
  • 项目类别:
Low-dose Myocardial Perfusion Imaging by CT
CT 低剂量心肌灌注成像
  • 批准号:
    8290709
  • 财政年份:
    2012
  • 资助金额:
    $ 13.51万
  • 项目类别:
Quantitative Cardiac PET/CT Imaging
定量心脏 PET/CT 成像
  • 批准号:
    7340109
  • 财政年份:
    2007
  • 资助金额:
    $ 13.51万
  • 项目类别:
Quantitative Cardiac PET/CT Imaging
定量心脏 PET/CT 成像
  • 批准号:
    7578264
  • 财政年份:
    2007
  • 资助金额:
    $ 13.51万
  • 项目类别:

相似海外基金

SBIR Phase II: Novel size-changing, gadolinium-free contrast agent for magnetic resonance angiography
SBIR II 期:用于磁共振血管造影的新型尺寸变化、无钆造影剂
  • 批准号:
    2322379
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
    Cooperative Agreement
ImproviNg rEnal outcomes following coronary angiograPhy and/or percuTaneoUs coroNary intErventions: a pragmatic, adaptive, patient-oriented randomized controlled trial
改善冠状动脉造影和/或经皮冠状动脉介入治疗后的肾脏结局:一项务实、适应性、以患者为导向的随机对照试验
  • 批准号:
    478732
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
    Operating Grants
Neonatal Optical Coherence Tomography Angiography to Assess the Effects of Postnatal Exposures on Retinal Development and Predict Neurodevelopmental Outcomes
新生儿光学相干断层扫描血管造影评估产后暴露对视网膜发育的影响并预测神经发育结果
  • 批准号:
    10588086
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
Motion-Resistant Background Subtraction Angiography with Deep Learning: Real-Time, Edge Hardware Implementation and Product Development
具有深度学习的抗运动背景减影血管造影:实时、边缘硬件实施和产品开发
  • 批准号:
    10602275
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
Highly Accelerated Magnetic Resonance Angiography using Deep Learning
使用深度学习的高加速磁共振血管造影
  • 批准号:
    2886357
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
    Studentship
Development of a method to simultaneously obtain cerebral blood flow information and progression of cerebral white matter lesions using head MR angiography.
开发一种使用头部磁共振血管造影同时获取脑血流信息和脑白质病变进展的方法。
  • 批准号:
    23K14839
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
    Grant-in-Aid for Early-Career Scientists
Development of a new diagnostic method for coronary artery disease using automated image analysis with postmortem coronary angiography CT
使用死后冠状动脉造影 CT 自动图像分析开发冠状动脉疾病的新诊断方法
  • 批准号:
    23K19795
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
    Grant-in-Aid for Research Activity Start-up
Novel ultrahigh speed swept source OCT angiography methods in diabetic retinopathy
糖尿病视网膜病变的新型超高速扫源 OCT 血管造影方法
  • 批准号:
    10656644
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
Automated Machine Learning-Based Brain Artery Segmentation, Anatomical Prior Labeling, and Feature Extraction on MR Angiography
基于自动机器学习的脑动脉分割、解剖先验标记和 MR 血管造影特征提取
  • 批准号:
    10759721
  • 财政年份:
    2023
  • 资助金额:
    $ 13.51万
  • 项目类别:
SCH: A physics-informed machine learning approach to dynamic blood flow analysis from static subtraction computed tomographic angiography imaging
SCH:一种基于物理的机器学习方法,用于从静态减影计算机断层血管造影成像中进行动态血流分析
  • 批准号:
    2205265
  • 财政年份:
    2022
  • 资助金额:
    $ 13.51万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了