Quantitative Coronary Hemodynamics Derived from CT Angiography

CT 血管造影得出的定量冠状动脉血流动力学

• 批准号: 8509973
• 负责人: Dimitrios Mitsouras
• 金额: $ 17.33万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8509973
• 关键词: 3D Print; Address; Adenosine; Algorithms; Angiography; Arteries; Back; Beryllium; Blood; Blood Circulation; Blood flow; Cardiac; Cardiovascular system; Catheters; Cause of Death; Clinical; Clinical Data; Clinical Management; Contrast Media; Coronary; Coronary Arteriosclerosis; Coronary artery; Data; Detection; Development; Diagnostic; Diagnostic Imaging; Drug Formulations; Engineering; Evaluation; Funding; Goals; Health; Health Care Costs; Health Sciences; Health Technology; High Performance Computing; Image; In Vitro; Individual; Intervention; K-Series Research Career Programs; Learning; Lesion; Light; Link; Liquid substance; Literature; Mathematics; Measurement; Measures; Medicine; Mentored Research Scientist Development Award; Mentors; Mentorship; Methods; Metric; Modeling; Myocardial perfusion; Noise; Patients; Pattern; Pattern Recognition; Performance; Perfusion; Photons; Physics; Population; Problem Solving; Protocols documentation; Publishing; Radiology Specialty; Research; Research Personnel; Research Project Grants; Residual state; Rest; Risk; Role; Science; Scientist; Sensitivity and Specificity; Severities; Signal Transduction; Simulate; Solutions; Stenosis; Stress; Structure; System; Techniques; Technology; Testing; Time; Training; Training Programs; Translating; Trees; Variant; Western World; Work; Workplace; X-Ray Computed Tomography; base; cardiovascular imaging; career; career development; clinical application; clinical care; cohort; computer science; computerized data processing; coronary computed tomography angiography; design; diagnostic accuracy; hemodynamics; improved; in vivo; information model; innovation; mathematical model; novel; novel strategies; professor; prospective; public health relevance; research study; simulation; skills; time use

DESCRIPTION (provided by applicant): This mentored career development award will enable the training and career transition of a productive, innovative, successful junior scientist. A key element is training and mentorship by thought-leaders in the field of cardiovascular diagnostic imaging and medicine. The applicant, Dr. Mitsouras, has been trained in computer science, physics, and applied mathematics at Brown and MIT. He is a successful MR physicist with important contributions to the field. He is Assistant Professor of Radiology at Harvard, and the Director of MR Physics and Engineering of the BWH Applied Imaging Science Lab (AISL), directed by Dr. Rybicki. A successful PI and skilled mentor, Dr. Rybicki will enthusiastically support and mentor Dr. Mitsouras to career independence. Noninvasive imaging for patients with coronary artery disease (CAD) is gravitating to CT; the proposed research addresses some of its current limitations in clinical care. The AISL evaluates new CT technologies, and has studied and published the potential of coronary enhancement to extend clinical information. It is recognized that the next leap in clinical cardiac CT is flow quantification; Dr. Mitsouras proposes a transformative new approach to this body of work. For the candidate, the transition from MR basic physics to clinical CT applications creates a large, unmet training need. Classroom based (Harvard/MIT Division of Health Sciences and Technology) and practical imaging based (BWH noninvasive cardiovascular imaging program) training will ideally bridge this gap. This K01 proposal also integrates mentorship from renowned experts who know and are eager to work with Dr. Mitsouras. The team includes Dr. Mel Clouse, an exceptional clinician and mentor, Dr. Xiaochuan Pan, an expert in CT physics, Dr. Tae Bae, an expert in contrast delivery, Dr. Al Lardo, an expert in modeling myocardial perfusion, and Dr. Joao Lima, an expert in advanced clinical applications. This exceptional team will navigate Dr. Mitsouras in a structured research project, capitalizing on their collective expertise and Dr. Mitsouras' existing skills. Together, tey will extend the role of noninvasive CAD imaging by applying innovative methods to extract blood flow from clinical CT angiography. At present, noninvasive access to flow is limited to simulated fluid dynamics using an arterial lumen segmented from clinical images. We advance this field with a novel transition from such CT-based flow, to CT-derived flow: quantitative flow that is derived either directly or indirectly from the spatio-temporal patterns of arterial contrast enhancement observed in vivo. A growing body of literature supports qualitative flow information is embedded in CTA enhancement patterns across an artery. We show these patterns in fact possess smooth space and time variations that are intrinsically linked to flow. By unifying signal processing and pattern recognition with high-performance fluid dynamics modeling, this information will be accessed for the first time to quantify true patient-specific flow and yield lesion-specific hemodynamic metrics such as fractional flow reserve.

描述（由申请人提供）：该指导性职业发展奖将促进富有成效、创新、成功的初级科学家的培训和职业转型。一个关键要素是心血管诊断成像和医学领域思想领袖的培训和指导。申请人 Mitsouras 博士曾在布朗大学和麻省理工学院接受过计算机科学、物理学和应用数学方面的培训。他是一位成功的 MR 物理学家，对该领域做出了重要贡献。他是哈佛大学放射学助理教授，也是 BWH 应用成像科学实验室 (AISL) 的 MR 物理和工程主任，由 Rybicki 博士领导。 Rybicki 博士是一位成功的 PI 和熟练的导师，他将热情支持和指导 Mitsouras 博士实现职业独立。 冠状动脉疾病 (CAD) 患者的无创成像正转向 CT；拟议的研究解决了目前临床护理中的一些局限性。 AISL 评估新的 CT 技术，并研究并发表了冠状动脉增强技术扩展临床信息的潜力。人们认识到临床心脏CT的下一个飞跃是血流量化； Mitsuuras博士建议 对这项工作采取变革性的新方法。对于候选人来说，从 MR 基础物理到临床 CT 应用的过渡产生了巨大的、未满足的培训需求。基于课堂（哈佛大学/麻省理工学院健康科学与技术部）和基于实用成像（BWH 无创心血管成像计划）的培训将理想地弥补这一差距。这项 K01 提案还融入了了解并渴望与 Mitsuuras 博士合作的知名专家的指导。该团队包括杰出的临床医生和导师 Mel Clouse 博士、CT 物理学专家潘晓川博士、造影剂输送专家 Tae Bae 博士、心肌灌注建模专家 Al Lardo 博士和Joao Lima，高级临床应用专家。 这个杰出的团队将利用他们的集体专业知识和 Mitsouras 博士的现有技能，引导 Mitsouras 博士完成一个结构化的研究项目。他们将共同应用创新方法从临床 CT 血管造影中提取血流，从而扩大无创 CAD 成像的作用。目前，无创血流获取仅限于使用从临床图像分割的动脉腔来模拟流体动力学。我们通过从基于 CT 的血流到 CT 衍生血流的新转变来推进这一领域：直接或间接从体内观察到的动脉对比增强的时空模式得出的定量血流。越来越多的文献支持将定性血流信息嵌入到动脉的 CTA 增强模式中。我们表明这些模式实际上具有平滑的空间和时间变化，这些变化与流动有着内在的联系。通过将信号处理和模式识别与高性能流体动力学建模相结合，将首次访问这些信息以量化真实的患者特定流量并产生特定于病变的血流动力学指标，例如血流储备分数。