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: 8650827
• 负责人: Dimitrios Mitsouras
• 金额: $ 17.37万
• 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/8650827
• 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; 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; signal processing; 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）：这个指导职业发展奖将使一个富有成效的，创新的，成功的初级科学家的培训和职业过渡。一个关键因素是由心血管诊断成像和医学领域的思想领袖提供培训和指导。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。他是一位成功的磁共振物理学家，对该领域做出了重要贡献。他是哈佛放射学助理教授，也是BWH应用成像科学实验室（AISL）的MR物理和工程主任，由Rybicki博士指导。作为一名成功的PI和熟练的导师，Rybicki博士将热情地支持和指导Mitsouras博士实现职业独立。 冠状动脉疾病（CAD）患者的非侵入性成像正被CT吸引;拟议的研究解决了其目前在临床护理中的一些局限性。AISL评估了新的CT技术，并研究和发表了冠状动脉增强的潜力，以扩展临床信息。人们认识到，临床心脏CT的下一个飞跃是流量量化; Mitsouras博士建议 一种全新的方法来完成这一系列的工作。对于候选人来说，从MR基础物理到临床CT应用的过渡产生了大量未满足的培训需求。基于课堂的（哈佛/麻省理工学院健康科学与技术部）和基于实践成像的（BWH无创心血管成像计划）培训将理想地弥合这一差距。K 01提案还整合了知名专家的指导，这些专家了解并渴望与Mitsouras博士合作。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Xiaochuan Pan博士、造影剂输送专家Tae Bae博士、心肌灌注建模专家Al Lardo博士和高级临床应用专家Joao Lima博士。 这个特殊的团队将在一个结构化的研究项目中指导Mitsouras博士，利用他们的集体专业知识和Mitsouras博士现有的技能。他们将通过应用创新方法从临床CT血管造影中提取血流来扩展非侵入性CAD成像的作用。目前，非侵入性的访问流量仅限于模拟流体动力学使用的动脉管腔分割从临床图像。我们推进这一领域的一个新的过渡，从这样的CT为基础的流量，CT衍生的流量：定量流量，直接或间接地从在体内观察到的动脉造影增强的时空模式。越来越多的文献支持将定性血流信息嵌入动脉CTA增强模式。我们表明，这些模式实际上具有平滑的空间和时间变化，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病变特定的血流动力学指标，如血流储备分数。