Cardiac CT Deblooming

心脏CT去晕

基本信息

批准号：
10674730
负责人：
MATTHEW J BUDOFF
金额：
$ 7.03万
依托单位：
GENERAL ELECTRIC GLOBAL RESEARCH CTR
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：
2020-09-01 至 2024-03-15
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10674730
关键词：
Address Algorithms American Area Calcium Cardiac Cardiovascular Diseases Clinical Clinical Data Collaborations Coronary Angiography Coronary Arteriosclerosis Coronary Stenosis Coronary artery Data Data Set Dedications Diagnosis Diameter Goals Heart Heart Diseases High Resolution Computed Tomography Hospitals Image In Vitro Lead Measurement Medicine Metals Methodology Methods Morbidity - disease rate Morphologic artifacts Motion New York Noise Outcome Patients Physics Presbyterian Church Prevention Procedures Protocols documentation Recording of previous events Research Residual state Resolution Scanning Speed Stenosis Stents Techniques Technology Therapeutic Intervention Training Validation X-Ray Computed Tomography calcification cohort coronary computed tomography angiography cost deep learning deep learning algorithm design diagnostic catheterization image reconstruction improved in silico in vivo innovation learning network machine learning method man microCT mortality prevent reconstruction recruit restenosis simulation success temporal measurement ultrasound virtual virtual reality simulation

项目摘要

PROJECT SUMMARY/ABSTRACT Coronary artery disease (CAD) is the most common type of heart disease, killing over 370,000 Americans annu- ally2. Cardiac CT is a safe, accurate, non-invasive method widely employed for diagnosis of CAD and planning therapeutic interventions. With the current CT technology, calcium blooming artifacts severely limit the accuracy of coronary stenosis assessment. Similarly, stent blooming artifacts lead to overestimation of in-stent restenosis. As a result, many coronary CT angiography (CCTA) scans are non-diagnostic and result in patients receiving costly and invasive coronary angiography (ICA) procedures. Based on extensive feasibility results, the goal of this project is to use deep learning innovations to fundamen- tally eliminate blooming artifacts without costly redesign of the CT hardware. A consortium between GE Re- search, Rensselaer Polytechnic Institute and Weill Cornell Medicine will develop dedicated imaging protocols and machine learning methods to avoid or minimize blooming artifacts and evaluate the clinical impact of the proposed solutions. In Aim 1, the CT scan protocol will be optimized and paired with deep learning reconstruc- tion and post-processing algorithms to generate high-resolution CT images and prevent blooming artifacts. In Aim 2, image-domain and raw-data-domain deep learning processing algorithms will be developed to correct for residual blooming. After successful demonstration of the proposed methods on phantom scans and emulated clinical datasets, in Aim 3 the proposed CT methods will be clinically demonstrated and optimized based on 100 patients with coronary artery disease, using intravascular ultrasound as the ground-truth reference. At the end of the project, we will have demonstrated and publicly disseminated a systematic methodology to essentially remove blooming artifacts in cardiac CT without a costly hardware upgrade. This will be another suc- cess of deep learning, enabling accurate coronary stenosis assessment and eliminating many unnecessary diag- nostic catheterizations.

项目概要/摘要冠状动脉疾病 (CAD) 是最常见的心脏病类型，每年导致超过 370,000 名美国人死亡盟友2。心脏 CT 是一种安全、准确、无创的方法，广泛应用于 CAD 的诊断和规划治疗干预。就目前的 CT 技术而言，钙霜伪影严重限制了准确性冠状动脉狭窄评估。同样，支架开花伪影会导致支架内再狭窄的高估。因此，许多冠状动脉 CT 血管造影 (CCTA) 扫描是非诊断性的，导致患者接受昂贵的侵入性冠状动脉造影（ICA）手术。基于广泛的可行性结果，该项目的目标是利用深度学习创新来基础计数消除了光晕伪影，无需昂贵的 CT 硬件重新设计。 GE Re- 之间的财团伦斯勒理工学院和威尔康奈尔医学院将开发专用成像协议和机器学习方法，以避免或最小化光晕伪影并评估临床影响提出的解决方案。在目标 1 中，CT 扫描协议将得到优化，并与深度学习重建相结合化和后处理算法来生成高分辨率 CT 图像并防止光晕伪影。在目标 2，将开发图像域和原始数据域深度学习处理算法来纠正残花。在幻影扫描和仿真上成功演示了所提出的方法后临床数据集，在目标 3 中，所提出的 CT 方法将基于 100 个临床数据集进行临床论证和优化患有冠状动脉疾病的患者，使用血管内超声作为真实参考。在项目结束时，我们将展示并公开传播一种系统的方法来本质上消除了心脏 CT 中的光晕伪影，而无需进行昂贵的硬件升级。这将是又一个成功深度学习的过程，能够准确评估冠状动脉狭窄并消除许多不必要的诊断鼻导管插入术。