Cardiac CT Deblooming

项目总结/摘要 冠状动脉疾病（CAD）是最常见的心脏病类型，每年有超过370，000名美国人死亡。 阿利2.心脏CT是一种安全、准确、无创的检查方法，广泛应用于CAD的诊断和制定手术计划 治疗干预。在当前的CT技术下，钙晕伪影严重限制了准确性 冠状动脉狭窄评估。同样，支架晕染伪影导致高估支架内再狭窄。 因此，许多冠状动脉CT血管造影（CCTA）扫描是非诊断性的，并导致患者接受 昂贵且有创的冠状动脉造影术（伊卡）。 基于广泛的可行性结果，该项目的目标是使用深度学习创新来奠定基础， 完全消除了模糊伪影，而无需重新设计CT硬件，成本高昂。通用电气公司（GE）和通用电气公司（GE）之间的一个财团， 搜索，伦斯勒理工学院和威尔康奈尔医学将开发专门的成像协议 和机器学习方法，以避免或最小化模糊伪影，并评估 建议的解决方案。在目标1中，CT扫描协议将得到优化，并与深度学习算法配对。 图像处理和后处理算法，以生成高分辨率CT图像并防止晕染伪影。在 目标2，将开发图像域和原始数据域深度学习处理算法， 残留晕染。在体模扫描和仿真上成功地演示了所提出的方法后， 在目标3中，将基于100个临床数据集对所提出的CT方法进行临床证明和优化， 冠状动脉疾病患者，使用血管内超声作为地面实况参考。 在项目结束时，我们将展示并公开传播一种系统的方法， 基本上消除了心脏CT中的模糊伪影，而无需昂贵的硬件升级。这将是另一个问题- 深度学习的成功，使准确的冠状动脉狭窄评估和消除许多不必要的诊断， 鼻导管插入术

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.