Cardiac CT Deblooming

项目摘要/摘要 冠心病(CAD)是最常见的心脏病类型，每年导致超过37万美国人死亡。 艾丽斯2。心脏CT是一种安全、准确、非侵入性的检查方法，被广泛应用于冠心病的诊断和规划 治疗性干预。在目前的CT技术中，钙华伪影严重限制了诊断的准确性 冠脉狭窄的评估。同样，支架内的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 昂贵且有创的冠状动脉造影术(ICA)程序。 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- 无需重新设计成本高昂的CT硬件即可消除开花伪影。通用电气再保险财团- Search、伦斯勒理工学院和威尔·康奈尔医学公司将开发专门的成像协议 和机器学习方法，以避免或最大限度地减少开花伪影，并评估 建议的解决方案。在目标1中，CT扫描方案将进行优化，并与深度学习重建配对- 图像分割和后处理算法，以生成高分辨率的CT图像并防止晕染伪影。在……里面 目标2，将开发图像域和原始数据域深度学习处理算法，以校正 残留的开花。在成功演示了所提出的方法后，对模型进行了扫描和仿真 临床数据集，在目标3中，所提出的CT方法将在临床上进行演示并基于100 冠心病患者，使用血管内超声作为地面真实参考。 在项目结束时，我们将演示并公开传播一种系统的方法来 从根本上消除心脏CT中的开花伪影，而无需昂贵的硬件升级。这将是又一次成功- 深度学习的CESS，实现准确的冠状动脉狭窄评估，并消除许多不必要的诊断 新发现的导尿术。

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.