Dual Energy CT-enabled Asymptomatic Pulmonary Embolism Detection on Non-contrast CT

双能 CT 平扫无症状肺栓塞检测

• 批准号: 10287287
• 负责人: Dufan Wu
• 金额: $ 44.87万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10287287
• 关键词: 3-Dimensional; Acute; Algorithms; American; Appearance; Benign; Biological; Brain hemorrhage; Calcium; Chest; Clinical; Data; Data Set; Detection; Diagnosis; Embolism; Emerging Technologies; Ensure; Generations; Goals; Hardness; Hemorrhage; Human; Image; Inherited; Iodine; Label; Location; Low Prevalence; Lung diseases; Malignant - descriptor; Manuals; Modeling; Noise; Outcome; Patients; Performance; Physicians; Population; Prevalence; Probability; Pulmonary Embolism; Reader; Reading; Recurrence; Scanning; Signal Transduction; Specificity; Testing; Texture; Training; Validation; X-Ray Computed Tomography; algorithm development; algorithm training; attenuation; base; cohort; college; contrast enhanced; contrast imaging; cost effectiveness; deep learning; deep learning algorithm; deep neural network; detector; digital; image reconstruction; improved; mortality; prevent; radiologist; screening; supervised learning; thrombolysis; validation studies; virtual

Project Summary Asymptomatic pulmonary embolism (PE) are often incidentally discovered from contrast computed tomography (CT) scans that do not target PE. It has a mean prevalence of 2.6% among patients and associated with increased mortality rate and recurrence of PE. Currently non-contrast CT are not read by radiologists for PE, because the hyperintensity signal of thrombolysis on NCCT is weak. Hence, around 2.6% of the patients with NCCT can have asymptomatic PE but are not diagnosed at all, which is potentially a large population. We propose a deep learning-based automatic PE detection algorithm for single-energy NCCT to improve the cost-effectiveness to discover asymptomatic PE from NCCT. The algorithm will be used to identify patients with higher probability of PE and call for human reading or contrast CT scans. A major challenge is training data accumulation due to the relatively low prevalence of asymptomatic PE and hardness of reading NCCT. To overcome this challenge, we propose to utilize dual energy CT (DECT), which is becoming routinely used for PE diagnosis, to generate virtual non-contrast (VNC) images as training images. We propose to use deep learning algorithm for the VNC generation to fill the image quality gap between VNC images and real single-energy NCCT, which ensures that our PE detection algorithm trained on VNC images can be readily applied to real NCCT. The expected outcome of the project is (1) a deep learning algorithm to generate realistic VNC images from contrast DECT; (2) a deep learning algorithm to screen PE from NCCT with high sensitivity.

项目概要 无症状肺栓塞 (PE) 常常是通过造影计算机断层扫描偶然发现的 (CT) 扫描不针对 PE。它在患者中的平均患病率为 2.6%，并且与增加 PE 的死亡率和复发率。目前，放射科医生不会通过非对比 CT 来判断 PE，因为 NCCT溶栓高信号弱。因此，大约 2.6% 的 NCCT 患者可能患有 无症状肺栓塞但根本没有被诊断出来，这可能是一个很大的人群。 我们提出了一种基于深度学习的单能量 NCCT 自动 PE 检测算法，以提高 通过 NCCT 发现无症状 PE 的成本效益。该算法将用于识别患有以下疾病的患者 PE 的可能性较高，需要进行人工读数或对比 CT 扫描。一个主要挑战是训练数据 由于无症状 PE 的患病率相对较低以及 NCCT 的阅读难度而导致积累。到 为了克服这一挑战，我们建议利用双能 CT (DECT)，它已成为 PE 的常规使用 诊断，生成虚拟非对比（VNC）图像作为训练图像。我们建议使用深度学习 VNC生成算法，填补VNC图像与真实单能NCCT之间的图像质量差距， 这确保了我们在 VNC 图像上训练的 PE 检测算法可以轻松应用于真实的 NCCT。 该项目的预期成果是 (1) 一种深度学习算法，用于生成真实的 VNC 图像 对比DECT； (2)深度学习算法从NCCT中高灵敏度筛选PE。