Data-driven Head Motion Correction in PET Imaging Using Deep Learning

使用深度学习在 PET 成像中进行数据驱动的头部运动校正

• 批准号: 10288215
• 负责人: Yihuan Lu
• 金额: $ 20.45万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10288215
• 关键词: Administrative Supplement; Aftercare; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Anatomy; Anxiety Disorders; Area; Back; Binding; Brain; Brain scan; Cigarette; Clinical; Cognitive; Data; Data Set; Detection; Development; Devices; Disease; Disease Progression; Evaluation; Event; Failure; Gold; Grant; Head; Hippocampus (Brain); Hour; Human; Image; Individual; Learning; Measurement; Measures; Metabolism; Methodology; Methods; Modeling; Monitor; Motion; Movement; Nature; Neurons; Parents; Parkinson Disease; Pathologic Processes; Patients; Performance; Pharmaceutical Preparations; Pharmacotherapy; Physiological Processes; Positron-Emission Tomography; Research; Research Personnel; Scanning; Smoking; Synapses; System; Testing; Time; Tracer; Training; Treatment Efficacy; United States National Institutes of Health; Variant; abeta oligomer; base; cognitive function; cohort; deep learning; deep neural network; density; drug efficacy; effectiveness evaluation; fluorodeoxyglucose; glucose metabolism; image reconstruction; improved; innovation; interest; kinetic model; motion sensor; neural network; novel; overtreatment; psychologic; receptor; reconstruction; simulation; statistics

Project Summary/Abstract In the parent R21, we are developing deep learning (DL)-based head motion estimation models, based on the PET raw data, to track head motion during a PET scan in real time without the need for external motion sensors. In this supplement, we will pursue the development of deep learning neural networks dedicated to estimating motion for Alzheimer's disease (AD) subjects. Brain PET imaging is highly sensitive to head motion. Problems due to head motion are exacerbated by the long duration of the scans, with scan times commonly over one hour, and by the small scale of disease-focused regions of interest, e.g., hippocampus, for AD subjects. The Yale PET Center recently acquired a set of AD PET data that includes AD patients under treatment using CT1812, a first-in-class drug that displaces Aβ oligomers bound to neuronal receptors at synapses. In the CT1812 study, AD patients underwent baseline and post-treatment scans using 11C-UCB-J and 18F-FDG. The longitudinal nature of this study requires the detection of small-scale changes in small-scale AD-related brain areas over time within the same individual. Existing Polaris Vicra motion tracking has a 5-10% failure rate, therefore, there is a compelling need to develop accurate head motion correction for this study. In this administrative supplement, we will pursue the development of DL neural networks dedicated to estimating motion for the AD PET dataset acquired under the CT1812 study, and perform rigorous evaluations. In Aim 1, we will develop a novel DL methodology to perform motion correction, which includes: (1) a DL model to generate synthetic AD PET images based on rapid back-projection images for every 1-sec frame, and (2) a second DL model to estimate the rigid motion between two synthetic AD PET images. We will evaluate our motion estimation models using the data from the twenty subjects acquired in the CT1812 study against Polaris Vicra motion tracking. In Aim 2, we will perform kinetic modeling analysis for all the CT1812 studies for both tracers. Dynamic motion corrected reconstruction will be performed using the DL estimated motion correction (from Aim 1) and be compared to reconstruction using Vicra-based motion correction. We will correlate the changes in synaptic density (11C-UCB-J), glucose metabolism (18F-FDG) and cognitive function following CT1812 treatment. We hypothesize that our proposed DL-based approach will outperform the Vicra- based approach by reducing cross-subject variations within cohorts for any quantitative PET measure in both 11C-UCB-J and 18F-FDG tracers. We also hypothesize the DL-based approach will outperform Vicra by increasing absolute correlation coefficient value for any correlation between changes in PET measures and cognitive improvement.

项目摘要/摘要 在父r21中，我们正在开发基于基于的深度学习（DL）的头部运动估计模型 PET原始数据，以实时跟踪PET扫描时的头部运动，而无需外部运动 传感器。在此补充中，我们将追求致力于的深度学习神经网络的发展 估计阿尔茨海默氏病（AD）受试者的运动。脑宠物成像对头部运动高度敏感。 由于扫描的持续时间，扫描时间通常会加剧由于头部运动引起的问题 超过一个小时，并且由于关注疾病的关注区域，例如海马，用于AD 主题。耶鲁大学宠物中心最近获得了一组广告宠物数据，其中包括AD患者 使用CT1812的治疗，这是一种替代与神经元受体的Aβ低聚物的第一类药物 突触。在CT1812研究中，AD患者使用11C-UCB-J进行了基线和治疗后扫描 和18f-fdg。这项研究的纵向性质需要检测小规模的小规模变化 随着时间的推移，与广告相关的大脑区域在同一个人中。现有的Polaris Vicra运动跟踪的5-10％ 因此，故障率迫切需要为这项研究开发准确的头部运动校正。在 这种行政补充，我们将追求致力于估算的DL神经网络的开发 根据CT1812研究获得的AD PET数据集的运动，并进行严格的评估。在AIM 1中， 我们将开发一种新型的DL方法来进行运动校正，其中包括：（1）DL模型 基于每个1-SEC框架的快速反射图像生成合成的AD PET图像，（2）A 第二个DL模型估算两个合成AD PET图像之间的刚性运动。我们将评估我们的 运动估算模型使用CT1812研究中获得的二十名受试者的数据反对 Polaris Vicra运动跟踪。在AIM 2中，我们将对所有CT1812研究进行动力学建模分析 两个示踪剂。动态运动校正的重建将使用DL估计运动进行 校正（来自AIM 1），并与基于VICRA的运动校正进行比较。我们将 将突触密度（11C-UCB-J），葡萄糖代谢（18F-FDG）和认知功能的变化相关联 CT1812处理后。我们假设我们提出的基于DL的方法将胜过Vicra- 通过减少队列中的跨主题变化的基于基于的方法，以便在两者中进行任何定量PET测量 11C-UCB-J和18F-FDG示踪剂。我们还假设基于DL的方法将优于Vicra 提高PET测量变化与 认知改善。