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.

项目概要/摘要 在母版 R21 中，我们正在开发基于深度学习 (DL) 的头部运动估计模型，基于 PET 原始数据，在 PET 扫描期间实时跟踪头部运动，无需外部运动 在本增刊中，我们将致力于开发专用于传感器的深度学习神经网络。 评估阿尔茨海默病 (AD) 受试者的运动 大脑 PET 成像对头部运动高度敏感。 扫描持续时间长，通常会加剧头部运动引起的问题 超过一小时，并通过小规模的以疾病为中心的感兴趣区域（例如海马体）来治疗 AD 耶鲁大学 PET 中心最近获得了一组 AD PET 数据，其中包括患有以下疾病的 AD 患者。 使用 CT1812 进行治疗，CT1812 是一种一流的药物，可取代与神经元受体结合的 Aβ 寡聚物 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范围的小范围变化。 随着时间的推移，现有的 Polaris Vicra 运动跟踪在同一个体中与 AD 相关的大脑区域有 5-10% 的变化。 因此，迫切需要为这项研究开发精确的头部运动校正。 在本行政补充文件中，我们将致力于开发用于估计的深度学习神经网络 针对 CT1812 研究中获取的 AD PET 数据集进行运动，并在目标 1 中执行严格的评估。 我们将开发一种新颖的深度学习方法来执行运动校正，其中包括：（1）一个深度学习模型 基于每 1 秒帧的快速背投影图像生成合成 AD PET 图像，以及 (2) 我们将评估我们的第二个 DL 模型，用于估计两个合成 AD PET 图像之间的刚性运动。 运动估计模型使用来自 CT1812 研究中 20 名受试者的数据 在目标 2 中，我们将对所有 CT1812 研究进行动力学建模分析。 两个跟踪器都将使用 DL 估计运动来执行动态运动校正重建。 校正（来自目标 1）并与使用基于 Vicra 的运动校正的重建进行比较。 将突触密度 (11C-UCB-J)、葡萄糖代谢 (18F-FDG) 和认知功能的变化关联起来 在 CT1812 治疗后，我们发现我们提出的基于 DL 的方法将优于 Vicra- 基于方法，通过减少队列中任何定量 PET 测量的跨受试者差异 我们还采用了 11C-UCB-J 和 18F-FDG 示踪剂，基于 DL 的方法将优于 Vicra。 增加 PET 测量值变化和 认知改善。