Tracer harmonization for amyloid and tau PET imaging using statistical and deep learning techniques

使用统计和深度学习技术协调淀粉样蛋白和 tau PET 成像的示踪剂

• 批准号: 10444803
• 负责人: Yi Su
• 金额: $ 228.24万
• 依托单位: BANNER HEALTH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10444803
• 关键词: Adopted; Affinity; Agreement; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease pathology; Amyloid; Anatomy; Appearance; Behavior; Binding; Brain; Calibration; Characteristics; Clinical; Communities; Data; Data Set; Derivation procedure; Detection; Development; Diagnosis; Diagnostic; Head; Image; Kinetics; Lead; Least-Squares Analysis; Linear Regressions; Machine Learning; Measurement; Modeling; Molecular Structure; Multicenter Studies; Noise; Participant; Pathogenesis; Pathology; Patient-Focused Outcomes; Phase; Play; Positron-Emission Tomography; Property; Protocols documentation; Publishing; Research; Research Support; Role; Scanning; Senile Plaques; Signal Transduction; Slice; Standardization; Symptoms; Techniques; Three-Dimensional Imaging; Tracer; Validation; Variant; Visual; amyloid imaging; artificial neural network; base; cohort; deep learning; deep learning model; design; effective therapy; head-to-head comparison; human imaging; imaging modality; improved; in vivo; indexing; large datasets; next generation; novel; prognostic; statistical and machine learning; statistical learning; success; tau Proteins; therapy development; white matter; working group; β-amyloid burden

PROJECT SUMMARY/ABSTRACT Amyloid and tau are two hallmark pathologies of Alzheimer's disease (AD), which start to accumulate in the brain long before clinical symptom onset. With the recent development of positron emission tomography (PET) tracers, in vivo measurements of these two pathologies became possible and play important roles in improving our understanding of AD pathogenesis and progression, and provide critical diagnostic and prognostic information that can facilitate the treatment development effort now and improve patient outcome when effective treatments become available. However, multiple PET tracers exist for both amyloid and tau imaging and generate images that have different visual appearances and quantitative characteristics. These differences lead to difficulties in comparing different studies, leveraging the large volume of data that have been collected to date, and defining a common criterion for positivity. In this project, we will focus on the development of novel techniques that can harmonize PET imaging data and generate highly compatible imaging derived measurements. We argue that by adopting advanced statistical and deep learning techniques and developing novel quantification strategies that take full use of the available information in imaging data, successful harmonization can be achieved via generalizable approaches. Several lines of research support this argument: 1) we recently demonstrated that using partial volume correction technique to reduce white matter signal contamination, improved agreements in amyloid burden measurements derived from PIB and florbetapir can be obtained; 2) in our preliminary analysis, using machine learning technique, we can substantially improve between-tracer agreements in global amyloid burden; 3) recent advancement in deep learning research demonstrated the ability to “impute” one imaging modality from another by taking advantage of the inherent information in large imaging datasets, and our preliminary results demonstrated its potential in image harmonization tasks. In this project, we will continue this research and develop a set of techniques that can be used to harmonize amyloid imaging data from different tracers and extent this approach for tau PET harmonization. Our specific aims are 1) to develop deep learning models that can generate imputed amyloid PET images from one tracer based on the images from another tracer; 2) to develop statistical learning approaches that can generate harmonized amyloid burden measurements; 3) to acquire head-to-head comparison tau PET imaging data and examine statistical and deep learning approaches to the harmonization of Tau PET imaging data.

项目摘要/摘要 淀粉样蛋白和tau蛋白是阿尔茨海默病(AD)的两种标志性病理，它们开始在大脑中积累 在临床症状出现之前很久。随着正电子发射断层扫描(PET)示踪剂的最新发展， 对这两种疾病的活体测量成为可能，并在改善我们的 了解AD的发病机制和进展，并提供关键的诊断和预后信息 这可以促进现在的治疗开发工作，并在有效的治疗时改善患者的结果 变得有空。然而，存在多个PET示踪剂，用于淀粉样蛋白和tau成像并生成图像 具有不同的视觉外观和数量特征。这些差异导致了在 比较不同的研究，利用迄今收集的大量数据，并确定 这是衡量积极程度的一个常见标准。在这个项目中，我们将专注于开发能够 协调PET成像数据并生成高度兼容的成像衍生测量结果。我们认为，通过 采用先进的统计和深度学习技术，开发新的量化策略， 充分利用成像数据中的可用信息，可以通过以下方式实现成功的协调 可概括的方法。有几条研究支持这一论点：1)我们最近证明了 使用部分体积校正技术减少脑白质信号污染，改进了 可以从PIB和florbetapir获得淀粉样蛋白负荷测量；2)在我们的初步分析中， 使用机器学习技术，我们可以显著改善全球淀粉样蛋白的示踪剂间协议 负担；3)深度学习研究的最新进展表明，有能力将一种成像“归因于” 通过利用大型成像数据集中的固有信息从另一种模式中分离出来，并且我们的 初步结果表明，它在图像协调任务中具有潜力。在这个项目中，我们将继续这样做 研究和开发一套可用于协调不同来源的淀粉样蛋白成像数据的技术 跟踪和扩展这种方法，以实现tau PET的协调。我们的具体目标是：1)发展深度学习 可以根据一种示踪剂的图像生成另一种示踪剂的淀粉样蛋白PET图像的模型； 2)开发统计学习方法，以产生协调的淀粉样蛋白负荷测量；3) 获取头对头对比的tau PET成像数据，并检查统计和深度学习 协调Tau PET成像数据的方法。