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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos 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

项目摘要

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.

项目概要/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聚随着正电子发射断层扫描 (PET) 示踪剂的最新发展，这两种病理的体内测量成为可能，并在改善我们的了解 AD 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，并在有效治疗后改善患者的治疗结果然而，有多种 PET 示踪剂可用于淀粉样蛋白和 tau 蛋白成像并生成图像。具有不同的视觉外观和数量特征，这些差异导致了困难。比较不同的研究，利用迄今为止收集的大量数据，并定义在这个项目中，我们将重点开发能够实现积极性的新技术。我们认为，通过协调 PET 成像数据并生成高度兼容的成像衍生测量结果。采用先进的统计和深度学习技术并开发新颖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多项研究支持这一论点：1）我们最近证明了这一点使用部分体积校正技术来减少白质信号污染，改善协议 2) 在我们的初步分析中，可以获得来自 PIB 和氟倍他吡的淀粉样蛋白负荷测量值；使用机器学习技术，我们可以显着改善全球淀粉样蛋白的示踪剂之间的协议 3）深度学习研究的最新进展证明了“估算”一种成像的能力通过利用大型成像数据集中的固有信息，我们可以从另一种模式中获得初步结果证明了它在图像协调任务中的潜力。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我们的具体目标是 1) 开发深度学习。可以根据来自一种示踪剂的图像从另一种示踪剂生成推定淀粉样蛋白 PET 图像的模型； 2) 开发可以生成统一的淀粉样蛋白负荷测量的统计学习方法；3) 获取头对头比较 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。