Super-Resolution Tau PET Imaging for Alzheimer's Disease

用于阿尔茨海默病的超分辨率 Tau PET 成像

• 批准号: 10724836
• 负责人: Joyita Dutta
• 金额: $ 15.17万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10724836
• 关键词: Address; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Anatomy; Artificial Intelligence; Binding; Biological Markers; Clinical; Clinical Data; Clinical Trials; Cognitive; Data; Data Set; Detection; Disease; Disease Progression; Dose; Early Diagnosis; Early Intervention; Ensure; Evaluation; Funding Opportunities; Future; Goals; Grant; Image; Imaging Techniques; Impaired cognition; Lead; Longitudinal Studies; Longitudinal cohort; Longitudinal cohort study; Measurable; Measures; Medial; Memory impairment; Methods; Modeling; Monitor; Names; Neural Network Simulation; Neurobehavioral Manifestations; Neurofibrillary Tangles; Outcome Measure; Output; Pathology; Performance; Phase; Positron-Emission Tomography; Recovery; Research; Resolution; Sample Size; Sampling; Scanning; Series; Statistical Data Interpretation; Supervision; Surrogate Markers; Techniques; Technology; Temporal Lobe; Tracer; Training; Treatment Efficacy; Validation; aging brain; arm; base; clinically translatable; cohort; data harmonization; deep learning; deep neural network; digital; disease diagnostic; drug development; generative adversarial network; high resolution imaging; high risk; human subject; improved; in vivo; innovation; interest; neural network; neural network architecture; neuroimaging; neuroimaging marker; novel; power analysis; pre-clinical; radioligand; radiotracer; rate of change; response; simulation; tau Proteins; tau aggregation; tau mutation

PROJECT SUMMARY Preclinical Alzheimer’s disease (the presymptomatic phase of Alzheimer’s disease) is characterized by pathophysiological changes without measurable cognitive decline and begins decades before the onset of cognitive symptoms. Preclinical Alzheimer’s disease research is in pressing need of new biomarker endpoints to enable disease monitoring before traditional cognitive endpoints are measurable. The overarching research objectives of this R03 Small Project Grant are to develop a super-resolution (SR) positron emission tomography (PET) imaging framework for tau (a pathophysiological hallmark of Alzheimer’s disease) and to assess the clinical utility of localized outcome measures obtained from SR PET images. Studies show that tau pathology in the medial temporal lobe is an important marker of cognitive decline in Alzheimer’s disease. Cohorts focused on preclinical Alzheimer’s now incorporate serialized 18F-flortaucipir PET scans for longitudinal tracking of tau accumulation in key anatomical regions-of-interest (ROIs). The quantitative accuracy of tau PET, however, is degraded by the limited spatial resolution capabilities of PET, which lead to inter-ROI spillover and partial volume effects. The problem is further compounded in studies spanning several decades, many of which were commenced on legacy scanners with even lower resolution capabilities than the current state of the art. Additionally, many longitudinal studies began on older scanners and later transitioned to newer models posing a multi-scanner data harmonization challenge. The proposed SR framework will perform a mapping from a low- resolution scanner’s image domain to a high-resolution scanner’s image domain and enable PET resolution recovery and data harmonization. Underlying the proposed framework is a neural network model that can be adversarially trained in self-supervised mode without requiring paired input/output image samples for training. This critical feature ensures practical clinical utility of the method as the need for paired low-resolution and high- resolution datasets from the same subject with similar tracer dose and scan settings is a major barrier for the clinical translatability of simpler supervised alternatives for SR. The proposed network, although trained using unpaired clinical data, receives guidance from an ancillary neural network separately pretrained using paired simulation datasets. For this purpose, we will synthesize paired low- and high-resolution images from a series of digital tau phantoms that will be created for this project. Training and validation of the self-supervised SR framework will be performed via secondary use of de-identified 18F-flortaucipir PET scans from the Harvard Aging Brain Study, a longitudinal cohort focused on preclinical Alzheimer’s disease. We will evaluate SR performance using a variety of image quality metrics. To assess the clinical utility of localized super-resolution measures, we will perform cross-sectional statistical power analyses that estimate sample sizes per arm needed to power clinical trials. Accurate localized measures of tau generated by this project could enable early diagnosis of Alzheimer’s disease and facilitate ongoing clinical trials by reducing sample sizes required for a given effect size.

项目概要 临床前阿尔茨海默病（阿尔茨海默病的症状前阶段）的特点是 病理生理变化没有可测量的认知能力下降，并且在发病前几十年就开始了 认知症状的临床前研究迫切需要新的生物标志物终点。 在传统认知终点可测量之前实现疾病监测。 R03 小项目拨款的目标是开发超分辨率 (SR) 正电子发射断层扫描 (PET) tau 蛋白（阿尔茨海默病的病理生理学标志）成像框架并评估 从 SR PET 图像获得的局部结果测量的临床实用性 研究表明 tau 病理学在 内侧颞叶是阿尔茨海默病人群认知能力下降的重要标志。 临床前阿尔茨海默病现在采用序列化 18F-flortaucipir PET 扫描来纵向追踪 tau 然而，tau PET 的定量准确性是关键解剖感兴趣区域 (ROI) 的积累。 PET 的空间分辨率有限，导致 ROI 间溢出和部分体积 在长达数十年的研究中，这个问题变得更加复杂，其中许多研究都是 从分辨率能力比当前技术水平更低的传统扫描仪开始。 此外，许多纵向研究始于较旧的扫描仪，后来过渡到较新的模型 所提出的 SR 框架将执行多扫描仪数据协调挑战。 分辨率扫描仪的图像域到高分辨率扫描仪的图像域并启用 PET 分辨率 所提出的框架的基础是可以的神经网络模型。 在自监督模式下进行对抗性训练，无需配对输入/输出图像样本进行训练。 这一关键特征确保了该方法的实际临床实用性，因为需要配对低分辨率和高分辨率 来自具有相似示踪剂剂量和扫描设置的同一受试者的分辨率数据集是 SR 的更简单的监督替代方案的临床可翻译性，尽管使用了训练。 未配对的临床数据，接收来自使用配对单独预训练的辅助神经网络的指导 为此，我们将从一系列的低分辨率和高分辨率图像中合成配对的图像。 将为该项目创建的数字 tau 模型的自我监督 SR 的训练和验证。 框架将通过二次使用来自哈佛老化中心的去识别 18F-flortaucipir PET 扫描来执行 Brain Study，一个专注于临床前阿尔茨海默病的纵向队列，我们​​将评估 SR 表现。 使用各种图像质量指标来评估局部超分辨率测量的临床效用。 将进行横截面统计功效分析，估计每个臂所需的样本量 该项目产生的 tau 蛋白的准确局部测量可以实现早期诊断。 阿尔茨海默病，并通过减少给定效应量所需的样本量来促进正在进行的临床试验。