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.

项目摘要 临床前阿尔茨海默氏病（阿尔茨海默氏病的持久性阶段）的特征是 病理生理的变化没有可衡量的认知能力下降，并在开始前几十年开始 认知症状。阿尔茨海默氏症疾病的研究是迫切需要新的生物标志物终点 可以在传统的认知终点之前进行疾病监测。总体研究 此R03小型项目赠款的目标是开发超分辨率（SR）正电子发射断层扫描 （PET）TAU的成像框架（阿尔茨海默氏病的病理生理标志）并评估 从SR PET图像获得的局部结果度量的临床实用性。研究表明，tau病理学 临时叶是阿尔茨海默氏病认知能力下降的重要标志。队列专注于 临床前阿尔茨海默氏症现在合并了序列化的18F-flortaucipir PET扫描，用于纵向跟踪tau 在关键解剖区域（ROI）中积累。但是，tau pet的定量准确性是 受PET的有限空间分辨率功能降低，这导致了Roi间的Spilover和部分体积 效果。在跨越几十年的研究中，问题进一步加剧了，其中许多是 对分辨率功能还低的遗产扫描仪比当前的现状发表了评论。 此外，许多纵向研究开始于较旧的扫描仪，后来转变为新的模型 多扫描仪数据协调挑战。提出的SR框架将执行从低 - 解决高分辨率扫描仪的图像域的分辨率扫描仪的图像域并实现宠物分辨率 恢复和数据协调。所提出的框架的基础是一种神经网络模型，可以是 以自我监督模式进行对抗训练，而不需要配对的输入/输出图像样本进行训练。 此关键特征可确保该方法的实际临床实用性，因为需要配对的低分辨率和高位 来自同一主题的分辨率数据集和类似的示踪剂剂量和扫描设置是该数据集的主要障碍 SR的更简单监督替代方案的临床翻译性。拟议的网络，尽管经过培训 未配对的临床数据，通过配对分别预处理的辅助神经网络获得指导 仿真数据集。为此，我们将合成一系列配对的低和高分辨率图像 将为该项目创建的数字Tau幻影。培训和验证自我监督的SR 框架将通过二次使用从哈佛大学衰老的去识别的18f flortaucipir pet扫描 大脑研究，一种纵向队列，重点是阿尔茨海默氏病。我们将评估SR性能 使用各种图像质量指标。为了评估局部超分辨率措施的临床实用性，我们 将执行横截面统计功率分析，以估计每个手臂所需的样本量 临床试验。该项目产生的TAU的准确局部度量可以早期诊断 阿尔茨海默氏病并通过减少给定效果大小所需的样本量来促进正在进行的临床试验。