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.

项目总结 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 没有可测量的认知功能下降的病理生理变化，开始于发病前几十年 认知症状。阿尔茨海默病临床前研究迫切需要新的生物标记物终点 在传统认知终点可测量之前启用疾病监测。最重要的研究 R03小项目基金的目标是开发一种超分辨率(SR)正电子发射断层扫描 (PET)tau(阿尔茨海默病的一个病理生理标志)的成像框架，并评估 从SR-PET图像获得的局部结果测量的临床应用。研究表明，tau的病理在 内侧颞叶是阿尔茨海默病认知功能下降的重要标志。关注以下问题的队列 临床前阿尔茨海默氏症现在纳入系列化18F-flortaucipir PET扫描，用于纵向跟踪tau 在感兴趣的关键解剖区域(ROI)积累。然而，tau PET的定量准确性是 由于PET的空间分辨率能力有限，从而导致ROI间溢出和部分体积 效果。这个问题在几十年的研究中进一步复杂化，其中许多是 开始于具有比当前技术水平更低的分辨率能力的传统扫描仪。 此外，许多纵向研究开始于较旧的扫描仪，后来过渡到较新的模型摆姿势 多扫描仪数据协调挑战。建议的SR框架将执行从低到低的映射- 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 恢复和数据协调。建议框架的基础是一个神经网络模型，该模型可以 在自我监督模式下进行相反的训练，而不需要成对的输入/输出图像样本来进行训练。 这一关键特征确保了该方法的实际临床实用性，因为需要配对的低分辨率和高分辨率图像。 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更简单的监督替代方案的临床可译性。建议的网络，尽管培训使用 未配对的临床数据，从辅助神经网络接收指导，该辅助神经网络使用配对 模拟数据集。为此，我们将从一系列低分辨率和高分辨率图像中合成成对的图像 将为该项目创建的数字tau幻影。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龄化的去识别的18F-flortaucipir PET扫描来执行 大脑研究，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性能 使用各种图像质量指标。为了评估局部超分辨措施的临床实用性，我们 将执行横截面统计功率分析，以估计供电所需的每臂样本大小 临床试验。该项目产生的tau的准确本地化测量可以使早期诊断 通过减少给定效应大小所需的样本大小，促进正在进行的临床试验。