Multi-atlas based Direct Estimation in Preclinical Alzheimer's Disease

项目总结： 在这一应用中，我们将建立一种新的基于图谱的阿尔茨海默病临床前脑MRI分析范式 疾病(AD)，其特点是通过独特的基于知识的信息来直接估计患者的属性- 检索技术。MRI图谱广泛应用于图像的自动分割，尤其是最近的研究进展 在多图谱中，方法产生了更高的分割精度。在传统的基于图谱的分析中， 图谱仅仅被用作分割患者图像的模板，然后体积、形状、强度 从分割的结构中提取以估计患者的诊断属性，例如认知状态 或临床评估。相反，在建议的基于多图谱的直接估计(MASE)方法中，我们 利用多图谱库作为与丰富的临床信息相关的知识数据库；解剖学 患者和地图集图像之间的相似性将被用来衡量来自多个地图集的信息，以及 将做出加权决策，绕过分割过程，直接估计患者属性。 我们的初步数据已经证明了基于Made-Based分析T1加权图像的优势 痴呆症患者，与容量分析比较。此外，非图像人口统计和临床信息 可以很容易地将患者的信息整合到Made框架中，以进一步提高估计的准确性。 我们的目标是开发基于制造的计算工具，并使用它们来改进诊断和预后 阿尔茨海默病临床前期。这项研究将得到BIOCARD队列的支持，该队列是一个精心设计的 这项纵向研究对350名参与者进行了20年的跟踪调查。综合认知评估和核磁共振 这些参与者自1995年以来一直在收集考试，当时他们在注册时认知正常。这 独特的数据库使我们能够研究长期的临床前阶段的大脑退化，并开发 在这一关键阶段可能有助于诊断决策的计算工具。在目标1中，我们将开发 并对自制的脑结构MRI分析图进行优化，以估计患者目前的认知状况 状态和疾病阶段，使用ADNI和BIOCARD数据。一旦工具变得成熟，我们就部署 它们安装在云计算平台--MRICout上，供公众使用。在目标2中，我们使用优化的Made 在BIOCARD队列中预测认知障碍的管道。具体地说，我们将使用Made管道 预测患者在基线后1-5年的认知能力下降；并预测他们从 在20年的随访中，从正常到认知障碍(MCI、AD或其他类型的异常)，以及 估计诊断的时间。拟议项目的成功可能会导致下一代 基于知识的计算机辅助诊断，并有可能提高早期诊断和预后的准确性 临床前AD患者。

Project summary: In this application, we will establish a novel paradigm of atlas-based brain MRI analysis of preclinical Alzheimer’s disease (AD), featuring in direct estimation of the patients’ attributes with a unique knowledge-based information- retrieval technology. MRI atlases are widely used for automated image parcellation, especially, recent advances in multi-atlas methods have yielded superior segmentation accuracy. In conventional atlas-based analysis, atlases are used merely as templates to segment a patient image, and then volumes, shapes, intensities are extracted from the segmented structures to estimate the patient’s diagnostic attributes, such as cognitive status or clinical assessments. In contrast, in the proposed multi-atlas based direct estimation (MADE) approach, we utilize the multi-atlas library as a knowledge database that is associated with rich clinical information; anatomical similarity between the patient and atlas images will be used to weigh the information from multiple atlases, and a weighted decision will be made to directly estimate the patient attributes, bypassing the segmentation process. Our preliminary data have demonstrated the advantages of MADE-based analysis of T1-weighted images in dementia patients, compared to volumetric analysis. In addition, non-image demographic and clinical information of the patients can be readily incorporated into the MADE framework to further enhance the estimation accuracy. Our goals is to develop MADE-based computational tools and use them to improve diagnosis and prognosis in preclinical phase of AD. This study will be supported by the BIOCARD cohort, which is a well-designed longitudinal study that followed 350 participants over 20 years. Comprehensive cognitive assessments and MRI exams have been collected in these participants since 1995 when they were cognitive normal at enrollment. This unique database allows us to investigate brain degeneration in the long preclinical phase, and develop computational tools that can possibly assist diagnostic decisions in this critical phase. In Aim 1, we will develop and optimize MADE-based brain structural MRI analysis diagram to estimate the patients’ current cognitive status and disease stages, using both the ADNI and BIOCARD data. Once the tools become mature, we deploy them on a cloud-computing platform—the MRICloud, for public use. In Aim 2, we use the optimized MADE pipeline to predict cognitive impairment in BIOCARD cohort. Specifically, we will used the MADE pipeline to predict patients’ cognitive decline at 1-5 years after baseline; and also predict their probability of conversion from normal to cognitive impairment (MCI, AD or other types of abnormality) across the 20 years of follow-up, and estimate the time-to-diagnosis. The success of the proposed project could lead to the next generation of knowledge-based computer-aided diagnosis, and potentially improve early diagnosis and prognosis accuracy in preclinical AD patients.