Optimizing automated MRI measures of atrophy in neurodegenerative disease

优化神经退行性疾病萎缩的自动 MRI 测量

• 批准号: 8395094
• 负责人: Ronald Pierson
• 金额: $ 30.41万
• 依托单位: PIERSON BRAIN IMAGE ANALYSIS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2014-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8395094
• 关键词: Alzheimer' s Disease; Atrophic; Biochemical; Biological Markers; Biological Neural Networks; Brain imaging; Caregivers; Caring; Clinical; Clinical Trials; Collaborations; Communities; Consensus; Data; Data Set; Dementia; Development; Disease; Disease Progression; Early Diagnosis; Family member; Genetic; Hippocampus (Brain); Image; Image Analysis; Institute of Medicine (U.S.); Life Expectancy; Magnetic Resonance Imaging; Measurement; Measures; Methods; Monitor; Multicenter Studies; Neurodegenerative Disorders; Neurosciences; Patients; Phase; Process; Relative (related person); Research; Research Design; Research Infrastructure; Research Personnel; Scanning; Services; Small Business Innovation Research Grant; Stream; Testing; United States; Work; arm; cost; design; experience; hippocampal atrophy; image processing; neuroimaging; programs

DESCRIPTION (provided by applicant): Alzheimer's disease afflicts an estimated 5.1 million people in the United States, and as life expectancies increase it is anticipated that this number will continue to rise (Institute of Medicine, 2008). Besides the personal toll on the patients and their caregivers (who often are family members), the cost of caring for those with dementia is anticipated to grow from the 2011 estimate of $187 billion to $1.1 trillion by the year 2050. Currently the most widely accepted automated method for measurement of the hippocampus in Alzheimer's disease is FreeSurfer. At Brain Image Analysis, LLC we have processed the MRI data from the ADNI 1 dataset (>3500 scans) using our automated pipeline, BRAINS AutoWorkup. In our analysis have found that, in comparisons with both the standard FreeSurfer and the longitudinal stream in FreeSurfer, our methods detect a higher annual atrophy rate with a lower relative standard deviation. This leads to a substantial reduction in the estimates of subjects needed per arm of a clinical trial from 131 or 204 using FreeSurfer (longitudinal and cross-sectional workflows) to 56 using BRAINS ANN methods. This application describes how we intend to implement and test a longitudinal ANN method and test our current hippocampal segmentations against those being prepared by a collaboration of Alzheimer's disease researchers and hippocampus experts. This will provide Brain Image Analysis, LLC with the information it needs to show our methods of subcortical segmentation are the most feasible in the field for commercial image processing in the study of Alzheimer's disease and its treatment. Phase II of this project will encompass implementation of these methods into the newest version of BRAINS and the additional program infrastructure to make our methods available on a larger commercial scale, as well as implementation on a large dataset to explore clinical correlates. Working with us on this project will be renowned neuroscience researchers. These include Dr. Vincent Magnotta and Dr. Nancy Andreasen, long-term leaders of development team for BRAINS, and Dr. Doug Langbehn, serving as our statistician with substantial experience in large imaging studies designed to support clinical trials. PUBLIC HEALTH RELEVANCE: Alzheimer's disease afflicts an estimated 5.1 million people in the United States, and as life expectancies increase it is anticipated that this number will continue to rise (Hebert et al., 2003). Besides the personal toll on the patients and their caregivers (who often are family members), the cost of caring for those with dementia is anticipated to grow from the 2011 estimate of $187 billion to $1.1 trillion by the year 2050 (Institute of Medicine, 2008). The Alzheimer's Disease Neuroimaging Initiative is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic and biochemical biomarkers for the early detection and tracking of Alzheimer's disease. One of the main focuses of work related to the ADNI dataset is to enhance clinical trials through providing sensitive biomarkers, such as hippocampus measures of atrophy rates, with which disease modifying effects may be monitored in clinical trials. At Brain Image Analysis, LLC we have used our current methods to analyze the ADNI MRI dataset and have found our methods to be very sensitive to hippocampal atrophy, requiring less than 1/2 the number of subjects compared to FreeSurfer to detect a 25 percent reduction in atrophy rates in a clinical trial. In this applicatin we propose to further develop and strengthen our methods as commercially available service in monitoring disease course and as biomarkers in clinical trials of agents designed to alter the course of the disease. This Phase I SBIR application proposes the following aims to more completely assess our current methods, expand them into a longitudinal pipeline, and evaluate them against a "consensus" hippocampal definition being developed in the Alzheimer's research community. AIM 1 - Implement a longitudinal artificial neural network hippocampal segmentation in BRAINS and assess its accuracy and sensitivity in following hippocampal atrophy in Alzheimer's disease, in comparison that the standard ANN used to process the preliminary data. AIM2 - Compare the results of our current hippocampal segmentation with the "consensus" methods being developed by Giovanni Frisoni and Clifford Jack. AIM3 - Calculate the ability to detect changes in longitudinal atrophy rates for the methods developed in Aims 1 and 2, and compare with those of FreeSurfer and other available methods. Working with us on this project will be renowned neuroscience researchers. These include Dr. Vincent Magnotta and Dr. Nancy Andreasen, long-term leaders of development team for BRAINS, and Dr. Doug Langbehn, serving as our statistician with substantial experience in large imaging studies designed to support clinical trials. Should Phase I be successful, Phase II of this project will encompass implementation of these methods into the newest version of BRAINS and the additional program infrastructure to make our methods available on a larger commercial scale, as well as application on the large ADNI dataset to explore clinical correlates relevant to monitoring disease progression and clinical trials.

描述（由申请人提供）：在美国，阿尔茨海默病估计折磨着510万人，随着预期寿命的增加，预计这一数字将继续上升（医学研究所，2008）。除了患者及其护理人员（通常是家庭成员）的个人损失之外，预计到2050年，照顾痴呆症患者的费用将从2011年估计的1870亿美元增长到1.1万亿美元。目前最广泛接受的测量阿尔茨海默病海马的自动化方法是FreeSurfer。在Brain Image Analysis， LLC，我们使用我们的自动化流水线BRAINS AutoWorkup处理了来自ADNI 1数据集（>3500次扫描）的MRI数据。在我们的分析中发现，与标准的FreeSurfer和FreeSurfer的纵向流相比，我们的方法检测到更高的年萎缩率和更低的相对标准偏差。这导致临床试验每组所需受试者的估计值从使用FreeSurfer（纵向和横断面工作流程）的131或204人大幅减少到使用BRAINS ANN方法的56人。本应用程序描述了我们打算如何实现和测试纵向神经网络方法，并将我们目前的海马分割与阿尔茨海默病研究人员和海马体专家合作准备的海马分割进行比较。这将为Brain Image Analysis， LLC提供所需的信息，以证明我们的皮质下分割方法在阿尔茨海默病及其治疗研究的商业图像处理领域是最可行的。该项目的第二阶段将包括将这些方法应用到最新版本的BRAINS和附加的程序基础设施中，以使我们的方法在更大的商业规模上可用，以及在大型数据集上实施以探索临床相关性。和我们一起参与这个项目的将是著名的神经科学研究者。其中包括BRAINS开发团队的长期领导者Vincent Magnotta博士和Nancy Andreasen博士，以及Doug Langbehn博士，他是我们的统计学家，在旨在支持临床试验的大型成像研究方面拥有丰富的经验。