QUANTITATIVE IMAGE ANALYSIS TECHNIQUES FOR STUDIES OF AGING PHENOTYPES AND AGE-RELATED DISEASES

用于研究衰老表型和年龄相关疾病的定量图像分析技术

• 批准号: 9044803
• 负责人: Sokratis Makrogiannis
• 金额: $ 6.36万
• 依托单位: DELAWARE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-02 至 2019-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9044803
• 关键词: Abdomen; Address; Adipose tissue; Affect; Age; Aging; Aging-Related Process; Algorithms; Anatomy; Biological Markers; Body Composition; Cardiovascular Diseases; Clinical; Clinical Trials; Computational Technique; Data; Data Analyses; Delaware; Descriptor; Development; Diabetes Mellitus; Diagnosis; Disease; Doctor of Medicine; Doctor of Philosophy; Early Diagnosis; Epidemic; Epidemiology; Fatty acid glycerol esters; Foundations; Gerontology; Goals; Health; Human body; Image; Image Analysis; Imaging technology; Individual; Intervention; Lead; Link; Longitudinal Studies; Lower Extremity; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Medical Imaging; Metabolic Diseases; Metabolic syndrome; Methods; Morphology; Muscle; Musculoskeletal Diseases; Noise; Non-Insulin-Dependent Diabetes Mellitus; Participant; Pathology; Pattern; Pennsylvania; Phenotype; Play; Prevention; Procedures; Quality of life; Research; Role; Skeletal Muscle; Techniques; Thigh structure; Tissues; Underrepresented Groups; United States National Institutes of Health; Work; X-Ray Computed Tomography; age related; biophysical model; bone; effective therapy; graduate student; image processing; image registration; in vivo; interest; longitudinal analysis; morphometry; muscle form; muscle strength; novel; outcome forecast; prognostic; quantitative imaging; reduced muscle strength; sarcopenia; statistics; tool; undergraduate student

 DESCRIPTION (provided by applicant): Tissue identification and quantification plays a significant role in the study of aging and age-related diseases. For example, the accumulation of fat in the human body and its regional distribution with aging is associated with type 2 diabetes and cardiovascular diseases. Changes in muscle composition are strongly linked to decline of muscle strength, decreased mobility caused by aging, or musculoskeletal disorders. Especially interesting is analysis of longitudinal changes of morphometric descriptors that is significant for studying the aging process and for the diagnosis and prevention of age-related diseases. Medical imaging has emerged as a major tool for estimation of body composition mainly due to being non- invasive and producing multi-dimensional information. Nowadays MRI and CT acquisition is a central component of clinical trials. An abundance of imaging data is collected, but this wealth of information has not been utilized to full extent. Therefore research on image analysis techniques for tissue quantification that are reproducible and can be used on large-scale clinical trials is of particular importance. The technical hypothesis of this work is that quantitative image processing can robustly and accurately segment, register, and fuse body composition data from modern MRI and CT imaging. The central hypothesis of this proposal is that qualitative body composition phenotypes on clinical imaging will differentiate individuals who are healthy versus those who are not. The goal of our work is to provide a foundation for image analysis of the abdomen and lower extremities and to study the relationship between body morphological changes and age-related pathologies. We will build upon recent advances in medical image computing to segment muscle, regional adipose tissue, and bone in clinical CT and MRI scans. We will also develop image registration procedures to achieve intra- and inter-subject correspondence and make efficient use of information provided by multi-modal and multi-temporal imaging data collected in clinical trials (aim 1). After these methods have been developed, we will address the hypothesis that quantitative use of clinical imaging can increase the prognostic accuracy of age-related pathologies (aim2).

 描述（由申请人提供）：组织鉴定和定量在衰老和年龄相关疾病的研究中发挥着重要作用。例如，人体内脂肪的积累及其随衰老的区域分布与2型糖尿病和心血管疾病有关。肌肉成分的变化与肌肉力量下降、衰老引起的活动能力下降或肌肉骨骼疾病密切相关。特别有趣的是形态测量描述符的纵向变化的分析，这对于 研究衰老过程以及诊断和预防与年龄相关的疾病。 医学成像已成为估计身体成分的主要工具，主要是因为它是非侵入性的并产生多维信息。如今，MRI 和 CT 采集是临床试验的核心组成部分。收集了大量的成像数据，但这些丰富的信息尚未得到充分利用。因此，研究可重复且可用于大规模临床试验的组织定量图像分析技术显得尤为重要。 这项工作的技术假设是定量图像处理可以稳健而准确地分割、注册和融合来自现代 MRI 和 CT 成像的身体成分数据。该提案的中心假设是，临床成像上的定性身体成分表型将区分健康个体和不健康个体。我们工作的目标是为腹部和下肢的图像分析提供基础，并研究身体形态变化与年龄相关病理之间的关系。 我们将利用医学图像计算的最新进展，在临床 CT 和 MRI 扫描中分割肌肉、区域脂肪组织和骨骼。我们还将开发图像配准程序，以实现受试者内和受试者间的对应，并有效利用临床试验中收集的多模态和多时相成像数据提供的信息（目标1）。在开发这些方法后，我们将提出这样的假设：临床成像的定量使用可以提高与年龄相关的病理的预后准确性（目标2）。