SCH: EAGER: New Approach: Early Diagnosis of Alzheimer's Disease Based on Magnetic Resonance Imaging (MRI) via High-Dimensional Image Feature Identification

SCH：EAGER：新方法：通过高维图像特征识别基于磁共振成像 (MRI) 的阿尔茨海默病早期诊断

• 批准号: 1723529
• 负责人: Xiuzhen Huang
• 金额: $ 24.18万
• 依托单位: Arkansas State University Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723529&HistoricalAwards=false
• 关键词: SCH; EAGER; New; Approach; Early

Alzheimer's disease (AD) is a form of progressive neurodegenerative dementia and one of the most common diseases in the aging population. Early diagnosis of AD is strongly recommended for several reasons. First, it can helps to significantly reduce the social and economic impacts caused by AD and allow people to better manage and plan ahead. Second, it may provide more information for researchers seeking new scientific approaches for early treatment and intervention. However, in current clinical practice early diagnosis is often a challenge. While neuroimaging is routinely collected in hospitals, it is very hard for radiologists to manually read the high-dimensional image data for analysis and interpretation. This project proposes untested but potentially transformative research approaches to identify high-dimensional image features for AD early diagnosis based on Magnetic Resonance Imaging (MRI). This project will advance the research in machine learning, optimization, statistics, image science and bioinformatics, and potentially be used to address other high-dimensional images besides brain images. The project also has broader impacts through cross-disciplinary research, training and education. This project has the following two aims: 1) Develop sparse coding based algorithms to identify features of structural MRI images for classifying AD patients and other diagnostic groups. This will allow the key structural features of images that separate AD patients, individuals with mild cognitive impairment (MCI) or healthy individuals to be identified. 2) Develop optimization and machine learning algorithms based on tensor Tucker core decomposition for high-dimensional image-marker detection from longitudinal functional MRI images. This approach should reduce the high computational complexity of marker detection from the longitudinal MRI images of AD patients. It is anticipated that the developed algorithms will enhance high-dimensional neuroimaging marker detection and diagnostic classification. This research project, if successful, will greatly impact the current practice of AD diagnosis by providing clinical doctors with the information from a larger population and also significantly easing the burden of radiologists.

阿尔茨海默氏病（AD）是一种进行性神经退行性痴呆的一种形式，也是老龄化人群中最常见的疾病之一。强烈建议对AD进行早期诊断，原因有几个。首先，它可以大大减少广告造成的社会和经济影响，并使人们能够更好地管理和计划。其次，它可以为寻求新科学方法进行早期治疗和干预的研究人员提供更多信息。但是，在当前的临床实践中，早期诊断通常是一个挑战。虽然通常在医院中收集神经影像学，但放射科医生很难手动阅读高维图像数据以进行分析和解释。该项目提出了未经测试但潜在的变革性研究方法，以鉴定基于磁共振成像（MRI）的AD早期诊断的高维图像特征。该项目将推进机器学习，优化，统计，图像科学和生物信息学的研究，并有可能用于解决大脑图像以外的其他高维图像。该项目还通过跨学科研究，培训和教育产生了更大的影响。该项目具有以下两个目的：1）开发基于稀疏的编码算法，以识别用于对AD患者和其他诊断组进行分类的结构MRI图像的特征。这将允许鉴定出AD患者，轻度认知障碍（MCI）或健康个体的个体的图像的关键结构特征。 2）基于张量的塔克核心分解来开发优化和机器学习算法，以从纵向功能MRI图像中检测高维图像标志物检测。这种方法应降低AD患者纵向MRI图像的标记检测的高计算复杂性。预计开发的算法将增强高维神经影像学标记检测和诊断分类。如果成功的话，该研究项目将通过为临床医生提供来自较大人群的信息，并显着减轻放射科医生的负担，从而极大地影响AD诊断的当前实践。

项目成果

Accelerated primal–dual proximal block coordinate updating methods for constrained convex optimization

• DOI: 10.1007/s10589-017-9972-z
• 发表时间: 2017-02
• 期刊: Computational Optimization and Applications
• 影响因子: 2.2
• 作者: Yangyang Xu;Shuzhong Zhang

NBT (No-Boundary Thinking): Needed to Attend to Ethical Implications of Data and AI

• DOI: 10.1145/3535508.3545595
• 发表时间: 2022-01-01
• 期刊: 13TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND HEALTH INFORMATICS, BCB 2022
• 作者: Peckham，Joan;Perkins，Andy;Huang，Xiuzhen
• 通讯作者: Huang，Xiuzhen

Arkansas AI-Campus Method for the 2019 Kidney Tumor Segmentation Challenge

阿肯色州 AI-Campus 方法应对 2019 年肾肿瘤分割挑战赛

• DOI: 10.24926/548719.050
• 发表时间: 2019
• 期刊: http://results.kits-challenge.org/miccai2019/manuscripts/ai_campus_5.pdf
• 作者: J. Causey, J. Stubblefield

Highly accurate model for prediction of lung nodule malignancy with CT scans.

• DOI: 10.1038/s41598-018-27569-w
• 发表时间: 2018-06-18
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Causey JL;Zhang J;Ma S;Jiang B;Qualls JA;Politte DG;Prior F;Zhang S;Huang X
• 通讯作者: Huang X

DNAp: A Pipeline for DNA-seq Data Analysis.

• DOI: 10.1038/s41598-018-25022-6
• 发表时间: 2018-05-01
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Causey JL;Ashby C;Walker K;Wang ZP;Yang M;Guan Y;Moore JH;Huang X
• 通讯作者: Huang X