Segmentation and volumetric quantification of thalamic nuclei for assessing MS

用于评估 MS 的丘脑核分割和体积定量

• 批准号: 8656167
• 负责人: Jerry L Prince
• 金额: $ 22.83万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8656167
• 关键词: Address; Affect; Algorithms; Anterior; Atrophic; Basal Ganglia; Behavioral; Biological Markers; Bipolar Disorder; Brain; Categories; Cessation of life; Chronic; Clinic; Clinical; Cognitive; Cognitive deficits; Communities; Computer software; Cues; Data; Data Set; Data Sources; Databases; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Epilepsy; Equilibrium; Fiber; Functional disorder; Grant; Histology; Image; Impaired cognition; Individual; Investigation; Joints; Label; Language; Learning; Lesion; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Manuals; Maps; Measurement; Measures; Methods; Modeling; Moods; Motor; Movement; Multiple Sclerosis; Neurosciences; Noise; Nuclear; Parkinson Disease; Participant; Pathology; Patients; Pattern; Performance; Pilot Projects; Play; Population; Primary Progressive Multiple Sclerosis; Principal Component Analysis; Process; Property; Relapse; Research; Research Design; Research Personnel; Research Project Grants; Resolution; Role; Running; Scanning; Sensory; Signal Transduction; Software Tools; Solvents; Source Code; Surface; Symptoms; Techniques; Testing; Thalamic Diseases; Thalamic structure; Tremor; Visual Acuity; Weight; White Matter Disease; Work; Writing; base; cohort; design; disability; disorder subtype; experience; falls; gray matter; illness length; improved; innovation; neuroimaging; novel; open source; programs; public health relevance; software development; success; web site; white matter

DESCRIPTION (provided by applicant): The thalamus plays a key role in integrating sensory information for further processing in the basal ganglia and cortex. In multiple sclerosis (MS), long thought to be primarily a white matter disease, it has recently been shown that cognitive decline is more strongly related to thalamic volume than to white matter magnetic resonance image (MRI) lesion load. Since the thalamus is made up of nuclei having specific physical connections within the brain, it may be possible to relate physical changes in thalamic nuclei caused by MS to specific cognitive, behavioral, or disease subtype differences. This grant proposes to develop an automated method and associated software tool to carry out thalamic nuclei parcellation using MRI. Specifically, it is proposed to: 1) optimize the computation of thalamic features from anatomical and diffusion MRI; 2) develop an integrated, multi-nuclear thalamus segmentation algorithm; 3) optimize the algorithm parameters using manual delineations; and 4) carry out a pilot study using an existing MRI database comprising 99 normal controls and 226 MS patients. The work builds on previous methods that exploit topology and connectivity in order to improve segmentation robustness. The primary innovation is to provide a coordinated multi-object approach that integrates intensity information from T1-weighted MRI with orientation information and connectivity information obtained from diffusion MRI. Primary diffusion directions will be mapped to a five- dimensional space in order to cluster nuclei by diffusion orientation and use this information in the parcellation algorithm. A machine learning approach applied to manual delineations will be used to learn boundary-specific properties that will be used to carry out a joint parcellation approach. The algorithm will be designed for conventional three tesla clinical MRI and will be validated using high-resolution, high signal-to-noise ratio seven tesla MRI on 15 subjects scanned contemporaneously with their three tesla scans. The pilot study will use 822 scans of 305 participants, and will examine longitudinal stability of the algorithm and a cross-sectional univariate statistical analysis relatng thalamic nuclei (or nuclear groups) volumes to various clinical measures including disease subtype, disease duration, visual acuity, and two standard MS composite disability scores. An exploratory principal component analysis of multiple thalamic nuclear volumes will be carried out to look for patterns of atrophy and their relationships to various clinical measures. The algorithm will be made publicly available as open source code on the NITRC website so that the entire neuroscience community will be able to use the algorithm to study other diseases or modify and extend it for other applications.

描述（由申请人提供）：丘脑在整合感官信息方面起着关键作用，以在基底神经节和皮层中进行进一步处理。在多发性硬化症（MS）中，长期认为主要是一种白质疾病，最近已经证明，认知能力下降与丘脑量更大，而不是与白质磁共振图像（MRI）病变负荷相比。由于丘脑是由大脑内具有特定物理联系的核组成的，因此有可能将由MS引起的丘脑核的物理变化与特定的认知，行为或疾病亚型差异联系起来。该赠款建议开发一种自动化方法和相关的软件工具，以使用MRI进行丘脑核。具体而言，提出的是：1）优化从解剖学和扩散MRI的丘脑特征的计算； 2）开发一种综合的多核丘脑分割算法； 3）使用手动描述优化算法参数； 4）使用现有的MRI数据库进行试点研究，该数据库包括99个正常对照和226毫秒患者。这项工作是基于利用拓扑和连接性以提高细分鲁棒性的先前方法的基础。主要的创新是提供一种协调的多对象方法，该方法将来自T1加权MRI的强度信息与从扩散MRI获得的方向信息和连接信息相结合。主要扩散方向将映射到五维空间，以通过扩散取向聚集核，并在分析算法中使用此信息。应用于手动描述的机器学习方法将用于学习针对边界特定的属性，该特性将用于执行联合分割方法。该算法将针对传统的三个特斯拉临床MRI设计，并将使用高分辨率，高信噪比验证，七个特斯拉MRI对15名受试者进行了三种特斯拉扫描，同时扫描。试点研究将使用822次扫描305名参与者，并将检查算法的纵向稳定性和横截面单变量统计分析与丘脑核核（或核基团）相关的各种临床措施，包括疾病亚型，包括疾病亚型，疾病，疾病持续时间，视觉浓度，视力，两种标准的MS综合能力。将对多个丘脑核体积进行探索性主成分分析，以寻找萎缩模式及其与各种临床指标的关系。算法 将在NITRC网站上公开作为开源代码公开使用，以便整个神经科学社区将能够使用算法来研究其他疾病或修改并将其扩展到其他应用程序。

项目成果

Thalamus parcellation using multi-modal feature classification and thalamic nuclei priors.

使用多模态特征分类和丘脑核先验进行丘脑分割。

• DOI: 10.1117/12.2216987
• 发表时间: 2016
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Glaister,Jeffrey;Carass,Aaron;Stough,JoshuaV;Calabresi,PeterA;Prince,JerryL
• 通讯作者: Prince,JerryL