Automatic Cerebellar MRI Labeling in Health and Disease

健康和疾病中的自动小脑 MRI 标记

• 批准号: 7231950
• 负责人: Jerry L Prince
• 金额: $ 30.57万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-10 至 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7231950
• 关键词: Achievement; Address; Adverse effects; Affect; Alcoholism; Algorithms; Anatomy; Anterior; Antiepileptic Agents; Ataxia; Atlases; Atrophic; Autoimmune Diseases; Behavioral; Blur; Brain; Cerebellar Ataxia; Cerebellar Diseases; Cerebellar cortex structure; Cerebellar degeneration; Cerebellum; Cerebral cortex; Cerebrum; Chemotherapy-Oncologic Procedure; Chronic; Classification; Clinical; Clinical Protocols; Cognitive deficits; Condition; Data; Data Set; Dementia; Diagnosis; Diplopia; Disease; Equilibrium; Functional disorder; Gait; Hand; Health; Homo; Image; Image Analysis; Imagery; Infection; Inherited Spinocerebellar Degenerations; Label; Learning; Lesion; Life; Lobule; Magnetic Resonance Imaging; Measures; Medical; Methods; Modeling; Morbidity - disease rate; Morus; Motor; Multimodal Imaging; Neurologic; Neurons; Neurosciences; Numbers; Patients; Pharmaceutical Preparations; Pilot Projects; Positioning Attribute; Procedures; Quality of life; Relative (related person); Research; Resolution; Score; Shapes; Specificity; Speech; Spinocerebellar Ataxias; Staging; Stroke; Structure-Activity Relationship; Surface; Testing; Tissues; Tremor; Type 6 Spinocerebellar Ataxia; United States; Vision; Work; base; bioimaging; clinical application; clinically significant; density; fundamental research; man; novel; programs; size; tool; tumor

DESCRIPTION (provided by applicant): Dysfunction of the cerebellum is associated with a wide variety of diseases that have significant morbidity yet inadequate therapy. Strokes, tumors, infection, autoimmune disease, medication side effects, and chronic alcoholism can all affect the cerebellum, as can more specific diseases of the cerebellum such as olivopontine cerebellar degeneration or cerebellar ataxia. These conditions may be associated with a host of disabling medical conditions, including gait and balance problems, slurred or slow speech, blurred or double vision, tremor, and mild dementia. Indeed, cerebellar dysfunction can cause considerable degradation in the quality of life and may also be life-shortening. There is increasing evidence that regional changes in the cerebellum can be identified with specific behavioral, motor, and cognitive deficits in patients. In order to study these relationships and then to make clinical protocols available, it is necessary to develop, and test automatic procedures for analysis of the cerebellar anatomy from magnetic resonance images. The proposed research program addresses this need. Specifically, we propose to (1) optimize imaging and tissue classification methods for magnetic resonance imaging of the cerebellum; (2) develop and evaluate automatic cerebellar labeling methods; (3) investigate and model shape differences of cerebellar regions; and (4) conduct a pilot study of regional cerebellum volumes in normal subjects and cerebellar ataxia patients. The proposed research program is novel in several ways. Tissue classification and segmentation will use multiple data sets for superresolution and will incorporate topology-preserving deformable models and kernel density prior shape models. Labeling (parcellation) will incorporate topology-preserving, multiple-atlas deformable registration using multiparametric features. Since our clinical application is to diseases associated with cerebellar atrophy, our methods must work on both normal and atrophied cerebella. Our pilot study on spinocerebellar ataxia (specifically SCA6) provides a unique opportunity to study a cerebellar-specific disease and to learn about regional atrophy and its correlation with neurological deficits. It is expected that the automatic procedures developed herein will not only facilitate large-scale neuroscientific studies of the structure/function relationships in the cerebellum, but will also provide the basis for a clinical tool to assist in treatment and management of all types of cerebellar diseases.

描述（由申请人提供）：小脑功能障碍与多种疾病相关，这些疾病具有显著的发病率，但治疗不足。中风、肿瘤、感染、自身免疫性疾病、药物副作用和慢性酒精中毒都可以影响小脑，更具体的小脑疾病如橄榄桥脑小脑变性或小脑共济失调也可以影响小脑。这些疾病可能与许多致残性疾病有关，包括步态和平衡问题、言语不清或缓慢、视力模糊或复视、震颤和轻度痴呆。事实上，小脑功能障碍会导致生活质量的显著下降，也可能缩短寿命。越来越多的证据表明，小脑的区域变化可以与患者的特定行为，运动和认知缺陷相鉴别。为了研究这些关系，然后使临床协议可用，有必要开发和测试自动程序，从磁共振图像的小脑解剖分析。该研究计划旨在满足这一需求。具体而言，我们建议（1）优化小脑磁共振成像的成像和组织分类方法;（2）开发和评估自动小脑标记方法;（3）调查和建模小脑区域的形状差异;（4）在正常受试者和小脑共济失调患者中进行局部小脑体积的初步研究。拟议的研究计划在几个方面是新颖的。组织分类和分割将使用多个数据集进行超分辨率，并将结合拓扑保持可变形模型和核密度先验形状模型。标记（分割）将采用多参数特征结合拓扑保持、多图谱可变形配准。由于我们的临床应用是与小脑萎缩有关的疾病，我们的方法必须对正常和萎缩的小脑都有效。我们对脊髓小脑共济失调（特别是SCA 6）的初步研究提供了一个独特的机会，研究小脑特异性疾病，了解区域萎缩及其与神经功能缺损的相关性。预计本文开发的自动程序不仅将促进小脑结构/功能关系的大规模神经科学研究，而且还将为临床工具提供基础，以帮助治疗和管理所有类型的小脑疾病。