Multimodal image registration by proxy image synthesis

通过代理图像合成进行多模态图像配准

• 批准号: 8614480
• 负责人: Jerry L Prince
• 金额: $ 34.38万
• 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: 2013-09-30 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8614480
• 关键词: Adopted; Affect; Aging; Algorithms; Appearance; Area; Atlases; Clinical; Clinical Medicine; Communities; Computer Vision Systems; Computer software; Data; Data Collection; Databases; Development; Diffusion Magnetic Resonance Imaging; Diffusion weighted imaging; Disease; Environment; Evaluation; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Grant; Growth; Health; Image; Java; Lead; Liquid substance; Magnetic Resonance Imaging; Manufacturer Name; Measures; Medical Imaging; Medical Research; Methods; Metric; Modality; Modeling; Motion; Multimodal Imaging; Neurosciences; Operative Surgical Procedures; Pathology; Performance; Physiologic pulse; Plague; Population; Positron-Emission Tomography; Predisposition; Procedures; Process; Proxy; Radial; Research; Research Personnel; Resources; Scanning; Science; Shapes; Specific qualifier value; Sum; Surface; Testing; Therapeutic; Tissues; Validation; Variant; Weight; Work; Writing; X-Ray Computed Tomography; base; clinical diagnosis; computer code; cone-beam computed tomography; image reconstruction; image registration; improved; intraoperative imaging; longitudinal analysis; neuroimaging; novel strategies; open source; outcome forecast; public health relevance; shape analysis; statistics; success; theories; tool; vector; web site; Adopted; Affect; Aging; Algorithms; Appearance; Area; Atlases; Clinical; Clinical Medicine; Communities; Computer Vision Systems; Computer software; Data; Data Collection; Databases; Development; Diffusion Magnetic Resonance Imaging; Diffusion weighted imaging; Disease; Environment; Evaluation; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Grant; Growth; Health; Image; Java; Lead; Liquid substance; Magnetic Resonance Imaging; Manufacturer Name; Measures; Medical Imaging; Medical Research; Methods; Metric; Modality; Modeling; Motion; Multimodal Imaging; Neurosciences; Operative Surgical Procedures; Pathology; Performance; Physiologic pulse; Plague; Population; Positron-Emission Tomography; Predisposition; Procedures; Process; Proxy; Radial; Research; Research Personnel; Resources; Scanning; Science; Shapes; Specific qualifier value; Sum; Surface; Testing; Therapeutic; Tissues; Validation; Variant; Weight; Work; Writing; X-Ray Computed Tomography; base; clinical diagnosis; computer code; cone-beam computed tomography; image reconstruction; image registration; improved; intraoperative imaging; longitudinal analysis; neuroimaging; novel strategies; open source; outcome forecast; public health relevance; shape analysis; statistics; success; theories; tool; vector; web site

Project Summary/Abstract (Max 30 lines of text) Image registration is a fundamentally important capability in modern neuroscience and clinical medicine. Normalization in functional imaging studies, studies of shape changes in growth, aging, and disease, overlaying surgical plans on intraoperative images, and geometric distortion correction are examples of important applications of image registration. There are dozens of needs for registration in both intrasubject and intersubject applications as well. Therefore, any improvement in image registration performance will have an immediate impact on the scientific and clinical communities. Despite numerous advances in image reconstruction algorithms, the use of multiple modalities (or tissue contrasts) to carry out image registration is a virtually untapped area. The vastly dominant framework is to register a single image of the subject to another single image of the target, and if multiple images are available of either subject or target, they are registered by using the transformation derived from the single image registration. The proposed research will develop, evaluate, and validate a very simply explained but quite radical idea for multi-modal registration. The basic idea is to synthesize a "proxy" image from the subject image that has the same tissue contrast and intensity range as the target image and then use a conventional metric such as sum-of-square difference to carry out the registration between the subject proxy and target. Preliminary results demonstrate significant benefits in this approach. In the grant we will: 1) Optimize "proxy" multimodal image registration by exploring its theoretical justification as well as key parameters of the overall approach; 2) Apply "proxy" multimodal image registration to three key applications in neuroscience in order to validate the method and develop principles of best practice; and 3) Write open source software to carry out image synthesis, similarity computation, and rigid and deformable registration using the "proxy" image concept. Both the software to synthesize images for use in a user's favorite image registration method as well as software to carry out the entire "proxy" registration process in an optimized way will be made publicly available as open source computer code. The results of this research will lead to a new era in image registration by changing the way researchers and practitioners acquire and use data for neuroscientific studies and clinical medicine.

项目摘要/摘要（最大30行文本） 图像注册是现代神经科学和临床的根本重要功能 药品。功能成像研究中的归一化，对生长，衰老和形状变化的研究 疾病，在术中图像上覆盖手术计划以及几何失真校正是 图像注册重要应用的示例。有数十个注册需求 主体内和主体间应用程序也是如此。因此，图像的任何改进 注册绩效将对科学和临床社区产生直接影响。 尽管图像重建算法取得了许多进步，但使用多种方式（或 进行图像注册的组织对比是几乎没有开发的区域。极大的主导地位 框架是将主题的单个图像注册为目标的另一个图像，如果 有多个受试者或目标的图像，它们通过使用 从单个图像注册得出的转换。拟议的研究将发展， 评估并验证一个非常简单的解释，但对多模式登记进行了非常激进的想法。这 基本思想是从主题图像中综合具有相同组织对比的“代理”图像 和强度范围作为目标图像，然后使用常规度量（例如 进行主题代理和目标之间的注册的差异。初步结果 在这种方法中表现出重大好处。在赠款中，我们将：1）优化“代理”多模式 图像注册通过探索其理论理由以及整体的关键参数 方法; 2）将“代理”多模式图像注册应用于神经科学的三个关键应用 为了验证方法并制定最佳实践原则； 3）写开源 进行图像综合，相似性计算以及刚性且可变形的注册的软件 使用“代理”图像概念。这两个软件都合成图像以用于用户的喜爱 图像注册方法以及软件，以执行整个“代理”注册过程 优化的方式将作为开源计算机代码公开提供。结果的结果 研究将通过改变研究人员的方式来导致图像注册的新时代 从业者获取并使用数据进行神经科学研究和临床医学。