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

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（最多 30 行文本） 图像配准是现代神经科学和临床中一项根本性的重要能力 药品。功能成像研究的标准化，生长、衰老和生长过程中形状变化的研究 病、在术中图像上叠加手术计划以及几何畸变校正 图像配准的重要应用示例。有几十个需要注册的 学科内和学科间应用也是如此。因此，任何形象上的改善 注册表现将对科学界和临床界产生直接影响。 尽管图像重建算法取得了许多进步，但多种模式（或 组织对比）来进行图像配准实际上是一个尚未开发的领域。占绝对优势的 框架是将主体的单个图像配准到目标的另一个单个图像，并且如果 主题或目标有多个图像可用，它们通过使用 从单个图像配准导出的变换。拟议的研究将发展， 评估并验证一个解释非常简单但相当激进的多模式注册想法。这 基本思想是从具有相同组织对比度的主体图像合成“代理”图像 和强度范围作为目标图像，然后使用传统的度量，例如平方和 差异来进行主题代理和目标之间的注册。初步结果 展示这种方法的显着优势。在赠款中，我们将： 1）优化“代理”多式联运 通过探索其理论依据以及整体的关键参数来进行图像配准 方法; 2）将“代理”多模态图像配准应用于神经科学中的三个关键应用 为了验证方法并制定最佳实践原则； 3) 编写开源代码 软件进行图像合成、相似性计算以及刚性和变形配准 使用“代理”图像概念。这两款软件都可以合成用户喜欢的图像使用 图像注册方法以及在一个过程中执行整个“代理”注册过程的软件 优化的方式将作为开源计算机代码公开提供。这样做的结果 研究将通过改变研究人员和图像配准的方式开创图像配准的新时代 从业者获取和使用数据进行神经科学研究和临床医学。