Multimodal image registration by proxy image synthesis

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医学中的一项重要功能 药功能成像研究中的标准化，生长、衰老和 疾病，在术中图像上覆盖手术计划和几何失真校正， 图像配准的重要应用实例。有几十个需要注册在 包括主体内和主体间的应用。因此，任何形象上的改善 注册绩效将对科学界和临床界产生直接影响。 尽管在图像重建算法中有许多进步，但是使用多模态（或 组织对比度）来执行图像配准实际上是一个未开发的领域。占主导地位的 框架的目的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 如果有多个受试者或目标的图像可用，则使用 从单个图像配准导出的变换。拟议的研究将发展， 评估和验证一个非常简单但非常激进的多模式配准想法。的 基本思想是从具有相同组织对比度的对象图像合成“代理”图像 和强度范围作为目标图像，然后使用平方和等传统度量 差异来执行主体代理和目标之间的注册。初步结果 在这种方法中显示出显著的好处。在赠款中，我们将：1）优化“代理”多式联运 图像配准，探讨其理论依据以及关键参数的整体 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， 为了验证方法和开发最佳实践的原则;和3）写开源 用于执行图像合成、相似性计算以及刚性和可变形配准的软件 使用“代理”图像概念。这两个软件来合成图像，用于用户的最爱， 图像配准方法以及软件，以在 优化的方法将作为开源计算机代码公开提供。的结果 研究将通过改变研究人员和 从业人员获取并使用神经科学研究和临床医学的数据。

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.