ROBUST AUTOMATIC MULTIMODALITY REGISTRATION

强大的自动多模式注册

• 批准号: 2008232
• 负责人: Charles Raymond Meyer
• 金额: $ 20.67万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-04-01 至 2000-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2008232
• 关键词: autoradiography; brain imaging /visualization /scanning; computer data analysis; computer program /software; human data; image processing; information theory; laboratory rat; magnetic resonance imaging; mathematical model; phantom model; autoradiography; brain imaging /visualization /scanning; computer data analysis; computer program /software; human data; image processing; information theory; laboratory rat; magnetic resonance imaging; mathematical model; phantom model

DESCRIPTION (Adapted from Applicant's Abstract): The goal of this application was to achieve the capability to routinely perform automatic, robust, rapid, and accurate multimodality registrations to facilitate comparisons and interpretations of image data set pairs from a variety of sources. The specific aims of the application were to 1) research and develop a quantitative, cost function-based method for automatic registration of multimodality data sets that has utility in clinical and biological research and is based on classical information theory, 2) fully characterize a number of data-related effects on the operation of the algorithm, and 3) demonstrate the algorithm's application to several diverse multimodal data sets in radiology and basic neuroscience where warping is often required for accurate registrations. The registration method proposed is based on the mutual information cost function (MI) which quantifies the mutual information content of two data sources. A geometric mapping that minimizes MI between data sets produces the most spatially correlated, i.e., registered, data sets. An optimizer drives the positions of control points in the homologous data set to effect a mapping between the reference and the homologous data sets that optimizes MI. Since MI is calculated from gray values, it is applicable to iso- and multi-modal sets without the need for preprocessing such as gray level segmentation. The proposed registration method applies to virtually any combination of data sources, both 2D (e.g., autoradiography, electron and light microscopy) and 3D (e.g., CT, MRI, fMRI, PET, SPECT, MEG, and confocal microscopy). Research design consists of several methods to extend our current MI-based prototype to automatically determine the complexity of the registration supported by the data. Three algorithms for automatic control point selection for warping based on local MI will be investigated. Several types of geometric inconsistencies will be systematically studied to elucidate the method's under different conditions. The effect of data set type and transform degrees of freedom on accuracy will be studied by measuring the information content in data from different modalities. Registration accuracy will be validated by use of phantoms; additionally, data sets from rats and humans will be used as a test bed for this work. Analysis of additional data including normal and abnormal cases will demonstrate the efficacy of automated warping registration in the clinical and basic sciences.

描述（根据申请人的摘要改编）：目的 应用是为了实现常规执行自动的能力， 强大，快速，准确的多模式注册以促进 图像数据集对的比较和解释对 来源。 该应用程序的具体目的是1）研究和 开发一种基于定量的基于成本函数的自动方法 在临床和 生物学研究，基于经典信息理论，2）完全 表征了许多与数据相关的影响对 算法和3）演示了该算法在几种不同的 放射学和基本神经科学的多模式数据集 通常需要进行准确的注册。 提出的注册方法 基于量化的相互信息成本函数（MI） 两个数据源的共同信息内容。 一个几何映射 最小化数据集之间的MI会产生最空间相关的MI，即 注册，数据集。 优化器驱动控制点的位置 在同源数据集中，以实现参考和参考的映射 优化MI的同源数据集。 由于MI是根据灰色计算的 值，它适用于ISO和多模式集，而无需 预处理，例如灰度分割。 拟议的注册 方法几乎适用于数据源的任何组合，包括2D（例如， 放射自显影，电子和光学显微镜）和3D（例如CT，MRI，fMRI， PET，SPECT，MEG和共聚焦显微镜）。 研究设计包括 将当前基于MI的原型扩展到自动的几种方法 确定数据支持的注册的复杂性。 三 基于本地的自动控制点选择的自动控制点选择算法 MI将被调查。 几种类型的几何不一致将是 系统地研究以在不同条件下阐明该方法。 数据集类型和转换自由度对准确性的影响 将通过测量来自不同的数据中的信息内容来研究 方式。 注册精度将通过使用幻象来验证； 此外，来自大鼠和人类的数据集将被用作测试床 这项工作。 分析其他数据，包括正常情况和异常情况 将证明自动翘曲注册的功效 临床和基础科学。