A Bayesian Framework for Inter-Modality Deformable Image Registration Evaluation

描述（由申请人提供）：随着医学成像文献中不断报告新的创新和日益复杂的图像处理技术，缺乏关键评价和质量控制方法的并发复杂性。尽管有许多关于新型的并行计算算法及其潜在的诊断和治疗医学应用的报道，但科学文献缺乏针对医学应用的并行计算性能评估、比较测试和验证的标准化程序。专家确定的解剖特征对有可能成为一个广泛采用的参考，用于评估三维空间的准确性，但是，仍然有很大的变化，在他们的使用。用于分析匹配标志对的统计方法已被限制为描述性统计，其总结了测量的配准误差，未能考虑解剖定位的不确定性、观察者之间的变异性以及图像空间的体素离散化。贝叶斯方法在解释现代医学研究数据中的效用早已得到认可。就我们的目的而言，贝叶斯方法的优势是允许从多个来源（包括用于特征对定位的多个观察者、多个成像模式和独立参考数据集）得出关于算法性能特征的判断。这有利于解释测量数据，并允许我们将成像采集和重建过程的知识纳入到制定反映底层物理过程的先验分布中。这导致比今天可用的算法的空间精度性能的更完整的表示。提出的研究的目标是开发一个计算框架和软件基础设施的贝叶斯分析变形图像配准空间精度。用于执行这些分析的软件将被纳入一个公开的参考图像数据库中，使研究人员能够在目前缺乏的标准分析框架内，在一个共同的数据集上定量评估和比较多个图像配准算法/实现。本研究的具体目的是：1.创建病例参考库，以测量模态间（CT-MRI）配准的空间准确性性能和不确定性。2.使用专家选择的地标特征方法，开发并验证贝叶斯分层模型，用于空间精度评估。3.传播用于标准化贝叶斯空间精度分析的软件。如果有一个广泛适用的共同数据集，可用于可持续发展评价，将有助于对科学文献进行精简的比较评价和元分析，并为制定目前缺乏的标准化评价方法提供基础。此外，采用多模态方法来进行预治疗放射治疗（RT）规划和图像引导的RT递送也很有意义，其中磁共振成像（MRI）的上级采集和软组织特性与计算机断层扫描（CT）固有的电子密度信息和几何保真度相结合。纳入CT-MRI参考数据将使研究者能够探索RT计划和图像引导输送的多模式方法的可行性，这需要对互补数据集进行准确的空间配准。通过提供一个严格的计算框架，将不确定性，在使用解剖特征对的评估，拟议的研究有可能塑造未来的协议指南的临床验证，验收测试，和质量保证，在医学成像中的骨关节炎。

DESCRIPTION (provided by applicant): As new innovative and increasingly sophisticated image processing techniques are continually reported in the medical imaging literature, concurrent sophistication in methods for critical evaluation and quality control is lacking. Despit numerous reports of novel DIR algorithms and their potential diagnostic and therapeutic medical applications, the scientific literature is lacking standardized procedures for DIR performance evaluation, comparison testing, and validation specific to medical application. Expert-determined anatomic feature-pairs have the potential to become a widely adopted reference for evaluating DIR spatial accuracy; however, there is still great variability in their use. Statistical methods fr analyzing the matched landmark pairs have been limited to descriptive statistics summarizing the measured registration errors, failing to account for uncertainty in anatomic localization, variability among observers, and voxel discretization of the image space. The utility of Bayesian methods in the interpretation of modern medical research data has long been recognized. For our purposes, the strength of a Bayesian approach is one that allows judgment regarding an algorithm's performance characteristics to be derived from multiple sources, including multiple observers for feature-pair localization, multiple imaging modalities, and independent reference datasets. This facilitates interpretation of the measured data, and allows us to incorporate knowledge of the imaging acquisition and reconstruction process into formulation of prior distributions reflective of the underlying physical processes. This results in a more complete representation of an algorithm's spatial accuracy performance than is available today. The goal of the proposed research is to develop a computational framework and software infrastructure for Bayesian analysis of deformable image registration spatial accuracy. Software for performing these analyses will be incorporated into a publicly available reference image database, allowing investigators to quantitatively evaluate and compare multiple image registration algorithms/implementations on a common dataset, within a standard analysis framework that is currently lacking. The Specific Aims of the proposed research are: 1. Create a reference library of cases to measure DIR spatial accuracy performance and uncertainty for inter-modality (CT-MRI) registration. 2. Develop and validate a Bayesian hierarchical model for DIR spatial accuracy evaluation using the expert selected landmark feature approach. 3. Disseminate software for standardized Bayesian analysis of DIR spatial accuracy. The availability of a common dataset for DIR evaluation that is broadly applicable will facilitate streamlined comparative evaluation and meta-analysis of the scientific literature, and provide a foundation upon which to develop a standardized evaluation methodology that is presently lacking. Additionally, there is much interest to adopt a multi-modality approach to pre-treatment radiotherapy (RT) planning and image guided RT delivery, in which the superior acquisition and soft-tissue characteristics of magnetic resonance imaging (MRI) are integrated with the electron density information and geometric fidelity inherent to computed tomography (CT). Inclusion of CT-MRI reference data will allow investigators to explore feasibility of a multi-modal approach to RT planning and image-guided delivery, which requires accurate spatial registration of the complementary datasets. By providing a rigorous computational framework for incorporating uncertainty in the use of anatomic feature-pairs for DIR evaluation, the proposed study has the potential to shape future protocol guidelines for clinical validation, acceptance testing, and quality assurance of DIR in medical imaging.