A Bayesian Framework for Inter-Modality Deformable Image Registration Evaluation

项目摘要 随着新的创新和日益复杂的图像处理技术不断报道， 医学影像学文献，同时复杂的方法，关键的评价和质量控制是 缺乏尽管有许多关于新的神经网络算法及其潜在的诊断和治疗的报道， 医学应用，科学文献缺乏标准化的程序， 评估、比较测试和医疗应用特定的验证。专家确定的解剖 特征对有潜力成为一个广泛采用的参考，用于评估三维空间精度; 然而，在其使用方面仍有很大变化。用于分析匹配界标对的统计方法 仅限于总结测量的配准误差的描述性统计，未能说明 解剖定位的不确定性、观察者之间的可变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应用已经很久了。 认可.就我们的目的而言，贝叶斯方法的优势在于， 算法的性能特征来自多个来源，包括多个观察者， 特征对定位、多种成像模式和独立参考数据集。这便于 测量数据的解释，并允许我们将成像采集的知识， 重构过程转化为反映底层物理过程的先验分布的公式化。 这导致比现有的更完整地表示算法的空间精度性能 今天该研究的目标是开发一个计算框架和软件基础设施 用于贝叶斯分析形变图像配准的空间精度。用于执行这些操作的软件 分析将被纳入一个公开的参考图像数据库，使研究人员， 定量评估和比较多个图像配准算法/实现， 数据集，在目前缺乏的标准分析框架内。建议的具体目标 研究是： 1.创建一个案例参考库，以衡量以下方面的空间精度性能和不确定性： 模态间（CT-MRI）配准。 2.开发并验证贝叶斯分层模型，用于使用 专家选择地标特征方法。 3.传播用于标准化贝叶斯空间精度分析的软件。 提供一个广泛适用的共同数据集，以进行可持续发展评估， 科学文献的比较评价和荟萃分析，并提供一个基础， 制定目前缺乏的标准化评价方法。此外，人们对 采用多模态方法进行治疗前放射治疗（RT）计划和图像引导的RT输送， 其中磁共振成像（MRI）的上级采集和软组织特性是 与计算机断层摄影（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多模式方法的可行性， RT计划和图像引导的输送，这需要互补的精确空间配准 数据集。通过提供一个严格的计算框架，将不确定性纳入解剖学的使用， 特征对的评估，拟议的研究有可能塑造未来的协议指南， 临床验证、验收测试和质量保证。

Project Summary 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. Despite 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 for 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.