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A Bayesian Framework for Inter-Modality Deformable Image Registration Evaluation

多模态变形图像配准评估的贝叶斯框架

基本信息

批准号：
9130137
负责人：
Richard Castillo
金额：
$ 11.95万
依托单位：
UNIVERSITY OF TEXAS MED BR GALVESTON
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-12 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9130137
关键词：
Accounting Adopted Algorithms Anatomy Bayesian Analysis Bayesian Method Bayesian Modeling Brachial plexus structure Case Study Characteristics Chest Clinical Communities Computer software Computing Methodologies Data Data Set Databases Diagnostic Diagnostic Imaging Dimensions Emerging Technologies Evaluation Evaluation Methodology Formulation Foundations Four-dimensional Future Goals Government Guidelines Health Human Image Internet Judgment Knowledge Lead Libraries Literature Liver Location Magnetic Resonance Imaging Measures Medical Medical Imaging Medical Research Meta-Analysis Methodology Methods Modality Modeling Neuraxis Online Systems Operative Surgical Procedures Patients Peer Review Pelvis Performance Positioning Attribute Procedures Process Protocols documentation Quality Control Radiation Oncology Radiation therapy Reader Reporting Research Research Infrastructure Research Personnel Review Literature Sampling Shapes Source Statistical Data Interpretation Statistical Methods Techniques Technology Testing Therapeutic Translating Uncertainty Validation Variant Work X-Ray Computed Tomography base comparative computer framework digital electron density image guided image guided radiation therapy image processing image registration imaging modality innovation interest novel physical process quality assurance reconstruction simulation soft tissue standard of care statistics targeted imaging

项目摘要

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.

描述（由申请人提供）：随着医学成像文献中不断报道新的创新且日益复杂的图像处理技术，同时缺乏关键评估和质量控制方法的复杂性。尽管有大量关于新型 DIR 算法及其潜在诊断和治疗医学应用的报道，但科学文献缺乏针对特定于医学应用的 DIR 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评估 DIR 空间精度的广泛采用的参考；然而，它们的使用仍然存在很大的差异。分析匹配的标志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，贝叶斯方法的优点是允许从多个来源得出算法性能特征的判断，包括用于特征对定位的多个观察者、多种成像模式和独立的参考数据集。这有助于对测量数据的解释，并使我们能够将成像采集和重建过程的知识纳入反映潜在物理过程的先验分布的公式中。这使得算法的空间精度性能比现在更完整。本研究的目标是开发用于可变形图像配准空间精度的贝叶斯分析的计算框架和软件基础设施。用于执行这些分析的软件将被纳入公开的参考图像数据库中，使研究人员能够在当前缺乏的标准分析框架内定量评估和比较通用数据集上的多个图像配准算法/实现。本研究的具体目标是： 1. 创建一个案例参考库来测量 DIR 空间精度性能和多模态 (CT-MRI) 配准的不确定性。 2. 使用专家选择的地标特征方法开发并验证用于 DIR 空间精度评估的贝叶斯分层模型。 3. 传播 DIR 空间精度标准化贝叶斯分析软件。广泛适用的 DIR 评估通用数据集的可用性将有助于简化科学文献的比较评估和荟萃分析，并为开发目前缺乏的标准化评估方法提供基础。此外，人们对采用多模态方法进行治疗前放疗 (RT) 计划和图像引导 RT 递送也很感兴趣，其中磁共振成像 (MRI) 的卓越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集成。 CT-MRI 参考数据的纳入将使研究人员能够探索 RT 计划和图像引导递送的多模式方法的可行性，这需要补充数据集的准确空间配准。通过提供严格的计算框架来纳入使用解剖特征对进行 DIR 评估的不确定性，拟议的研究有可能为医学成像中 DIR 的临床验证、验收测试和质量保证制定未来的协议指南。