Medical Image Registration: Statistical Models of Performance in Relation to the Statistical Characteristics of the Image Data

For image-guided interventions, the imaging task often pertains to registering preoperative and intraoperative images within a common coordinate system. While the accuracy of the registration is directly tied to the accuracy of targeting in the intervention (and presumably the success of the medical outcome), there is relatively little quantitative understanding of the fundamental factors that govern image registration accuracy. A statistical framework is presented that relates models of image noise and spatial resolution to the task of registration, giving theoretical limits on registration accuracy and providing guidance for the selection of image acquisition and post-processing parameters. The framework is further shown to model the confounding influence of soft-tissue deformation in rigid image registration — accurately predicting the reduction in registration accuracy and revealing similarity metrics that are robust against such effects. Furthermore, the framework is shown to provide conceptual guidance in the development of a novel CT-to-radiograph registration method that accounts for deformation. The work also examines a learning-based method for deformable registration to investigate how the statistical characteristics of the training data affect the ability of the model to generalize to test data with differing statistical characteristics. The analysis provides insight on the benefits of statistically diverse training data in generalizability of a neural network and is further applied to the development of a learning-based MR-to-CT synthesis method. Overall, the work yields a quantitative approach to theoretically and experimentally relate the accuracy of image registration to the statistical characteristics of the image data, providing a rigorous guide to the development of new registration methods

Medical Image Registration: Statistical Models of Performance in Relation to the Statistical Characteristics of the Image Data

For image-guided interventions, the imaging task often pertains to registering preoperative and intraoperative images within a common coordinate system. While the accuracy of the registration is directly tied to the accuracy of targeting in the intervention (and presumably the success of the medical outcome), there is relatively little quantitative understanding of the fundamental factors that govern image registration accuracy. A statistical framework is presented that relates models of image noise and spatial resolution to the task of registration, giving theoretical limits on registration accuracy and providing guidance for the selection of image acquisition and post-processing parameters. The framework is further shown to model the confounding influence of soft-tissue deformation in rigid image registration — accurately predicting the reduction in registration accuracy and revealing similarity metrics that are robust against such effects. Furthermore, the framework is shown to provide conceptual guidance in the development of a novel CT-to-radiograph registration method that accounts for deformation. The work also examines a learning-based method for deformable registration to investigate how the statistical characteristics of the training data affect the ability of the model to generalize to test data with differing statistical characteristics. The analysis provides insight on the benefits of statistically diverse training data in generalizability of a neural network and is further applied to the development of a learning-based MR-to-CT synthesis method. Overall, the work yields a quantitative approach to theoretically and experimentally relate the accuracy of image registration to the statistical characteristics of the image data, providing a rigorous guide to the development of new registration methods