Self-reference-based and during-registration detection of motion artifacts in spatio-temporal image data

Respiration-correlated or 4D CT imaging represents the standard of care in radiation therapy treatment planning for patients with tumors subject to significant breathing-induced motion. Applications like motion field estimation, correspondence modeling and 4D dose simulation further rely on deformable image registration (DIR) of the individual phase images of the 4D CT data set with DIR accuracy and reliability of derived information being impeded by common 4D CT motion artifacts. Development of image-based approaches for reduction of artifacts or dampening their influence on DIR would benefit from precise artifact detection and localization. In this work, we propose applying groupwise non-linear registration of the 4D CT phase images and during-registration analysis of phase-based contributions to the DIR cost function to detect and localize artifacts. In detail, we build on the B-spline-based elastix framework and focus on the variance metric with the rational being that contributions of artifact-affected phase images and image regions to the variance metric and respective distances to the implicit reference frame (= self reference) are significantly larger than those of non-affected. Evaluation is based on selected artifact-free 4D CT data sets of lung tumor patients. By manipulation of the 4D CT reconstruction, we introduced artifacts at specific breathing phases and known localization. Results show that both detecting artifact-affected breathing phases and localizing the artifacts during registration is feasible. The present proof-of-concept opens up the opportunity for targeted local adjustment of, e.g., regularization weights for artifact-affected image regions to increase robustness of DIR in artifact-affected spatio-temporal image data.