Local Motion Detection, Characterization, and Quantification for X-ray CT

Motion detection and correction are key to good image quality in x-ray CT. Various approaches have been explored in the past. Methods to combat patient motion include the use of gating devices to optimize the data acquisition, reducing the patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post-processing algorithms to minimize motion artifacts. In clinical practice, these approaches often rely on CT operators to detect local motion artifacts and initiate appropriate corrective actions. Because physiologically induced motion is complex and often varies spatially and temporally, the effectiveness of the existing approaches is often compromised. In this paper, we propose a data consistency-metric to automatically detect and characterize motion. The metric takes advantage of the fact that the size and shape of small rigid objects, such as blood vessels or lung nodules, do not change significantly over the data acquisition window. The characteristics of the tomographic reconstruction ensures that the magnitude of the circular integral surrounding these objects is small. Therefore, the differential signal of the back-projected intensity between a stationary object and its background integral is linear with respect to the projection view. The "goodness of fit" of the differential signal, therefore, represents the level of motion present at each local region and provides a four-dimensional (spatial plus temporal) motion map of the reconstructed volume. We first present a mathematical model to establish a theoretical foundation. Extensive computer simulations are then utilized to confirm the validity of our theory. Our theory is further tested using a swine cardiac scan to demonstrate its efficacy and robustness. Future expansion and potential clinical benefits of the proposed approach are also discussed.