4D CT image reconstruction with diffeomorphic motion model

Four-dimensional (4D) respiratory correlated computed tomography (RCCT) has been widely used for studying organ motion. Most current RCCT imaging algorithms use binning techniques that are susceptible to artifacts and challenge the quantitative analysis of organ motion. In this paper, we develop an algorithm for analyzing organ motion which uses the raw, time-stamped imaging data to reconstruct images while simultaneously estimating deformation in the subject’s anatomy. This results in reduction of artifacts and facilitates a reduction in dose to the patient during scanning while providing equivalent or better image quality as compared to RCCT. The framework also incorporates fundamental physical properties of organ motion, such as the conservation of local tissue volume. We demonstrate that this approach is accurate and robust against noise and irregular breathing patterns. We present results for a simulated cone beam CT phantom, as well as a detailed real porcine liver phantom study demonstrating accuracy and robustness of the algorithm. An example of applying this algorithm to real patient image data is also presented.

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► We model respiratory motion during acquisition in a fluid flow framework.
► We jointly reconstruct image and motion.
► Our framework includes an optional incompressibility constraint.
► Joint estimation improves SNR over binning and allows reduction in imaging dose.