Rigid motion compensation in interventional C-arm CT using consistency measure on projection data

Abstract

Interventional C-arm CT has the potential to visualize brain hemorrhages in the operating suite and save valuable time for stroke patients. Due to the critical constitution of the patients, C-arm CT images are frequently affected by patient motion artifacts, which often makes the reliable diagnosis of hemorrhages impossible. In this work, we propose a geometric optimization algorithm to compensate for these artifacts and present first results. The algorithm is based on a projection data consistency measure, which avoids computationally expensive forward- and backprojection steps in the optimization process. The ability to estimate movements with this measure is investigated for different rigid degrees of freedom. It was shown that out-of-plane parameters, i. e. geometrical deviations perpendicular to the plane of rotation, can be estimated with high precision. Movement artifacts in reconstructions are consistently reduced throughout all analyzed clinical datasets. With its low computational cost and high robustness, the proposed algorithm is well-suited for integration into clinical software prototypes for further evaluation.