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Task-Specific Trajectory Optimisation for Twin-Robotic X-Ray Tomography

With the advent of robotic C-arm computed tomography (CT) systems in medicine and twin-robotic CT systems in industry, new possibilities for the realisation of complex trajectories for CT scans are emerging. These trajectories will increase the range of CT applications, enable optimisation of image quality for many applications and open up new possibilities to reduce scan time and radiation dose. In this work, trajectory optimisation methods for optimising both, task-based data quality and data completeness, are presented by combining two different metrics. On the one hand, task-based data quality is optimised with a proven observer model. On the other hand, a Tuy-based metric is utilised to optimise data completeness. Both metrics capture mutually exclusive properties of the trajectory which are necessary, but alone are not sufficient for trajectory optimisation. Hence, existing task-driven trajectory optimisation approaches require additional input to decide on an overall optimal trajectory, e.g. in most cases constraints on the trajectory. Advantages and disadvantages of the presented methods are investigated. It is shown that by combining both metrics, trajectory optimisation for arbitrary geometries becomes possible. In application examples it is shown that this can be used for trajectory optimisation of challenging scanning tasks involving metal parts as well as for trajectory optimisation to reduce the number of projections while ensuring task-dependently high image quality. In total, the results of this work enable new applications for X-ray CT, especially for twin-robotic CT systems which are able to benefit from a high number of degrees of freedom.