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Purpose: Computed tomography (CT) guided minimally invasive interventions such as biopsies or ablation therapies often involve insertion of a needle-shaped instrument into the target organ (e.g., the liver). Today, these interventions still require manual planning of a suitable trajectory to the target (e.g., the tumor) based on the slice data provided by the imaging modality. However, taking into account the critical structures and other parameters crucial to the success of the intervention—such as instrument shape and penetration angle—is challenging and requires a lot of experience. Methods: To overcome these problems, we present a system for the automatic or semiautomatic planning of optimal trajectories to a target, based on 3D reconstructions of all relevant structures. The system determines possible insertion zones based on so-calledhard constraints and rates the quality of these zones by so-called soft constraints. The concept of pareto optimality is utilized to allow for a weight-independent proposal of insertion trajectories. In order to demonstrate the benefits of our method, automatic trajectory planning was applied retrospectively to n = 10 data sets from interventions in which complications occurred. Results: The efficient (graphics processing unit-based) implementation of the constraints results in a mean overall planning time of about 9 s. The examined trajectories, originally chosen by the physician, have been rated as follows: in six cases, the insertion point was labeled invalid by the planning system. For two cases, the system would have proposed points with a better rating according to thesoft constraints. For the remaining two cases the system would have indicated poor rating with respect to one of the soft constraints. The paths proposed by our system were rated feasible and qualitatively good by experienced interventional radiologists. Conclusions: The proposed computer-assisted trajectory planning system is able to detect unsafe and propose safe insertion trajectories and may especially be helpful for interventional radiologist at the beginning or during their interventional training.

Computed tomography (CT) guided minimally invasive interventions such as biopsies or ablation therapies often involve insertion of a needle-shaped instrument into the target organ (e.g., the liver). Today, these interventions still require manual planning of a suitable trajectory to the target (e.g., the tumor) based on the slice data provided by the imaging modality. However, taking into account the critical structures and other parameters crucial to the success of the intervention--such as instrument shape and penetration angle--is challenging and requires a lot of experience.To overcome these problems, we present a system for the automatic or semiautomatic planning of optimal trajectories to a target, based on 3D reconstructions of all relevant structures. The system determines possible insertion zones based on so-called hard constraints and rates the quality of these zones by so-called soft constraints. The concept of pareto optimality is utilized to allow for a weight-independent proposal of insertion trajectories. In order to demonstrate the benefits of our method, automatic trajectory planning was applied retrospectively to n = 10 data sets from interventions in which complications occurred.The efficient (graphics processing unit-based) implementation of the constraints results in a mean overall planning time of about 9 s. The examined trajectories, originally chosen by the physician, have been rated as follows: in six cases, the insertion point was labeled invalid by the planning system. For two cases, the system would have proposed points with a better rating according to the soft constraints. For the remaining two cases the system would have indicated poor rating with respect to one of the soft constraints. The paths proposed by our system were rated feasible and qualitatively good by experienced interventional radiologists.The proposed computer-assisted trajectory planning system is able to detect unsafe and propose safe insertion trajectories and may especially be helpful for interventional radiologist at the beginning or during their interventional training.