A major challenge during endovascular interventions is visualising the position and orientation of the catheter being inserted. This is typically achieved by intermittent X-ray imaging. Since the radiation exposure to the surgeon is considerable, it is desirable to reduce X-ray exposure to the bare minimum needed. Additionally, transferring two-dimensional (2D) X-ray images to 3D locations is challenging. The authors present the development of a real-time navigation framework, which allows a 3D holographic view of the vascular system without any need of radiation. They extract the patient's surface and vascular tree from pre-operative computed tomography data and register it to the patient using a magnetic tracking system. The system was evaluated on an anthropomorphic full-body phantom by experienced clinicians using a four-point questionnaire. The average score of the system (maximum of 20) was found to be 17.5. The authors’ approach shows great potential to improve the workflow for endovascular procedures, by simultaneously reducing X-ray exposure. It will also improve the learning curve and help novices to more quickly master the required skills.

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We present a novel framework for rigid point cloud registration. Our approach is based on the principles of mechanics and thermodynamics. We solve the registration problem by assuming point clouds as rigid bodies consisting of particles. Forces can be applied between both particle systems so that they attract or repel each other. These forces are used to cause rigid-body motion of one particle system toward the other, until both are aligned. The framework supports physics-based registration processes with arbitrary driving forces, depending on the desired behaviour. Additionally, the approach handles feature-enhanced point clouds, e.g. by colours or intensity values. Our framework is freely accessible for download. In contrast to already existing algorithms, our contribution is to precisely register high-resolution point clouds with nearly constant computational effort and without the need for pre-processing, subsampling or pre-alignment. At the same time, the quality is up to 28% higher than for state-of-the-art algorithms and up to 49% higher when considering feature-enhanced point clouds. Even in the presence of noise, our registration approach is one of the most robust, on par with state-of-the-art implementations.

While the blocking of beam directions has a negative effect on the plan quality, the results indicate that a careful choice of the ultrasound robot's pose and a large solid angle covered by beam starting positions can offset this effect. Identifying robot poses that yield acceptable plan quality and allow for intra-fraction ultrasound image guidance, therefore, appears feasible.