Enhanced markerless surgical robotic guidance system for keyhole neurosurgery

Image-based surgical techniques can support minimally invasive keyhole neurosurgery through the segmentation of computed tomography or magnetic resonance imaging, optimal surgical planning, and surgical guidance. Keyhole neurosurgery requires automatic surgical planning and accuracy guidance for a target of the non-invasive or minimally invasive treatment of an anatomical structure. For supporting these requirements, we propose a markerless surgical robotic guidance system for keyhole neurosurgery. Our proposed robotic system consists of a six-degree of freedom (DoF) robotic arm, a needle guidance device, a mobile cart, a control workstation, and a 3D surface scanner. A 3D surface scanner is used for markerless registration between a preoperative reference image and a patient’s face in the operating room. This system configuration has merits of minimally invasive surgery. Traditional frame-based registration requires a stereotactic frame onto the patient’s head. Also, it requires an additional CT/MRI scanning to identify the coordinate system of the frame. However, the proposed markerless system can register without an additional CT scan by using a surface scanner. First, we show the system configuration to perform robotic-assisted surgery precisely. Next, we analyze subcomponents and calibrations such as the repeatability of the robotic arm, hand-tool calibration error for the relationship between needle guidance device and flange of the robotic arm, 3D scanning accuracy of the surface scanner, and hand-eye calibration error for the relationship between the surface scanner and the flange of the robotic arm. After calibrating each component of proposed system, the system accuracy is affected by the propagation of small errors. We sampled 15 paths located in the working space to check and compensate the residual transformation. We also test the total system accuracy with a phantom model; the proposed system allows us to obtain translation error (0.75 ± 0.38 mm) and rotation error (0.85 ± 0.16°) as a residual median error, which satisfies a surgical requirement (< 1 mm). With placements of medical devices to the planned pathway, it is crucial to automatically calculate a surgical path for keyhole neurosurgery. Thus, we proposed a path-planning algorithm that can support surgical decision-making process for keyhole neurosurgery. In the proposed algorithm, a possible trajectory is defined by a pair of a possible entry point on skull surface and a target point inside of target region. Then, the surgical paths are evaluated to satisfy the surgical rules such as the avoidance of vital organs, the maximization of the coverage of the target volume, and orthogonal insertion into the scalp surface.