Kirst, Alexander
Towards Safe and Collaborative Robotic Ultrasound Tissue Scanning in Neurosurgery
Dyck, Michael, Weld, Alistair, Klodmann, Julian, Kirst, Alexander, Dixon, Luke, Anichini, Giulio, Camp, Sophie, Albu-Schäffer, Alin, Giannarou, Stamatia
Intraoperative ultrasound imaging is used to facilitate safe brain tumour resection. However, due to challenges with image interpretation and the physical scanning, this tool has yet to achieve widespread adoption in neurosurgery. In this paper, we introduce the components and workflow of a novel, versatile robotic platform for intraoperative ultrasound tissue scanning in neurosurgery. An RGB-D camera attached to the robotic arm allows for automatic object localisation with ArUco markers, and 3D surface reconstruction as a triangular mesh using the ImFusion Suite software solution. Impedance controlled guidance of the US probe along arbitrary surfaces, represented as a mesh, enables collaborative US scanning, i.e., autonomous, teleoperated and hands-on guided data acquisition. A preliminary experiment evaluates the suitability of the conceptual workflow and system components for probe landing on a custom-made soft-tissue phantom. Further assessment in future experiments will be necessary to prove the effectiveness of the presented platform.
Automated robotic intraoperative ultrasound for brain surgery
Dyck, Michael, Weld, Alistair, Klodmann, Julian, Kirst, Alexander, Anichini, Giulio, Dixon, Luke, Camp, Sophie, Giannarou, Stamatia, Albu-Schäffer, Alin
During brain tumour resection, localising cancerous tissue and delineating healthy and pathological borders is challenging, even for experienced neurosurgeons and neuroradiologists [1]. Intraoperative imaging is commonly employed for determining and updating surgical plans in the operating room. Ultrasound (US) has presented itself a suitable tool for this task, owing to its ease of integration into the operating room and surgical procedure. However, widespread establishment of this tool has been limited because of the difficulty of anatomy localisation and data interpretation. Experimental setup showing the robotic arm with it's attached This ensures the presence [3] presents an automated method for lung diagnosis, using of random features within the US recordings of the phantom.