ABSTRACT
Understanding the underlying pathology in different tissues and organs is crucial when fighting pandemics like COVID-19. During conventional autopsy, large tissue sample sets of multiple organs can be collected from cadavers. However, direct contact with an infectious corpse is associated with the risk of disease transmission and relatives of the deceased might object to a conventional autopsy. To overcome these drawbacks, we consider minimally invasive autopsies with robotic needle placement as a practical alternative. One challenge in needle based biopsies is avoidance of dense obstacles, including bones or embedded medical devices such as pacemakers. We demonstrate an approach for automated planning and visualising suitable needle insertion points based on computed tomography (CT) scans. Needle paths are modeled by a line between insertion and target point and needle insertion path occlusion from obstacles is determined by using central projections from the biopsy target to the surface of the skin. We project the maximum and minimum CT attenuation, insertion depth, and standard deviation of CT attenuation along the needle path and create two-dimensional intensity-maps projected on the skin. A cost function considering these metrics is introduced and minimized to find an optimal biopsy needle path. Furthermore, we disregard insertion points without sufficient room for needle placement. For visualisation, we display the color-coded cost function so that suitable points for needle insertion become visible. We evaluate our system on 10 post mortem CTs with six biopsy targets in abdomen and thorax annotated by medical experts. For all patients and targets an optimal insertion path is found. The mean distance to the target ranges from (49.9 ± 12.9)mm for the spleen to (90.1 ± 25.8)mm for the pancreas. © 2021 by Walter de Gruyter Berlin/Boston.
ABSTRACT
Introduction Understanding the underlying pathology in different tissues is crucial when fighting a pandemic like COVID-19. During a conventional autopsy, large tissue sample sets of multiple organs can be collected from cadavers. However, direct contact with an infectious corpse is associated with the risk of disease transmission and relatives of the deceased might object to an conventional autopsy. To overcome these drawbacks, we consider minimally invasive autopsies with robotic needle placement as practical alternative. One challenge in needle based biopsies is the avoidance of dense obstacles including bones or embedded medical devices such as pacemakers. We demonstrate an approach for planning and visualizing suitable needle insertion points based on post mortem CT scans. Methods The needle paths are modeled by a line between insertion and target point. We project the maximum and minimum CT intensity, insertion depth, and standard deviation of CT intensity along the needle path and create two-dimensional intensity-maps projected on the skin. A cost function considering these metrics is introduced and minimized to find an optimal biopsy needle path. Furthermore, we disregard insertion points without sufficient room for needle placement. For visualization, we segment the surface of the corpse and display a colormap of a function depending on the estimated metrics such that viable points for needle insertion become visible. Results We evaluate our system on CTs of 10 corpses with six biopsy targets in the abdomen and thorax annotated by medical experts. For all patients and targets an optimal insertion path could be found. The mean distance to the target ranges from (49. 9 ± 12. 9) mm for the spleen to (90. 1 ± 25. 8) mm for the pancreas. Conclusion Robots are a valuable tool for minimally invasive needle biopsies from infectious corpses and allow for pre-planned needle insertion. We developed a tool for visualizing and optimizing for viable needle insertion points. Depending on the type of needle insertion, the objective function can be adjusted for optimal needle insertion points.