The main challenges of Computed Tomography (CT)-guided organ puncture are the mental registration of the medical imaging data with the patient anatomy, required when planning a trajectory, and the subsequent precise insertion of a needle along it. An interventional telerobotic system, such as Robopsy, enables precise needle insertion, however, in order to minimize procedure time and number of CT scans, this system should be driven by an interface that is directly integrated with the medical imaging data. In this study we have developed and evaluated such an interface that provides the user with a point-and-click functionality for specifying the desired trajectory, segmenting the needle and automatically calculating the insertion parameters (angles and depth). In order to highlight the advantages of such an interface, we compared robotic-assisted targeting using the old interface (non-image-based) where the path planning was performed on the CT console and transferred manually to the interface with the targeting procedure using the new interface (image-based). We found that the mean procedure time (n=5) was 22±5 min (non-image-based) and 19±1 min (image-based) with a mean number of CT scans of 6±1 (non-image-based) and 5±1 (image-based). Although the targeting experiments were performed in gelatin with homogenous properties our results indicate that an image-based interface can reduce procedure time as well as number of CT scans for percutaneous needle biopsies.
Computed tomography (CT) guided percutaneous punctures of the liver for cancer diagnosis and therapy (e.g. tumor biopsy, radiofrequency ablation) are well-established procedures in clinical routine. One of the main challenges related to these interventions is the accurate placement of the needle within the lesion. Several navigation concepts have been introduced to compensate for organ shift and deformation in real-time, yet, the operator error remains an important factor influencing the overall accuracy of the developed systems. The aim of this study was to investigate whether the operator error and, thus, the overall insertion error of an existing navigation system could be further reduced by replacing the user with the medical robot Robopsy. For this purpose, we performed navigated needle insertions in a static abdominal phantom as well as in a respiratory liver motion simulator and compared the human operator error with the targeting error performed by the robot. According to the results, the Robopsy driven needle insertion system is able to more accurately align the needle and insert it along its axis compared to a human operator. Integration of the robot into the current navigation system could thus improve targeting accuracy in clinical use.
A quasi-passive leg exoskeleton is presented for load-carrying augmentation during walking. The exoskeleton has no actuators, only ankle and hip springs and a knee variable damper. Without a payload, the exoskeleton weighs 11.7kg and requires only 2 Watts of electrical power during loaded walking. For a 36kg payload, we demonstrate that the quasi-passive exoskeleton transfers on average 80% of the load to the ground during the single support phase of walking. By measuring the rate of oxygen consumption on a study participant walking at a self-selected speed, we find that the exoskeleton slightly increases the walking metabolic cost of transport (COT) as compared to a standard loaded backpack (10% increase). However, a similar exoskeleton without joint springs or damping control (zero-impedance exoskeleton) is found to increase COT by 23% compared to the loaded backpack, highlighting the benefits of passive and quasi-passive joint mechanisms in the design of efficient, low-mass leg exoskeletons.