The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safely and accurately navigating a needle through living tissue to a target is currently often challenging or infeasible because of the presence of anatomical obstacles, high levels of uncertainty, and natural tissue motion. Medical robots capable of automating needle-based procedures have the potential to overcome these challenges and enable enhanced patient care and safety. However, autonomous navigation of a needle around obstacles to a predefined target in vivo has not been shown. Here, we introduce a medical robot that autonomously navigates a needle through living tissue around anatomical obstacles to a target in vivo. Our system leverages a laser-patterned highly flexible steerable needle capable of maneuvering along curvilinear trajectories. The autonomous robot accounts for anatomical obstacles, uncertainty in tissue/needle interaction, and respiratory motion using replanning, control, and safe insertion time windows. We applied the system to lung biopsy, which is critical for diagnosing lung cancer, the leading cause of cancer-related deaths in the United States. We demonstrated successful performance of our system in multiple in vivo porcine studies achieving targeting errors less than the radius of clinically relevant lung nodules. We also demonstrated that our approach offers greater accuracy compared with a standard manual bronchoscopy technique. Our results show the feasibility and advantage of deploying autonomous steerable needle robots in living tissue and how these systems can extend the current capabilities of physicians to further improve patient care.

Laser ablation of the hippocampus offers medically refractory epilepsy patients an alternative to invasive surgeries. Emerging commercial solutions deliver the ablator through a burr hole in the back of the head. We recently introduced a new access path through the foremen ovale, using a helical needle, which minimizes the amount of healthy brain tissue the needle must pass through on its way to the hippocampus, and also enables the needle to follow the medial axis of the hippocampus more closely. In this paper, we investigate whether helical needles should be designed and fabricated on a patient-specific basis as we had previously proposed, or whether a small collection of pre-defined needle shapes can apply across many patients. We propose a new optimization strategy to determine this needle set using patient data, and investigate the accuracy with which these needles can reach the the medial axis of the hippocampus. We find that three basic tube shapes (mirrored as necessary for left vs. right hippocampi) are all that is required, across 20 patient datasets (obtained from 10 patient CT scans), to reduce worst-case maximum error below 2 mm.

This short communication describes experimental evaluation of a new granular jamming cap (GJC) recently introduced in Wellborn et al. (Int J Comput Assist Radiol Surg 12(6):1069-1077, 2017). The contributions beyond [8] are (1) to evaluate accuracy across multiple human subjects, and (2) to determine how much of the accuracy improvement is attributable to improved fiducial marker arrangement alone, and how much is due to granular jamming. The motivation for this GJC is to improve the accuracy of image-guidance interfaces in transnasal skull base surgery. Accuracy depends on a rigid connection between tracked fiducial markers and the patient. By molding itself to the unique contours of the individual patient's head and then solidifying, the GJC can firmly attach fiducial markers to a patient, increasing accuracy in the presence of disturbances.

We present a new framework for in vivo image guidance evaluation and provide a case study on robotic partial nephrectomy. This framework (called the "bystander protocol") involves two surgeons, one who solely performs the therapeutic process without image guidance, and another who solely periodically collects data to evaluate image guidance. This isolates the evaluation from the therapy, so that in-development image guidance systems can be tested without risk of negatively impacting the standard of care. We provide a case study applying this protocol in clinical cases during robotic partial nephrectomy surgery. The bystander protocol was performed successfully in 6 patient cases. We find average lesion centroid localization error with our IGS system to be 6.5 mm in vivo compared to our prior result of 3.0 mm in phantoms. The bystander protocol is a safe, effective method for testing in-development image guidance systems in human subjects.