No abstract

Continuum manipulators enable minimally-invasive surgery on the beating heart, but the challenges involved in manually controlling the manipulator's tip position and contact force with the tissue result in failed procedures and complications. The objective of this work is to achieve autonomous robotic control of a continuum manipulator's position and force in a beating heart model. We present a model-less hybrid control approach that regulates the tip position/force of manipulators with unknown kinematics/ mechanics, under unknown constraints along the manipulator's body. The algorithms estimate the Jacobian in the presence of heartbeat disturbances and sensor noise in real time, enabling closed-loop control. Using this model-less control approach, a robotic catheter autonomously traced clinically relevant paths on a simulated beating heart environment while regulating contact force. A gating procedure is used to tighten the treatment margins and improve precision. Experimental results demonstrate the capabilities of the robot (1:4 AE 1:1 mm-1:9 AE 1:4 mm tracking error) while user demonstrations show the difficulty of manually performing the same task (2:6 AE 2:0 mm-4:3 AE 3:9 mm tracking error). This new, robotically-enabled contiguous ablation method could reduce ablation path discontinuities, improve consistency of treatment, and therefore improve clinical outcomes.

Lung cancer is the leading cause of cancer-related death worldwide, and early diagnosis is critical to improving patient outcomes. To diagnose cancer, a highly trained pulmonologist must navigate a flexible bronchoscope deep into the branched structure of the lung for biopsy. The biopsy fails to sample the target tissue in 26-33% of cases largely because of poor registration with the preoperative CT map. We developed two deep learning approaches to localize the bronchoscope in the preoperative CT map in real time and tested the algorithms across 13 trajectories in a lung phantom and 68 trajectories in 11 human cadaver lungs. In the lung phantom, we observe performance reaching 95% precision and recall of visible airways and 3 mm average position error. On a successful cadaver lung sequence, the algorithms trained on simulation alone achieved 77%-94% precision and recall of visible airways and 4-6 mm average position error. We also compare the effect of GAN-stylizing images and we look at aggregate statistics over the entire set of trajectories.One Sentence Summary: Neural networks trained on simulated data can track a bronchoscope's movement through a plastic lung phantom and a human cadaver lung.

This analysis quantifies the importance of head positioning prior to impact, and may help to explain why other species are naturally more resilient to head impacts than humans.

In studying traumatic brain injury (TBI), it has been long hypothesized that the head is more vulnerable to injury from impacts in certain directions or locations, as the relationship between impact force and the resulting neurological outcome is complex and can vary significantly between individual cases. Many studies have identified head angular acceleration to be the putative cause of brain trauma, but it is not well understood how impact location can affect the resulting head kinematics and tissue strain. Here, we identify the susceptibility of the head to accelerations and brain strain from normal forces at contact points across the surface of the skull and jaw using a three-dimensional, 20-degree-of-freedom rigid-body head and cervical spine model. We find that head angular acceleration and brain tissue strain resulting from an input force can vary by orders of magnitude based on impact location on the skull, with the mandible as the most vulnerable region. Conversely, head linear acceleration is not sensitive to contact location. Using these analyses, we present an optimization scheme to distribute helmet padding thickness to minimize angular acceleration, resulting in a reduction of angular acceleration by an estimated 25% at the most vulnerable contact point compared to uniform thickness padding. This work gives intuition behind the relationship between input force and resulting brain injury risk, and presents a framework for developing and evaluating novel head protection gear.