Investigating uncertainty in augmented reality enhanced renal navigation using in vitro patient-specific tissue-mimicking phantoms

To improve the outcome of minimally invasive renal interventions, traditional video-guided needle navigation can be enhanced by tracking the needle, guiding the needle using video imaging, and augmenting the surgical scene with pre-procedural images or models of the anatomy. In our previous work we studied, both through simulations and in vitro experiments, the uncertainty associated with the model-to-phantom registration, as well as the camera-tracker calibration and video-guided navigation. In this work, we characterize the overall navigation uncertainty using tissue emulating patient-specific kidney phantoms featuring both virtual and physical internal targets. Pre-procedural models of the kidney phantoms and internal targets are generated from cone-beam CT images, and are registered to their intra-operative physical counter-parts. The user then guides the needle insertion to reach the internal targets using video-based imaging augmented with a virtual representation of the needle tracked in real time. Following navigation, we acquire post-procedural cone-beam CT images of the phantoms and inserted needles. These images are used to determine the ground truth needle navigation accuracy (i.e., needle to target distance) against which the intra-operative navigation accuracy (i.e., intra-op needle tip to target distance) is assessed. We also explore a method to update the pre-procedural model to physical phantom registration intra-operatively using tracked video imaging, with the overall goal to improve overall navigation accuracy in the event of sub-optimal initial image-to-phantom registration. Our results showed a navigation error of less than 3.5 mm in gelatin phantoms and less than 6.5 mm in PVA phantoms. Following registration correction intra-operatively, we showed an overall improvement in navigation from roughly 6 mm RMS to approximately 2 mm RMS error, which is acceptable given the inherent tracking, 3D printing and phantom manufacturing limitations.

I. Introduction

As minimally invasive image-guided interventions experience increased traction in the clinic, it is critical to provide surgeons with sufficient information to not compromise the outcome of the intervention. Ongoing research includes better intra-operative guidance to reduce navigation uncertainty to ensure that targets are accurately reached or successfully removed. Significant technological advancements to improve guidance for minimally invasive surgery in recent years have focused on augmented reality (AR) or virtual reality (VR) techniques [1], [2], which have posed numerous advantages to various procedures [3]–[5], leading to improved outcome [6]