Our long-term hypothesis is that the technology proposed being investigated above will improve the fidelity and reliability of single-pass negative-margin resection breast conserving surger and will impact the demographic by becoming a standard of care surgical technology as ubiquitous as image-guided surgery is within neurosurgery.
Positional changes during surgery can compromise the use of preoperative breast imaging to inform tumor excision. Biomechanical finite-element models that incorporate breast tissue heterogeneity and anisotropy can be used to accurately correct for these tissue deformations. We are focused on further developing these modeling methods so that they can be used for surgical guidance applications.
Ongoing clinical studies are being performed to validate the accuracy of our image guidance system that compensates for intraoperative soft tissue deformations during minimally invasive liver surgery. This technology enables the precise localization and targeting of subsurface anatomical structures for the purposes of improving interventional safety, enhancing surgical management, and expanding the feasibility of curative resective therapy in broader patient cohorts.
Traditional methods for modeling linear elasticity in soft tissue can be cumbersome and computationally intensive, which is a barrier when incorporating simulation technologies into a clinical workflow. We are investigating innovative methods to make tissue deformation modeling near-real time by utilizing analytical solutions to linear elasticity that were originally proposed for computer animation software.
In this project, our research in surgical ablation leverages patient-biomarker MR imaging to develop “digital twin” computational models to forecast ablative treatments for liver cancer- specifically in patients with fatty liver disease. Our goal is to realistically capture the biophysical effects of ablation and validate ablation predictions against other computational models, surgical phantoms, ex-vivo tissue, and surgical reports.
The goal of this project is to create a mixed reality to help with neurosurgical planning. The current standard for craniotomy planning and teaching revolves around the use of image guidance systems, but training on these systems takes a substantial amount of time and they can be fairly inaccessible for training due to cost. In addition, extended time to teach neuronavigational approaches and consequences is also not possible. To address these concerns, we are working to create a mixed reality application on the Microsoft HoloLens to create an immersive neurosurgery simulation that will allow residents to practice craniotomy planning and understand the consequences of proposed surgical navigation approaches.
Despite significant technological advances, high-resolution electrodes, and improved anatomical understanding of human peripheral nerves, many stimulation clinical trials are empirically driven and have had limited success. Ultimately, patient-specific physiology, anatomy, and clinical attributes should drive ‘when’, ‘where’ and ‘how’ a system should be optimally modulated. The general hypothesis of this work is that a subject-specific digital twin can be used to optimize electrode array geometry and stimulation parameters to improve therapeutic control, and better understand and interpret the vagus nerve anatomy and function as well.
The goals of this research are to design, integrate, deploy, and assess low-cost solutions for accelerated laparoscopic surgeon training and skill assessment in resource-limited developing countries.