William "Liam" Wang

Hello! I am a PhD student in Computer Science at The University of Michigan under Dr. JJ Park. I have participated in research projects involving robotic navigation systems, mixed reality interfaces for medical procedures, advance motion planning for remote operation of a satellite repair robotic arm, and machine learning for bioinformatics.

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Research

My current research interests are at the intersection of deep learning for robotics and visual computing (computer vision and 3D graphics).

We built a system to determine the changing 3D pose of hardware elements (K-wires) during insertion for percutaneous pelvic fracture fixation surgery using only standard intraoperative X-ray fluoroscopy images. Our approach dynamically creates a 3D anatomic 'digital twin' by estimating the 3D position of hardware during insertion using a deep neural network (DNN) and a global optimization method for per-frame localization.

Leveraging virtual reality (VR), we evaluate a curriculum in which surgeons and non-surgeons swap roles inside a realistic VR operating room. Our study focuses on X-ray guided pelvic trauma surgery, a procedure where successful communication depends on the shared model between the surgeon and a C-arm technologist. Exposing non-surgeons to VR surgical training results in higher engagement with the C-arm technologist role in VR. It also has a significant effect on non-surgeon’s mental model of the overall task; novice participants’ estimation of the mental demand and effort required for the surgeon’s task increases after training.

We present a complete lumbar puncture guidance system with the integration of (1) a wearable mechatronic ultrasound imaging device, (2) volume-reconstruction and bone surface esti- mation algorithms and (3) two alternative augmented reality user interfaces for needle guidance, including a HoloLens-based and a tablet-based solution.

Teleoperation of robots in space is challenging due to high latency and limited workspace visibility. We developed a 3D virtual reality (VR) interface for the Interactive Planning and Supervised Execution (IPSE) system and implemented it on a Meta Quest 2 head-mounted display (HMD). We demonstrated improved operator load with the 3D VR interface, with no decrease in task performance, while also providing cost and portability benefits compared to the conventional 2D interface.

We built an augmented reality (AR) patient education iOS/Android application that allows patients to view a virtual simulation of themselves receiving radiation treatment. We read DICOM-RT data from the clinical treatment planning system, convert their CT data into 3D translucent mesh objects, and render the patient’s actual radiotherapy plan in real time on any smartphone or tablet. We conducted a patient study and found that this simplified low-cost tablet-based personalized AR simulation can be a helpful educational tool and reduces patient anxiety.

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