Multimodal mixed reality visualisation for intraoperative surgical guidance

Abstract

Purpose In the last decade, there has been a great effort to bring mixed reality (MR) into the operating room to assist surgeons intraoperatively. However, progress towards this goal is still at an early stage. The aim of this paper is to propose a MR visualisation platform which projects multiple imaging modalities to assist intraoperative surgical guidance. Methodology In this work, a MR visualisation platform has been developed for the Microsoft HoloLens. The platform contains three visualisation components, namely a 3D organ model, volumetric data, and tissue morphology captured with intraoperative imaging modalities. Furthermore, a set of novel interactive functionalities have been designed including scrolling through volumetric data and adjustment of the virtual objects’ transparency. A pilot user study has been conducted to evaluate the usability of the proposed platform in the operating room. The participants were allowed to interact with the visualisation components and test the different functionalities. Each surgeon answered a questionnaire on the usability of the platform and provided their feedback and suggestions. Results The analysis of the surgeons’ scores showed that the 3D model is the most popular MR visualisation component and neurosurgery is the most relevant speciality for this platform. The majority of the surgeons found the proposed visualisation platform intuitive and would use it in their operating rooms for intraoperative surgical guidance. Our platform has several promising potential clinical applications, including vascular neurosurgery. Conclusion The presented pilot study verified the potential of the proposed visualisation platform and its usability in the operating room. Our future work will focus on enhancing the platform by incorporating the surgeons’ suggestions and conducting extensive evaluation on a large group of surgeons.

HoloLens and mobile augmented reality in medical and health science education: A randomised controlled trial

“…Although the use of augmented reality technology for teaching remains relatively new, benefits have already been identified from using the HoloLens for medical use, such as for visualising organs prior to surgery (Cartucho, Shapira, Ashrafian, & Giannarou, 2020), teaching dental students (Zafar & Zachar, 2020) and in pathology education (Hanna, Ahmed, Nine, Prajapati, & Pantanowitz, 2018). However, this technology has not yet been commonly utilised in the tertiary medical or health education environments.…”

Intraoperative holography navigation using a mixed-reality wearable computer during laparoscopic cholecystectomy

“…The 3D models displayed by HL can be manipulated at any time by the surgeon's own hands while wearing sterile gloves and can be viewed from any angle without changing the standing position of the surgeon. 11,13 Moreover, because the 3D model can be displayed freely in all directions, not only in the surgical field, surgeons can check the anatomy side by side with the 2D image or can display the 3D model next to the laparoscopic view during surgery and check the anatomy while comparing them, similar to a car navigation system. Therefore, the time spent referring to images may have become greater than that spent referring to conventional images, leading to a prolonged operative time.…”

“…This reinforces the surgeons' spatial awareness, making it possible to understand the anatomy more intuitively, compared with conventional 2D images presented on a monitor. Cartucho et al 13 compared 2D images and 3D models displayed by a wearable mixed-reality device and reported that the 3D models were more highly evaluated by the surgeons. As surgeons with less surgical experience evaluated 3D models more highly than experienced surgeons, 10,14 we speculate that less experienced surgeons with lower spatial awareness may benefit more from 3D models.…”

Multimodal MRI Analysis of Brain Metabolism in Maintenance Hemodialysis Patients Based on Cognitive Computing

“…The sample entropy expression is defined as S ( x , y ) for an embedding dimension of m and a similarity threshold of y . Thus, for an EEG time series of time length T , the dynamic sample entropy expression is shown in the following equation, where the subscript k represents the sliding time window [ 26 ]: …”