Mixed reality ultrasound guidance system: a case study in system development and a cautionary tale.

Abstract

Real-time ultrasound has become a crucial aspect of several image-guided interventions. One of the main constraints of such an approach is the difficulty in interpretability of the limited field of view of the image, a problem that has recently been addressed using mixed reality, such as augmented reality and augmented virtuality. The growing popularity and maturity of mixed reality has led to a series of informal guidelines to direct development of new systems and to facilitate regulatory approval. However, the goals of mixed reality image guidance systems and the guidelines for their development have not been thoroughly discussed. The purpose of this paper is to identify and critically examine development guidelines in the context of a mixed reality ultrasound guidance system through a case study. A mixed reality ultrasound guidance system tailored to central line insertions was developed in close collaboration with an expert user. This system outperformed ultrasound-only guidance in a novice user study and has obtained clearance for clinical use in humans. A phantom study with 25 experienced physicians was carried out to compare the performance of the mixed reality ultrasound system against conventional ultrasound-only guidance. Despite the previous promising results, there was no statistically significant difference between the two systems. Guidelines for developing mixed reality image guidance systems cannot be applied indiscriminately. Each design decision, no matter how well justified, should be the subject of scientific and technical investigation. Iterative and small-scale evaluation can readily unearth issues and previously unknown or implicit system requirements. We recommend a wary eye in development of mixed reality ultrasound image guidance systems emphasizing small-scale iterative evaluation alongside system development. Ultimately, we recommend that the image-guided intervention community furthers and deepens this discussion into best practices in developing image-guided interventions.