Estimation of Nasal Airway Cross-sectional Area From Endoscopy Using Depth Maps: A Proof-of-Concept Study.

OBJECTIVE: Endoscopy is routinely used to diagnose obstructive airway diseases. Currently, endoscopy is only a visualization technique and does not allow quantification of airspace cross-sectional areas (CSAs). This pilot study tested the hypothesis that CSAs can be accurately estimated from depth maps created from virtual endoscopy videos. STUDY DESIGN: Cross-sectional. SETTING: Academic tertiary medical center. METHODS: Virtual endoscopy and depth map videos of the nasal cavity were digitally created based on anatomically accurate three-dimensional (3D) models built from computed tomography scans of 30 subjects. A software tool was developed to outline the airway perimeter and estimate the airspace CSA from the depth maps. Two otolaryngologists used the software tool to estimate the nasopharynx CSA and the nasal valve minimal CSA (mCSA) in the left and right nasal cavities. Model validation statistics were performed. RESULTS: Nasopharynx CSA had a median percent error of 3.7% to 4.6% when compared to the true values measured in the 3D models. Nasal valve mCSA had a median percent error of 22.7% to 33.6% relative to the true values. Raters successfully used the software tool to identify subjects with nasal valve stenosis (ie, mCSA < 0.20 cm2) with a sensitivity of 83.3%, specificity ≥ 90.7%, and classification accuracy ≥ 90.0%. Interrater and intrarater agreements were high. CONCLUSION: This study demonstrates that airway CSAs in 3D models can be accurately estimated from depth maps. The development of artificial intelligence algorithms to compute depth maps may soon allow the quantification of airspace CSAs from clinical endoscopies.

A workflow for viewing biomedical computational fluid dynamics results and corresponding data within virtual and augmented reality environments.

Researchers conducting computational fluid dynamics (CFD) modeling can spend weeks obtaining imaging data, determining boundary conditions, running simulations and post-processing files. However, results are typically viewed on a 2D display and often at one point in time thus reducing the dynamic and inherently three-dimensional data to a static image. Results from different pathologic states or cases are rarely compared in real-time, and supplementary data are seldom included. Therefore, only a fraction of CFD results are typically studied in detail, and associations between mechanical stimuli and biological response may be overlooked. Virtual and augmented reality facilitate stereoscopic viewing that may foster extraction of more information from CFD results by taking advantage of improved depth cues, as well as custom content development and interactivity, all within an immersive approach. Our objective was to develop a straightforward, semi-automated workflow for enhanced viewing of CFD results and associated data in an immersive virtual environment (IVE). The workflow supports common CFD software and has been successfully completed by novice users in about an hour, demonstrating its ease of use. Moreover, its utility is demonstrated across clinical research areas and IVE platforms spanning a range of cost and development considerations. We are optimistic that this advancement, which decreases and simplifies the steps to facilitate more widespread use of immersive CFD viewing, will foster more efficient collaboration between engineers and clinicians. Initial clinical feedback is presented, and instructional videos, manuals, templates and sample data are provided online to facilitate adoption by the community.

Immersive visualization for enhanced computational fluid dynamics analysis.

Modern biomedical computer simulations produce spatiotemporal results that are often viewed at a single point in time on standard 2D displays. An immersive visualization environment (IVE) with 3D stereoscopic capability can mitigate some shortcomings of 2D displays via improved depth cues and active movement to further appreciate the spatial localization of imaging data with temporal computational fluid dynamics (CFD) results. We present a semi-automatic workflow for the import, processing, rendering, and stereoscopic visualization of high resolution, patient-specific imaging data, and CFD results in an IVE. Versatility of the workflow is highlighted with current clinical sequelae known to be influenced by adverse hemodynamics to illustrate potential clinical utility.