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Proceedings Paper

Augmenting real-time video with virtual models for enhanced visualization for simulation, teaching, training and guidance
Author(s): Michael Potter; Alexander Bensch; Alexander Dawson-Elli; Cristian A. Linte
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Paper Abstract

In minimally invasive surgical interventions direct visualization of the target area is often not available. Instead, clinicians rely on images from various sources, along with surgical navigation systems for guidance. These spatial localization and tracking systems function much like the Global Positioning Systems (GPS) that we are all well familiar with. In this work we demonstrate how the video feed from a typical camera, which could mimic a laparoscopic or endoscopic camera used during an interventional procedure, can be used to identify the pose of the camera with respect to the viewed scene and augment the video feed with computer-generated information, such as rendering of internal anatomy not visible beyond the imaged surface, resulting in a simple augmented reality environment. This paper describes the software and hardware environment and methodology for augmenting the real world with virtual models extracted from medical images to provide enhanced visualization beyond the surface view achieved using traditional imaging. Following intrinsic and extrinsic camera calibration, the technique was implemented and demonstrated using a LEGO structure phantom, as well as a 3D-printed patient-specific left atrial phantom. We assessed the quality of the overlay according to fiducial localization, fiducial registration, and target registration errors, as well as the overlay offset error. Using the software extensions we developed in conjunction with common webcams it is possible to achieve tracking accuracy comparable to that seen with significantly more expensive hardware, leading to target registration errors on the order of 2 mm.

Paper Details

Date Published: 17 March 2015
PDF: 10 pages
Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 941615 (17 March 2015); doi: 10.1117/12.2082545
Show Author Affiliations
Michael Potter, Rochester Institute of Technology (United States)
Alexander Bensch, Rochester Institute of Technology (United States)
Alexander Dawson-Elli, Rochester Institute of Technology (United States)
Cristian A. Linte, Rochester Institute of Technology (United States)

Published in SPIE Proceedings Vol. 9416:
Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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