Hannes Götz Kenngott1, Anas Amin Preukschas1, Martin Wagner1, Felix Nickel1, Michael Müller2, Nadine Bellemann3, Christian Stock4, Markus Fangerau3, Boris Radeleff3, Hans-Ulrich Kauczor3, Hans-Peter Meinzer2, Lena Maier-Hein2, Beat Peter Müller-Stich5. 1. Department of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany. 2. Division of Medical and Biological Informatics, German Cancer Research Center, Heidelberg, Germany. 3. Department of Diagnostic and Interventional Radiology, Heidelberg University, Heidelberg, Germany. 4. Institute for Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany. 5. Department of General, Visceral and Transplantation Surgery, Heidelberg University, Im Neuenheimer Feld 110, 69120, Heidelberg, Germany. beat.mueller@med.uni-heidelberg.de.
Abstract
BACKGROUND: Augmented reality (AR) systems are currently being explored by a broad spectrum of industries, mainly for improving point-of-care access to data and images. Especially in surgery and especially for timely decisions in emergency cases, a fast and comprehensive access to images at the patient bedside is mandatory. Currently, imaging data are accessed at a distance from the patient both in time and space, i.e., at a specific workstation. Mobile technology and 3-dimensional (3D) visualization of radiological imaging data promise to overcome these restrictions by making bedside AR feasible. METHODS: In this project, AR was realized in a surgical setting by fusing a 3D-representation of structures of interest with live camera images on a tablet computer using marker-based registration. The intent of this study was to focus on a thorough evaluation of AR. Feasibility, robustness, and accuracy were thus evaluated consecutively in a phantom model and a porcine model. Additionally feasibility was evaluated in one male volunteer. RESULTS: In the phantom model (n = 10), AR visualization was feasible in 84% of the visualization space with high accuracy (mean reprojection error ± standard deviation (SD): 2.8 ± 2.7 mm; 95th percentile = 6.7 mm). In a porcine model (n = 5), AR visualization was feasible in 79% with high accuracy (mean reprojection error ± SD: 3.5 ± 3.0 mm; 95th percentile = 9.5 mm). Furthermore, AR was successfully used and proved feasible within a male volunteer. CONCLUSIONS: Mobile, real-time, and point-of-care AR for clinical purposes proved feasible, robust, and accurate in the phantom, animal, and single-trial human model shown in this study. Consequently, AR following similar implementation proved robust and accurate enough to be evaluated in clinical trials assessing accuracy, robustness in clinical reality, as well as integration into the clinical workflow. If these further studies prove successful, AR might revolutionize data access at patient bedside.
BACKGROUND: Augmented reality (AR) systems are currently being explored by a broad spectrum of industries, mainly for improving point-of-care access to data and images. Especially in surgery and especially for timely decisions in emergency cases, a fast and comprehensive access to images at the patient bedside is mandatory. Currently, imaging data are accessed at a distance from the patient both in time and space, i.e., at a specific workstation. Mobile technology and 3-dimensional (3D) visualization of radiological imaging data promise to overcome these restrictions by making bedside AR feasible. METHODS: In this project, AR was realized in a surgical setting by fusing a 3D-representation of structures of interest with live camera images on a tablet computer using marker-based registration. The intent of this study was to focus on a thorough evaluation of AR. Feasibility, robustness, and accuracy were thus evaluated consecutively in a phantom model and a porcine model. Additionally feasibility was evaluated in one male volunteer. RESULTS: In the phantom model (n = 10), AR visualization was feasible in 84% of the visualization space with high accuracy (mean reprojection error ± standard deviation (SD): 2.8 ± 2.7 mm; 95th percentile = 6.7 mm). In a porcine model (n = 5), AR visualization was feasible in 79% with high accuracy (mean reprojection error ± SD: 3.5 ± 3.0 mm; 95th percentile = 9.5 mm). Furthermore, AR was successfully used and proved feasible within a male volunteer. CONCLUSIONS: Mobile, real-time, and point-of-care AR for clinical purposes proved feasible, robust, and accurate in the phantom, animal, and single-trial human model shown in this study. Consequently, AR following similar implementation proved robust and accurate enough to be evaluated in clinical trials assessing accuracy, robustness in clinical reality, as well as integration into the clinical workflow. If these further studies prove successful, AR might revolutionize data access at patient bedside.
Entities:
Keywords:
Augmented reality; Image visualization; Mobile device; Visual assistance
Authors: Timothy J Carter; Maxime Sermesant; David M Cash; Dean C Barratt; Christine Tanner; David J Hawkes Journal: Med Eng Phys Date: 2005-11-03 Impact factor: 2.242
Authors: H G Kenngott; J Neuhaus; B P Müller-Stich; I Wolf; M Vetter; H-P Meinzer; J Köninger; M W Büchler; C N Gutt Journal: Surg Endosc Date: 2007-12-20 Impact factor: 4.584
Authors: T E Wurmb; C Quaisser; H Balling; M Kredel; R Muellenbach; W Kenn; N Roewer; J Brederlau Journal: Emerg Med J Date: 2010-07-20 Impact factor: 2.740
Authors: H G Kenngott; J J Wünscher; M Wagner; A Preukschas; A L Wekerle; P Neher; S Suwelack; S Speidel; F Nickel; D Oladokun; Lorenzo Albala; L Maier-Hein; R Dillmann; H P Meinzer; B P Müller-Stich Journal: Surg Endosc Date: 2015-02-12 Impact factor: 4.584
Authors: Rachel Hecht; Ming Li; Quirina M B de Ruiter; William F Pritchard; Xiaobai Li; Venkatesh Krishnasamy; Wael Saad; John W Karanian; Bradford J Wood Journal: Cardiovasc Intervent Radiol Date: 2020-01-08 Impact factor: 2.740