Registration Techniques for Clinical Applications of Three-Dimensional Augmented Reality Devices

被引:51
作者
Andrews, Christopher M. [1 ,2 ]
Henry, Alexander B. [2 ]
Soriano, Ignacio M. [2 ]
Southworth, Michael K. [2 ]
Silva, Jonathan R. [1 ]
机构
[1] Washington Univ, McKelvey Sch Engn, Dept Biomed Engn, St Louis, MO 63130 USA
[2] SentiAR Inc, St Louis, MO 63108 USA
基金
美国国家卫生研究院;
关键词
Augmented reality (AR); HoloLens; medical imaging; image registration; surgery; HOLOLENS; INTERVENTIONS; TECHNOLOGY; PATIENT; FIELD;
D O I
10.1109/JTEHM.2020.3045642
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Many clinical procedures would benefit from direct and intuitive real-time visualization of anatomy, surgical plans, or other information crucial to the procedure. Three-dimensional augmented reality (3D-AR) is an emerging technology that has the potential to assist physicians with spatial reasoning during clinical interventions. The most intriguing applications of 3D-AR involve visualizations of anatomy or surgical plans that appear directly on the patient. However, commercially available 3D-AR devices have spatial localization errors that are too large for many clinical procedures. For this reason, a variety of approaches for improving 3D-AR registration accuracy have been explored. The focus of this review is on the methods, accuracy, and clinical applications of registering 3D-AR devices with the clinical environment. The works cited represent a variety of approaches for registering holograms to patients, including manual registration, computer vision-based registration, and registrations that incorporate external tracking systems. Evaluations of user accuracy when performing clinically relevant tasks suggest that accuracies of approximately 2 mm are feasible. 3D-AR device limitations due to the vergence-accommodation conflict or other factors attributable to the headset hardware add on the order of 1.5 mm of error compared to conventional guidance. Continued improvements to 3D-AR hardware will decrease these sources of error.
引用
收藏
页数:14
相关论文
共 63 条
[1]   Effectiveness of the HoloLens mixed-reality headset in minimally invasive surgery: a simulation-based feasibility study [J].
Al Janabi, Hasaneen Fathy ;
Aydin, Abdullatif ;
Palaneer, Sharanya ;
Macchione, Nicola ;
Al-Jabir, Ahmed ;
Khan, Muhammad Shamim ;
Dasgupta, Prokar ;
Ahmed, Kamran .
SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, 2020, 34 (03) :1143-1149
[2]   On-the-fly augmented reality for orthopedic surgery using a multimodal fiducial [J].
Andress, Sebastian ;
Johnson, Alex ;
Unberath, Mathias ;
Winkler, Alexander Felix ;
Yu, Kevin ;
Fotouhi, Javad ;
Weidert, Simon ;
Osgood, Greg ;
Navab, Nassir .
JOURNAL OF MEDICAL IMAGING, 2018, 5 (02)
[3]   Extended Reality in Medical Practice [J].
Andrews C. ;
Southworth M.K. ;
Silva J.N.A. ;
Silva J.R. .
Current Treatment Options in Cardiovascular Medicine, 2019, 21 (4)
[4]  
[Anonymous], 2018, ARXIV180411142
[5]  
[Anonymous], 2019, LOCATABLE CAMERA MIX
[6]   LEAST-SQUARES FITTING OF 2 3-D POINT SETS [J].
ARUN, KS ;
HUANG, TS ;
BLOSTEIN, SD .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1987, 9 (05) :699-700
[7]  
Avari Silva J.N., 2020, Virtual, P341, DOI DOI 10.1007/978-3-030-49698-223
[8]  
Azimi E., 2017, ARXIV170305834
[9]   Management of Patient and Staff Radiation Dose in Interventional Radiology: Current Concepts [J].
Bartal, Gabriel ;
Vano, Eliseo ;
Paulo, Graciano ;
Miller, Donald L. .
CARDIOVASCULAR AND INTERVENTIONAL RADIOLOGY, 2014, 37 (02) :289-298
[10]   HoloLens-Based AR System with a Robust Point Set Registration Algorithm [J].
Chien, Jong-Chih ;
Tsai, Yao-Ren ;
Wu, Chieh-Tsai ;
Lee, Jiann-Der .
SENSORS, 2019, 19 (16)