3D camera-based markerless navigation system for robotic osteotomies

AbstractA markerless system for the registration of a bone’s pose is presented which reduces the setup time and the damage to the bone to a minimum. For the registration, a particle filter is implemented which is able to estimate a bone’s pose using depth images. In a phantom study, the pose of 3D-printed bones has been estimated at a rate of 90 Hz and with a precision of a few millimeters. The particle filter is stable under partial occlusions and only diverges when the bone is fully occluded. During a cadaver study, the preoperatively planned cutting edges have been projected as augmented reality (AR) templates onto the hip bones of five cadavers. By cutting manually along the AR templates, surgeons were able to extract ten transplants in the same time as with conventional osteotomy templates. Using the presented navigation system can save hours spent on the construction and production of conventional templates. In conclusion, this work represents one step towards a broader acceptance of robotic osteotomies.

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Development of an inside-out augmented reality technique for neurosurgical navigation

Neurosurgical FOCUS ◽

10.3171/2021.5.focus21184 ◽

2021 ◽

Vol 51 (2) ◽

pp. E21

Author(s):

Yun-Sik Dho ◽

Sang Joon Park ◽

Haneul Choi ◽

Youngdeok Kim ◽

Hyeong Cheol Moon ◽

...

Keyword(s):

Augmented Reality ◽

Navigation System ◽

Fine Tuning ◽

Image Feature ◽

Patient Specific ◽

Registration Accuracy ◽

Pilot Model ◽

Patient Model ◽

3D Printed ◽

Inside Out

OBJECTIVE With the advancement of 3D modeling techniques and visualization devices, augmented reality (AR)–based navigation (AR navigation) is being developed actively. The authors developed a pilot model of their newly developed inside-out tracking AR navigation system. METHODS The inside-out AR navigation technique was developed based on the visual inertial odometry (VIO) algorithm. The Quick Response (QR) marker was created and used for the image feature–detection algorithm. Inside-out AR navigation works through the steps of visualization device recognition, marker recognition, AR implementation, and registration within the running environment. A virtual 3D patient model for AR rendering and a 3D-printed patient model for validating registration accuracy were created. Inside-out tracking was used for the registration. The registration accuracy was validated by using intuitive, visualization, and quantitative methods for identifying coordinates by matching errors. Fine-tuning and opacity-adjustment functions were developed. RESULTS ARKit-based inside-out AR navigation was developed. The fiducial marker of the AR model and those of the 3D-printed patient model were correctly overlapped at all locations without errors. The tumor and anatomical structures of AR navigation and the tumors and structures placed in the intracranial space of the 3D-printed patient model precisely overlapped. The registration accuracy was quantified using coordinates, and the average moving errors of the x-axis and y-axis were 0.52 ± 0.35 and 0.05 ± 0.16 mm, respectively. The gradients from the x-axis and y-axis were 0.35° and 1.02°, respectively. Application of the fine-tuning and opacity-adjustment functions was proven by the videos. CONCLUSIONS The authors developed a novel inside-out tracking–based AR navigation system and validated its registration accuracy. This technical system could be applied in the novel navigation system for patient-specific neurosurgery.

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A Study on Route Guidance of a Car Navigation System Based on Augmented Reality

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.131.897 ◽

2011 ◽

Vol 131 (7) ◽

pp. 897-906

Author(s):

Kengo Akaho ◽

Takashi Nakagawa ◽

Yoshihisa Yamaguchi ◽

Katsuya Kawai ◽

Hirokazu Kato ◽

...

Keyword(s):

Augmented Reality ◽

Navigation System ◽

Route Guidance ◽

Car Navigation System ◽

Car Navigation

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Efficacy of an Augmented Reality Navigation System (SIRIO) for Percutaneous Computed Tomographic-Guided Pulmonary Ground-Glass Opacity Biopsies

10.1055/s-0041-1729031 ◽

2020 ◽

Author(s):

Faiella Eliodoro ◽

Pacella Giuseppina ◽

Altomare Carlo ◽

Andresciani Flavio ◽

Zobel Beomonte Bruno ◽

...

Keyword(s):

Augmented Reality ◽

Navigation System ◽

Ground Glass Opacity ◽

Ground Glass ◽

Computed Tomographic

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Comparison of implant placement accuracy in two different preoperative digital workflows: navigated vs. pilot-drill-guided surgery

International Journal of Implant Dentistry ◽

10.1186/s40729-021-00322-1 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Johannes Spille ◽

Feilu Jin ◽

Eleonore Behrens ◽

Yahya Açil ◽

Jürgen Lichtenstein ◽

...

Keyword(s):

Navigation System ◽

Entry Point ◽

Angular Deviation ◽

Guided Surgery ◽

Implant Placement ◽

Drill Guide ◽

Navigation Group ◽

3D Printed ◽

The Mean ◽

Master Model

Abstract Background The aim of the study is to evaluate the accuracy of a new implant navigation system on two different digital workflows. Methods A total of 18 phantom jaws consisting of hard and non-warping plastic and resembling edentulous jaws were used to stimulate a clinical circumstance. A conventional pilot-drill guide was conducted by a technician, and a master model was set by using this laboratory-produced guide. After cone beam computed tomography (CBCT) and 3D scanning of the master models, two different digital workflows (marker tray in CBCT and 3D-printed tray) were performed based on the Digital Imaging Communication in Medicine files and standard tessellation language files. Eight Straumann implants (4.1 mm × 10 mm) were placed in each model, six models for each group, resulting in 144 implant placements in total. Postoperative CBCT were taken, and deviations at the entry point and apex as well as angular deviations were measured compared to the master model. Results The mean total deviations at the implant entry point for MTC (marker tray in CBCT), 3dPT (3d-printed tray), and PDG (pilot-drill guide) were 1.024 ± 0.446 mm, 1.027 ± 0.455 mm, and 1.009 ± 0.415 mm, respectively, and the mean total deviations at the implant apex were 1.026 ± 0.383 mm, 1.116 ± 0.530 mm, and 1.068 ± 0.384 mm. The angular deviation for the MTC group was 2.22 ± 1.54°. The 3dPT group revealed an angular deviation of 1.95 ± 1.35°, whereas the PDG group showed a mean angular deviation of 2.67 ± 1.58°. Although there were no significant differences among the three groups (P > 0.05), the navigation groups showed lesser angular deviations compared to the pilot-drill-guide (PDG) group. Implants in the 3D-printed tray navigation group showed higher deviations at both entry point and apex. Conclusions The accuracy of the evaluated navigation system was similar with the accuracy of a pilot-drill guide. Accuracy of both preoperative workflows (marker tray in CBCT or 3D-printed tray) was reliable for clinical use.

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Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study

Applied Sciences ◽

10.3390/app11052315 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2315

Author(s):

Yu-Cheng Lo ◽

Guan-An Chen ◽

Yin Chun Liu ◽

Yuan-Hou Chen ◽

Jui-Ting Hsu ◽

...

Keyword(s):

Augmented Reality ◽

Navigation System ◽

Computer Aided Design ◽

Control Group ◽

Orthodontic Bracket ◽

Bracket Position ◽

Aided Design ◽

Clinical Error ◽

And Control

To improve the accuracy of bracket placement in vivo, a protocol and device were introduced, which consisted of operative procedures for accurate control, a computer-aided design, and an augmented reality–assisted bracket navigation system. The present study evaluated the accuracy of this protocol. Methods: Thirty-one incisor teeth were tested from four participators. The teeth were bonded by novice and expert orthodontists. Compared with the control group by Boone gauge and the experiment group by augmented reality-assisted bracket navigation system, our study used for brackets measurement. To evaluate the accuracy, deviations of positions for bracket placement were measured. Results: The augmented reality-assisted bracket navigation system and control group were used in the same 31 cases. The priority of bonding brackets between control group or experiment group was decided by tossing coins, and then the teeth were debonded and the other technique was used. The medium vertical (incisogingival) position deviation in the control and AR groups by the novice orthodontist was 0.90 ± 0.06 mm and 0.51 ± 0.24 mm, respectively (p < 0.05), and by the expert orthodontist was 0.40 ± 0.29 mm and 0.29 ± 0.08 mm, respectively (p < 0.05). No significant changes in the horizontal position deviation were noted regardless of the orthodontist experience or use of the augmented reality–assisted bracket navigation system. Conclusion: The augmented reality–assisted bracket navigation system increased the accuracy rate by the expert orthodontist in the incisogingival direction and helped the novice orthodontist guide the bracket position within an acceptable clinical error of approximately 0.5 mm.

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