Precision of a Novel Craniofacial Surgical Navigation System Based on Augmented Reality Using an Occlusal Splint as a Registration Strategy

Jiang, Taoran; Zhu, Ming; Chai, Gang; Li, Qingfeng

doi:10.1038/s41598-018-36457-2

Cited by 20 publications

(23 citation statements)

References 26 publications

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“…For instance, Pietruski et al [10] reported errors of 4.2 ± 1° and 5.4 ± 3.9° in sagittal and frontal osteotomy planes and Viehöfer et al [33] found similar errors of 4.9 ± 4.2°. Smaller errors of 2 ± 1.2° and 1.3 ± 1.2° were reported in recent studies [34,35]. In our study, only 30.2% of cases presented ≤ 2° inclination angle errors (Table 2).…”

Section: Error In the Position And Dimensions Of The Virtual Modelssupporting

confidence: 49%

Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks

et al. 2021

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Purpose Emerging holographic headsets can be used to register patient-specific virtual models obtained from medical scans with the patient’s body. Maximising accuracy of the virtual models’ inclination angle and position (ideally, ≤ 2° and ≤ 2 mm, respectively, as in currently approved navigation systems) is vital for this application to be useful. This study investigated the accuracy with which a holographic headset registers virtual models with real-world features based on the position and size of image markers. Methods HoloLens® and the image-pattern-recognition tool Vuforia Engine™ were used to overlay a 5-cm-radius virtual hexagon on a monitor’s surface in a predefined position. The headset’s camera detection of an image marker (displayed on the monitor) triggered the rendering of the virtual hexagon on the headset’s lenses. 4 × 4, 8 × 8 and 12 × 12 cm image markers displayed at nine different positions were used. In total, the position and dimensions of 114 virtual hexagons were measured on photographs captured by the headset’s camera. Results Some image marker positions and the smallest image marker (4 × 4 cm) led to larger errors in the perceived dimensions of the virtual models than other image marker positions and larger markers (8 × 8 and 12 × 12 cm). ≤ 2° and ≤ 2 mm errors were found in 70.7% and 76% of cases, respectively. Conclusion Errors obtained in a non-negligible percentage of cases are not acceptable for certain surgical tasks (e.g. the identification of correct trajectories of surgical instruments). Achieving sufficient accuracy with image marker sizes that meet surgical needs and regardless of image marker position remains a challenge.

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Section: Error In the Position And Dimensions Of The Virtual Modelssupporting

confidence: 49%

Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks

et al. 2021

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“…The spatial information embedded in medical three‐dimensional (3D) images is being increasingly used to personalize treatment by means of computer‐assisted surgery (CAS) . This new field of medicine encompasses virtual surgical planning, 3D printing of personalized constructs, such as anatomical models, surgical saw guides, or implants, virtual and augmented reality and robot‐guided surgery . The use of such emerging technologies in medicine has resulted in better treatment outcomes and a reduction in both operating times and costs .…”

Section: Introductionmentioning

confidence: 99%

Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

et al. 2019

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Purpose: In order to attain anatomical models, surgical guides and implants for computer-assisted surgery, accurate segmentation of bony structures in cone-beam computed tomography (CBCT) scans is required. However, this image segmentation step is often impeded by metal artifacts. Therefore, this study aimed to develop a mixed-scale dense convolutional neural network (MS-D network) for bone segmentation in CBCT scans affected by metal artifacts. Method: Training data were acquired from 20 dental CBCT scans affected by metal artifacts. An experienced medical engineer segmented the bony structures in all CBCT scans using global thresholding and manually removed all remaining noise and metal artifacts. The resulting gold standard segmentations were used to train an MS-D network comprising 100 convolutional layers using far fewer trainable parameters than alternative convolutional neural network (CNN) architectures. The bone segmentation performance of the MS-D network was evaluated using a leave-2-out scheme and compared with a clinical snake evolution algorithm and two state-of-the-art CNN architectures (U-Net and ResNet). All segmented CBCT scans were subsequently converted into standard tessellation language (STL) models and geometrically compared with the gold standard. Results: CBCT scans segmented using the MS-D network, U-Net, ResNet and the snake evolution algorithm demonstrated mean Dice similarity coefficients of 0.87 AE 0.06, 0.87 AE 0.07, 0.86 AE 0.05, and 0.78 AE 0.07, respectively. The STL models acquired using the MS-D network, U-Net, ResNet and the snake evolution algorithm demonstrated mean absolute deviations of 0.44 mm AE 0.13 mm, 0.43 mm AE 0.16 mm, 0.40 mm AE 0.12 mm and 0.57 mm AE 0.22 mm, respectively. In contrast to the MS-D network, the ResNet introduced wave-like artifacts in the STL models, whereas the U-Net incorrectly labeled background voxels as bone around the vertebrae in 4 of the 9 CBCT scans containing vertebrae. Conclusion: The MS-D network was able to accurately segment bony structures in CBCT scans affected by metal artifacts.

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“…The dentition may be used as a reference in a direct or indirect way in Augmented Reality (AR) [34][35][36] . Wang et al designed a method in which an intra-oral scan is matched to the CT scan (based on an Iterative Closest Point approach) 36 .…”

Section: Discussionmentioning

confidence: 99%

“…Exposure of the dentition, within the field-of-view, is a requirement for the workflow. Jiang et al have proposed an AR workflow resembling the registration-free workflow described in this study 34 . An intra-oral scan of the gypsum cast with the DRF in place is acquired, and the resulting model is matched to the CT scan using user-indicated landmarks on the dental cusps in the CT model and intra-oral scan.…”

Section: Discussionmentioning

confidence: 99%

Registration-Free Workflow for Electromagnetic and Optical Navigation in Orbital and Craniofacial Surgery

Schreurs

Baan

Klop

et al. 2020

Preprint

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The accuracy of intra-operative navigation is largely dependent on the intra-operative registration procedure. Next to accuracy, important factors to consider for the registration procedure are invasiveness, time consumption, logistical demands, user-dependency, compatibility and radiation exposure. In this study, a workflow is presented that eliminates the need for a registration procedure altogether: registration-free navigation. In the workflow, the maxillary dental model is fused to the pre-operative imaging data using commercially available virtual planning software. A virtual Dynamic Reference Frame on a splint is designed on the patient’s fused maxillary dentition: during surgery, the splint containing the reference frame is positioned on the patient’s dentition. This alleviates the need for any registration procedure, since the position of the reference frame is known from the design. The accuracy of the workflow was evaluated in a cadaver set-up, and compared to bone-anchored fiducial, virtual splint and surface-based registration. The results showed that accuracy of the workflow was greatly dependent on tracking technique used: the workflow was the most accurate with electromagnetic tracking, but the least accurate with optical tracking. Although this method offers a time-efficient, non-invasive, radiation-free automatic alternative for registration, clinical implementation is hampered by the unexplained differences in accuracy between tracking techniques.

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Precision of a Novel Craniofacial Surgical Navigation System Based on Augmented Reality Using an Occlusal Splint as a Registration Strategy

Cited by 20 publications

References 26 publications

Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks

Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks

Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

Registration-Free Workflow for Electromagnetic and Optical Navigation in Orbital and Craniofacial Surgery

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