Augmented reality in neurosurgical navigation: A survey

Liu, Tao; Tai, Yongshang; Zhao, Chengming; Wei, Lei; Zhang, Jun; Pan, Junjun; Shi, Junsheng

doi:10.1002/rcs.2160

Cited by 24 publications

(17 citation statements)

References 83 publications

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“…It is thought that the use of AR in neurosurgery will open a new horizon in surgical planning and will play a major role in the future. 13 Intracranial tumor surgery is the field with the most studies on AR navigation in neurosurgery. The importance of the pathways in the cerebral cortex and white matter makes it necessary to navigate this region with high acuity.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, another limitation is, again, low battery life. 13 All neuronavigation systems have important practical concerns about error when using preoperative images. Intraoperative imaging increases the accuracy of neuronavigation systems by addressing the changing position of the lesion after dural opening, tumor resection, and cyst decompression on removal of CSF.…”

Section: Figmentioning

confidence: 99%

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Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique

Yavas

Calıskan

Cagli

2021

Neurosurgical Focus

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OBJECTIVE The aim of this study was to assess the precision and feasibility of 3D-printed marker–based augmented reality (AR) neurosurgical navigation and its use intraoperatively compared with optical tracking neuronavigation systems (OTNSs). METHODS Three-dimensional–printed markers for CT and MRI and intraoperative use were applied with mobile devices using an AR light detection and ranging (LIDAR) camera. The 3D segmentations of intracranial tumors were created with CT and MR images, and preoperative registration of the marker and pathology was performed. A patient-specific, surgeon-facilitated mobile application was developed, and a mobile device camera was used for neuronavigation with high accuracy, ease, and cost-effectiveness. After accuracy values were preliminarily assessed, this technique was used intraoperatively in 8 patients. RESULTS The mobile device LIDAR camera was found to successfully overlay images of virtual tumor segmentations according to the position of a 3D-printed marker. The targeting error that was measured ranged from 0.5 to 3.5 mm (mean 1.70 ± 1.02 mm, median 1.58 mm). The mean preoperative preparation time was 35.7 ± 5.56 minutes, which is longer than that for routine OTNSs, but the amount of time required for preoperative registration and the placement of the intraoperative marker was very brief compared with other neurosurgical navigation systems (mean 1.02 ± 0.3 minutes). CONCLUSIONS The 3D-printed marker–based AR neuronavigation system was a clinically feasible, highly precise, low-cost, and easy-to-use navigation technique. Three-dimensional segmentation of intracranial tumors was targeted on the brain and was clearly visualized from the skin incision to the end of surgery.

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Section: Discussionmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique

Yavas

Calıskan

Cagli

2021

Neurosurgical Focus

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“…The fusion of augmented reality virtual data into the real scene of neurosurgery reduces the difficulty of analysis and planning for surgeons during the surgery process. This technique is widely used in the medical field [4]. In maxillofacial surgery, Zhu et al [5,6] used augmented reality technology to register the dental membrane with two-dimensional reconstructed images through the dental membrane that can be identified.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Augmented reality display of neurosurgery craniotomy lesions based on feature contour matching

Zhang

Sun

Liu

2021

Cognitive Comp and Systems

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Traditional neurosurgical craniotomy primarily uses two-dimensional cranial medical images to estimate the location of a patient's intracranial lesions. Such work relies on the experience and skills of the doctor and may result in accidental injury to important intracranial physiological tissues. To help doctors more intuitively determine patient lesion information and improve the accuracy of surgical route formulation and craniotomy safety, an augmented reality method for displaying neurosurgery craniotomy lesions based on feature contour matching is proposed. This method uses threshold segmentation and region growing algorithms to reconstruct a 3-D Computed tomography image of the patient's head. The augmented reality engine is used to adjust the reconstruction model's relevant parameters to meet the doctor's requirements and determine the augmented reality matching method for feature contour matching. By using the mobile terminal to align the real skull model, the virtual lesion model is displayed. Using the designed user interface, doctors can view the patient's personal information and can zoom in, zoom out, and rotate the virtual model. Therefore, the patient's lesions information can be visualized accurately, which provides a visual basis for preoperative preparation.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Recently developed image segmentation and rendering technologies have facilitated the production of 3D brain virtual models and the subsequent introduction of several augmented reality (AR) navigation systems using these 3D virtual models. [11][12][13] AR navigation systems use various visualization devices, such as video projectors, tablet PCs, and head-mounted displays (HMDs). However, most of the systems incorporate the classic outside-in tracking methods.…”

mentioning

confidence: 99%

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

Dho

Park²,

Choi³

et al. 2021

Neurosurgical Focus

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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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Augmented reality in neurosurgical navigation: A survey

Abstract: Background: Neurosurgery has exceptionally high requirements for minimally invasive and safety. This survey attempts to analyse the practical application of AR in neurosurgical navigation. Also, this survey describes future trends in augmented reality neurosurgical navigation systems.

Cited by 24 publications

References 83 publications

Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique

Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique

Augmented reality display of neurosurgery craniotomy lesions based on feature contour matching

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

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