121. Machine-vision image guided surgery (MvIGS): an intraoperative and radiation-free spine navigation system workflow analysis

Brecevich, Antonio T.; Dowe, Christina; Lebl, Darren R.; Sama, Andrew A.; Abjornson, Celeste; Cammisa, Frank P.

doi:10.1016/j.spinee.2019.05.135

Cited by 5 publications

(3 citation statements)

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“…The fastest commercial system requires 20 seconds for reregistrations. 42,43 Furthermore, information from prior alignments can seed the search for subsequent fluoroscopic captures. Our algorithm can generate one full-resolution digital radiographic projection and perform a gradient descent iteration in 27 msec on a single modern consumer graphics card.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph

Abumoussa,

Gopalakrishnan,

Succop

et al. 2023

Neurosurgical Focus

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OBJECTIVE The goal of this work was to methodically evaluate, optimize, and validate a self-supervised machine learning algorithm capable of real-time automatic registration and fluoroscopic localization of the spine using a single radiograph or fluoroscopic frame. METHODS The authors propose a two-dimensional to three-dimensional (2D-3D) registration algorithm that maximizes an image similarity metric between radiographic images to identify the position of a C-arm relative to a 3D volume. This work utilizes digitally reconstructed radiographs (DRRs), which are synthetic radiographic images generated by simulating the x-ray projections as they would pass through a CT volume. To evaluate the algorithm, the authors used cone-beam CT data for 127 patients obtained from an open-source de-identified registry of cervical, thoracic, and lumbar scans. They systematically evaluated and tuned the algorithm, then quantified the convergence rate of the model by simulating C-arm registrations with 80 randomly simulated DRRs for each CT volume. The endpoints of this study were time to convergence, accuracy of convergence for each of the C-arm’s degrees of freedom, and overall registration accuracy based on a voxel-by-voxel measurement. RESULTS A total of 10,160 unique radiographic images were simulated from 127 CT scans. The algorithm successfully converged to the correct solution 82% of the time with an average of 1.96 seconds of computation. The radiographic images for which the algorithm converged to the solution demonstrated 99.9% registration accuracy despite utilizing only single-precision computation for speed. The algorithm was found to be optimized for convergence when the search space was limited to a ± 45° offset in the right anterior oblique/left anterior oblique, cranial/caudal, and receiver rotation angles with the radiographic isocenter contained within 8000 cm3 of the volumetric center of the CT volume. CONCLUSIONS The investigated machine learning algorithm has the potential to aid surgeons in level localization, surgical planning, and intraoperative navigation through a completely automated 2D-3D registration process. Future work will focus on algorithmic optimizations to improve the convergence rate and speed profile.

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Section: Limitations and Future Directionsmentioning

confidence: 99%

Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph

Abumoussa,

Gopalakrishnan,

Succop

et al. 2023

Neurosurgical Focus

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“…The initial clinical experience with the 7D machine vision navigation system has demonstrated that it significantly improves upon the intraoperative workflow and efficiency of the navigation process when compared to other navigation options. It provides the same level of screw insertion accuracy in less time and without the need for any intraoperative imaging or radiation exposure ( 27 ).…”

Section: Machine Vision Navigation Technologymentioning

confidence: 99%

Machine Vision Navigation in Spine Surgery

Kalfas

2021

Front. Surg.

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The advancements in computing and digital localizer technologies has led to the evolving clinical application of image-guided technology for the surgical management of spinal disorders. Image-guided spinal navigation addresses the limitations of fluoroscopy and improves the accurate placement of fixation screws. Several navigation platforms are currently available, each having its own unique advantages and disadvantages. The most recent spinal navigation system developed utilizes machine vision structured light imaging which creates a precise and detailed three-dimensional image of the exposed surface anatomy and co-registers it to a pre-operatively or intra-operatively acquired image. This system improves upon the intraoperative workflow and efficiency of the navigation process. With the continued advancements in machine vision, there is a potential for clinical applications that extend beyond surgical navigation. These applications include reducing the potential for wrong level spine surgery and providing for real-time tracking of spinal deformity correction. As the adoption and clinical experience with navigation continues to expand and evolve, the technology that enables navigation also continues to evolve.

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“…The system projects visible light to construct a digital terrain map of the exposed vertebral body during surgery, which is then correlated with preoperative CT data of the spine anatomy. 34,35 However, this system is limited in its application to minimally invasive surgery, as it requires exposure of the bone to be registered. Spatial registration needs to realise high precision and high realtime, and the process should also be as simple as possible without adding too much extra work for surgery, and the trauma caused to the patient during the registration process should be as small as possible.…”

mentioning

confidence: 99%

A spatial registration method based on 2D–3D registration for an augmented reality spinal surgery navigation system

Zhang,

Yang,

Jiang

et al. 2023

Robotics Computer Surgery

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BackgroundIn order to provide accurate and reliable image guidance for augmented reality (AR) spinal surgery navigation, a spatial registration method has been proposed.MethodsIn the AR spinal surgery navigation system, grayscale‐based 2D/3D registration technology has been used to register preoperative computed tomography images with intraoperative X‐ray images to complete the spatial registration, and then the fusion of virtual image and real spine has been realised.ResultsIn the image registration experiment, the success rate of spine model registration was 90%. In the spinal model verification experiment, the surface registration error of the spinal model ranged from 0.361 to 0.612 mm, and the total average surface registration error was 0.501 mm.ConclusionThe spatial registration method based on 2D/3D registration technology can be used in AR spinal surgery navigation systems and is highly accurate and minimally invasive.

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121. Machine-vision image guided surgery (MvIGS): an intraoperative and radiation-free spine navigation system workflow analysis

Cited by 5 publications

References 0 publications

Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph

Machine learning for automated and real-time two-dimensional to three-dimensional registration of the spine using a single radiograph

Machine Vision Navigation in Spine Surgery

A spatial registration method based on 2D–3D registration for an augmented reality spinal surgery navigation system

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