Open-source software for ultrasound-based guidance in spinal fusion surgery

Ultrasound-based navigated pedicle screw insertion without intraoperative radiation: feasibility study on porcine cadavers

BACKGROUND

Navigation systems for spinal fusion surgery rely on intraoperative computed tomography (CT) or fluoroscopy imaging. Both expose patient, surgeons and operating room staff to significant amounts of radiation. Alternative methods involving intraoperative ultrasound (iUS) imaging have recently shown promise for image-to-patient registration. Yet, the feasibility and safety of iUS navigation in spinal fusion have not been demonstrated.

PURPOSE

To evaluate the accuracy of pedicle screw insertion in lumbar and thoracolumbar spinal fusion using a fully automated iUS navigation system.

STUDY DESIGN

Prospective porcine cadaver study.

METHODS

Five porcine cadavers were used to instrument the lumbar and thoracolumbar spine using posterior open surgery. During the procedure, iUS images were acquired and used to establish automatic registration between the anatomy and preoperative CT images. Navigation was performed with the preoperative CT using tracked instruments. The accuracy of the system was measured as the distance of manually collected points to the preoperative CT vertebral surface and compared against fiducial-based registration. A postoperative CT was acquired, and screw placements were manually verified. We report breach rates, as well as axial and sagittal screw deviations.

RESULTS

A total of 56 screws were inserted (5.50 mm diameter n=50, and 6.50 mm diameter n=6). Fifty-two screws were inserted safely without breach. Four screws (7.14%) presented a medial breach with an average deviation of 1.35±0.37 mm (all <2 mm). Two breaches were caused by 6.50 mm diameter screws, and two by 5.50 mm screws. For vertebrae instrumented with 5.50 mm screws, the average axial diameter of the pedicle was 9.29 mm leaving a 1.89 mm margin in the left and right pedicle. For vertebrae instrumented with 6.50 mm screws, the average axial diameter of the pedicle was 8.99 mm leaving a 1.24 mm error margin in the left and right pedicle. The average distance to the vertebral surface was 0.96 mm using iUS registration and 0.97 mm using fiducial-based registration.

CONCLUSIONS

We successfully implanted all pedicle screws in the thoracolumbar spine using the ultrasound-based navigation system. All breaches recorded were minor (<2 mm) and the breach rate (7.14%) was comparable to existing literature. More investigation is needed to evaluate consistency, reproducibility, and performance in surgical context.

CLINICAL SIGNIFICANCE

Intraoperative US-based navigation is feasible and practical for pedicle screw insertion in a porcine model. It might be used as a low-cost and radiation-free alternative to intraoperative CT and fluoroscopy in the future.

Systematic Review: Applications of Intraoperative Ultrasound in Spinal Surgery

BACKGROUND

Due to increased practicality and decreased costs and radiation, interest has risen for intraoperative ultrasound (iUS) in spinal surgery applications; however, few studies have provided a robust overview of its use in spinal surgery. We synthesize findings of existing literature on usage of iUS in navigation, pedicle screw placement, and identification of anatomy during spinal interventions.

METHODS

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were utilized in this systematic review. Studies were identified through PubMed, Scopus, and Google Scholar databases using the search string. Abstracts mentioning iUS in spine applications were included. Upon full-text review, exclusion criteria were implemented, including outdated studies or those with weak topic relevance or statistical power. Upon elimination of duplicates, multi-reviewer screening for eligibility, and citation search, 44 manuscripts were analyzed.

RESULTS

Navigation using iUS is safe, effective, and economical. iUS registration accuracy and success is within clinically acceptable limits for image-guided navigation (Table 2). Pedicle screw instrumentation with iUS is precise with a favorable safety profile (Table 2). Anatomical landmarks are reliably identified with iUS, and surgeons are overwhelmingly successful in neural or vascular tissue identification with iUS modalities including standard B mode, doppler, and contrast-enhanced ultrasound (CE-US) (Table 3). iUS use in traumatic reduction of fractures properly identifies anatomical structures, intervertebral disc space, and vasculature (Table 3).

CONCLUSION

iUS eliminates radiation, decreases costs, and provides sufficient accuracy and reliability in identification of anatomical and neurovascular structures in various spinal surgery settings.

Evaluation of an Ultrasound-Based Navigation System for Spine Neurosurgery: A Porcine Cadaver Study

Background

With the growing incidence of patients receiving surgical treatment for spinal metastatic tumours, there is a need for developing cost-efficient and radiation-free alternatives for spinal interventions. In this paper, we evaluate the capabilities and limitations of an image-guided neurosurgery (IGNS) system that uses intraoperative ultrasound (iUS) imaging for guidance.

Methods

Using a lumbosacral section of a porcine cadaver, we explored the impact of CT image resolution, ultrasound depth and ultrasound frequency on system accuracy, robustness and effectiveness. Preoperative CT images with an isotropic resolution of , and were acquired. During surgery, vertebrae L1 to L6 were exposed. For each vertebra, five iUS scans were acquired using two depth parameters (5 cm and 7 cm) and two frequencies (6 MHz and 12 MHz). A total of 120 acquisition trials were evaluated. Ultrasound-based registration performance is compared to the standard alignment procedure using intraoperative CT. We report target registration error (TRE) and computation time. In addition, the scans’ trajectories were analyzed to identify vertebral regions that provide the most relevant features for the alignment.

Results

For all acquisitions, the median TRE ranged from 1.42 mm to 1.58 mm and the overall computation time was 9.04 s ± 1.58 s. Fourteen out of 120 iUS acquisitions (11.66%) yielded a level-to-level mismatch (and these are included in the accuracy measurements reported). No significant effect on accuracy was found with CT resolution (F_(2,10) = 1.70, p = 0.232), depth (F_(1,5) = 0.22, p= 0.659) nor frequency (F_(1,5) = 1.02, p = 0.359). While misalignment increases linearly with the distance from the imaged vertebra, accuracy was satisfactory for directly adjacent levels. A significant relationship was found between iUS scan coverage of laminae and articular processes, and accuracy.

Conclusion

Intraoperative ultrasound can be used for spine surgery neuronavigation. We demonstrated that the IGNS system yield acceptable accuracy and high efficiency compared to the standard CT-based navigation procedure. The flexibility of the iUS acquisitions can have repercussions on the system performance, which are not fully identified. Further investigation is needed to understand the relationship between iUS acquisition and alignment performance.

The Essential Role of Open Data and Software for the Future of Ultrasound-Based Neuronavigation

With the recent developments in machine learning and modern graphics processing units (GPUs), there is a marked shift in the way intra-operative ultrasound (iUS) images can be processed and presented during surgery. Real-time processing of images to highlight important anatomical structures combined with in-situ display, has the potential to greatly facilitate the acquisition and interpretation of iUS images when guiding an operation. In order to take full advantage of the recent advances in machine learning, large amounts of high-quality annotated training data are necessary to develop and validate the algorithms. To ensure efficient collection of a sufficient number of patient images and external validity of the models, training data should be collected at several centers by different neurosurgeons, and stored in a standard format directly compatible with the most commonly used machine learning toolkits and libraries. In this paper, we argue that such effort to collect and organize large-scale multi-center datasets should be based on common open source software and databases. We first describe the development of existing open-source ultrasound based neuronavigation systems and how these systems have contributed to enhanced neurosurgical guidance over the last 15 years. We review the impact of the large number of projects worldwide that have benefited from the publicly available datasets “Brain Images of Tumors for Evaluation” (BITE) and “Retrospective evaluation of Cerebral Tumors” (RESECT) that include MR and US data from brain tumor cases. We also describe the need for continuous data collection and how this effort can be organized through the use of a well-adapted and user-friendly open-source software platform that integrates both continually improved guidance and automated data collection functionalities.