Image-guided Navigation System Research Articles

Towards overcoming barriers to the clinical deployment of mixed reality image-guided navigation systems supporting percutaneous ablation of liver focal lesions

In recent years, we have observed a rise in the popularity of minimally invasive procedures for treating liver tumours, with percutaneous thermoablation being one of them, conducted using image-guided navigation systems with mixed reality technology. However, the application of this method requires adequate training in using the employed system. In our study, we assessed which skills pose the greatest challenges in performing such procedures. The article proposes a training module characterized by an innovative approach: the possibility of practicing the diagnosis, planning, execution stages and the physical possibility of performing the execution stage on the radiological phantom of the abdominal cavity. The proposed approach was evaluated by designing a set of 4 exercises corresponding to the 3 phases mentioned. To the research group included 10 radiologists and 5 residents in the study. Based on 20 clinical cases of liver tumors subjected to percutaneous thermoablation, we developed assessment tasks evaluating four skill categories: head-mounted display (HMD), ultrasound (US)/computed tomography (CT) image fusion interpretation, tracking system use, and the ability to insert a needle. The results were presented using the Likert scale. The results of our study indicate that the most challenging aspect for radiology specialists is adapting to HMD gesture control, while residents point to intraoperative images of fusion and respiratory movements in the liver as the most problematic. In terms of improving the ability to perform procedures on new patients, the module also allows you to create a new hologram for a different clinical case.

Use of artificial intelligence in total mesorectal excision in rectal cancer surgery: State of the art and perspectives

BACKGROUND Colorectal cancer is a major public health problem, with 1.9 million new cases and 953000 deaths worldwide in 2020. Total mesorectal excision (TME) is the standard of care for the treatment of rectal cancer and is crucial to prevent local recurrence, but it is a technically challenging surgery. The use of artificial intelligence (AI) could help improve the performance and safety of TME surgery. AIM To review the literature on the use of AI and machine learning in rectal surgery and potential future developments. METHODS Online scientific databases were searched for articles on the use of AI in rectal cancer surgery between 2020 and 2023. RESULTS The literature search yielded 876 results, and only 13 studies were selected for review. The use of AI in rectal cancer surgery and specifically in TME is a rapidly evolving field. There are a number of different AI algorithms that have been developed for use in TME, including algorithms for instrument detection, anatomical structure identification, and image-guided navigation systems. CONCLUSION AI has the potential to revolutionize TME surgery by providing real-time surgical guidance, preventing complications, and improving training. However, further research is needed to fully understand the benefits and risks of AI in TME surgery.

Letter to the Editor Regarding “Full Endoscopic Spine Surgery with Image-Guided Navigation System as ‘Hybrid Endoscopic Spine Surgery’”

Letter to the Editor Regarding “Full Endoscopic Spine Surgery with Image-Guided Navigation System as ‘Hybrid Endoscopic Spine Surgery’”

Endoscopic Endonasal Trans-Sphenoidal Minimally Invasive Pituitary Surgery with Image Guided Navigation System (Igns): Learning Experience of Ent Surgeon: First Author.

During the period of April 2015 to August 2022 a total 104 patients, diagnosed with pituitary adenoma underwent surgery and further followed up in a multidisciplinary team approach in a tertiary care hospital of Kolkata, India. The data obtained were reviewed statistically to satisfy the study objectives. Total 104 operations were done on 98 patients and total cases taken for calculation and analysis was 98, which consist of 11 microadenomas, 81 macroadenomas. Among 35 patients with normal preoperative hormonal assay, one patient developed postoperative hypopituitarism. Among 6 patients with preoperative hypopituitarism 4 patients (66.6%) recovered after surgery. Overall, 85 cases had total disease clearance as detected on post-operative MRI. In functioning pituitary adenoma (FPA) clinical and endocrinological improvement occurred after primary surgery in 85.36% (n = 35) and after revision surgery it was 84.44% (n = 38). Macroadenomas, giant adenomas were found to have statistically significant higher risk of incomplete disease clearance but large adenomas do not have statistically higher risk of incomplete clearance. IGNS requires extra time for setup, but with proper registration of tracker instruments it adds precision to the surgery. IGNS supplements endoscopic visualization with localization of target lesion by real time stereotactic feedback using preset preoperative imaging data, thus increasing accuracy, safety and effectiveness of minimally invasive surgery.

Advancements in Image-Guided Navigation for Surgical Procedures

Surgeons may now conduct complex surgeries with more precision, accuracy, and safety thanks to image-guided navigation. This article analyzes how image-guided navigation devices have changed surgical operations. Surgeons now approach procedures differently thanks to the integration of imaging modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound with surgical navigation systems. Surgeons can confidently navigate complex anatomical features and plan surgical paths using these technologies' real-time, three-dimensional anatomy visualization. Image-guided navigation has advanced with Augmented Reality (AR) and Virtual Reality (VR). Augmented Reality (AR) devices improve spatial awareness and reduce errors by superimposing patient-specific anatomical data onto the surgeon's field of view. VR allows surgeons to perform complex surgeries in a virtual environment, increasing their skills and aiding preoperative planning. Another innovation is machine learning and Artificial Intelligence (AI) algorithms in image-guided navigation systems. These algorithms evaluate massive volumes of patient data, including medical pictures, surgery plans, and results, to help surgeons make predictions and judgments. Data-driven algorithms in AI-driven navigation systems optimize surgical operations, accuracy, and patient outcomes. Miniaturized and wireless tracking systems provide less invasive image-guided navigation. Electromagnetic and optical monitoring devices allow surgeons to track surgical tools inside the patient's body, providing real-time feedback and instruction. Image-guided navigation is being used in laparoscopic, endoscopic, and robotic-assisted surgeries. Image-guided navigation still faces challenges. The incorporation of diverse imaging modalities, system accuracy, and smooth compatibility with surgical instruments are ongoing research and development. To promote widespread adoption of these technologies, cost-effectiveness, training, and regulations must be addressed. Image-guided navigation has improved surgical visualization, navigation, and decision-making. Augmented reality, virtual reality, machine learning, and tiny tracking have improved surgery and patient outcomes. Image-guided navigation could become common surgery with continued study and improvement in this sector.

Real-Time Acoustic Simulation Framework for tFUS: A Feasibility Study Using Navigation System

Transcranial focused ultrasound (tFUS), in which acoustic energy is focused on a small region in the brain through the skull, is a non-invasive therapeutic method with high spatial resolution and depth penetration. Image-guided navigation has been widely utilized to visualize the location of acoustic focus in the cranial cavity. However, this system is often inaccurate because of the significant aberrations caused by the skull. Therefore, acoustic simulations using a numerical solver have been widely adopted to compensate for this inaccuracy. Although the simulation can predict the intracranial acoustic pressure field, real-time application during tFUS treatment is almost impossible due to the high computational cost. In this study, we propose a neural network-based real-time acoustic simulation framework and test its feasibility by implementing a simulation-guided navigation (SGN) system. Real-time acoustic simulation is performed using a 3D conditional generative adversarial network (3D-cGAN) model featuring residual blocks and multiple loss functions. This network was trained by the conventional numerical acoustic simulation program (i.e., k-Wave). The SGN system is then implemented by integrating real-time acoustic simulation with a conventional image-guided navigation system. The proposed system can provide simulation results with a frame rate of 5 Hz (i.e., about 0.2 s), including all processing times. In numerical validation (3D-cGAN vs. k-Wave), the average peak intracranial pressure error was 6.8 ± 5.5%, and the average acoustic focus position error was 5.3 ± 7.7 mm. In experimental validation using a skull phantom (3D-cGAN vs. actual measurement), the average peak intracranial pressure error was 4.5%, and the average acoustic focus position error was 6.6 mm. These results demonstrate that the SGN system can predict the intracranial acoustic field according to transducer placement in real-time.

Full Endoscopic Spine Surgery with Image-Guided Navigation System as “Hybrid Endoscopic Spine Surgery”: A Narrative Review

Endoscopic surgery is adopted as a minimally invasive technique in several surgical fields. Endoscopic spine surgery (ESS) was performed initially for lumbar discectomy but is currently widely utilized for various pathologies. Similar to other endoscopic techniques, ESS has a steep learning curve that has recently been a topic of discussion. Image-guided navigation systems have been developed for spine surgery. Intraoperative computed tomography enables the use of an image-guided navigation system in ESS, which is a suitable approach for managing complex lesions. Full-ESS is currently being adopted for certain cervical pathologies, and the incorporation of an image-guided navigation system will soon enable surgery for other cervical pathologies.

A novel intrarenal multimodal 2D/3D registration algorithm and preliminary phantom study.

Retrograde intrarenal surgery (RIRS) is a widely utilized diagnostic and therapeutic tool for multiple upper urinary tract pathologies. The image-guided navigation system can assist the surgeon to perform precise surgery by providing the relative position between the lesion and the instrument after the intraoperative image is registered with the preoperative model. However, due to the structural complexity and diversity of multi-branched organs such as kidneys, bronchi, etc., the consistency of the intensity distribution of virtual and real images will be challenged, which makes the classical pure intensity registration method prone to bias and random results in a wide search domain. In this paper, we propose a structural feature similarity-based method combined with a semantic style transfer network, which significantly improves the registration accuracy when the initial state deviation is obvious. Furthermore, multi-view constraints are introduced to compensate for the collapse of spatial depth information and improve the robustness of the algorithm. Experimental studies were conducted on two models generated from patient data to evaluate the performance of the method and competing algorithms. The proposed method obtains mean target error (mTRE) of 0.971±0.585mm and 1.266±0.416mm respectively, with better accuracy and robustness overall. Experimental results demonstrate that the proposed method has the potential to be applied to RIRS and extended to other organs with similar structures.

Image-guided navigation system for minimally invasive total hip arthroplasty (MITHA) using an improved position-sensing marker.

Minimally invasive total hip arthroplasty (MITHA) is a treatment for hip arthritis, and it causes less tissue trauma, blood loss, and recovery time. However, the limited incision makes it difficult for surgeons to perceive the instruments' location and orientation. Computer-assisted navigation systems can help improve the medical outcome of MITHA. Directly applying existing navigation systems for MITHA, however, suffers from problems of bulky fiducial marker, severe feature-loss, multiple instruments tracking confusion, and radiation exposure. To tackle these problems, we propose an image-guided navigation system for MITHA using a novel position-sensing marker. A position-sensing marker is proposed to serve as the fiducial marker with high-density and multi-fold ID tags. It results in less feature span and enables the use of ID for each feature, overcoming the problem of bulky fiducial markers and multiple instruments tracking confusion. And the marker can be recognized even when a large part of locating features is obscured. As for the elimination of intraoperative radiation exposure, we propose a point-based method to achieve patient-image registration based on anatomical landmarks. Quantitative experiments are conducted to evaluate the feasibility of our system. The accuracy of instrument positioning is achieved at 0.33 ± 0.18mm, and that of patient-image registration is achieved at 0.79 ± 0.15mm. And qualitative experiments are also performed, verifying that our system can be used in compact surgical spatial volume and can address severe feature-loss and tracking confusion problems. In addition, our system does not require any intraoperative medical scans. Experimental results indicate that our proposed system can assist surgeons without larger space occupations, radiation exposure, and extra incision, showing its potential application value in MITHA.

A review of deep learning-based deformable medical image registration.

The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.

Does the 3D Feature Descriptor Impact The Registration Accuracy in Laparoscopic Liver Surgery?

Abstract In laparoscopic liver surgery (LLS) image-guided navigation systems could support the surgeon by providing subsurface information such as the positions of tumors and vessels. For this purpose, one option is to perform a registration of preoperative 3D data and 3D surface patches reconstructed from laparoscopic images. Part of an automatic 3D registration pipeline is the feature description, which takes into account various geometric and spatial information. Since there is no leading feature descriptor in the field of LLS, two feature descriptors are compared in this paper: The Fast Point Feature Histogram (FPFH) and Triple Orthogonal Local Depth Images (TOLDI). To evaluate their performance, three perturbations were induced: varying surface patch sizes, spatial displacement, and Gaussian deformation. Registration was performed using the RANSAC algorithm. FPFH outperformed TOLDI for small surface patches and in case of Gaussian deformations in terms of registration accuracy. In contrast, TOLDI showed lower registration errors for patches with spatial displacement. While developing a 3D-3D registration pipeline, the choice of the feature descriptor is of importance, consequently a careful choice suitable for the application in LLS is necessary.

Image-guided navigation for locally advanced primary and locally recurrent rectal cancer: evaluation of its early cost-effectiveness.

BackgroundA first pilot study showed that an image-guided navigation system could improve resection margin rates in locally advanced (LARC) and locally recurrent rectal cancer (LRRC) patients. Incremental surgical innovation is often implemented without reimbursement consequences, health economic aspects should however also be taken into account. This study evaluates the early cost-effectiveness of navigated surgery compared to standard surgery in LARC and LRRC.MethodsA Markov decision model was constructed to estimate the expected costs and outcomes for navigated and standard surgery. The input parameters were based on pilot data from a prospective (navigation cohort n = 33) and retrospective (control group n = 142) data. Utility values were measured in a comparable group (n = 63) through the EQ5D-5L. Additionally, sensitivity and value of information analyses were performed.ResultsBased on this early evaluation, navigated surgery showed incremental costs of €3141 and €2896 in LARC and LRRC. In LARC, navigated surgery resulted in 2.05 Quality-Adjusted Life Years (QALYs) vs 2.02 QALYs for standard surgery. For LRRC, we found 1.73 vs 1.67 QALYs respectively. This showed an Incremental Cost-Effectiveness Ratio (ICER) of €136.604 for LARC and €52.510 for LRRC per QALY gained. In scenario analyses, optimal utilization rates of the navigation technology lowered the ICER to €61.817 and €21.334 for LARC and LRRC. The ICERs of both indications were most sensitive to uncertainty surrounding the risk of progression in the first year after surgery, the risk of having a positive surgical margin, and the costs of the navigation system.ConclusionAdding navigation system use is expected to be cost-effective in LRRC and has the potential to become cost-effective in LARC. To increase the probability of being cost-effective, it is crucial to optimize efficient use of both the hybrid OR and the navigation system and identify subgroups where navigation is expected to show higher effectiveness.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12885-022-09561-w.

Artificial Intelligence-Based Total Mesorectal Excision Plane Navigation in Laparoscopic Colorectal Surgery.

Total mesorectal excision is the standard surgical procedure for rectal cancer because it is associated with low local recurrence rates. To the best of our knowledge, this is the first study to use an image-guided navigation system with total mesorectal excision. The impact of innovation is the development of a deep learning-based image-guided navigation system for areolar tissue in the total mesorectal excision plane. Such a system might be helpful to surgeons because areolar tissue can be used as a landmark for the appropriate dissection plane. This was a single-center experimental feasibility study involving 32 randomly selected patients who had undergone laparoscopic left-sided colorectal resection between 2015 and 2019. Deep learning-based semantic segmentation of areolar tissue in the total mesorectal excision plane was performed. Intraoperative images capturing the total mesorectal excision scene extracted from left colorectal laparoscopic resection videos were used as training data for the deep learning model. Six hundred annotation images were created from 32 videos, with 528 images in the training and 72 images in the test data sets. The experimental feasibility study was conducted at the Department of Colorectal Surgery, National Cancer Center Hospital East, Chiba, Japan. Dice coefficient was used to evaluate semantic segmentation accuracy for areolar tissue. The developed semantic segmentation model helped locate and highlight the areolar tissue area in the total mesorectal excision plane. The accuracy and generalization performance of deep learning models depend mainly on the quantity and quality of the training data. This study had only 600 images; thus, more images for training are necessary to improve the recognition accuracy. We successfully developed a total mesorectal excision plane image-guided navigation system based on an areolar tissue segmentation approach with high accuracy. This may aid surgeons in recognizing the total mesorectal excision plane for dissection.

Development of recognised position-guided navigation system.

Previously, we developed an image-guided navigation system (IG-NS) incorporating augmented reality technology. Nevertheless, the system could still only aid the operator by presenting imagery and was short of achieving the goal of developing a real navigation system. Therefore, we developed a recognised position-guided navigation system (RP-NS) and herein reported the functionality and usefulness of this system in a phantom model for clinical applications. We developed RP-NS which was reconstructed by adding the positional recognition and instruction functions with the cautions by displaying the images on the monitor with a voice to the IG-NS. We evaluated accuracy of positional recognition and instruction functions using phantom model. By utilising the chronological recording of the tip position of the surgical apparatus, the surgical precision of the operators was assessed. Finally, the feasibility of improvements in surgical precision using this system was evaluated. The RP-NS indicated an accuracy of the position recognition functions with an error of 2.7mm. The surgeons could perform partial hepatectomies within mean value of 7.5% error as compared with calculated volume according to the instruction. Improvements in surgical precision using this system were obtained on the surgeons with different levels. The RP-NS was highly effective as a navigation system owing to precise positional recognition and adequate instruction functions. Therefore, these results indicate that the use of this system may complement differences in proficiency, and numerically evaluate surgical skills and analyse tendencies of surgeons.

Treating Lumbar Fracture Using the Mixed Reality Technique.

The mixed reality (MR) technique has recently been widely used in the orthopedic surgery with satisfactory results reported. However, few studies have focused on the application of MR in the Lumbar fracture (LF). In this retrospective study, our aim is to analyze some findings by investigating the feasibility of MR applied to lumbar fracture treatment. Posterior vertebrectomy has been operated on 7 patients. The MR–based intraoperative three-dimensional image-guided navigation system (MITINS) was used to assist implantation of pedicle screws. The feasibility and safety of pedicle screw implantation were assessed by postsurgery radiography. The visual analog scale (VAS) and Oswestry Disability Index (ODI) were used to assess the pain level and recovery situation before and after surgery. 57 pedicle screws were safely and precisely placed into three-dimensional lumbar models by using MITINS. No screw was found outside the pedicle of the models, and it was not necessary for the X-ray to provide extra locative information during the operation with the use of MITINS. In summary, the application of MITINS is feasible, safe, and accurate while the lumbar fracture surgery is processing, providing satisfactory assistance for spine surgeons.

The Use of Image-Guided Navigation Systems During Spine Surgeries in Saudi Arabia: A Cross-Sectional Study.

We used a cross-sectional study design (questionnaire) to investigate the use of image-guided navigation (IGN) in Saudi Arabia and explore possible differences in implementing IGN for daily practice. An internet-based survey was sent to all spine surgeons who are practicing in Saudi Arabia (orthopedics or neurosurgery). The survey is composed of 12 items that collected demographic and academic data. Ninety-nine answered the questionnaire from 197; 80% were from Riyadh, the capital, and 50% were consultants (attending physicians). Orthopedic surgeons were almost 60% of responders compared to 40% neurosurgeons. The use of navigation in Saudi hospitals was high (76.8%). There was a significant difference between specialties in the preference of using navigation (23.2% for orthopedics versus 81.4% for neurosurgery, P < .001) and routine use in surgical spine cases (88.4% for neurosurgery versus 50.0% orthopedics, P < .001). The majority of responders from neurosurgery learned to use navigation during residency compared to orthopedics responders (51.2% versus 28.6%, P = .001). More than 30% of orthopedics responders expressed they never learned navigation compared to only 4% of neurosurgery responders. The comfort level of > 75% with performing surgery using navigation was significantly different between specialties (25% for orthopedics versus 46.5% for neurosurgery, P < .001). Saudi spine surgeons are among the highest users of IGN systems. The strong healthcare infrastructure and the availability of these devices across the country are among the most important factors for its prevalence. Enhancing surgical exposure and education of postgraduate trainees to use these tools, especially within orthopedics, could increase use and comfort level rates.

Accuracy assessment of a novel optical image guided system for trans-nasal sinus and skull base surgeries

Purpose: Many studies have been conducted on assessment of navigation accuracy on cadaver head using pre-installed CT fiducials. However, there are fewer studies on assessing accuracy on real patient due to its complexity in measuring Target Registration Error (TRE). In this paper we propose a new approach for quantifying navigation accuracy on the real patients. This paper summarizes the results of navigation accuracy of a novel optical image guided navigation system evaluated on over 20 patients. Method: Commercially available optical navigation system (Navient Image guided navigation system - ClaroNav Kolahi Inc., Toronto, Canada) was used on over 20 patients from 2017 to 2019. Navient employs both landmarks based and trace based registration. Since mounting CT fiducials on the real patients is invasive and impractical, we developed a new approach to measure the distance from the navigation probe to the patient’s skin as a new metric to quantify navigation accuracy. The measurement was conducted in dozens of locations including patient’s forehead, temples and base of the nose. These anatomical locations were chosen very carefully to represent navigation accuracy in coronal, sagittal and axial directions respectively. Furthermore, our method also eliminates human errors involved in the TRE measurements which is the main source of error in TRE studies. Results: The average Euclidian distance from skin on 23 patients were 1.47(mm). the standard deviation of the accuracy was 0.28(mm). Low STD demonstrates consistent accuracy during the two year course of the study. Conclusion: A new noninvasive approach for accuracy assessment of image guided navigation systems on real patient was proposed and validated using a novel optical navigation system. The low STD of navigation accuracy indicates its reliable accuracy.

Navigated transoral odontoidectomy to treat congenital basilar invagination after failed posterior reduction and fusion.

Transoral odontoidectomy is a traditional technique to treat congenital basilar invagination (BI) associated with atlantoaxial dislocation (AAD). Although posterior surgery has been a trend to treat most cases, there are still cases that need to be treated through a transoral approach. In addition, intraoperative modern image-guided navigation systems help identify any remnants of the dens and decrease the risk of vertebral artery injury. For symptomatic cases with a history of previous posterior fusion and severe osteoporosis, transoral odontoidectomy is preferred over a posterior-only approach. Our video demonstrates the surgical technique for transoral revision odontoidectomy to treat congenital basilar invagination associated with atlantoaxial dislocation after previous posterior craniovertebral junction surgery.The video can be found here: https://youtu.be/vzcAW8oLcZY

In Reply to the Letter to the Editor Regarding “Accuracy of Pedicle Screw Insertion Among 3 Image-Guided Navigation Systems: A Systematic Review and Meta-Analysis”

In Reply to the Letter to the Editor Regarding “Accuracy of Pedicle Screw Insertion Among 3 Image-Guided Navigation Systems: A Systematic Review and Meta-Analysis”

Letter to the Editor Regarding “Accuracy of Pedicle Screw Insertion Among 3 Image-Guided Navigation Systems: A Systematic Review and Meta-Analysis”

Letter to the Editor Regarding “Accuracy of Pedicle Screw Insertion Among 3 Image-Guided Navigation Systems: A Systematic Review and Meta-Analysis”