Image-guided Neurosurgery System Research Articles

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

BackgroundWith 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.MethodsUsing 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.ResultsFor 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.ConclusionIntraoperative 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.

An Automatic Spatial Registration Method for Image-Guided Neurosurgery System.

This study aimed to investigate the feasibility of an automatic marker-free patient-to-image spatial registration method based on the 4-points congruent sets (4PCS) and iterative closest point (ICP) algorithm for the image-guided neurosurgery system (IGNS). A portable scanner was used to obtain the point cloud of the patient's entire head. The 4PCS algorithm, which is resilient to noise and outliers, automatically registered the point cloud in the patient space to the surface reconstructed from the patient's preoperative images in the image space without any assumptions about initial alignment. A variant of the ICP algorithm was then used to finish the fine registration. Two phantoms and 3 patients' experiments were performed to demonstrate the effectiveness of the proposed method. In the phantom experiments, the mean target registration error of 15 targets on the surface of the rigid and the elastic phantoms were 1.02 ± 0.18 mm and 1.27 ± 0.36 mm, respectively. In the clinical experiments, the mean target registration error of 7 targets on the first, second and third patient's head were 1.88 ± 0.19 mm, 1.84 ± 0.19 mm, and 1.89 ± 0.18 mm, respectively, which was sufficient to meet clinical requirements. The registration accuracy and registration time using the proposed method are better than that using the method based on manually coarse registration and automatic fine registration. It is feasible to use the automatic spatial registration method based on the 4PCS and ICP algorithm for the IGNS. Moreover, it can replace the spatial registration method based on manually selected anatomical landmarks combined with the automatic fine registration in the currently used IGNS.

Wireless transmission‐based brain shift compensation system

The brain deforms during neurosurgery, resulting in a decrease in the accuracy of the image guided neurosurgery system (IGNS). In this study, a wireless transmission-based brain shift compensation system (WBSCS) is implemented. The system consists of a laser range scanner for acquiring a cortical surface, and a mobile workstation equipped with a brain deformation correction software platform. The brain tissue deformation correction software platform includes a three-dimensional visualisation module, a calibration module, a brain tissue extraction module, a meshing module, a boundary condition acquisition module, a finite element calculation module, a preoperative image update module, and a communication module. The system exchanges data with the IGNS through wireless communication. The authors used five pigs to test the system. Results of experiments show that the system can compensate for brain deformation. It worked with IGNS to provide brain-shift-compensation-based guidance, improving the accuracy of the IGNS.

Neurosurgical Craniotomy Localization Using Interactive 3D Lesion Mapping for Image-Guided Neurosurgery

Precise craniotomy localization is essential in neurosurgical procedures, especially during the preoperative planning. The mainstream craniotomy localization method utilizing image-guided neurosurgery system (IGNS) or augmented reality (AR) navigation system require experienced neurosurgeons to point out the lesion margin by probe and draw the craniotomy manually on the patient's head according to cranial anatomy. However, improper manual operation and dither from the AR model will bring in errors about craniotomy localization. In addition, there is no specific standard to evaluate the accuracy of craniotomy. This paper attempts to propose a standardized interactive 3D method using orthogonal transformation to map the lesion onto the scalp model and generate a conformal virtual incision in real time. Considering clinical requirements, the incision can be amended by 3D interaction and margin modification. According to the IGNS and the virtual incision, an actual craniotomy will be located on the patient's head and the movement path of the probe will be recorded and evaluated by an indicator, which is presented as an evaluated standard to measure the error between virtual and actual craniotomies. After the experiment, an incision is drawn on a 3D printing phantom based on the generated virtual one. The results show that the proposed method can generate a lesion-consistent craniotomy according to the size of the lesion and the mapping angle and delineate the incision on the patient's head precisely under the IGNS.

A new robust markerless method for automatic image-to-patient registration in image-guided neurosurgery system

Background: Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty.Methods: A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration.Results: Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18–26 mm) and different support radii (2–6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm.Conclusion: This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

A Surface-Based Spatial Registration Method Based on Sense Three-Dimensional Scanner.

The purpose of this study was to investigate the feasibility of a surface-based registration method based on a low-cost, hand-held Sense three-dimensional (3D) scanner in image-guided neurosurgery system. The scanner was calibrated prior and fixed on a tripod before registration. During registration, a part of the head surface was scanned at first and the spatial position of the adapter was recorded. Then the scanner was taken off from the tripod and the entire head surface was scanned by moving the scanner around the patient's head. All the scan points were aligned to the recorded spatial position to form a unique point cloud of the head by the automatic mosaic function of the scanner. The coordinates of the scan points were transformed from the device space to the adapter space by a calibration matrix, and then to the patient space. A 2-step patient-to-image registration method was then performed to register the patient space to the image space. The experimental results showed that the mean target registration error of 15 targets on the surface of the phantom was 1.61±0.09 mm. In a clinical experiment, the mean target registration error of 7 targets on the patient's head surface was 2.50±0.31 mm, which was sufficient to meet clinical requirements. It is feasible to use the Sense 3D scanner for patient-to-image registration, and the low-cost Sense 3D scanner can take the place of the current used scanner in the image-guided neurosurgery system.

In Vivo Investigation of the Effectiveness of a Hyper-viscoelastic Model in Simulating Brain Retraction.

Intraoperative brain retraction leads to a misalignment between the intraoperative positions of the brain structures and their previous positions, as determined from preoperative images. In vitro swine brain sample uniaxial tests showed that the mechanical response of brain tissue to compression and extension could be described by the hyper-viscoelasticity theory. The brain retraction caused by the mechanical process is a combination of brain tissue compression and extension. In this paper, we first constructed a hyper-viscoelastic framework based on the extended finite element method (XFEM) to simulate intraoperative brain retraction. To explore its effectiveness, we then applied this framework to an in vivo brain retraction simulation. The simulation strictly followed the clinical scenario, in which seven swine were subjected to brain retraction. Our experimental results showed that the hyper-viscoelastic XFEM framework is capable of simulating intraoperative brain retraction and improving the navigation accuracy of an image-guided neurosurgery system (IGNS).

Assessment of an image-guided neurosurgery system using a head phantom

Objective: To acknowledge the challenges and limitations of image-guided neurosurgery systems, we compared the application accuracy of two different image registration methods for one commercial system. (VectorVision, BrainLab, Germany).Methods: We used an anthropomorphic head phantom for radiosurgery and a custom built add-on to simulate surgical targets inside the brain during an image-guided neurosurgery. We used two image registration methods, fiducial registration using attachable surface markers for computed tomography (CT) and surface registration using infrared laser face scanning. After simulation, we calculated the three-dimensional (3D) distance between the predicted position of a target, and its actual position using a registered pointer and an infrared camera. Deviations were measured for both superficial fiducial markers and internal surgical targets by five different users.Results: Deviations from the location of fiducial markers after each registration method were 2.15 ± 0.93 mm after CT surface marker registration and 1.25 ± 0.64 mm after infrared face scanner registration. The mean target registration errors were 2.95 ± 1.4 mm using fiducial registration and 2.90 ± 1.3 mm using surface registration. The largest deviations (6.2 mm) were found for the targets in the skull base and posterior cranial fossa. Fiducial deviations and target registration errors were statistically uncorrelated. The total application accuracy was 4.87 ± 0.97 mm after CT surface marker registration and 4.14 ± 0.64 mm after infrared face scanner registration.Conclusions: Despite others have reported differences, we did not find significant variations between both registration methods for the target registration error, although application accuracy was slightly better after surface face registration. Superficial registration errors, but not the target registration error, can be routinely evaluated in the operating room. Since both errors were uncorrelated, surgeons may neglect the achievable accuracy of the procedure. The described method is recommended to assess application accuracy in the operating room.

Towards estimating fiducial localization error of point-based registration in image-guided neurosurgery.

Fiducial Localization Error (FLE) is one of the major reasons of inaccuracy in point-based spatial registration of Image-Guided Neurosurgery System (IGNS), and minimizing FLE is the fundamental way to improve spatial registration accuracy. A reliable estimation of FLE is needed, as it cannot be measured directly in real application of IGNS. In this paper, we propose a method to estimate the FLE in a point-based registration of IGNS. Test fiducial point sets were generated in one coordinate system around the given fiducial point set by utilizing simple random sampling. Further, these points were registered to the fiducial point set in the other coordinate system. The average position of the test fiducial point sets with small FRE are calculated and its displacement from the given fiducial point set is the parameter used to estimate the FLE of each fiducial point. The correlation between the displacement and the FLE of each fiducial point is greater than 0.75 when nine or more fiducial points were utilized. This correlation gradually increases up to 0.9 with the increase of the number of fiducial points.

A novel registration method for image-guided neurosurgery system based on stereo vision.

This study presents a novel spatial registration method of Image-guided neurosurgery system (IGNS) based on stereo-vision. Images of the patient's head are captured by a video camera, which is calibrated and tracked by an optical tracking system. Then, a set of sparse facial data points are reconstructed from them by stereo vision in the patient space. Surface matching method is utilized to register the reconstructed sparse points and the facial surface reconstructed from preoperative images of the patient. Simulation experiments verified the feasibility of the proposed method. The proposed method it is a new low-cost and easy-to-use spatial registration method for IGNS, with good prospects for clinical application.

Automated Global Optimization Surface-Matching Registration Method for Image-to-Patient Spatial Registration in an Image-Guided Neurosurgery System

Automated Global Optimization Surface-Matching Registration Method for Image-to-Patient Spatial Registration in an Image-Guided Neurosurgery System

Multi-mode navigation in image-guided neurosurgery using a wireless tablet PC.

The authors present a tablet-based image-guided neurosurgery system that transmits navigation information from a host to a movable tablet PC via a wireless local area network, and displays this information on the tablet screen. With this new system, surgeons can obtain standard navigation information on the tablet screen to avoid large view switching between the surgical field and the navigation screen of the computer monitor. In addition, this system can also provide additional navigation information by displaying arbitrary sectional images and maximum intensity projection images on the tablet screen. These images are generated according to the position and orientation of the tablet screen, and can be used to locate intracranial tumor for preoperative planning and intraoperative procedures in neurosurgery. The average tracking error was approximately 1.25mm. A dry skull specimen study verified the feasibility of the proposed system. Furthermore, an actual patient's surgery with this system showed its clinical applicability.

A framework for correcting brain retraction based on an eXtended Finite Element Method using a laser range scanner

BackgroundBrain retraction causes great distortion that limits the accuracy of an image-guided neurosurgery system that uses preoperative images. Therefore, brain retraction correction is an important intraoperative clinical application.MethodsWe used a linear elastic biomechanical model, which deforms based on the eXtended Finite Element Method (XFEM) within a framework for brain retraction correction. In particular, a laser range scanner was introduced to obtain a surface point cloud of the exposed surgical field including retractors inserted into the brain. A brain retraction surface tracking algorithm converted these point clouds into boundary conditions applied to XFEM modeling that drive brain deformation. To test the framework, we performed a brain phantom experiment involving the retraction of tissue. Pairs of the modified Hausdorff distance between Canny edges extracted from model-updated images, pre-retraction, and post-retraction CT images were compared to evaluate the morphological alignment of our framework. Furthermore, the measured displacements of beads embedded in the brain phantom and the predicted ones were compared to evaluate numerical performance.ResultsThe modified Hausdorff distance of 19 pairs of images decreased from 1.10 to 0.76 mm. The forecast error of 23 stainless steel beads in the phantom was between 0 and 1.73 mm (mean 1.19 mm). The correction accuracy varied between 52.8 and 100 % (mean 81.4 %).ConclusionsThe results demonstrate that the brain retraction compensation can be incorporated intraoperatively into the model-updating process in image-guided neurosurgery systems.

Distribution Templates of the Fiducial Points in Image-Guided Neurosurgery

Point-pair registration is widely used in an image-guided neurosurgery system. Poor distribution of the fiducial points leads to an increase in the target registration error (TRE). This study aimed to provide templates consisting of optimized positioning of the fiducial points to reduce the TRE in image-guided neurosurgery. We divided the head into 6 regions and provided distribution templates of the fiducial points for each of them. A variable termed TREM(r) was used to express the approximate expected square of the TRE at the target point with a specified distribution of fiducial points. We randomly selected 85 patients from 5 hospitals who underwent image-guided neurosurgery and compared the TREM(r) of the real fiducial points with that of the templates. We grouped the patients by hospitals and regions. The mean TREM(r)s of the templates were much smaller than those of the real fiducial points. In each group, the range of the TREM(r) values of the templates was much smaller than that of the real fiducial points. This study provides an easy method to implement a good distribution of the fiducial points to help reduce TRE in image-guided neurosurgery. The templates are simple and exact and can be easily integrated into current workflow.

CASMIL: a comprehensive software/toolkit for image-guided neurosurgeries

CASMIL aims to develop a cost-effective and efficient approach to monitor and predict deformation during surgery, allowing accurate, and real-time intra-operative information to be provided reliably to the surgeon. CASMIL is a comprehensive Image-guided Neurosurgery System with extensive novel features. It is an integration of various modules including rigid and non-rigid body co-registration (image-image, image-atlas, and image-patient), automated 3D segmentation, brain shift predictor, knowledge based query tools, intelligent planning, and augmented reality. One of the vital and unique modules is the Intelligent Planning module, which displays the best surgical corridor on the computer screen based on tumor location, captured surgeon knowledge, and predicted brain shift using patient specific Finite Element Model. Also, it has multi-level parallel computing to provide near real-time interaction with iMRI (Intra-operative MRI). In addition, it has been securely web-enabled and optimized for remote web and PDA access. A version of this system is being used and tested using real patient data and is expected to be in use in the operating room at the Detroit Medical Center in the first half of 2006. CASMIL is currently under development and is targeted for minimally invasive surgeries. With minimal changes to the design, it can be easily extended and made available for other surgical procedures.

Image-guided neurosurgery system integrating AR-based navigation and open-MRI monitoring

Image-guided neurosurgery system integrating AR-based navigation and open-MRI monitoring

Image-guided neurosurgery system integrating AR-based navigation and open-MRI monitoring

As endoscopic surgery has become a popular form of minimally invasive surgery, it increasingly requires useful imaging tools to help the surgeons perform safe and secure operations. Our navigation system provides surgeons with visual information by overlaying 3D wire frame models of tumor onto live images, as well as by displaying relative the positions of surgical tools and the target tumor. Such 3D wire frame models are generated from pre-operative CT/MR images with the help of a 3D surgical simulation software. Another important function of our system is real-time volume rendering of intra-operative MR images for the target tumor. This function allows surgeons to carefully observe the vicinity of the tumor regions to be removed, by rendering the sectional views with respect to the surgical tool position, so that surgical performance can be easily monitored during the operation. We tested this navigation system in more than 10 clinical operations and verified the effectiveness of the navigation and surgical performance.

A low-cost PCI-bus-based ultrasound system for use in image-guided neurosurgery

A low-cost PCI-bus-based ultrasound sub-system has been developed and integrated into the image-guided neurosurgery system currently in use at the Cleveland Clinic. Two software applications have been developed that integrate real-time ultrasound images with preoperative MR and CT data sets. By tracking the position of the ultrasound probe during surgery, it is possible to display a real time ultrasound image and the corresponding (preoperative) oblique CT or MR slice. This provides immediate positional feedback to the neurosurgeon during the surgical procedure.

An ultrasonic approach to localization of fiducial markers for interactive, image-guided neurosurgery--Part II: Implementation and automation.

Registration of image space and physical space lies at the heart of any interactive, image-guided neurosurgery system. This paper, in conjunction with the previous companion paper [1], describes a localization technique that enables bone-implanted fiducial markers to be used for the registration of these spaces. The nature of these subcutaneous markers allows for their long-term use for registration which is desirable for surgical follow-up, monitoring of therapy efficacy, and performing fractionated stereotactic radiosurgery. The major challenge to using implanted markers is determining the location of the markers in physical space after implantation. The A-mode ultrasonic technique described here is capable of determining the three-dimensional (3-D) location of small implanted cylindrical markers. Accuracy tests were conducted on a phantom representing a human head. The accuracy of the system was characterized by comparing the location of a marker analogue as determined with an optically tracked pointer and the location as determined with the ultrasonic localization. Analyzing the phantom in several orientations revealed a mean system accuracy of 0.5 mm with a +/- 0.1-mm 95% confidence interval. These tests indicate that transcutaneous localization of implanted fiducial markers is possible with a high degree of accuracy.