Fiducial optimization for minimal target registration error in image-guided neurosurgery

Deep Learning-Based Fine-Tuning Approach of Coarse Registration for Ear-Nose-Throat (ENT) Surgical Navigation Systems

Accurate registration between medical images and patient anatomy is crucial for surgical navigation systems in minimally invasive surgeries. This study introduces a novel deep learning-based refinement step to enhance the accuracy of surface registration without disrupting established workflows. The proposed method integrates a machine learning model between conventional coarse registration and ICP fine registration. A deep-learning model was trained using simulated anatomical landmarks with introduced localization errors. The model architecture features global feature-based learning, an iterative prediction structure, and independent processing of rotational and translational components. Validation with silicon-masked head phantoms and CT imaging compared the proposed method to both conventional registration and a recent deep-learning approach. The results demonstrated significant improvements in target registration error (TRE) across different facial regions and depths. The average TRE for the proposed method (1.58 ± 0.52 mm) was significantly lower than that of the conventional (2.37 ± 1.14 mm) and previous deep-learning (2.29 ± 0.95 mm) approaches (p < 0.01). The method showed a consistent performance across various facial regions and enhanced registration accuracy for deeper areas. This advancement could significantly enhance precision and safety in minimally invasive surgical procedures.

Review of robotic systems for thoracoabdominal puncture interventional surgery

Cancer, with high morbidity and high mortality, is one of the major burdens threatening human health globally. Intervention procedures via percutaneous puncture have been widely used by physicians due to its minimally invasive surgical approach. However, traditional manual puncture intervention depends on personal experience and faces challenges in terms of precisely puncture, learning-curve, safety and efficacy. The development of puncture interventional surgery robotic (PISR) systems could alleviate the aforementioned problems to a certain extent. This paper attempts to review the current status and prospective of PISR systems for thoracic and abdominal application. In this review, the key technologies related to the robotics, including spatial registration, positioning navigation, puncture guidance feedback, respiratory motion compensation, and motion control, are discussed in detail.

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

BACKGROUND

In order to provide accurate and reliable image guidance for augmented reality (AR) spinal surgery navigation, a spatial registration method has been proposed.

METHODS

In 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.

RESULTS

In 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.

CONCLUSION

The 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.

Accuracy and precision of navigated transcranial magnetic stimulation

Objective.Transcranial magnetic stimulation (TMS) induces an electric field (E-field) in the cortex. To facilitate stimulation targeting, image-guided neuronavigation systems have been introduced. Such systems track the placement of the coil with respect to the head and visualize the estimated cortical stimulation location on an anatomical brain image in real time. The accuracy and precision of the neuronavigation is affected by multiple factors. Our aim was to analyze how different factors in TMS neuronavigation affect the accuracy and precision of the coil-head coregistration and the estimated E-field.Approach.By performing simulations, we estimated navigation errors due to distortions in magnetic resonance images (MRIs), head-to-MRI registration (landmark- and surface-based registrations), localization and movement of the head tracker, and localization of the coil tracker. We analyzed the effect of these errors on coil and head coregistration and on the induced E-field as determined with simplistic and realistic head models.Main results.Average total coregistration accuracies were in the range of 2.2-3.6 mm and 1°; precision values were about half of the accuracy values. The coregistration errors were mainly due to head-to-MRI registration with average accuracies 1.5-1.9 mm/0.2-0.4° and precisions 0.5-0.8 mm/0.1-0.2° better with surface-based registration. The other major source of error was the movement of the head tracker with average accuracy of 1.5 mm and precision of 1.1 mm. When assessed within an E-field method, the average accuracies of the peak E-field location, orientation, and magnitude ranged between 1.5 and 5.0 mm, 0.9 and 4.8°, and 4.4 and 8.5% across the E-field models studied. The largest errors were obtained with the landmark-based registration. When computing another accuracy measure with the most realistic E-field model as a reference, the accuracies tended to improve from about 10 mm/15°/25% to about 2 mm/2°/5% when increasing realism of the E-field model.Significance.The results of this comprehensive analysis help TMS operators to recognize the main sources of error in TMS navigation and that the coregistration errors and their effect in the E-field estimation depend on the methods applied. To ensure reliable TMS navigation, we recommend surface-based head-to-MRI registration and realistic models for E-field computations.

Phantom and Animal Study of a Robot-Assisted, CT-Guided Targeting System using Image-Only Navigation for Stereotactic Needle Insertion without Positional Sensors

PURPOSE

To evaluate the feasibility and accuracy of a robotic system to integrate and map computed tomography (CT) and robotic coordinates, followed by automatic trajectory execution by a robotic arm. The system was hypothesized to achieve a targeting error of <5 mm without significant influence from variations in angulation or depth.

MATERIALS AND METHODS

An experimental study was conducted using a robotic system (Automated Needle Targeting device for CT [ANT-C]) for needle insertions into a phantom model on both moving patient table and moving gantry CT scanners. Eight spherical markers were registered as targets for 90 insertions at different trajectories. After a single ANT-C registration, the closed-loop software targeted multiple markers via the insertion of robotically aligned 18-gauge needles. Accuracy (distance from the needle tip to the target) was assessed by postinsertion CT scans. Similar procedures were repeated to guide 10 needle insertions into a porcine lung. A regression analysis was performed to test the effect of needle angulation and insertion depth on the accuracy of insertion.

RESULTS

In the phantom model, all needle insertions (median trajectory depth, 64.8 mm; range, 46.1-153 mm) were successfully performed in single attempts. The overall accuracy was 1.36 mm ± 0.53, which did not differ between the 2 types of CT scanners (1.39 mm ± 0.54 [moving patient table CT] vs 1.33 mm ± 0.52 [moving gantry CT]; P = .54) and was not significantly affected by the needle angulation and insertion depth. The accuracy for the porcine model was 9.09 mm ± 4.21.

CONCLUSIONS

Robot-assisted needle insertion using the ANT-C robotic device was feasible and accurate for targeting multiple markers in a phantom model.

Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration Algorithms

Landmark correspondences are a widely used type of gold standard in image registration. However, the manual placement of corresponding points is subject to high inter-user variability in the chosen annotated locations and in the interpretation of visual ambiguities. In this paper, we introduce a principled strategy for the construction of a gold standard in deformable registration. Our framework: (i) iteratively suggests the most informative location to annotate next, taking into account its redundancy with previous annotations; (ii) extends traditional pointwise annotations by accounting for the spatial uncertainty of each annotation, which can either be directly specified by the user, or aggregated from pointwise annotations from multiple experts; and (iii) naturally provides a new strategy for the evaluation of deformable registration algorithms. Our approach is validated on four different registration tasks. The experimental results show the efficacy of suggesting annotations according to their informativeness, and an improved capacity to assess the quality of the outputs of registration algorithms. In addition, our approach yields, from sparse annotations only, a dense visualization of the errors made by a registration method. The source code of our approach supporting both 2D and 3D data is publicly available at https://github.com/LoicPeter/evaluation-deformable-registration.

Augmented reality based navigation for distal interlocking of intramedullary nails utilizing Microsoft HoloLens 2

BACKGROUND AND OBJECTIVE

The distal interlocking of intramedullary nail remains a technically demanding procedure. Existing augmented reality based solutions still suffer from hand-eye coordination problem, prolonged operation time, and inadequate resolution. In this study, an augmented reality based navigation system for distal interlocking of intramedullary nail is developed using Microsoft HoloLens 2, the state-of-the-art optical see-through head-mounted display.

METHODS

A customized registration cube is designed to assist surgeons with better depth perception when performing registration procedures. During drilling, surgeons can obtain accurate and in-situ visualization of intramedullary nail and drilling path, and dynamic navigation is enabled. An intraoperative warning system is proposed to provide intuitive feedback of real-time deviations and electromagnetic disturbances.

RESULTS

The preclinical phantom experiment showed that the reprojection errors along the X, Y, and Z axes were 1.55 ± 0.27 mm, 1.71 ± 0.40 mm, and 2.84 ± 0.78 mm, respectively. The end-to-end evaluation method indicated the distance error was 1.61 ± 0.44 mm, and the 3D angle error was 1.46 ± 0.46°. A cadaver experiment was also conducted to evaluate the feasibility of the system.

CONCLUSION

Our system has potential advantages over the 2D-screen based navigation system and the pointing device based navigation system in terms of accuracy and time consumption, and has tremendous application prospects.

Strain Energy Decay Predicts Elastic Registration Accuracy From Intraoperative Data Constraints

Image-guided intervention for soft tissue organs depends on the accuracy of deformable registration methods to achieve effective results. While registration techniques based on elastic theory are prevalent, no methods yet exist that can prospectively estimate registration uncertainty to regulate sources and mitigate consequences of localization error in deforming organs. This paper introduces registration uncertainty metrics based on dispersion of strain energy from boundary constraints to predict the proportion of target registration error (TRE) remaining after nonrigid elastic registration. These uncertainty metrics depend on the spatial distribution of intraoperative constraints provided to registration with relation to patient-specific organ geometry. Predictive linear and bivariate gamma models are fit and cross-validated using an existing dataset of 6291 simulated registration examples, plus 699 novel simulated registrations withheld for independent validation. Average uncertainty and average proportion of TRE remaining after elastic registration are strongly correlated ( r = 0.78 ), with mean absolute difference in predicted TRE equivalent to 0.9 ± 0.6 mm (cross-validation) and 0.9 ± 0.5 mm (independent validation). Spatial uncertainty maps also permit localized TRE estimates accurate to an equivalent of 3.0 ± 3.1 mm (cross-validation) and 1.6 ± 1.2 mm (independent validation). Additional clinical evaluation of vascular features yields localized TRE estimates accurate to 3.4 ± 3.2 mm. This work formalizes a lower bound for the inherent uncertainty of nonrigid elastic registrations given coverage of intraoperative data constraints, and demonstrates a relation to TRE that can be predictively leveraged to inform data collection and provide a measure of registration confidence for elastic methods.

A multiple closed-loops robotic calibration for accurate surgical puncture

BACKGROUND

Robotic puncture system increasingly demands stringent standard in target location accuracy. The positional and orientational transformation relationships among all components of the system are supposed to be calibrated and identified preoperatively.

AIMS

The target location performance is directly determined by the calibration result. Therefore, a multiple closed-loops calibration approach is proposed to achieve high-level calibration accuracy in robotic puncture system. MATERIALS & METHODS: This method takes as input the three-dimensional position information of the retro-reflective markers mounted on the surgical tool, which is detected by the optical tracking system in real time during robotic movement. There is less complicated mathematical derivation and calculation in the presented algorithm by applying the closed-loop principle.

RESULTS

Experimental results validate that it can achieve accurate robotic target location with less input data and computation-cost, satisfying the clinical puncture requirements.

DISCUSSION

The spatial calibration between robotic arm and optical tracking system efficiently realised by the presented approach present an alternative which can be safely applied to the robotic puncture system for accurate insertion.

CONCLUSION

Overall, a multiple closed-loops calibration approach is proposed in this work, which may increase surgical efficiency.

Current Accuracy of Augmented Reality Neuronavigation Systems: Systematic Review and Meta-Analysis

BACKGROUND

Augmented reality neuronavigation (ARN) systems can overlay three-dimensional anatomy and disease without the need for a two-dimensional external monitor. Accuracy is crucial for their clinical applicability. We performed a systematic review regarding the reported accuracy of ARN systems and compared them with the accuracy of conventional infrared neuronavigation (CIN).

METHODS

PubMed and Embase were searched for ARN and CIN systems. For ARN, type of system, method of patient-to-image registration, accuracy method, and accuracy of the system were noted. For CIN, navigation accuracy, expressed as target registration error (TRE), was noted. A meta-analysis was performed comparing the TRE of ARN and CIN systems.

RESULTS

Thirty-five studies were included, 12 for ARN and 23 for CIN. ARN systems could be divided into head-mounted display and heads-up display. In ARN, 4 methods were encountered for patient-to-image registration, of which point-pair matching was the one most frequently used. Five methods for assessing accuracy were described. Ninety-four TRE measurements of ARN systems were compared with 9058 TRE measurements of CIN systems. Mean TRE was 2.5 mm (95% confidence interval, 0.7-4.4) for ARN systems and 2.6 mm (95% confidence interval, 2.1-3.1) for CIN systems.

CONCLUSIONS

In ARN, there seems to be lack of agreement regarding the best method to assess accuracy. Nevertheless, ARN systems seem able to achieve an accuracy comparable to CIN systems. Future studies should be prospective and compare TREs, which should be measured in a standardized fashion.

Differential Image Based Robot to MRI Scanner Registration with Active Fiducial Markers for an MRI-Guided Robotic Catheter System

In magnetic resonance imaging (MRI) guided robotic catheter ablation procedures, reliable tracking of the catheter within the MRI scanner is needed to safely navigate the catheter. This requires accurate registration of the catheter to the scanner. This paper presents a differential, multi-slice image-based registration approach utilizing active fiducial coils. The proposed method would be used to preoperatively register the MRI image space with the physical catheter space. In the proposed scheme, the registration is performed with the help of a registration frame, which has a set of embedded electromagnetic coils designed to actively create MRI image artifacts. These coils are detected in the MRI scanner's coordinate system by background subtraction. The detected coil locations in each slice are weighted by the artifact size and then registered to known ground truth coil locations in the catheter's coordinate system via least-squares fitting. The proposed approach is validated by using a set of target coils placed withing the workspace, employing multi-planar capabilities of the MRI scanner. The average registration and validation errors are respectively computed as 1.97 mm and 2.49 mm. The multi-slice approach is also compared to the single-slice method and shown to improve registration and validation by respectively 0.45 mm and 0.66 mm.

Optimization Model for the Distribution of Fiducial Markers in Liver Intervention

The distribution of fiducial markers is one of the main factors affected the accuracy of optical navigation system. However, many studies have been focused on improving the fiducial registration accuracy or the target registration accuracy, but few solutions involve optimization model for the distribution of fiducial markers. In this paper, we propose an optimization model for the distribution of fiducial markers to improve the optical navigation accuracy. The strategy of optimization model is reducing the distribution from three dimensional to two dimensional to obtain the 2D optimal distribution by using optimization algorithm in terms of the marker number and the expectation equation of target registration error (TRE), and then extend the 2D optimal distribution in two dimensional to three dimensional to calculate the optimal distribution according to the distance parameter and the expectation equation of TRE. The results of the experiments show that the averaged TRE for the human phantom is approximately 1.00 mm by applying the proposed optimization model, and the averaged TRE for the abdominal phantom is 0.59 mm. The experimental results of liver simulator model and ex-vivo porcine liver model show that the proposed optimization model can be effectively applied in liver intervention.

Comparative Study of Two Pose Measuring Systems Used to Reduce Robot Localization Error

The performance of marker-based, six degrees of freedom (6DOF) pose measuring systems is investigated. For instruments in this class, the pose is derived from locations of a few three-dimensional (3D) points. For such configurations to be used, the rigid-body condition—which requires that the distance between any two points must be fixed, regardless of orientation and position of the configuration—must be satisfied. This report introduces metrics that gauge the deviation from the rigid-body condition. The use of these metrics is demonstrated on the problem of reducing robot localization error in assembly applications. Experiments with two different systems used to reduce the localization error of the same industrial robot yielded two conflicting outcomes. The data acquired with one system led to substantial reduction in both position and orientation error of the robot, while the data acquired with a second system led to comparable reduction in the position error only. The difference is attributed to differences between metrics used to characterize the two systems.

Reliable navigation registration in cranial and spine surgery based on intraoperative computed tomography

OBJECTIVE

Low registration errors are an important prerequisite for reliable navigation, independent of its use in cranial or spinal surgery. Regardless of whether navigation is used for trajectory alignment in biopsy or implant procedures, or for sophisticated augmented reality applications, all depend on a correct registration of patient space and image space. In contrast to fiducial, landmark, or surface matching–based registration, the application of intraoperative imaging allows user-independent automatic patient registration, which is less error prone. The authors’ aim in this paper was to give an overview of their experience using intraoperative CT (iCT) scanning for automatic registration with a focus on registration accuracy and radiation exposure.

METHODS

A total of 645 patients underwent iCT scanning with a 32-slice movable CT scanner in combination with navigation for trajectory alignment in biopsy and implantation procedures (n = 222) and for augmented reality (n = 437) in cranial and spine procedures (347 craniotomies and 42 transsphenoidal, 56 frameless stereotactic, 59 frame-based stereotactic, and 141 spinal procedures). The target registration error was measured using skin fiducials that were not part of the registration procedure. The effective dose was calculated by multiplying the dose length product with conversion factors.

RESULTS

Among all 1281 iCT scans obtained, 1172 were used for automatic patient registration (645 initial registration scans and 527 repeat iCT scans). The overall mean target registration error was 0.86 ± 0.38 mm (± SD) (craniotomy, 0.88 ± 0.39 mm; transsphenoidal, 0.92 ± 0.39 mm; frameless, 0.74 ± 0.39 mm; frame-based, 0.84 ± 0.34 mm; and spinal, 0.80 ± 0.28 mm). Compared with standard diagnostic scans, a distinct reduction of the effective dose could be achieved using low-dose protocols for the initial registration scan with mean effective doses of 0.06 ± 0.04 mSv for cranial, 0.50 ± 0.09 mSv for cervical, 4.12 ± 2.13 mSv for thoracic, and 3.37 ± 0.93 mSv for lumbar scans without impeding registration accuracy.

CONCLUSIONS

Reliable automatic patient registration can be achieved using iCT scanning. Low-dose protocols ensured a low radiation exposure for the patient. Low-dose scanning had no negative effect on navigation accuracy.

Soft tissue deformation tracking by means of an optimized fiducial marker layout with application to cancer tumors

OBJECTIVE

Interventional radiology methods have been adopted for intraoperative control of the surgical region of interest (ROI) in a wide range of minimally invasive procedures. One major obstacle that hinders the success of procedures using interventional radiology methods is the preoperative and intraoperative deformation of the ROI. While fiducial markers (FM) tracing has been shown to be promising in tracking such deformations, determining the optimal placement of the FM in the ROI remains a significant challenge. The current study proposes a computational framework to address this problem by preoperatively optimizing the layout of FM, thereby enabling an accurate tracking of the ROI deformations.

METHODS

The proposed approach includes three main components: (1) creation of virtual deformation benchmarks, (2) method of predicting intraoperative tissue deformation based on FM registration, and (3) FM layout optimization. To account for the large variety of potential ROI deformations, virtual benchmarks are created by applying a multitude of random force fields on the tumor surface in physically based simulations. The ROI deformation prediction is carried out by solving the inverse problem of finding the smoothest force field that leads to the observed FM displacements. Based on this formulation, a simulated annealing approach is employed to optimize the FM layout that produces the best prediction accuracy.

RESULTS

The proposed approach is capable of finding an FM layout that outperforms the rationally chosen layouts by 40% in terms of ROI prediction accuracy. For a maximum induced displacement of 20 mm on the tumor surface, the average maximum error between the benchmarks and our FM-optimized predictions is about 1.72 mm, which falls within the typical resolution of ultrasound imaging.

CONCLUSIONS

The proposed framework can optimize FM layout to effectively reduce the errors in the intraoperative deformation prediction process, thus bridging the gap between preoperative imaging and intraoperative tissue deformation.

Intraoperative computed tomography as reliable navigation registration device in 200 cranial procedures

BACKGROUND

Registration accuracy is a main factor influencing overall navigation accuracy. Standard fiducial- or landmark-based patient registration is user dependent and error-prone. Intraoperative imaging offers the possibility for user-independent patient registration. The aim of this paper is to evaluate our initial experience applying intraoperative computed tomography (CT) for navigation registration in cranial neurosurgery, with a special focus on registration accuracy and effective radiation dose.

METHODS

A total of 200 patients (141 craniotomy, 19 transsphenoidal, and 40 stereotactic burr hole procedures) were investigated by intraoperative CT applying a 32-slice movable CT scanner, which was used for automatic navigation registration. Registration accuracy was measured by at least three skin fiducials that were not part of the registration process.

RESULTS

Automatic registration resulted in high registration accuracy (mean registration error: 0.93 ± 0.41 mm). Implementation of low-dose scanning protocols did not impede registration accuracy (registration error applying the full dose head protocol: 0.87 ± 0.36 mm vs. the low dose sinus protocol 0.72 ± 0.43 mm) while a reduction of the effective radiation dose by a factor of 8 could be achieved (mean effective radiation dose head protocol: 2.73 mSv vs. sinus protocol: 0.34 mSv).

CONCLUSION

Intraoperative CT allows highly reliable navigation registration with low radiation exposure.

An Augmented Reality System Using Improved-Iterative Closest Point Algorithm for On-Patient Medical Image Visualization

In many surgery assistance systems, cumbersome equipment or complicated algorithms are often introduced to build the whole system. To build a system without cumbersome equipment or complicated algorithms, and to provide physicians the ability to observe the location of the lesion in the course of surgery, an augmented reality approach using an improved alignment method to image-guided surgery (IGS) is proposed. The system uses RGB-Depth sensor in conjunction with the Point Cloud Library (PCL) to build and establish the patient's head surface information, and, through the use of the improved alignment algorithm proposed in this study, the preoperative medical imaging information obtained can be placed in the same world-coordinates system as the patient's head surface information. The traditional alignment method, Iterative Closest Point (ICP), has the disadvantage that an ill-chosen starting position will result only in a locally optimal solution. The proposed improved para-alignment algorithm, named improved-ICP (I-ICP), uses a stochastic perturbation technique to escape from locally optimal solutions and reach the globally optimal solution. After the alignment, the results will be merged and displayed using Microsoft's HoloLens Head-Mounted Display (HMD), and allows the surgeon to view the patient's head at the same time as the patient's medical images. In this study, experiments were performed using spatial reference points with known positions. The experimental results show that the proposed improved alignment algorithm has errors bounded within 3 mm, which is highly accurate.

Estimating Maximum Target Registration Error Under Uniform Restriction of Fiducial Localization Error in Image Guided System

In this paper, we investigate the estimation of the maximum target registration error (TRE) magnitude of the target location while using point-based rigid registration in the image guided system. Under the uniform restriction of fiducial localization error (FLE) magnitude, we explicitly formulate the estimation as an optimization problem. Through analyzing the approximated problem which assumes the rigidity of the fiducial set holds with the perturbation of FLE, we present a strict lower bound for the maximum TRE magnitude. The simulations show that the lower bound is close to the actual maximum TRE magnitude for the target locations lying far away from the fiducial points. Unlike the expected TRE magnitude in which all fiducial points contribute, the lower bound is only related to the fiducial points serving as the vertices of the convex hull of the fiducial set. Our analysis provides a new perspective of investigating the problem of TRE estimation and is helpful for the surgeons to learn about the worst situation during using the image guided system.

How does adding anatomical landmarks as fiducial points in the point-matching registration of neuronavigation influence registration accuracy?

Skin markers (SMs) are usually used as fiducial points in registration of neuronavigation, but the areas in which they can be adhered to are restricted, which usually results in poor distribution of the SMs and a large registration error. In this research, we studied whether the registration accuracy can be improved by adding anatomical landmarks (ALs), which are thought to have a larger localization error than SMs. A series of random SM configurations were generated, and for each SM configuration, we generated a corresponding SM-AL configuration by adding several ALs. We then compared the accuracy of the point-matching registration of the SM configurations with that of the corresponding SM-AL configurations. Experiment results indicated that adding ALs always made the mean target registration error of the whole head fall into a lower and narrower range, which meant that the registration became more accurate and more stable. In addition, adding more ALs resulted in a better performance.

Comparison of digital surface displacements of maxillary dentures based on noninvasive anatomic landmarks

STATEMENT OF PROBLEM

Artificial markers called fiducials are commonly used to orient digitized surfaces for analysis. However, when these markers are tangible and placed in the region of interest, they may alter surface topography and influence data analysis.

PURPOSE

The purpose of this in vitro study was to apply a modified digital surface fitting method based on anatomic landmarks to evaluate denture accuracy and to use 2 different denture processing techniques to evaluate the method. The goal was to noninvasively measure and describe any surface differences in denture processing techniques at the intaglio and denture tooth levels.

MATERIAL AND METHODS

Twenty standardized maxillary complete dentures were waxed on standardized edentulous casts and processed by using acrylic resin compression (COM, n=10) and injection molding (INJ, n=10) methods. Digital scans were recorded of the anatomic surface of the cast, the intaglio and cameo surfaces of the acrylic resin dentures, and the cameo surface of the wax dentures. Three anatomic fiducials were identified on denture intaglio and cast scans and 4 on the cameo surfaces of waxed and acrylic resin denture scans. These fiducials were then used to digitally align the anatomic with the processed intaglio surfaces and the waxed with the processed cameo surfaces. Surface displacements were compared among processed dentures expressed at specific points (9 tissue landmarks and 8 tooth landmarks). The accuracy of surface displacements was assessed by changes in the number and location of anatomic fiducials. The scanning precision and the intraobserver repeatability in the selection of dental landmarks were also determined. For each landmark, the spatial (x, y, and z) mean differences between the 2 processing techniques were calculated for the intaglio and the cameo surfaces and presented on each orthogonal plane. Statistical nonparametric comparison of these means was analyzed with the Mann-Whitney U test (α=.05). Benjamini-Hochberg corrections for multiple comparisons were used.

RESULTS

Changing the number and the location of anatomic landmarks had a small effect on the precision of the surface fitting. Repeated scans yielded high precision levels. In contrast, intraobserver repeatability had a larger error. In general, injection-molded dentures showed less displacement after polymerization than did the compression-molded ones. These differences were more substantial at the denture tooth level than on the intaglio surfaces.

CONCLUSIONS

Anatomic noninvasive fiducials chosen at distinct locations of maxillary edentulous areas seem to be reliable markers for the superposition of corresponding digital surface scans. Maxillary dentures processed with the injection molding technique have minimal deformation. Posterior denture teeth displace in 3 dimensions with the compression molding technique.

Orientation Uncertainty Characteristics of Some Pose Measuring Systems

We investigate the performance of pose measuring systems which determine an object's pose from measurement of a few fiducial markers attached to the object. Such systems use point-based, rigid body registration to get the orientation matrix. Uncertainty in the fiducials' measurement propagates to the uncertainty of the orientation matrix. This orientation uncertainty then propagates to points on the object's surface. This propagation is anisotropic, and the direction along which the uncertainty is the smallest is determined by the eigenvector associated with the largest eigenvalue of the orientation data's covariance matrix. This eigenvector in the coordinate frame defined by the fiducials remains almost fixed for any rotation of the object. However, the remaining two eigenvectors vary widely and the direction along which the propagated uncertainty is the largest cannot be determined from the object's pose. Conditions that result in such a behavior and practical consequences of it are presented.

Experimental validation of predicted application accuracies for computer-assisted (CAS) intraoperative navigation with paired-point registration

PURPOSE

The target registration error (TRE) is a crucial parameter to estimate the potential usefulness of computer-assisted navigation intraoperatively. Both image-to-patient registration on base of rigid-body registration and TRE prediction methods are available for spatially isotropic and anisotropic data. This study presents a thorough validation of data obtained in an experimental operating room setting with CT images.

METHODS

Optical tracking was used to register a plastic skull, an anatomic specimen, and a volunteer to their respective CT images. Plastic skull and anatomic specimen had implanted bone fiducials for registration; the volunteer was registered with anatomic landmarks. Fiducial localization error, fiducial registration error, and total target error (TTE) were measured; the TTE was compared to isotropic and anisotropic error prediction models. Numerical simulations of the experiment were done additionally.

RESULTS

The user localization error and the TTE were measured and calculated using predictions, both leading to results as expected for anatomic landmarks and screws used as fiducials. TRE/TTE is submillimetric for the plastic skull and the anatomic specimen. In the experimental data a medium correlation was found between TRE and target localization error (TLE). Most of the predictions of the application accuracy (TRE) fall in the 68% confidence interval of the measured TTE. For the numerically simulated data, a prediction of TTE was not possible; TRE and TTE show a negligible correlation.

CONCLUSION

Experimental application accuracy of computer-assisted navigation could be predicted satisfactorily with adequate models in an experimental setup with paired-point registration of CT images to a patient. The experimental findings suggest that it is possible to run navigation and prediction of navigation application accuracy basically defined by the spatial resolution/precision of the 3D tracker used.

Registration and fusion quantification of augmented reality based nasal endoscopic surgery

This paper quantifies the registration and fusion display errors of augmented reality-based nasal endoscopic surgery (ARNES). We comparatively investigated the spatial calibration process for front-end endoscopy and redefined the accuracy level of a calibrated endoscope by using a calibration tool with improved structural reliability. We also studied how registration accuracy was combined with the number and distribution of the deployed fiducial points (FPs) for positioning and the measured registration time. A physically integrated ARNES prototype was customarily configured for performance evaluation in skull base tumor resection surgery with an innovative approach of dynamic endoscopic vision expansion. As advised by surgical experts in otolaryngology, we proposed a hierarchical rendering scheme to properly adapt the fused images with the required visual sensation. By constraining the rendered sight in a known depth and radius, the visual focus of the surgeon can be induced only on the anticipated critical anatomies and vessel structures to avoid misguidance. Furthermore, error analysis was conducted to examine the feasibility of hybrid optical tracking based on point cloud, which was proposed in our previous work as an in-surgery registration solution. Measured results indicated that the error of target registration for ARNES can be reduced to 0.77 ± 0.07 mm. For initial registration, our results suggest that a trade-off for a new minimal time of registration can be reached when the distribution of five FPs is considered. For in-surgery registration, our findings reveal that the intrinsic registration error is a major cause of performance loss. Rigid model and cadaver experiments confirmed that the scenic integration and display fluency of ARNES are smooth, as demonstrated by three clinical trials that surpassed practicality.

Selection of Fiducial Locations and Performance Metrics for Point-Based Rigid-Body Registration

A method is described to select the location and number of fiducials used in point-based, rigid-body registration of two coordinate frames. Two indices are introduced which are used to search for the optimum configuration of fiducials. They can be used to quickly evaluate a large number of configurations because no actual registration is involved in their calculation. Furthermore, configurations yielding small values of the indices correlate well with configurations which result in optimum registrations. Three registration performance metrics are discussed, and it is shown that optimization of different metrics leads to different selection of fiducial configurations. If an optimized configuration is selected as a starting configuration of N fiducials, the addition of extra fiducials does not significantly improve the registration in most cases. This work is based on 3D data acquired with three different instruments, each having different noise and bias characteristics.

Virtual wall-based haptic-guided teleoperated surgical robotic system for single-port brain tumor removal surgery

This article presents a haptic-guided teleoperation for a tumor removal surgical robotic system, so-called a SIROMAN system. The system was developed in our previous work to make it possible to access tumor tissue, even those that seat deeply inside the brain, and to remove the tissue with full maneuverability. For a safe and accurate operation to remove only tumor tissue completely while minimizing damage to the normal tissue, a virtual wall-based haptic guidance together with a medical image-guided control is proposed and developed. The virtual wall is extracted from preoperative medical images, and the robot is controlled to restrict its motion within the virtual wall using haptic feedback. Coordinate transformation between sub-systems, a collision detection algorithm, and a haptic-guided teleoperation using a virtual wall are described in the context of using SIROMAN. A series of experiments using a simplified virtual wall are performed to evaluate the performance of virtual wall-based haptic-guided teleoperation. With haptic guidance, the accuracy of the robotic manipulator's trajectory is improved by 57% compared to one without. The tissue removal performance is also improved by 21% ( p < 0.05). The experiments show that virtual wall-based haptic guidance provides safer and more accurate tissue removal for single-port brain surgery.

Cramér-Rao Lower Bound for Point Based Image Registration With Heteroscedastic Error Model for Application in Single Molecule Microscopy

The Cramér-Rao lower bound for the estimation of the affine transformation parameters in a multivariate heteroscedastic errors-in-variables model is derived. The model is suitable for feature-based image registration in which both sets of control points are localized with errors whose covariance matrices vary from point to point. With focus given to the registration of fluorescence microscopy images, the Cramér-Rao lower bound for the estimation of a feature's position (e.g., of a single molecule) in a registered image is also derived. In the particular case where all covariance matrices for the localization errors are scalar multiples of a common positive definite matrix (e.g., the identity matrix), as can be assumed in fluorescence microscopy, then simplified expressions for the Cramér-Rao lower bound are given. Under certain simplifying assumptions these expressions are shown to match asymptotic distributions for a previously presented set of estimators. Theoretical results are verified with simulations and experimental data.

Strategy for accurate liver intervention by an optical tracking system

Image-guided navigation for radiofrequency ablation of liver tumors requires the accurate guidance of needle insertion into a tumor target. The main challenge of image-guided navigation for radiofrequency ablation of liver tumors is the occurrence of liver deformations caused by respiratory motion. This study reports a strategy of real-time automatic registration to track custom fiducial markers glued onto the surface of a patient's abdomen to find the respiratory phase, in which the static preoperative CT is performed. Custom fiducial markers are designed. Real-time automatic registration method consists of the automatic localization of custom fiducial markers in the patient and image spaces. The fiducial registration error is calculated in real time and indicates if the current respiratory phase corresponds to the phase of the static preoperative CT. To demonstrate the feasibility of the proposed strategy, a liver simulator is constructed and two volunteers are involved in the preliminary experiments. An ex-vivo porcine liver model is employed to further verify the strategy for liver intervention. Experimental results demonstrate that real-time automatic registration method is rapid, accurate, and feasible for capturing the respiratory phase from which the static preoperative CT anatomical model is generated by tracking the movement of the skin-adhered custom fiducial markers.

A Microscopic Optically Tracking Navigation System That Uses High-resolution 3D Computer Graphics

Three-dimensional (3D) computer graphics (CG) are useful for preoperative planning of neurosurgical operations. However, application of 3D CG to intraoperative navigation is not widespread because existing commercial operative navigation systems do not show 3D CG in sufficient detail. We have developed a microscopic optically tracking navigation system that uses high-resolution 3D CG. This article presents the technical details of our microscopic optically tracking navigation system. Our navigation system consists of three components: the operative microscope, registration, and the image display system. An optical tracker was attached to the microscope to monitor the position and attitude of the microscope in real time; point-pair registration was used to register the operation room coordinate system, and the image coordinate system; and the image display system showed the 3D CG image in the field-of-view of the microscope. Ten neurosurgeons (seven males, two females; mean age 32.9 years) participated in an experiment to assess the accuracy of this system using a phantom model. Accuracy of our system was compared with the commercial system. The 3D CG provided by the navigation system coincided well with the operative scene under the microscope. Target registration error for our system was 2.9 ± 1.9 mm. Our navigation system provides a clear image of the operation position and the surrounding structures. Systems like this may reduce intraoperative complications.

A new markerless patient-to-image registration method using a portable 3D scanner

PURPOSE

Patient-to-image registration is critical to providing surgeons with reliable guidance information in the application of image-guided neurosurgery systems. The conventional point-matching registration method, which is based on skin markers, requires expensive and time-consuming logistic support. Surface-matching registration with facial surface scans is an alternative method, but the registration accuracy is unstable and the error in the more posterior parts of the head is usually large because the scan range is limited. This study proposes a new surface-matching method using a portable 3D scanner to acquire a point cloud of the entire head to perform the patient-to-image registration.

METHODS

A new method for transforming the scan points from the device space into the patient space without calibration and tracking was developed. Five positioning targets were attached on a reference star, and their coordinates in the patient space were measured prior. During registration, the authors moved the scanner around the head to scan its entire surface as well as the positioning targets, and the scanner generated a unique point cloud in the device space. The coordinates of the positioning targets in the device space were automatically detected by the scanner, and a spatial transformation from the device space to the patient space could be calculated by registering them to their coordinates in the patient space that had been measured prior. A three-step registration algorithm was then used to register the patient space to the image space. The authors evaluated their method on a rigid head phantom and an elastic head phantom to verify its practicality and to calculate the target registration error (TRE) in different regions of the head phantoms. The authors also conducted an experiment with a real patient's data to test the feasibility of their method in the clinical environment.

RESULTS

In the phantom experiments, the mean fiducial registration error between the device space and the patient space, the mean surface registration error, and the mean TRE of 15 targets on the surface of each phantom were 0.34 ± 0.01 mm and 0.33 ± 0.02 mm, 1.17 ± 0.02 mm and 1.34 ± 0.10 mm, and 1.06 ± 0.11 mm and 1.48 ± 0.21 mm, respectively. When grouping the targets according to their positions on the head, high accuracy was achieved in all parts of the head, and the TREs were similar across different regions. The authors compared their method with the current surface registration methods that use only a part of the facial surface on the elastic phantom, and the mean TRE of 15 targets was 1.48 ± 0.21 mm and 1.98 ± 0.53 mm, respectively. In a clinical experiment, the mean TRE of seven targets on the patient's head surface was 1.92 ± 0.18 mm, which was sufficient to meet clinical requirements.

CONCLUSIONS

The proposed surface-matching registration method provides sufficient registration accuracy even in the posterior area of the head. The 3D point cloud of the entire head, including the facial surface and the back of the head, can be easily acquired using a portable 3D scanner. The scanner does not need to be calibrated prior or tracked by the optical tracking system during scanning.

Assessing the reliability of MRI-CBCT image registration to visualize temporomandibular joints

OBJECTIVES

To evaluate image quality of two methods of registering MRI and CBCT images of the temporomandibular joint (TMJ), particularly regarding TMJ articular disc-condyle relationship and osseous abnormality.

METHODS

MR and CBCT images for 10 patients (20 TMJs) were obtained and co-registered using two methods (non-guided and marker guided) using Mirada XD software (Mirada Medical Ltd, Oxford, UK). Three radiologists independently and blindly evaluated three types of images (MRI, CBCT and registered MRI-CBCT) at two times (T1 and T2) on two criteria: (1) quality of MRI-CBCT registrations (excellent, fair or poor) and (2) TMJ disc-condylar position and articular osseous abnormalities (osteophytes, erosions and subcortical cyst, surface flattening, sclerosis).

RESULTS

75% of the non-guided registered images showed excellent quality, and 95% of the marker-guided registered images showed poor quality. Significant difference was found between the non-guided and marker-guided registration (χ(2) = 108.5; p < 0.01). The interexaminer variability of the disc position in MRI [intraclass correlation coefficient (ICC) = 0.50 at T1, 0.56 at T2] was lower than that in MRI-CBCT registered images [ICC = 0.80 (0.52-0.92) at T1, 0.84 (0.62-0.93) at T2]. Erosions and subcortical cysts were noticed less frequently in the MRI-CBCT images than in CBCT images.

CONCLUSIONS

Non-guided registration proved superior to marker-guided registration. Although MRI-CBCT fused images were slightly more limited than CBCT alone to detect osseous abnormalities, use of the fused images improved the consistency among examiners in detecting disc position in relation to the condyle.

Accuracy assessment of a marker-free method for registration of CT and stereo images applied in image-guided implantology: a phantom study

To assess the accuracy of a proposed marker-free registration method as opposed to the conventional marker-based method using an image-guided dental system, and investigating the best configurations of anatomical landmarks for various surgical fields in a phantom study, a CT-compatible dental phantom consisting of implanted targets was used. Two marker-free registration methods were evaluated, first using dental anatomical landmarks and second, using a reference marker tool. Six implanted markers, distributed in the inner space of the phantom were used as the targets; the values of target registration error (TRE) for each target were measured and compared with the marker-based method. Then, the effects of different landmark configurations on TRE values, measured using the Parsiss IV Guided Navigation system (Parsiss, Tehran, Iran), were investigated to find the best landmark arrangement for reaching the minimum registration error in each target region. It was proved that marker-free registration can be as precise as the marker-based method. This has a great impact on image-guided implantology systems whereby the drawbacks of fiducial markers for patient and surgeon are removed. It was also shown that smaller values of TRE could be achieved by using appropriate landmark configurations and moving the center of the landmark set closer to the surgery target. Other common factors would not necessarily decrease the TRE value so the conventional rules accepted in the clinical community about the ways to reduce TRE should be adapted to the selected field of dental surgery.

A region-based anatomical landmark configuration for sinus surgery using image guided navigation system: a phantom-study

PURPOSE

To evaluate the current beliefs about the ways to reduce target registration error (TRE) values in image guided Sinus surgery by rearranging the fiducial configuration, and investigating the best configurations for various surgical fields in a phantom study.

METHODS

A new CT-compatible skull phantom consisting of implanted targets was designed to enable direct measurement of TRE in four fields of sinus surgery, Frontal, Ethmoid, Sphenoid and Maxillary. The effects of different landmark configurations on TRE values, measured by the Parsiss-IV navigation system were investigated to find the best landmark arrangement for each region, and compared to the TRE prediction formula to assess the clinically accepted landmark selection approaches based on this formula.

RESULTS

It was shown that smaller values of TRE could be attained by arranging the center of the fiducials to be more focused on the surgery target. The addition of more fiducials and keeping non-linear arrangement of landmark would not necessarily decrease the TRE value.

CONCLUSION

Optimizing the landmark configuration is important for increasing the localization accuracy in image guided sinus surgery. The common beliefs accepted in the clinical community about the ways to reduce the TRE are very general and should be adapted to specific field of image guided surgery.

Quantitative error analysis for computer assisted navigation: a feasibility study

PURPOSE

The benefit of computer-assisted navigation depends on the registration process, at which patient features are correlated to some preoperative imagery. The operator-induced uncertainty in localizing patient features-the user localization error (ULE)-is unknown and most likely dominating the application accuracy. This initial feasibility study aims at providing first data for ULE with a research navigation system.

METHODS

Active optical navigation was done in CT-images of a plastic skull, an anatomic specimen (both with implanted fiducials), and a volunteer with anatomical landmarks exclusively. Each object was registered ten times with 3, 5, 7, and 9 registration points. Measurements were taken at 10 (anatomic specimen and volunteer) and 11 targets (plastic skull). The active NDI Polaris system was used under ideal working conditions (tracking accuracy 0.23 mm root-mean-square, RMS; probe tip calibration was 0.18 mm RMS). Variances of tracking along the principal directions were measured as 0.18 mm(2), 0.32 mm(2), and 0.42 mm(2). ULE was calculated from predicted application accuracy with isotropic and anisotropic models and from experimental variances, respectively.

RESULTS

The ULE was determined from the variances as 0.45 mm (plastic skull), 0.60 mm (anatomic specimen), and 4.96 mm (volunteer). The predicted application accuracy did not yield consistent values for the ULE.

CONCLUSIONS

Quantitative data of application accuracy could be tested against prediction models with iso- and anisotropic noise models and revealed some discrepancies. This could potentially be due to the facts that navigation and one prediction model wrongly assume isotropic noise (tracking is anisotropic), while the anisotropic noise prediction model assumes an anisotropic registration strategy (registration is isotropic in typical navigation systems). The ULE data are presumably the first quantitative values for the precision of localizing anatomical landmarks and implanted fiducials. Submillimetric localization is possible for implanted screws; anatomic landmarks are not suitable for high-precision clinical navigation.