Anatomical landmarks for point-matching registration in image-guided neurosurgery

Registration of preoperative temporal bone CT-scan to otoendoscopic video for augmented-reality based on convolutional neural networks

PURPOSE

Patient-to-image registration is a preliminary step required in surgical navigation based on preoperative images. Human intervention and fiducial markers hamper this task as they are time-consuming and introduce potential errors. We aimed to develop a fully automatic 2D registration system for augmented reality in ear surgery.

METHODS

CT-scans and corresponding oto-endoscopic videos were collected from 41 patients (58 ears) undergoing ear examination (vestibular schwannoma before surgery, profound hearing loss requiring cochlear implant, suspicion of perilymphatic fistula, contralateral ears in cases of unilateral chronic otitis media). Two to four images were selected from each case. For the training phase, data from patients (75% of the dataset) and 11 cadaveric specimens were used. Tympanic membranes and malleus handles were contoured on both video images and CT-scans by expert surgeons. The algorithm used a U-Net network for detecting the contours of the tympanic membrane and the malleus on both preoperative CT-scans and endoscopic video frames. Then, contours were processed and registered through an iterative closest point algorithm. Validation was performed on 4 cases and testing on 6 cases. Registration error was measured by overlaying both images and measuring the average and Hausdorff distances.

RESULTS

The proposed registration method yielded a precision compatible with ear surgery with a 2D mean overlay error of 0.65 ± 0.60 mm for the incus and 0.48 ± 0.32 mm for the round window. The average Hausdorff distance for these 2 targets was 0.98 ± 0.60 mm and 0.78 ± 0.34 mm respectively. An outlier case with higher errors (2.3 mm and 1.5 mm average Hausdorff distance for incus and round window respectively) was observed in relation to a high discrepancy between the projection angle of the reconstructed CT-scan and the video image. The maximum duration for the overall process was 18 s.

CONCLUSIONS

A fully automatic 2D registration method based on a convolutional neural network and applied to ear surgery was developed. The method did not rely on any external fiducial markers nor human intervention for landmark recognition. The method was fast and its precision was compatible with ear surgery.

Automatic image registration on intraoperative CBCT compared to Surface Matching registration on preoperative CT for spinal navigation: accuracy and workflow

INTRODUCTION

Spinal navigation solutions have been slower to develop compared to cranial ones. To facilitate greater adoption and use of spinal navigation, the relatively cumbersome registration processes need to be improved upon. This study aims to validate a new solution for automatic image registration and compare it to a traditional Surface Matching method.

METHOD

Adult patients undergoing spinal surgery requiring navigation were enrolled after providing consent. A registration matrix-Universal AIR (= Automatic Image Registration)-was placed in the surgical field and used for automatic registration based on intraoperative 3D imaging. A standard Surface Matching method was used for comparison. Accuracy measurements were obtained by comparing planned and acquired coordinates on the vertebrae.

RESULTS

Thirty-nine patients with 42 datasets were included. The mean accuracy of Universal AIR registration was 1.20 ± 0.42 mm, while the mean accuracy of Surface Matching registration was 1.94 ± 0.64 mm. Universal AIR registration was non-inferior to Surface Matching registration. Post hoc analysis showed a significantly greater accuracy for Universal AIR registration. In Surface Matching, but not automatic registration, user-related errors such as incorrect identification of the vertebral level were seen.

CONCLUSION

Automatic image registration for spinal navigation using Universal AIR and intraoperative 3D imaging provided improved accuracy compared to Surface Matching registration. In addition, it minimizes user errors and offers a standardized workflow, making it a reliable registration method for navigated spinal procedures.

Image-to-Patient Registration in Computer-Assisted Surgery of Head and Neck: State-of-the-Art, Perspectives, and Challenges

Today, image-guided systems play a significant role in improving the outcome of diagnostic and therapeutic interventions. They provide crucial anatomical information during the procedure to decrease the size and the extent of the approach, to reduce intraoperative complications, and to increase accuracy, repeatability, and safety. Image-to-patient registration is the first step in image-guided procedures. It establishes a correspondence between the patient's preoperative imaging and the intraoperative data. When it comes to the head-and-neck region, the presence of many sensitive structures such as the central nervous system or the neurosensory organs requires a millimetric precision. This review allows evaluating the characteristics and the performances of different registration methods in the head-and-neck region used in the operation room from the perspectives of accuracy, invasiveness, and processing times. Our work led to the conclusion that invasive marker-based methods are still considered as the gold standard of image-to-patient registration. The surface-based methods are recommended for faster procedures and applied on the surface tissues especially around the eyes. In the near future, computer vision technology is expected to enhance these systems by reducing human errors and cognitive load in the operating room.

"Image to patient" equal-resolution surface registration supported by a surface scanner: analysis of algorithm efficiency for computer-aided surgery

PURPOSE

The "image to patient" registration procedure is crucial for the accuracy of surgical instrument tracking relative to the medical image while computer-aided surgery. The main aim of this work was to create an equal-resolution surface registration algorithm (ERSR) and analyze its efficiency.

METHODS

The ERSR algorithm provides two datasets with equal, high resolution and approximately corresponding points. The registered sets are obtained by projection of a user-designed rectangle(s)-shaped uniform clouds of points on DICOM and surface scanner datasets. The tests of the algorithm were performed on a phantom with titanium microscrews. We analyzed the influence of DICOM resolution on the effect of the ERSR algorithm and compared the ERSR to standard paired-points landmark transform registration. The methods of analysis were Target Registration Error, distance maps, and their histogram evaluation.

RESULTS

The mean TRE in case of ERSR equaled 0.8 ± 0.3 mm (resolution A), 0.8 ± 0.5 mm (resolution B), and 1.0 ± 0.7 mm (resolution C). The mean values were at least 0.4 mm lower than in the case of landmark transform registration. The distance maps between the model achieved from the scanner and the CT-based model were analyzed by histogram. The frequency of the first bin in a histogram of the distance map for ERSR was about 0.6 for all three resolutions of DICOM dataset and three times higher than in the case of landmark transform registration. The results were statistically analyzed using the Wilcoxon signed-rank test (alpha = 0.05).

CONCLUSION

The tests proved a statistically significant higher efficiency of equal resolution surface registration related to the landmark transform algorithm. It was proven that the lower resolution of the CT DICOM dataset did not degrade the efficiency of the ERSR algorithm. We observed a significantly lower response to decreased resolution than in the case of paired-points landmark transform registration.

Predictive Value of Ablative Margin Assessment After Microwave Ablation for Local Tumor Progression in Medium and Large Hepatocellular Carcinoma: Computed Tomography-Computed Tomography Image Fusion Method Versus Side-by-Side Method

OBJECTIVE

This study aimed to explore the feasibility and predictive value for local tumor progression (LTP) of the computed tomography (CT)-CT image fusion method versus side-by-side method to assess ablative margin (AM) in hepatocellular carcinoma ≥3 cm in diameter.

MATERIALS AND METHODS

We selected patients with hepatocellular carcinoma ≥3 cm in diameter who underwent microwave ablation and had complete tumor ablation. We used the CT-CT image fusion method and side-by-side method to assess AM separately and divided the lesions into 3 groups: group I, minimum ablative margin (min-AM) <0 mm (the ablation zone did not fully cover the tumor); group II, 0 mm ≤ min-AM <5 mm; and group III, min-AM ≥5 mm.

RESULTS

A total of 71 patients involving 71 lesions were included. The κ coefficient for the agreement between the CT-CT image fusion method and the side-by-side method in assessing min-AM was 0.14 (P = 0.028). Cumulative LTP rate was significantly different between groups by min-AM from the CT-CT image fusion method (P < 0.05) but not by min-AM from the side-by-side method (P = 0.807). Seventeen of the 20 LTP lesions were located at min-AM on fused CT images, with consistency rate of 85%.

CONCLUSIONS

Compared with the side-by-side method, the CT-CT image fusion method is more accurate in assessing the AM of eccentrically ablated lesions and shows better predictive value for LTP. The min-AM based on CT-CT image fusion assessment is an important influencing factor for LTP.

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.

Automated Machine Learning (AutoML)-based Surface Registration Methodology for Image-guided Surgical Navigation System

BACKGROUND

While the surface registration technique has the advantage of being relatively safe and the operation time is short, it generally has the disadvantage of low accuracy.

PURPOSE

This research proposes automated machine learning (AutoML)-based surface registration to improve the accuracy of image-guided surgical navigation systems.

METHODS

The state-of-the-art surface registration concept is that first, using a neural network model, a new point-cloud that matches the facial information acquired by a passive probe of an optical tracking system (OTS) is extracted from the facial information obtained by computerized tomography (CT). Target registration error (TRE) representing the accuracy of surface registration is then calculated by applying the iterative closest point (ICP) algorithm to the newly extracted point-cloud and OTS information. In this process, the hyperparameters used in the neural network model and ICP algorithm are automatically optimized using Bayesian Optimization with Expected Improvement to yield improved registration accuracy.

RESULTS

Using the proposed surface registration methodology, the average TRE for the targets located in the sinus space and nasal cavity of the soft phantoms is (0.939 ± 0.375) mm, which shows 57.8 % improvement compared to the average TRE of (2.227 ± 0.193) mm calculated by the conventional surface registration method (p < 0.01). The performance of the proposed methodology is evaluated, and the average TREs computed by the proposed methodology and the conventional method are (0.767 ± 0.132) mm and (2.615 ± 0.378) mm, respectively. Additionally, for one healthy adult, the clinical applicability of the AutoML-based surface registration is also presented.

CONCLUSION

Our findings showed that the registration accuracy could be improved while maintaining the advantages of the surface registration technique. This article is protected by copyright. All rights reserved.

A fully automatic surgical registration method for percutaneous abdominal puncture surgical navigation

Surgical registration that maps surgical space onto image space plays an important role in surgical navigation. Accurate surgical registration can help surgeons efficiently locate surgical instruments. The complicated marker-based surgical registration method is highly accurate, but it is time-consuming. Therefore, a marker-less surgical registration method with high-precision and high-efficiency is proposed without human intervention. Firstly, the surgical navigation system based on the multi-vision system is calibrated by using a specially-designed calibration board. When extracting the abdominal point cloud acquired by the structured light vision system, the constraint is constructed by using Computed Tomography (CT) image to filter out the points in irrelevant areas to improve the computational efficiency. The Coherent Point Drift (CPD) algorithm based on Gaussian Mixture Model (GMM) is applied in the registration of abdominal point cloud with lack of surface features. To enhance the efficiency of the CPD algorithm, firstly, the system calibration result is used in rough registration of the point cloud, and then the proper point cloud pretreatment method and its parameters are studied through experiments. Finally, the puncturing simulation experiments were carried out by using the abdominal phantom. The experimental results show that the proposed surgical registration method has high accuracy and efficiency, and has potential clinical application value.

How precise are oral splints for frameless stereotaxy in guided ear, nose, throat, and maxillofacial surgery: a cadaver study

Background

Computer-assisted surgery optimises accuracy and serves to improve precise surgical procedures. We validated oral splints with fiducial markers by testing them against rigid bone markers.

Methods

We screwed twenty bone anchors as fiducial markers into different regions of a dried skull and measured the distances. After computed tomography (CT) scanning, the accuracy was evaluated by determining the markers’ position using frameless stereotaxy on a dry cadaver and indicated on the CT scan. We compared the accuracy of chairside fabricated oral splints to standard registration with bone markers immediately after fabrication and after a ten-time use. Accuracy was calculated as deviation (mean ± standard deviation). For statistical analysis, t test, Kruskal-Wallis, Tukey's, and various linear regression models, such as the Pearson's product–moment correlation coefficient, were used.

Results

Oral splints showed an accuracy of 0.90 mm ± 0.27 for viscerocranium, 1.10 mm ± 0.39 for skull base, and 1.45 mm ± 0.59 for neurocranium. We found an accuracy of less than 2 mm for both splints for a distance of up to 152 mm. The accuracy persisted even after ten times removing and reattaching the splints.

Conclusions

Oral splints offer a non-invasive indicator to improve the accuracy of image-guided surgery. The precision is dependent on the distance to the target. Up to 150-mm distance, a precision of fewer than 2 mm is possible. Dental splints provide sufficient accuracy than bone markers and may opt for higher precision combined with other non-invasive registration methods.

A hybrid feature-based patient-to-image registration method for robot-assisted long bone osteotomy

PURPOSE

The purpose of this study is to provide a simple, feasible and effective patient-to-image registration method for robot-assisted long bone osteotomy, which has rarely been systematically reported. The practical requirement is to meet the accuracy of 1 mm or even higher without bone-implanted markers.

METHODS

A hybrid feature-based registration method termed CR-RAMSICP is proposed. Point-based coarse registration (CR) is accomplished relying on the optical retro-reflective markers attached to the tracked rigid body fixed out of the bone. In surface-based fine registration, an improved iterative closest point (ICP) algorithm based on the range-adaptive matching strategy (termed RAMSICP) is presented to cope with the robust precise matching between the asymmetric patient and image point clouds, which avoids converging to a local minimum.

RESULTS

A series of registration experiments based on the isolated porcine iliums are carried out. The results illustrate that CR-RAMSICP not only significantly outperforms CR and CR-ICP in the accuracy and reproducibility, but also exhibits better robustness to the CR errors and less sensitiveness to the distribution and number of fiducial points located in the patient point cloud than CR-ICP.

CONCLUSION

The proposed registration method CR-RAMSICP can stably satisfy the desired registration accuracy without the use of bone-implanted markers like fiducial screws. Besides, the RAMSICP algorithm used in fine registration is convenient for programming because any complex metrics or models are not involved.

ROBUST SURFACE-MATCHING REGISTRATION BASED ON THE STRUCTURE INFORMATION FOR IMAGE-GUIDED NEUROSURGERY SYSTEM

Image-to-patient space registration is to make the accurate alignment between the actual operating space and the image space. Although the image-to-patient space registration using paired-point is used in some image-guided neurosurgery systems, the current paired-point registration method has some drawbacks and usually cannot achieve the best registration result. Therefore, surface-matching registration is proposed to solve this problem. This paper proposes a surface-matching method that accomplishes image-to-patient space registration automatically. We represent the surface point clouds by the Gaussian Mixture Model (GMM), which can smoothly approximate the probability density distribution of an arbitrary point set. We also use mutual information as the similarity measure between the point clouds and take into account the structure information of the points. To analyze the registration error, we introduce a method for the estimation of Target Registration Error (TRE) by generating simulated data. In the experiments, we used the point sets of the cranium surface and the model of the human head determined by a CT and laser scanner. The TRE was less than 2[Formula: see text]mm, and the TRE had better accuracy in the front and the posterior region. Compared to the Iterative Closest Point algorithm, the surface registration based on GMM and the structure information of the points proved superior in registration robustness and accurate implementation of image-to-patient registration.

Virtual splint registration for electromagnetic and optical navigation in orbital and craniofacial surgery

In intra-operative navigation, a registration procedure is performed to register the patient's position to the pre-operative imaging data. The registration process is the main factor that determines accuracy of the navigation feedback. In this study, a novel registration protocol for craniofacial surgery is presented, that utilizes a virtual splint with marker points. The accuracy of the proposed method was evaluated by two observers in five human cadaver heads, for optical and electromagnetic navigation, and compared to maxillary bone-anchored fiducial registration (optical and electromagnetic) and surface-based registration (electromagnetic). The results showed minimal differences in accuracy compared to bone-anchored fiducials at the level of the infra-orbital rim. Both point-based techniques had lower error estimates at the infraorbital rim than surface-based registration, but surface-based registration had the lowest loss of accuracy over target distance. An advantage over existing point-based registration methods (bone-anchored fiducials, existing splint techniques) is that radiological imaging does not need to be repeated, since the need for physical fiducials to be present in the image volume is eradicated. Other advantages include reduction of invasiveness compared to bone-achnored fiducials and a possible reduction of human error in the registration process.

Holographic patient tracking after bed movement for augmented reality neuronavigation using a head-mounted display

BACKGROUND

Holographic neuronavigation has several potential advantages compared to conventional neuronavigation systems. We present the first report of a holographic neuronavigation system with patient-to-image registration and patient tracking with a reference array using an augmented reality head-mounted display (AR-HMD).

METHODS

Three patients undergoing an intracranial neurosurgical procedure were included in this pilot study. The relevant anatomy was first segmented in 3D and then uploaded as holographic scene in our custom neuronavigation software. Registration was performed using point-based matching using anatomical landmarks. We measured the fiducial registration error (FRE) as the outcome measure for registration accuracy. A custom-made reference array with QR codes was integrated in the neurosurgical setup and used for patient tracking after bed movement.

RESULTS

Six registrations were performed with a mean FRE of 8.5 mm. Patient tracking was achieved with no visual difference between the registration before and after movement.

CONCLUSIONS

This first report shows a proof of principle of intraoperative patient tracking using a standalone holographic neuronavigation system. The navigation accuracy should be further optimized to be clinically applicable. However, it is likely that this technology will be incorporated in future neurosurgical workflows because the system improves spatial anatomical understanding for the surgeon.

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.

A robust automated surface-matching registration method for neuronavigation

PURPOSE

The surface-matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient-to-image registration.

METHODS

A coarse-to-fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three-dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4-points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine-registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed method.

RESULTS

Keypoints were extracted within 7.0 s with a minimum threshold 0.001. In the phantom experiment, the mean target registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17 ± 0.04 mm, and the average registration time was 17.4 s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient's head surface were 1.70 ± 0.32 mm, 1.83 ± 0.38 mm, 1.64 ± 0.3 mm, 1.67 ± 0.35 mm, and 1.72 ± 0.31 mm, respectively, and the average registration time was 21.4 s. Compared with the method only based on the 4PCS and ICP algorithm and the current clinical method, the proposed method has obvious speed advantage while ensuring the registration accuracy.

CONCLUSIONS

The proposed method greatly improves the registration speed while guaranteeing the equivalent or higher registration accuracy, and avoids a tedious manual process for the coarse registration.

sEVD-smartphone-navigated placement of external ventricular drains

BACKGROUND

Currently, the trajectory for insertion of an external ventricular drain (EVD) is mainly determined using anatomical landmarks. However, non-assisted implantations frequently require multiple attempts and are associated with EVD malpositioning and complications. The authors evaluated the feasibility and accuracy of a novel smartphone-guided, angle-adjusted technique for assisted implantations of an EVD (sEVD) in both a human artificial head model and a cadaveric head.

METHODS

After computed tomography (CT), optimal insertion angles and lengths of intracranial trajectories of the EVDs were determined. A smartphone was calibrated to the mid-cranial sagittal line. Twenty EVDs were placed using both the premeasured data and smartphone-adjusted insertion angles, targeting the center of the ipsilateral ventricular frontal horn. The EVD positions were verified with post-interventional CT.

RESULTS

All 20 sEVDs (head model, 8/20; cadaveric head, 12/20) showed accurate placement in the ipsilateral ventricle. The sEVD tip locations showed a mean target deviation of 1.73° corresponding to 12 mm in the plastic head model, and 3.45° corresponding to 33 mm in the cadaveric head. The mean duration of preoperative measurements on CT data was 3 min, whereas sterile packing, smartphone calibration, drilling, and implantation required 9 min on average.

CONCLUSIONS

By implementation of an innovative navigation technique, a conventional smartphone was used as a protractor for the insertion of EVDs. Our ex vivo data suggest that smartphone-guided EVD placement offers a precise, rapidly applicable, and patient-individualized freehand technique based on a standard procedure with a simple, cheap, and widely available multifunctional device.

An Automatic Spatial Registration Method for Image-Guided Neurosurgery System

OBJECTIVE

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Development and characterization of the InVesalius Navigator software for navigated transcranial magnetic stimulation

BACKGROUND

Neuronavigation provides visual guidance of an instrument during procedures of neurological interventions, and has been shown to be a valuable tool for accurately positioning transcranial magnetic stimulation (TMS) coils relative to an individual's anatomy. Despite the importance of neuronavigation, its high cost, low portability, and low availability of magnetic resonance imaging facilities limit its insertion in research and clinical environments.

NEW METHOD

We have developed and validated the InVesalius Navigator as the first free, open-source software for image-guided navigated TMS, compatible with multiple tracking devices. A point-based, co-registration algorithm and a guiding interface were designed for tracking any instrument (e.g. TMS coils) relative to an individual's anatomy.

RESULTS

Localization, precision errors, and repeatability were measured for two tracking devices during navigation in a phantom and in a simulated TMS study. Errors were measured in two commercial navigated TMS systems for comparison. Localization error was about 1.5 mm, and repeatability was about 1 mm for translation and 1° for rotation angles, both within limits established in the literature.

COMPARISON WITH EXISTING METHODS

Existing TMS neuronavigation software programs are not compatible with multiple tracking devices, and do not provide an easy to implement platform for custom tools. Moreover, commercial alternatives are expensive with limited portability.

CONCLUSIONS

InVesalius Navigator might contribute to improving spatial accuracy and the reliability of techniques for brain interventions by means of an intuitive graphical interface. Furthermore, the software can be easily integrated into existing neuroimaging tools, and customized for novel applications such as multi-locus and/or controllable-pulse TMS.

Simultaneous combination of electromagnetic navigation with visual evoked potential in endoscopic transsphenoidal surgery: clinical experience and technical considerations

OBJECTIVE

The combination of electromagnetic navigation with continuous monitoring techniques allows for the best available anatomic and real-time functional intraoperative monitoring. Methodological aspects and technical adaptations for this combination of methods and the results from 19 patients with tumors in the pituitary region are reported.

METHODS

We retrospectively identified 19 patients who were treated with transsphenoidal surgery using high-resolution endoscopy (eTSS) at our hospital between June 2015 and June 2016. All patients underwent surgery under electromagnetic navigation with visual evoked potential (VEP) monitoring. The cases were reviewed for information on disease, and the distance between the patient tracker and emitter was measured.

RESULTS

In 19 patients, 17 had pituitary adenomas, 1 had a Rathke cleft cyst, and 1 had an arachnoid cyst. The optimal distance between the patient tracker and emitter was 20-25 cm. VEP monitoring could be performed with unaffected recording quality under electromagnetic navigation. Also we were able to perform the registration and eTSS at this distance using both navigation and VEP monitoring.

CONCLUSIONS

We performed eTSS for pituitary tumor by simultaneously using electromagnetic navigation and VEP. The optimal distance between the emitter and tracker minimizes VEP monitoring noise and allows accurate electromagnetic navigation.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

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.

Electromagnetic navigation-guided surgery in the semi-sitting position for posterior fossa tumours: a safety and feasibility study

BACKGROUND

Electromagnetic (EM)-guided neuronavigation is an innovative technique and a viable alternative to opto-electric navigation. We have performed a safety and feasibility study using EM-guided neuronavigation for posterior fossa surgery in the semi-sitting position in a selected subset of patients.

METHODS

Out of 284 patients with posterior fossa tumours operated upon over a period of 40 months, a subset of 15 patients was thought to possibly benefit from EM navigational guidance and was included in this study. There were six children and nine adults (aged between 8 and 84 years; mean age, 34.6 years) with different neoplasms in the brainstem or close to the midline. All patients had contrast-enhanced three-dimensional (3D) magnetic resonance imaging (MRI) of the head preoperatively. EM-guided navigation was used to identify and preserve the venous sinuses during craniotomy and to determine the trajectory to the lesion using various approaches. Neuronavigation accuracy was repeatedly checked for deviations measured in millimetres on screen shots during surgery before and after dural opening in the coronal (z = vertical), axial (x = mediolateral) and sagittal (y = anteroposterior) plane.

RESULTS

Referencing of the patient in the supine position was fast and easy. There was no loss of navigation accuracy after repositioning of the patient in the semi-sitting position (mean, 2.5 mm ± 0.92 mm). Identification of the pathological structure using EM navigation was achieved in all instances. Optimal angulation of the neck was selected individually to permit a comfortable position for the surgeon with full access to the lesion avoiding over-flexion. Deviation of accuracy at the surface of the target lesion ranged between 2.5 and 5.8 mm (mean, 3.9 mm ± 1.1 mm).

CONCLUSIONS

EM-guided neuronavigation in the semi-sitting position was safe and technically feasible. It enabled fast and accurate referencing without loss of navigation accuracy despite repositioning of the patient. In contrast to conventional opto-electric neuronavigation there were no line of sight problems.

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.

Preoperative fiducial coil placement facilitates robot-assisted laparoscopic excision of retroperitoneal small solitary metastasis of kidney cancer

Image-guided fiducial markers are being used in surgery, especially in spine and breast surgery, and radiotherapy, allowing localization of tumor sites precisely. We report a case of fiducial coil use in a man undergoing a robot-assisted laparoscopic resection of a metastatic nodule under the ipsilateral diaphragm after robot-assisted partial nephrectomy performed 2 years ago for a left upper pole renal tumor. The fiducial coil facilitated the localization of the lesion, which would otherwise have been challenging because of its small size and location. In addition, the fiducial coil was helpful to avoid cutting into the lesion directly.