Surface Registration Method Research Articles

Points of interest linear attention network for real-time non-rigid liver volume to surface registration.

In laparoscopic liver surgery, accurately predicting the displacement of key intrahepatic anatomical structures is crucial for informing the doctor's intraoperative decision-making. However, due to the constrained surgical perspective, only a partial surface of the liver is typically visible. Consequently, the utilization of non-rigid volume to surface registration methods becomes essential. But traditional registration methods lack the necessary accuracy and cannot meet real-timerequirements. To achieve high-precision liver registration with only partial surface information and estimate the displacement of internal liver tissues inreal-time. We propose a novel neural network architecture tailored for real-time non-rigid liver volume to surface registration. The network utilizes a voxel-based method, integrating sparse convolution with the newly proposed points of interest (POI) linear attention module. POI linear attention module specifically calculates attention on the previously extracted POI. Additionally, we identified the most suitable normalization methodRMSINorm. We evaluated our proposed network and other networks on a dataset generated from real liver models and two real datasets. Our method achieves an average error of 4.23mm and a mean frame rate of 65.4fps in the generation dataset. It also achieves an average error of 8.29mm in the human breathing motiondataset. Our network outperforms CNN-based networks and other attention networks in terms of accuracy and inferencespeed.

Tooth Surface Registration of Spiral Bevel Gear Based on Improved Genetic Algorithm

To reduce the tooth surface measurement error of spiral bevel gear, a tooth surface registration method using an improved genetic algorithm (GA) was proposed according to the measuring principle of a coordinate measuring machine (CMM). To improve the performance of GA, the unit quaternion method was used to limit the initial population generation space, and then the simulated annealing algorithm was used to improve the search process. Finally, the measured and theoretical tooth surface was matched. Experimental results show that the improved genetic algorithm can compensate for more than 55% of the measurement error of the tooth surface.

Hierarchical particle optimization for cortical shape correspondence in temporal lobe resection

BackgroundAnterior temporal lobe resection is an effective treatment for temporal lobe epilepsy. The post-surgical structural changes could influence the follow-up treatment. Capturing post-surgical changes necessitates a well-established cortical shape correspondence between pre- and post-surgical surfaces. Yet, most cortical surface registration methods are designed for normal neuroanatomy. Surgical changes can introduce wide ranging artifacts in correspondence, for which conventional surface registration methods may not work as intended. MethodsIn this paper, we propose a novel particle method for one-to-one dense shape correspondence between pre- and post-surgical surfaces with temporal lobe resection. The proposed method can handle partial structural abnormality involving non-rigid changes. Unlike existing particle methods using implicit particle adjacency, we consider explicit particle adjacency to establish a smooth correspondence. Moreover, we propose hierarchical optimization of particles rather than full optimization of all particles at once to avoid trappings of locally optimal particle update. ResultsWe evaluate the proposed method on 25 pairs of T1-MRI with pre- and post-simulated resection on the anterior temporal lobe and 25 pairs of patients with actual resection. We show improved accuracy over several cortical regions in terms of ROI boundary Hausdorff distance with 4.29 mm and Dice similarity coefficients with average value 0.841, compared to existing surface registration methods on simulated data. In 25 patients with actual resection of the anterior temporal lobe, our method shows an improved shape correspondence in qualitative and quantitative evaluation on parcellation-off ratio with average value 0.061 and cortical thickness changes. We also show better smoothness of the correspondence without self-intersection, compared with point-wise matching methods which show various degrees of self-intersection. ConclusionThe proposed method establishes a promising one-to-one dense shape correspondence for temporal lobe resection. The resulting correspondence is smooth without self-intersection. The proposed hierarchical optimization strategy could accelerate optimization and improve the optimization accuracy. According to the results on the paired surfaces with temporal lobe resection, the proposed method outperforms the compared methods and is more reliable to capture cortical thickness changes.

Measuring Respiratory Motion for Supporting the Minimally Invasive Destruction of Liver Tumors.

Objective: Destroying liver tumors is a challenge for contemporary interventional radiology. The aim of this work is to compare different techniques used for the measurement of respiratory motion, as this is the main hurdle to the effective implementation of this therapy. Methods: Laparoscopic stereoscopic reconstruction of point displacements on the surface of the liver, observation of breathing using external markers placed on the surface of the abdominal cavity, and methods for registration of the surface of the abdominal cavity during breathing were implemented and evaluated. Results: The following accuracies were obtained: above 4 mm and 0.5 mm, and below 8 mm for laparoscopic, skin markers, and skin surface registration methods, respectively. Conclusions: The clinical techniques and accompanying imaging modalities employed to destroy liver tumors, as well as the advantages and limitations of the proposed methods, are presented. Further directions for their development are also indicated.

Global optimization point-set registration based on translation/rotation decoupling for image-guided surgery applications.

In image-guided surgery systems, image-to-patient spatial registration is to get the spatial transformation between the image space and the actual operating space. Although the image-to-patient spatial registration methods using paired point or surface matching are used in some image-guided neurosurgery systems, the key problem is that the global optimization registration result cannot be achieved. Therefore, this paper proposes a new rotation invariant feature for decoupling rotation and translation space, based on which global optimization point set registration method is proposed. The new rotation invariant features, constructed based on the edges and the angles, are the rotation invariant, which has high feature resolution. Some of them are not only the rotation invariant, but also the translation invariant. To obtain the global optimal solution, branch and bound search strategy is used to search the parameter space of the translation and the computational cost is reduced simultaneously. The registration accuracy of the spatial registration method is analyzed by comparing the difference between the estimated transform and the standard transform to calculate the registration error. To validate the performance of the spatial registration method proposed, the registration performance was analyzed by comparing the experimental results with the results of the two mainstream registration methods (the iterative closest point [ICP] registration method and the coherent point drift method). In the experiments, the comparison was based on the registration accuracy and the execution time. We show our registration method can obtain higher accuracy in a shorter time in most cases. At the same time, when using ICP to further refine our results, the ICP method can converge in a very short time, which also shows that our method provides a good initial pose for the ICP method and can help the ICP converge to the global optimal solution faster. Our method can achieve an average rotation error of 0.124 degrees and an average translation error of 0.38 mm on 10 clinical data. The results reveal that the surface registration method based on translation rotation decoupling can achieve superior performance regarding both the registration accuracy and the time efficiency in the image-to-patient spatial registration.

Biomechanical model registration for monitoring and simulating large orthodontic tooth movements in the maxilla and mandible.

Superimposition of digital dental-arch models allows quantification of orthodontic tooth movements (OTM). Currently, this procedure requires stable reference surfaces usually only present in the maxilla. This study aimed to investigate the accuracy of anovel superimposition approach based on biomechanical principles of OTM and the equilibrium of forces and moments (EFM)-applicable in both jaws-for monitoring and simulating large OTM. The study included 7patients who had undergone extraction of the first (PM1-Ex) or second (PM2-Ex) premolar in each quadrant. Digital models taken at start and end of the T‑Loop treatment phase were superimposed by applying 3 EFM variants differing in the number of teeth used for registration. Maxillary OTM results for EFM were validated against those for aconventional surface registration method (SRM). In an additional case study, OTM were simulated for PM1-Ex, PM2-Ex and non-extraction treatment strategies. The EFM variant that included all teeth of the dental arch achieved the highest accuracy, with median translational and rotational OTM deviations from SRM of only 0.37 mm and 0.56°, respectively. On average, retracted canines and first premolars were distalized by 3.0 mm, accompanied by 6.2° distal crown tipping and 12.2° distorotation. The share of space closure by molar mesialization was 19.4% for PM1-Ex quadrants and 34.5% for PM2-Ex quadrants. EFM allows accurate OTM quantification relative to the maxillary and mandibular bases even in challenging situations involving large OTM. Superimposition of malocclusion and setup models enables realistic simulation of final tooth positions. This may greatly enhance the value of digital setups for decision-making in orthodontic treatment planning.

Automated machine learning (AutoML)-based surface registration methodology for image-guided surgical navigation system.

Although 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. This research proposes automated machine learning (AutoML)-based surface registration to improve the accuracy of image-guided surgical navigation systems. 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. 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. 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±0.375mm, which shows 57.8% improvement compared to the average TRE of 2.227±0.193mm 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 and 2.615±0.378mm, respectively. Additionally, for one healthy adult, the clinical applicability of the AutoML-based surface registration is also presented. Our findings showed that the registration accuracy could be improved while maintaining the advantages of the surface registration technique.

In Vivo Metacarpophalangeal Joint Kinematics After Silicone Implant Arthroplasty in Patients With Rheumatoid Arthritis

The aim of this study was to determine the potential mechanism of implant fracture using 3-dimensional motion analysis of patients with rheumatoid arthritis. Active flexion motion in 9 hands (34 fingers) of 6 female patients with rheumatoid arthritis who previously underwent hinged silicone metacarpophalangeal joint arthroplasty was examined using 4-dimensional computed tomography. Positions of the proximal phalanges relative to the metacarpals were quantified using a surface registration method. The deformation of the silicone implant was classified in the sagittal plane in the maximum flexion frame. The longitudinal bone axis of the proximal phalanx and the helical axis of the proximal phalanx were evaluated in 3-dimensional coordinates based on the hinge of the silicone implant. Nineteen fingers were classified into group 1, in which the silicone implant moved volarly during flexion without buckling of the distal stem. Twelve fingers were classified into group 2, in which the distal stem of the silicone implant buckled. Three fingers were classified into group 3, in which the base of the distal stem had already fractured. Quantitatively, the longitudinal bone axes of the proximal phalanges were displaced from dorsal to volar in the middle stage of flexion and migrated in the proximal direction in the late phase of flexion. The helical axes of the proximal phalanges were located on the dorsal and proximal sides of the hinge, and these tended to move in the volar and proximal directions as the metacarpophalangeal joint flexed. Volar and proximal translation of the proximal phalange was observed on 4-dimensional computed tomography. Proximal displacement of the bone axis late in flexion appears to be a contributing factor inducing implant fractures, because the pistoning motion does not allow the implant to move in the proximal direction.

Progress and limitations of current surface registration methods when measuring natural enamel wear

ObjectivesOur ability to detect dental wear on sequential scans is improving. This experiment aimed to determine if widely used surface registration methods were sufficiently accurate to distinguish differences between intervention groups on early wear lesions. MethodsBaseline measurements were taken on human molar buccal enamel samples (n = 96) with a confocal scanning profilometer (Taicaan, UK). Samples were randomly assigned to subgroups of brushing (30 linear strokes 300 g force) before or after an acid challenge (10 min citric acid 0.3% immersion) for four test dentifrices (medium abrasivity NaF, medium abrasivity SnF2, low abrasivity NaF and a water control). Post-experimental profilometry was repeated. 3D step height was analysed using WearCompare (www.leedsdigitaldentistry.co.uk/wearcompare, UK). Percentage Sa change was calculated using Boddies (Taicaan Technologies, Southampton, UK). Data were analysed in SPSS (IBM, USA). ResultsThe mean 3D step height (SD) observed when samples were brushed before the erosive challenge was −2.33 µm (3.46) and after was −3.5 µm (5.6). No significant differences were observed between timing of toothbrushing or dentifrice used. The mean % Sa change for the low abrasivity group (water control and low abrasivity NaF) was −10.7% (16.8%) and +28.0% (42.0%) for the medium abrasivity group (medium abrasivity NaF and SnF2). ConclusionsDetectable wear scars were observed at early stages of wear progression. However standard deviations were high and the experiment was underpowered to detect significant changes. Brushing with a low abrasivity dentifrice or water control produced a smoother surface whereas brushing with a high abrasivity dentifrice produced a rougher surface. Clinical SignificanceThe methodology currently used to align sequential scans of teeth and measure change is too imprecise to measure early wear on natural enamel surfaces unless a large sample size is used. Further improvements are required before we can fully assess early wear processes on natural teeth using profilometry.

S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.

Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to facilitate neuroimaging studies. Though achieving good performance, available methods are either time consuming or not flexible to extend to multiple or high dimensional features. Considering the explosive availability of large-scale and multimodal brain MRI data, fast surface registration methods that can flexibly handle multimodal features are desired. In this study, we develop a Superfast Spherical Surface Registration (S3Reg) framework for the cerebral cortex. Leveraging an end-to-end unsupervised learning strategy, S3Reg offers great flexibility in the choice of input feature sets and output similarity measures for registration, and meanwhile reduces the registration time significantly. Specifically, we exploit the powerful learning capability of spherical Convolutional Neural Network (CNN) to directly learn the deformation fields in spherical space and implement diffeomorphic design with "scaling and squaring" layers to guarantee topology-preserving deformations. To handle the polar-distortion issue, we construct a novel spherical CNN model using three orthogonal Spherical U-Nets. Experiments are performed on two different datasets to align both adult and infant multimodal cortical features. Results demonstrate that our S3Reg shows superior or comparable performance with state-of-the-art methods, while improving the registration time from 1 min to 10 sec.

Validation of Group-wise Registration for Surface-based Functional MRI Analysis.

Resting-state functional MRI (rsfMRI) provides important information for studying and mapping the activities and functions of the brain. Conventionally, rsfMRIs are often registered to structural images in the Euclidean space without considering cortical surface geometry. Meanwhile, a surface-based representation offers a relaxed coordinate chart, but this still requires surface registration for group-wise data analysis. In this work, we investigate the performance of two existing surface registration methods in a surface-based rsfMRI analysis framework: FreeSurfer and Hierarchical Spherical Deformation (HSD). To minimize registration bias, we establish shape correspondence using both methods in a group-wise manner that estimates the unbiased average of a given cohort. To evaluate their performance, we focus on neuroanatomical alignment as well as the amount of distortion that can potentially bias surface tessellation for secondary level rsfMRI data analyses. In the pilot analysis, we examine a single timepoint of imaging data from 100 subjects out of an aging cohort. Overall, HSD establishes improved shape correspondence with reduced mean curvature deviation (10.94% less on average per subject, paired t-test: p <10-10) and reduced registration distortion (FreeSurfer: average 41.91% distortion per subject, HSD: 18.63%, paired t-test: p <10-10). Furthermore, HSD introduces less distortion than FreeSurfer in the areas identified in the individual components that were extracted by surface-based independent component analysis (ICA) after spatial smoothing and time series normalization. Consequently, we show that FreeSurfer capture individual components with globally similar but locally different patterns in ICA in visual inspection.

Establishing Surface Correspondence for Post-surgical Cortical Thickness Changes in Temporal Lobe Epilepsy.

In pre- and post-surgical surface shape analysis, establishing shape correspondence is necessary to investigate the postoperative surface changes. However, structural absence after the operation accompanies focal non-rigid changes, which leads to challenges in existing surface registration methods. In this paper, we present a fully automatic particle-based method to establish surface correspondence that can handle partial structural abnormality in the temporal lobe resection. Our method optimizes the coordinates of points which are modeled as particles on surfaces in a hierarchical way to reduce a chance of being trapped in a local minimum during the optimization. In the experiments, we evaluate the effectiveness of our method in comparison with conventional spherical registration (FreeSurfer) on two scenarios: cortical thickness changes in healthy controls within a short scan-rescan time window and patients with temporal lobe resection. The post-surgical scan is acquired at least 1 year after the presurgical scan. In region of interest-wise (ROI-wise) analysis, no changes on cortical thickness are found in both methods on the healthy control group. In patients, since there is no ground truth available, we instead investigated the disagreement between our method and FreeSurfer. We see poorly matched ROIs and large cortical thickness changes using FreeSurfer. On the contrary, our method shows well-matched ROIs and subtle cortical thickness changes. This suggests that the proposed method can establish a stable shape correspondence, which is not fully captured in a conventional spherical registration.

A phantom study to evaluate three different registration platform of 3D/3D, 2D/3D, and 3D surface match with 6D alignment for precise image-guided radiotherapy.

PurposeTo evaluate two three‐dimensional (3D)/3D registration platforms, one two‐dimensional (2D)/3D registration method, and one 3D surface registration method (3DS). These three technologies are available to perform six‐dimensional (6D) registrations for image‐guided radiotherapy treatment.MethodsFiducial markers were asymmetrically placed on the surfaces of an anthropomorphic head phantom (n = 13) and a body phantom (n = 8), respectively. The point match (PM) solution to the six‐dimensional (6D) transformation between the two image sets [planning computed tomography (CT) and cone beam CT (CBCT)] was determined through least‐square fitting of the fiducial positions using singular value decomposition (SVD). The transformation result from SVD was verified and was used as the gold standard to evaluate the 6D accuracy of 3D/3D registration in Varian’s platform (3D3DV), 3D/3D and 2D/3D registration in the BrainLab ExacTrac system (3D3DE and 2D3D), as well as 3DS in the AlignRT system. Image registration accuracy from each method was quantitatively evaluated by root mean square of target registration error (rmsTRE) on fiducial markers and by isocenter registration error (IRE). The Wilcoxon signed‐rank test was utilized to compare the difference of each registration method with PM. A P < 0.05 was considered significant.Results rmsTRE was in the range of 0.4 mm/0.7 mm (cranial/body), 0.5 mm/1 mm, 1.0 mm/1.5 mm, and 1.0 mm/1.2 mm for PM, 3D3D, 2D3D, and 3DS, respectively. Comparing to PM, the mean errors of IRE were 0.3 mm/1 mm for 3D3D, 0.5 mm/1.4 mm for 2D3D, and 1.6 mm/1.35 mm for 3DS for the cranial and body phantoms respectively. Both of 3D3D and 2D3D methods differed significantly in the roll direction as compared to the PM method for the cranial phantom. The 3DS method was significantly different from the PM method in all three translation dimensions for both the cranial (P = 0.003–P = 0.03) and body (P < 0.001–P = 0.008) phantoms.Conclusion3D3D using CBCT had the best image registration accuracy among all the tested methods. 2D3D method was slightly inferior to the 3D3D method but was still acceptable as a treatment position verification device. 3DS is comparable to 2D3D technique and could be a substitute for X‐ray or CBCT for pretreatment verification for treatment of anatomical sites that are rigid.

Improving a probabilistic cytoarchitectonic atlas of auditory cortex using a novel method for inter-individual alignment.

The human superior temporal plane, the site of the auditory cortex, displays high inter-individual macro-anatomical variation. This questions the validity of curvature-based alignment (CBA) methods for in vivo imaging data. Here, we have addressed this issue by developing CBA+, which is a cortical surface registration method that uses prior macro-anatomical knowledge. We validate this method by using cytoarchitectonic areas on 10 individual brains (which we make publicly available). Compared to volumetric and standard surface registration, CBA+ results in a more accurate cytoarchitectonic auditory atlas. The improved correspondence of micro-anatomy following the improved alignment of macro-anatomy validates the superiority of CBA+ compared to CBA. In addition, we use CBA+ to align in vivo and postmortem data. This allows projection of functional and anatomical information collected in vivo onto the cytoarchitectonic areas, which has the potential to contribute to the ongoing debate on the parcellation of the human auditory cortex.

Data registration for multi-method qualification of additive manufactured components

This work refines surface registration methods for metrological datasets to improve the multi-method qualification accuracy of additively manufactured (AM) lattices. Datasets acquired from X-ray computed tomography and a coordinate measurement machine of an AM lattice were aligned using derived geometry datum features based on a theoretical supplemental surface definition, which has been established in recent draft standards, but has had limited examination using complex AM structures. A refined sampling registration approach for lattice geometry based on spatially-dependent subsampling is derived and shown to statistically decrease variation between measurement sources. This importance of well-defined sampling practice and definition is highlighted. The applicability of this approach for multi-method qualification of complex AM parts is discussed. This work lays the foundation of utilizing specifications under consideration in a new standard with possible verification techniques that can be employed.

DIFFEOMORPHIC REGISTRATION FOR RETINOTOPIC MAPPING VIA QUASICONFORMAL MAPPING.

Human visual cortex is organized into several functional regions/areas. Identifying these visual areas of the human brain (i.e., V1, V2, V4, etc) is an important topic in neurophysiology and vision science. Retinotopic mapping via functional magnetic resonance imaging (fMRI) provides a non-invasive way of defining the boundaries of the visual areas. It is well known from neurophysiology studies that retinotopic mapping is diffeomorphic within each local area (i.e. locally smooth, differentiable, and invertible). However, due to the low signal-noise ratio of fMRI, the retinotopic maps from fMRI are often not diffeomorphic, making it difficult to delineate the boundaries of visual areas. The purpose of this work is to generate diffeomorphic retinotopic maps and improve the accuracy of the retinotopic atlas from fMRI measurements through the development of a specifically designed registration procedure. Although there are sophisticated existing cortical surface registration methods, most of them cannot fully utilize the features of retinotopic mapping. By considering unique retinotopic mapping features, we form a quasiconformal geometry-based registration model and solve it with efficient numerical methods. We compare our registration with several popular methods on synthetic data. The results demonstrate that the proposed registration is superior to conventional methods for the registration of retinotopic maps. The application of our method to a real retinotopic mapping dataset also results in much smaller registration errors.

A Multisensor Data Fusion Method Based on Gaussian Process Model for Precision Measurement of Complex Surfaces.

As multisensor measurement technology is rapidly applied in industrial production, one key issue is the data fusion procedure by combining several datasets from multiple sensors to obtain the overall geometric measurement. In this paper, a multisensor data fusion method based on a Gaussian process model is proposed for complex surface measurements. A robust surface registration method based on the adaptive distance function is firstly used to unify the coordinate systems of different measurement datasets. By introducing an adjustment model, the residuals between several independent datasets from different sensors are then approximated to construct a Gaussian process model-based data fusion system. The proposed method is verified through both simulation verification and actual experiments, indicating that the proposed method can fuse multisensor measurement datasets with better fusion accuracy and faster computational efficiency compared to the existing method.

Unsupervised Learning for Spherical Surface Registration.

Current spherical surface registration methods achieve good performance on alignment and spatial normalization of cortical surfaces across individuals in neuroimaging analysis. However, they are computationally intensive, since they have to optimize an objective function independently for each pair of surfaces. In this paper, we present a fast learning-based algorithm that makes use of the recent development in spherical Convolutional Neural Networks (CNNs) for spherical cortical surface registration. Given a set of surface pairs without supervised information such as ground truth deformation fields or anatomical landmarks, we formulate the registration as a parametric function and learn its parameters by enforcing the feature similarity between one surface and the other one warped by the estimated deformation field using the function. Then, given a new pair of surfaces, we can quickly infer the spherical deformation field registering one surface to the other one. We model this parametric function using three orthogonal Spherical U-Nets and use spherical transform layers to warp the spherical surfaces, while imposing smoothness constraints on the deformation field. All the layers in the network are well-defined and differentiable, thus the parameters can be effectively learned. We show that our method achieves accurate cortical alignment results on 102 subjects, comparable to two state-of-the-art methods: Spherical Demons and MSM, while runs much faster.

Any-degrees-of-freedom (anyDOF) registration for the characterization of freeform surfaces

This paper presents an any-degrees-of-freedom (anyDOF) registration method for the characterization of freeform surfaces. The method attempts to fill the research gap regarding traditional surface registration methods which are normally dedicated to solving the global optimization problem with all DOF but they lack flexibility. The proposed anyDOF method is capable of registering surfaces with any specified combination of DOF. This is particularly useful when some of the DOF are known to be unchanged according to the a priori knowledge. The anyDOF surface registration method is regarded as a typical optimization problem of finding the minimum distance from target surface to the reference surface, with constraints of the unwanted DOF. The problem is solved by the Levenberg-Marquardt method. Simulated experiments for a two-dimensional (2D) profile and a three-dimensional (3D) surface were undertaken, together with three measurement experiments including a fluid-jet polished surface, a bonnet polished surface and a diamond machined freeform surface. Experimental results show that the anyDOF registration method is highly flexible in the characterization of freeform surfaces.

Precision study of stereotactic electroencephalograph electrode implantation under neuronavigation in children with refractory epilepsy

Objective To explore the accuracy of stereotactic electroencephalograph (SEEG) electrode implantation under neuronavigation in children with refractory epilepsy. Methods Twenty-one children with refractory epilepsy underwent SEEG electrode implantation for identification of epileptogenic zone at Neurosurgery Department of Shenzhen Children's Hospital from March 2016 to December 2017 and were retrospective enrolled into this study MR surface registration and CT registration with anatomic markers were used for the electrode implantation. The accuracy of electrode implantation was compared between the 2 registration methods. The correlation between the depth of electrode implantation (the distance from cortical entry point to target) and the error of target and the error of entry point was analyzed. Results A total of 139 electrodes were implanted in 21 patients. Among them, 56 electrodes were implanted by MR surface registration method, and 83 implanted electrodes were placed by CT anatomical marker registration method. The registration time, registration error, electrode entry error and target error of MR Surface registration group were (37±9) min, (2.3±0.5) mm, (2.7±0.7) mm and (3.1±0.5) mm; those in anatomical marker registration group were (10±4) min, (1.1±0.3) mm, (1.5±0.5) mm, (2.2±0.6) mm respectively. The differences were statistically significant (all P<0.05) in the comparison between the 2 groups. In 139 electrodes, the electrode implantation depth was (4.7±1.7) mm, the entry point error was (2.1±1.3) mm, and the target error was (2.8±1.2) mm. Correlation analysis showed that the depth of electrode implantation was positively correlated with the target error (r=0.57, P=0.034), and there was no correlation with the error of the entry point (r=0.27, P=0.121). None of the patients had complications such as bleeding and infection after electrode implantation. After epileptogenic zonectomy, all patients were followed up for 6 months and included 20 cases as Engel Ⅰ and 1 case as Engel Ⅱ. Conclusion In children with refractory epilepsy, the accuracy of SEEG electrode implantation under neuronavigation seems to be relatively high. Compared with MR surface registration method, the registration efficiency and the accuracy of electrode implantation can be improved by using CT anatomical marker registration. Key words: Epilepsy; Child; Error; Neuronavigation; Stereoelectroencephalography