Surface-based Registration Method Research Articles

Create virtual dentoskeletal model by superimposing digital dental cast into cone-beam computed tomography scan.

Many artifacts and obstacles associated with cone-beam computed tomography (CBCT) scan can obscure or distort the details of the teeth and occlusal surface, like distorted teeth, streak artifacts, noise, and some malocclusion cases with excessive overlapping between jaws cause decrease the interocclusal space, which can impact diagnosis and treatment planning, and the 3D reconstruction accuracy. Optimizing dental precision by Integrating CBCT scans with other imaging modalities, supply more information to enhance CBCT accuracy, mainly in dental areas with limited clarity. Performing the Structure-from-Motion (SfM) photogrammetry method, using phone camera and photograph studio setup using simple hardware, to digitize the dental casts and obtain an accurate digital dental model. Using this digital dental model to enhance dental precision in the CBCT data by performing the superimposition process, using a surface-based registration method and integration process to create a virtual dentoskeletal model. Evaluate the accuracy and quality of the superimposition results using qualitative (visual inspection) and quantitative measures. The differences between the virtual dentoskeletal model and the reference CBCT model are calculating by the 3D Euclidean distance, the mean ± SD are 0.212 ± 0.169mm and 0.26 ± 0.149mm for the maxilla and mandible, respectively. The color-coded map shows that the two surfaces are similar, but the extremist values are concentrated in the dental region due to the presence of the noise in the reference model and the gingiva in the virtual dentoskeletal model. The resulting virtual dentoskeletal model can be viewed and manipulated on a computer screen, allowing for a detailed analysis of the teeth and supporting structures. The 3D model generated by the SfM photogrammetry technique did well during the superimposition process, representing a reliable method for virtual-based processing such as orthognathic surgery planning and splint design.

Reliability of Artificial Intelligence-based Cone Beam Computed Tomography Integration with Digital Dental Images.

This study aimed to introduce cone-beam computed tomography (CBCT) digitization and integration of digital dental images (DDI) based on artificial intelligence (AI)-based registration (ABR) and to evaluate the reliability and reproducibility using this method compared with those of surface-based registration (SBR). This retrospective study consisted of CBCT images and DDI of 17 patients who had undergone computer-aided bimaxillary orthognathic surgery. The digitization of CBCT images and their integration with DDI were repeated using an AI-based program. CBCT images and DDI were integrated using a point-to-point registration. In contrast, with the SBR method, the three landmarks were identified manually on the CBCT and DDI, which were integrated with the iterative closest points method. After two repeated integrations of each method, the three-dimensional coordinate values of the first maxillary molars and central incisors and their differences were obtained. Intraclass coefficient (ICC) testing was performed to evaluate intra-observer reliability with each method's coordinates and compare their reliability between the ABR and SBR. The intra-observer reliability showed significant and almost perfect ICC in each method. There was no significance in the mean difference between the first and second registrations in each ABR and SBR and between both methods; however, their ranges were narrower with ABR than with the SBR method. This study shows that AI-based digitization and integration are reliable and reproducible.

Evaluation of surface-based hippocampal registration using ground-truth subfield definitions

The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches.

Evaluation of surface-based hippocampal registration using ground-truth subfield definitions.

The hippocampus is an archicortical structure, consisting of subfields with unique circuits. Understanding its microstructure, as proxied by these subfields, can improve our mechanistic understanding of learning and memory and has clinical potential for several neurological disorders. One prominent issue is how to parcellate, register, or retrieve homologous points between two hippocampi with grossly different morphologies. Here, we present a surface-based registration method that solves this issue in a contrast-agnostic, topology-preserving manner. Specifically, the entire hippocampus is first analytically unfolded, and then samples are registered in 2D unfolded space based on thickness, curvature, and gyrification. We demonstrate this method in seven 3D histology samples and show superior alignment with respect to subfields using this method over more conventional registration approaches.

Contactless surface registration of featureless anatomy using structured light camera: application to fibula navigation in mandible reconstruction.

Mandibular reconstruction using fibula free flap is a challenging surgical procedure. To assist osteotomies, computer-assisted surgery (CAS) can be used. Nevertheless, precise registration is required and often necessitates anchored markers that disturb the patient and clinical flow. This work proposes a new contactless surface-based method adapted to featureless anatomies such as fibula to achieve a fast, precise, and reproducible registration. Preoperatively, a CT-scan of the patient is realized and osteotomies are virtually planned. During surgery, a structured light camera digitizes the fibula. The obtained intraoperative point cloud is coarsely registered with the preoperative model using 3 points defined in the CT-scan and located on the patient's bone with a laser beam. Then, a fine registration is performed using an ICP algorithm. The registration accuracy was evaluated comparing the position of points engraved in a 3D-printed fibula with their position in the registered model and evaluating resulting osteotomies. Accuracy and execution time were compared to a conventional stylus-based registration method. The work was validated in vivo. The experiment performed on a 3D-printed model showed that execution time is equivalent to surface-based registration using a stylus, with a better accuracy (mean TRE of 0.9mm vs 1.3mm using stylus) and guarantee good osteotomies. The preliminary in vivo study proved the feasibility of the method. The proposed contactless surface-based registration method using structured light camera gave promising results in terms of accuracy and execution speed and should be useful to implement CAS for mandibular reconstruction.

Regional-surface-based registration for image-guided neurosurgery: effects of scan modes on registration accuracy.

The conventional surface-based method only registers the facial zone with preoperative point cloud, resulting in low accuracy away from the facial area. Acquiring a point cloud of the entire head for registration can improve registration accuracy in all parts of the head. However, it takes a long time to collect a point cloud of the entire head. It may be more practical to selectively scan part of the head to ensure high registration accuracy in the surgical area of interest. In this study, we investigate the effects of different scan regions on registration errors in different target areas when using a surface-based registration method. We first evaluated the correlation between the laser scan resolution and registration accuracy to determine an appropriate scan resolution. Then, with the appropriate resolution, we explored the effects of scan modes on registration error in computer simulation experiments, phantom experiments and two clinical cases. The scan modes were designed based on different combinations of five zones of the head surface, i.e., the sphenoid-frontal zone, parietal zone, left temporal zone, right temporal zone and occipital zone. In the phantom experiment, a handheld scanner was used to acquire a point cloud of the head. A head model containing several tumors was designed, enabling us to calculate the target registration errors deep in the brain to evaluate the effect of regional-surface-based registration. The optimal scan modes for tumors located in the sphenoid-frontal, parietal and temporal areas are mode 4 (i.e., simultaneously scanning the sphenoid-frontal zone and the temporal zone), mode 4 and mode 6 (i.e., simultaneously scanning the sphenoid-frontal zone, the temporal zone and the parietal zone), respectively. For the tumor located in the occipital area, no modes were able to achieve reliable accuracy. The results show that selecting an appropriate scan resolution and scan mode can achieve reliable accuracy for use in sphenoid-frontal, parietal and temporal area surgeries while effectively reducing the operation time.

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

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

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

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

Biomechanical modeling constrained surface-based image registration for prostate MR guided TRUS biopsy.

Adding magnetic resonance (MR)-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound (US) by using MR-US registration. A common approach is to use surface-based registration. The authors hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular nonrigid surface-based registration method. The authors developed a novel method by extending a nonrigid surface-based registration algorithm with biomechanical finite element (FE) modeling to better predict internal deformations of the prostate. Data were collected from ten patients and the MR and TRUS images were rigidly registered to anatomically align prostate orientations. The prostate was manually segmented in both images and corresponding surface meshes were generated. Next, a tetrahedral volume mesh was generated from the MR image. Prostate deformations due to the TRUS probe were simulated using the surface displacements as the boundary condition. A three-dimensional thin-plate spline deformation field was calculated by registering the mesh vertices. The target registration errors (TREs) of 35 reference landmarks determined by surface and volume mesh registrations were compared. The median TRE of a surface-based registration with biomechanical regularization was 2.76 (0.81-7.96) mm. This was significantly different than the median TRE of 3.47 (1.05-7.80) mm for regular surface-based registration without biomechanical regularization. Biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to a regular nonrigid surface-based registration algorithm and can help to improve the effectiveness of MR guided TRUS biopsy procedures.

Segmenting the Brain Surface from CT Images with Artifacts Using Dictionary Learning for Non-rigid MR-CT Registration.

This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons require accurate registration with pre-implantation functional and structural MR imaging to guide surgical resection of epileptic tissue. In this work, we use a surface-based registration method to align the MR and CT brain surfaces. The key challenge here is not the registration, but rather the extraction of the cortical surface from the CT image, which includes missing parts of the skull and artifacts introduced by the electrodes. To segment the brain from these images, we propose learning a model of appearance that captures both the normal tissue and the artifacts found along this brain surface boundary. Using clinical data, we demonstrate that our method both accurately extracts the brain surface and better localizes electrodes than intensity-based rigid and non-rigid registration methods.

Artifact-Resistant Superimposition of Digital Dental Models and Cone-Beam Computed Tomography Images

Combining the maxillofacial cone-beam computed tomography (CBCT) model with its corresponding digital dental model enables an integrated 3-dimensional (3D) representation of skeletal structures, teeth, and occlusions. Undesired artifacts, however, introduce difficulties in the superimposition of both models. We have proposed an artifact-resistant surface-based registration method that is robust and clinically applicable and that does not require markers. A CBCT bone model and a laser-scanned dental model obtained from the same patient were used in developing the method and examining the accuracy of the superimposition. Our method included 4 phases. The first phase was to segment the maxilla from the mandible in the CBCT model. The second phase was to conduct an initial registration to bring the digital dental model and the maxilla and mandible sufficiently close to each other. Third, we manually selected at least 3 corresponding regions on both models by smearing patches on the 3D surfaces. The last phase was to superimpose the digital dental model into the maxillofacial model. Each superimposition process was performed twice by 2 operators with the same object to investigate the intra- and interoperator differences. All collected objects were divided into 3 groups with various degrees of artifacts: artifact-free, critical artifacts, and severe artifacts. The mean errors and root-mean-square (RMS) errors were used to evaluate the accuracy of the superimposition results. Repeated measures analysis of variance and the Wilcoxon rank sum test were used to calculate the intraoperator reproducibility and interoperator reliability. Twenty-four maxilla and mandible objects for evaluation were obtained from 14 patients. The experimental results showed that the mean errors between the 2 original models in the residing fused model ranged from 0.10 to 0.43 mm and that the RMS errors ranged from 0.13 to 0.53 mm. These data were consistent with previously used methods and were clinically acceptable. All measurements of the proposed study exhibited desirable intraoperator reproducibility and interoperator reliability. Regarding the intra- and interoperator mean errors and RMS errors in the nonartifact or critical artifact group, no significant difference between the repeated trials or between operators (P < .05) was observed. The results of the present study have shown that the proposed regional surface-based registration can robustly and accurately superimpose a digital dental model into its corresponding CBCT model.

Surface-Based CT–TEE Registration of the Aortic Root

Transcatheter aortic valve implantation (TAVI) is a minimally invasive alternative to conventional aortic valve replacement for severe aortic stenosis in high-risk patients in which a stent-based bioprosthetic valve is delivered into the heart via a catheter. TAVI relies largely on single-plane fluoroscopy for intraoperative navigation and guidance, which provides only gross imaging of anatomical structures. Inadequate imaging leading to suboptimal valve positioning contributes to many of the early complications experienced by TAVI patients, including valve embolism, coronary ostia obstruction, paravalvular leak, heart block, and secondary nephrotoxicity from excessive contrast use. Improved visualization can be provided using intraoperative registration of a CT-derived surface to transesophageal echo (TEE) images. In this study, the accuracy and robustness of a surface-based registration method suitable for intraoperative use are evaluated, and the performances of different TEE surface extraction methods are compared. The use of cross-plane TEE contours demonstrated the best accuracy, with registration errors of less than 5 mm. This guidance system uses minimal intraoperative interaction and workflow modification, does not require tool calibration or additional intraoperative hardware, and can be implemented at all cardiac centers at extremely low cost.

Automated landmark identification for human cortical surface-based registration

Volume-based registration (VBR) is the predominant method used in human neuroimaging to compensate for individual variability. However, surface-based registration (SBR) techniques have an inherent advantage over VBR because they respect the topology of the convoluted cortical sheet. There is evidence that existing SBR methods indeed confer a registration advantage over affine VBR. Landmark-SBR constrains registration using explicit landmarks to represent corresponding geographical locations on individual and atlas surfaces. The need for manual landmark identification has been an impediment to the widespread adoption of Landmark-SBR. To circumvent this obstacle, we have implemented and evaluated an automated landmark identification (ALI) algorithm for registration to the human PALS-B12 atlas. We compared ALI performance with that from two trained human raters and one expert anatomical rater (ENR). We employed both quantitative and qualitative quality assurance metrics, including a biologically meaningful analysis of hemispheric asymmetry. ALI performed well across all quality assurance tests, indicating that it yields robust and largely accurate results that require only modest manual correction (<10 min per subject). ALI largely circumvents human error and bias and enables high throughput analysis of large neuroimaging datasets for inter-subject registration to an atlas.

A multi-resolution scheme for distortion-minimizing mapping between human subcortical structures based on geodesic construction on Riemannian manifolds

In this paper, we deal with a subcortical surface registration problem. Subcortical structures including hippocampi and caudates have a small number of salient features such as heads and tails unlike cortical surfaces. Therefore, it is hard, if not impossible, to perform subcortical surface registration with only such features. It is also non-trivial for neuroanatomical experts to select landmarks consistently for subcortical surfaces of different subjects. We therefore present a landmark-free approach for subcortical surface registration by measuring the amount of mesh distortion between subcortical surfaces assuming that the surfaces are represented by meshes. The input meshes can be constructed using any surface modeling tool available in the public domain since our registration method is independent of a surface modeling process. Given the source and target surfaces together with their representing meshes, the vertex positions of the source mesh are iteratively displaced while preserving the underlying surface shape in order to minimize the distortion to the target mesh. By representing each surface mesh as a point on a high-dimensional Riemannian manifold, we define a distance metric on the manifold that measures the amount of distortion from a given source mesh to the target mesh, based on the notion of isometry while penalizing triangle flipping. Under this metric, we reduce the distortion minimization problem to the problem of constructing a geodesic curve from the moving source point to the fixed target point on the manifold while satisfying the shape-preserving constraint. We adopt a multi-resolution framework to solve the problem for distortion-minimizing mapping between the source and target meshes. We validate our registration scheme through several experiments: distance metric comparison, visual validation using real data, robustness test to mesh variations, feature alignment using anatomic landmarks, consistency with previous clinical findings, and comparison with a surface-based registration method, LDDMM-surface.

Evaluation of intersession 3D‐TRUS to 3D‐TRUS image registration for repeat prostate biopsies

3D-TRUS-guided prostate biopsy permits a 3D record of biopsy cores, supporting the planning of targets to resample or avoid during repeat biopsy sessions. Image registration is required in order to map biopsy targets planned on a previous session's 3D-TRUS image into the context of the current session. The authors evaluated the performance of surface- and intensity-based rigid and nonrigid registration algorithms for this task using a clinically motivated success criterion of a maximum 2.5 mm target registration error (TRE). The authors collected two 3D-TRUS images for each of 13 patients, where each image was collected in a separate biopsy session, and the sessions were 1 week apart. The authors tested the iterative closest point and thin-plate spline surface-based registration methods, and the block matching and B-spline intensity-based methods. Manually marked intrinsic fiducials (calcifications) were used to calculate a TRE for each of the tested methods. In addition, error ellipsoids, anisotropy, and variability due to image segmentation were analyzed. All analysis was performed separately for the peripheral zone since this area harbors up to 80% of all prostate cancer. Only the intensity-based nonrigid registration method met the success criterion for both the whole gland and the peripheral zone. Segmentation was a substantial contributor to registration error variability for the surface-based methods, and the surface-based methods resulted in greater error volumes and anisotropy. Intensity-based rigid registration is clinically sufficient to register regions outside the peripheral zone, but nonrigid registration is required in order to register the peripheral zone with clinically needed accuracy. The clinical advantage of using nonrigid registration is questionable since the difference between the RMS TREs for rigid and nonrigid intensity-based registration could be considered to be small (0.3 mm) and is statistically significant. If the added clinical value in performing a nonrigid registration is insufficient given the additional time required for this computation, rigid registration alone may be suitable.

Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation.

A successful surface-based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information to surgeons and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperative presentation. The surface based registration is then performed via a traditional iterative closest point (ICP) algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intraoperatively acquired point cloud of the liver surface provided by a laser range scanner. Using this more conventional method, the registration accuracy can be compromised by poor initial pose estimation as well as tissue deformation due to the laparotomy and liver mobilization performed prior to tumor resection. In order to increase the robustness of the current surface-based registration method used in IGLS, we propose the incorporation of salient anatomical features, identifiable in both the preoperative image sets and intraoperative liver surface data, to aid in the initial pose estimation and play a more significant role in the surface-based registration via a novel weighting scheme. Examples of such salient anatomical features are the falciform groove region as well as the inferior ridge of the liver surface. In order to validate the proposed weighted patch registration method, the alignment results provided by the proposed algorithm using both single and multiple patch regions were compared with the traditional ICP method using six clinical datasets. Robustness studies were also performed using both phantom and clinical data to compare the resulting registrations provided by the proposed algorithm and the traditional method under conditions of varying initial pose. The results provided by the robustness trials and clinical registration comparisons suggest that the proposed weighted patch registration algorithm provides a more robust method with which to perform the image-to-physical space registration in IGLS. Furthermore, the implementation of the proposed algorithm during surgical procedures does not impose significant increases in computation or data acquisition times.

Accuracy evaluation of surface-based registration methods in a computer navigation system for hip surgery performed through a posterolateral approach.

Many computer navigation systems have recently been developed for brain surgery, and the use of such systems in orthopedic surgery is increasing. Intraoperative registration of preoperative images is one of the most important steps in controlling the overall accuracy of computer navigation systems. Various parameters, such as CT-scan slice thickness, reconstruction pitch, intraoperative data sampling area, and data sampling volume, may affect the accuracy of registration. The purpose of this study was to evaluate the effect of the aforementioned parameters on the accuracy of registration for hip surgery performed through a posterolateral approach, and to find a clinically suitable trade-off between accuracy and surgical invasiveness. One cadaveric pelvis and one cadaveric femur were used for this study. Four alumina ceramic balls with a diameter of 28 mm and within 1 micrometer of sphericity were attached to the pelvis, and three similar balls attached to the femur, to determine relative position. CT-scan images of the pelvis and femur were obtained with a helical scanner. Three sets of slice thickness and slice pitch were chosen for data acquisition, and two additional sets of reconstructed data were made. Bone contours were extracted by cutting out the surrounding substrate at a given CT number threshold, and surface models of the bone were made from the resultant data. The positions of the pelvis and femur were tracked by LED markers attached to the bone using an optical three-dimensional position sensor (OPTOTRAK). Registration of the computer models to the real objects was performed by measuring the position of a certain number of surface points on each object with an OPTOTRAK pen-probe. Slice thickness and reconstruction pitch affected the accuracy of registration. As the sampling area was expanded from the periarticular area to the distant peripheral area, accuracy increased slightly. Accuracy did not increase when the whole area was used, but in fact decreased, especially in the femur. The positive effect of increasing the number of sampling points was saturated at 30 points when the surface of the periarticular area was sampled. The following trade-off between accuracy and invasiveness, in terms of various parameters of preoperative and intraoperative data, is proposed as clinically optimal: perform the CT scan with 3-mm slice thickness and 1-mm reconstruction pitch, and sample the periarticular area with 30 sampling points. With these parameters, the accuracy of registration was 1.2 mm and 0.9 degrees of bias with 0.7 mm and 0.3 degrees of RMS in the pelvis, and 1.4 mm and 0.6 degrees of bias with 1.3 mm and 0.3 degrees of RMS in the femur.

Accuracy Evaluation of Surface-Based Registration Methods in a Computer Navigation System for Hip Surgery Performed Through a Posterolateral Approach

Many computer navigation systems have recently been developed for brain surgery, and the use of such systems in orthopedic surgery is increasing. Intraoperative registration of preoperative images is one of the most important steps in controlling the overall accuracy of computer navigation systems. Various parameters, such as CT-scan slice thickness, reconstruction pitch, intraoperative data sampling area, and data sampling volume, may affect the accuracy of registration. The purpose of this study was to evaluate the effect of the aforementioned parameters on the accuracy of registration for hip surgery performed through a posterolateral approach, and to find a clinically suitable trade-off between accuracy and surgical invasiveness.Materials and Methods: One cadaveric pelvis and one cadaveric femur were used for this study. Four alumina ceramic balls with a diameter of 28 mm and within 1 micrometer of sphericity were attached to the pelvis, and three similar balls attached to the femur, to determine relative position. CT-scan images of the pelvis and femur were obtained with a helical scanner. Three sets of slice thickness and slice pitch were chosen for data acquisition, and two additional sets of reconstructed data were made. Bone contours were extracted by cutting out the surrounding substrate at a given CT number threshold, and surface models of the bone were made from the resultant data. The positions of the pelvis and femur were tracked by LED markers attached to the bone using an optical three-dimensional position sensor (OPTOTRAK). Registration of the computer models to the real objects was performed by measuring the position of a certain number of surface points on each object with an OPTOTRAK pen-probe.Results and Conclusion: Slice thickness and reconstruction pitch affected the accuracy of registration. As the sampling area was expanded from the periarticular area to the distant peripheral area, accuracy increased slightly. Accuracy did not increase when the whole area was used, but in fact decreased, especially in the femur. The positive effect of increasing the number of sampling points was saturated at 30 points when the surface of the periarticular area was sampled. The following trade-off between accuracy and invasiveness, in terms of various parameters of preoperative and intraoperative data, is proposed as clinically optimal: perform the CT scan with 3-mm slice thickness and 1-mm reconstruction pitch, and sample the periarticular area with 30 sampling points. With these parameters, the accuracy of registration was 1.2 mm and 0.9 degrees of bias with 0.7 mm and 0.3 degrees of RMS in the pelvis, and 1.4 mm and 0.6 degrees of bias with 1.3 mm and 0.3 degrees of RMS in the femur.