Coherent Point Drift Algorithm Research Articles

MRI non-rigid registration with tumor contraction correction for ablative margin assessment after microwave ablation of hepatocellular carcinomas.

This study aims to develop and assess a tumor contraction model, enhancing the precision of ablative margin(AM) evaluation after microwave ablation (MWA) treatment for hepatocellular carcinomas (HCCs). 
Approach: We utilize a probabilistic method called the Coherent Point Drift algorithm to align pre-and post-ablation MRI images. Subsequently, a nonlinear regression method quantifies local tumor contraction induced by MWA, utilizing data from 47 HCC with viable ablated tumors in post-ablation MRI. After automatic non-rigid registration, correction for tumor contraction involves contracting the 3D contour of the warped tumor towards its center in all orientations.
Main Results: We evaluate the performance of our proposed method on 30 HCC patients who underwent MWA. The Dice similarity coefficient between the post-ablation liver and the warped pre-ablation livers is found to be 0.95±0.01, with a mean corresponding distance between the corresponding landmarks measured at 3.25±0.62mm. Additionally, we conduct a comparative analysis of clinical outcomes assessed through MRI over a 3-month follow-up period, noting that the AM, as evaluated by our proposed method, accurately detects residual tumor after MWA.
Significance: Our proposed method showcases a high level of accuracy in MRI liver registration and AM assessment following ablation treatment. It introduces a potentially approach for predicting incomplete ablations and gauging treatment success.

Macroscopic differences in adult human femora are linked to body mass index.

Bone functional adaptation is routinely invoked to interpret skeletal morphology despite ongoing debate regarding the limits of the bone response to mechanical stimuli. The wide variation in human body mass presents an opportunity to explore the relationship between mechanical load and skeletal response in weight-bearing elements. Here, we examine variation in femoral macroscopic morphology as a function of body mass index (BMI), which is used as a metric of load history. A sample of 80 femora (40 female; 40 male) from recent modern humans was selected from the Texas State University Donated Skeletal Collection. Femora were imaged using x-ray computed tomography (voxel size ~0.5 mm), and segmented to produce surface models. Landmark-based geometric morphometric analyses based on the Coherent Point Drift algorithm were conducted to quantify shape. Principal components analyses were used to summarize shape variation, and component scores were regressed on BMI. Within the male sample, increased BMI was associated with a mediolaterally expanded femoral shaft, as well as increased neck-shaft angle and decreased femoral neck anteversion angle. No statistically significant relationships between shape and BMI were found in the female sample. While mechanical stimulus has traditionally been applied to changes in long bong diaphyseal shape it appears that bone functional adaptation may also result in fundamental changes in the shape of skeletal elements.

MINING OF BIOMECHANICAL AND GEOMETRY DATA OF INTERVERTEBRAL DISC FINITE ELEMENT SIMULATIONS

This study investigates the relationships between Intervertebral Disc (IVD) morphology and biomechanics using patient-specific (PS) finite element (FE) models and poromechanical simulations.169 3D lumbar IVD shapes from the European project MySpine (FP7-269909), spanning healthy to Pfirrmann grade 4 degeneration, were obtained from MRIs. A Bayesian Coherent Point Drift algorithm aligned meshes to a previously validated structural FE mesh of the IVD. After mesh quality analyses and Hausdorff distance measurements, mechanical simulations were performed: 8 and 16 hours of sleep and daytime, respectively, applying 0.11 and 0.54 MPa of pressure on the upper cartilage endplate (CEP). Simulation results were extracted from the anterior (ATZ) and posterior regions (PTZ) and the center of the nucleus pulposus (CNP). Data mining was performed using Linear Regression, Support Vector Machine, and eXtreme Gradient Boosting techniques. Mechanical variables of interest in DD, such as pore fluid velocity (FLVEL), water content, and swelling pressure, were examined. The morphological variables of the simulated discs were used as input features.Local morphological variables significantly impacted the local mechanical response. The local disc heights, respectively in the mid (mh), anterior (ah), and posterior (ph) regions, were key factors in general. Additionally, fluid transport, reflected by FLVEL, was greatly influenced (r2 0.69) by the shape of the upper and lower cartilage endplates (CEPs).This study suggests that disc morphology affects Mechanical variables of interest in DD. Attention should be paid to the antero-posterior distribution and local effects of disc heights. Surprisingly, the CEP morphology remotely affected the fluid transport in NP volumes around mid-height, and mechanobiological implications shall be explored. In conclusion, patient-specific IVD modeling has strong potential to unravel important correlations between IVD phenotypes and local tissue regulation.Acknowledgments: European Commission: Disc4All-MSCA-2020-ITN-ETN GA: 955735; O-Health-ERC-CoG-2021-101044828

Computer vision application for industrial Li-ion battery module disassembly

Automated robot-assisted disassembly is essential for the flexible disassembly of Li-ion battery modules for economic and safety reasons. In such a case, a CAD model for the planning process is of immense benefit. The geometric uncertainties due to the breathing of the Li-ion cells as well as the presence of component tolerances underline the importance of a sensor-based detection approach to determine the actual state of the battery module, which is crucial to ensure an automated and reliable disassembly process. In this paper, we present a method for 3D camera-based localization of points on deformed battery modules, aiding in identifying support points for milling operations in robot-assisted disassembly cells. This separation operation planning employs a CAD model, and our introduced computer vision “data processing pipeline”—a systematic series of processing steps—bridges the gap between the CAD model and the actual battery module. This involves capturing the module using a 3D camera and subsequently registering its points with the CAD model’s points. Central to this process are two algorithms: The Bayesian Coherent Point Drift (BCPD) algorithm ensures accurate non-rigid registration, while TEASER++ aids in reducing computational time. We demonstrate the effectiveness of these combined algorithms in our pipeline through rigorous testing and metrics, evidencing that a balance between accuracy and computational speed can be attained by adjusting point density.

Advancements in total knee arthroplasty kinematics: 3D implant computer aided design model creation through X-ray or fluoroscopic images

Background3D-to-2D fluoroscopic registration is a popular and important step for analyzing total-knee-arthroplasty weight-bearing kinematics. Unfortunately, in vivo analyses using these techniques cannot be completed if the associated computer-aided design implant models are not available. This study introduces a novel method that enables the accessible computation of knee replacement patients' kinematics from fluoroscopy, achieved through the reconstruction of 3-dimensional knee component models using a limited set of 2-dimensional X-ray or fluoroscopic images. MethodsThe proposed non-rigid morphing algorithm, based on the coherent point drift algorithm, scales and transforms the shape of the template model to fit with the silhouette of the corresponding fluoroscopic images without changing the structure of the knee implant. While a greater number of fluoroscopic images can lead to higher accuracy, our study utilizes only 4 images. FindingsThe morphed models show excellent results in comparison with known models with a 0.52 mm average root-mean-square error and a 2.82 mm largest source error for 17 tested knee models of various implant types. The proposed algorithm also enables direct output of patient kinematics using fluoroscopy, with an average error of only 0.54 ± 0.42 mm for femorotibial contact and 0.86 ± 0.34 degrees for axial rotation. InterpretationA novel methodology was introduced to overcome common 3-dimentional to 2-dimensional registration limitations by recreating entire families of 3 dimensional models from a limited number of fluoroscopic images for both cruciate-retaining and posterior-stabilized knee replacement implants. Our algorithm has demonstrated high levels of accuracy with multiple potential extended applications.

Automatic generation of subject-specific finite element models of the spine from magnetic resonance images.

The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93-0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.

Improving Estimation of Tree Parameters by Fusing ALS and TLS Point Cloud Data Based on Canopy Gap Shape Feature Points

Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) are two ways to obtain forest three-dimensional (3D) spatial information. Due to canopy occlusion and the features of different scanning methods, some of the forest point clouds acquired by a single scanning platform may be missing, resulting in an inaccurate estimation of forest structure parameters. Hence, the registration of ALS and TLS point clouds is an alternative for improving the estimation accuracy of forest structure parameters. Currently, forest point cloud registration is mainly conducted based on individual tree attributes (e.g., location, diameter at breast height, and tree height), but the registration is affected by individual tree segmentation and is inefficient. In this study, we proposed a method to automatically fuse ALS and TLS point clouds by using feature points of canopy gap shapes. First, the ALS and TLS canopy gap boundary vectors were extracted by the canopy point cloud density model, and the turning or feature points were obtained from the canopy gap vectors using the weighted effective area (WEA) algorithm. The feature points were then aligned, the transformation parameters were solved using the coherent point drift (CPD) algorithm, and the TLS point clouds were further aligned using the recovery transformation matrix and refined by utilizing the iterative closest point (ICP) algorithm. Finally, individual tree segmentations were performed to estimate tree parameters using the TLS and fusion point clouds, respectively. The results show that the proposed method achieved more accurate registration of ALS and TLS point clouds in four plots, with the average distance residuals of coarse and fine registration of 194.83 cm and 2.14 cm being much smaller compared with those from the widely used crown feature point-based method. Using the fused point cloud data led to more accurate estimates of tree height than using the TLS point cloud data alone. Thus, the proposed method has the potential to improve the registration of ALS and TLS point cloud data and the accuracy of tree height estimation.

A fast methodology for generating skeletal FEM with detailed human geometric features based on CPD and RBF algorithms

Due to the significant effects of the human anatomical characteristics on the injury mechanism of passenger in traffic accidents, it is necessary to develop human body FEM (Finite Element Model) with detailed anatomical characteristics. However, traditional development of a human body FEM is an extremely complicated process. In particular, the meshing of human body is a huge and time-consuming project. In this paper, a new fast methodology based on CPD (Coherent Point Drift) and RBF (Radial Basis Function) was proposed to achieve the rapid developing the FEM of human bone with detailed anatomical characteristics. In this methodology, the mesh morphing technology based the RBF was used to generate FEM mesh in the geometry extracted from the target CT (Computed Tomography) data. In order to further improve the accuracy and speed of mesh morphing, the target geometric feature points required in the mesh morphing process were realized via the rapid and automatic generation based on the point-cloud registration technology of the CPD algorithm. Finally, this new methodology was used to generate a 3-year-old ribcage FEM consisting of a total of 27,728 elements with mesh size 3–5 mm based on the THUMS (Total Human Model for Safety) adult model. In the entire process of generating this new ribcage model, it only took about 2.7 s. The average error between the new FEM and target geometries was only about 2.7 mm. This indicated that the new FEM well described the detailed anatomical characteristics of target geometry, thus importantly revealing that the mesh quality of the new FEM was basically similar to that of source FEM.

An Accurate Point Set Matching Method for Hull Blocks With Engineering Constraints

Abstract To effectively improve the precision matching technique of measurement point set and design point set for hull blocks, an accurate point set matching method for hull blocks with engineering constraints (verticality, levelness, symmetry, flatness, etc.) and elimination error points is presented. It is divided into initial matching and refine matching. In initial matching, the improved Random Sample Consensus algorithm is proposed to eliminate error points rapidly and accurately; the improved Coherent Point Drift algorithm is proposed to obtain more accurate initial matching. In refine matching, the Analytic Hierarchy Process is used to obtain each weight of engineering constraints automatically, and the weight vector is introduced into the multi-optimization objective function to achieve the more reasonable matching results. The results proved that this method can rapidly and automatically eliminate error points, and get more accurate and reasonable results meeting engineering constraints. It can provide the basis for the subsequent assembly of hull blocks.

Spatial Perception of Tagged Cargo Using Fused RFID and CV Data in Intelligent Storage

Radio-frequency identification (RFID) and computer vision (CV) technology are widely employed in intelligent storage systems for sensing, locating, identifying, and monitoring storage cargo. However, both of them have applicability scenarios and limitations. In this article, we propose a system for spatial perception of storage cargo based on the fusion of RFID and CV data. Specifically, we employ a mobile robot carrying an RFID reader and an RGB camera to move between shelves. The reader is connected to two vertically deployed antennas that receive phase values from the tags on the cargo to perform phase unwrapping. Then, we construct a linear system of equations to solve synthetic aperture radar (SAR)-based 3-D localization to obtain the location of the target tag efficiently and accurately. For the images of the shelves captured by the RGB camera, we use image localization techniques based on color and texture features to obtain the pixel coordinates of the cargo in the image. Since the sampling range of the RFID reader antenna is larger than the shooting range of the RGB camera, we use the coherent point drift (CPD) algorithm with threshold judgment to match the localization results of the two subsystems, enabling us to display the inherent information of the target cargo on the plane rendering. Our proposed system is evaluated through various experiments, and the results show that our localization algorithm achieves high accuracy in 3-D space, and the matching algorithm has high robustness to the number of cargo and missing cargo or tags.

Bidirectional prediction of facial and bony shapes for orthognathic surgical planning.

This paper proposes a deep learning framework to encode subject-specific transformations between facial and bony shapes for orthognathic surgical planning. Our framework involves a bidirectional point-to-point convolutional network (P2P-Conv) to predict the transformations between facial and bony shapes. P2P-Conv is an extension of the state-of-the-art P2P-Net and leverages dynamic point-wise convolution (i.e., PointConv) to capture local-to-global spatial information. Data augmentation is carried out in the training of P2P-Conv with multiple point subsets from the facial and bony shapes. During inference, network outputs generated for multiple point subsets are combined into a dense transformation. Finally, non-rigid registration using the coherent point drift (CPD) algorithm is applied to generate surface meshes based on the predicted point sets. Experimental results on real-subject data demonstrate that our method substantially improves the prediction of facial and bony shapes over state-of-the-art methods.

Vein pattern recognition based on RGB images using Monte Carlo simulation and ridge tracking.

In forensic investigations, images of evidence can often be obtained from crimes such as child pornography and masked violent riots. However, identifying criminals is usually very difficult and sometimes impossible because these images usually contain skin of body parts, while their faces and other commonly used biometrics are unavailable. Vein patterns are a potential biometric to solve this problem. Traditional systems use near-infrared (NIR) imaging technologies to obtain vein patterns, which cannot be applied to forensic analysis since only RGB images are available. However, veins are unobservable in RGB images. In this paper, a comprehensive scheme including a vein uncovering algorithm, a vein extraction algorithm, and a vein pattern matching algorithm is presented. Based on the Monte Carlo (MC) simulation of light transmission in a skin optical model, physical parameters corresponding to different skin colors are obtained, and vein patterns are uncovered from the parameter distribution images. After preprocessing with cubic convolution and Gabor filtering, vein lines are extracted based on ridge tracking. Local gradient orientation and the geometric direction of veins are utilized to guarantee the correct tracking direction. Hessian-based Frangi filters are adopted to locate potential veins. In the matching step, effective minutiae are extracted to represent the topology of vein patterns. A modified coherent point drift (CPD) algorithm is proposed utilizing coordinates, Gabor energy values, and curvatures of minutiae to match vein patterns. Comprehensive experiments were carried out to evaluate the proposed three algorithms. Experimental results show the superiority of the proposed algorithms to various state-of-the-art methods.

A FAST assembly simulation analysis method for hull blocks with engineering constraints

In order to effectively improve the assembly efficiency for hull blocks, an assembly simulation analysis method considering engineering constraints is proposed in this paper, and an integrated system of shipbuilding accuracy analysis and assembly analysis considering multi-constraints is developed. The method is divided into pre-matching model and fine matching model. In the pre-matching model, an Improved Coherent Point Drift (ICPD) algorithm is used to obtain more accurate initial matching values. The fine matching model firstly uses the Analytic Hierarchy Process (AHP) algorithm to automatically obtain the constrained weights, then the weights vector is used to add the assembly constraints for hull blocks such as straightness, hard point constraints etc. into the multi-objective optimization function. By solving the function, the optimum positioning location and the most reasonable adjustment scheme are obtained. This method shortens the occupancy time of the equipment used to build the hull, reduces the workload of the staff, and improves the efficiency and quality of shipbuilding. The integrated system adds engineering constraints analysis module and the function of automatically finding and eliminating error measurement points. Through the verification of the examples, the integrated system realizes the automation and intellectualization of the assembly for hull blocks.

Visible and infrared missile-borne image registration based on improved SIFT and joint features

For the acquired visible and infrared missile-borne images, in order to provide accurate and reliable image data for the subsequent processing of images to achieve complementary information, we carried out visible and infrared missile-borne image registration research. Firstly, the shape context is used to extract the rough edge features of the image. Then we improve the SIFT gradient definition to overcome the difference in image gray value, and combine with the coherent point drift algorithm to increase the number of correct matching points to achieve global image registration. Experimental results show that, compared with the existing registration methods, the proposed method has a good visual effect of registration, greatly improves the correct registration points and registration accuracy, and can better solve the problem of visible infrared missile-borne image registration.

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.

Augmented reality navigation for liver surgery: an enhanced coherent point drift algorithm based hybrid optimization scheme

Augmented reality navigation for liver surgery: an enhanced coherent point drift algorithm based hybrid optimization scheme

Approximating the maximum tibial coverage in total knee arthroplasty does not necessarily result in implant malrotation

Traditionally, the practice of the tibial component placement in total knee arthroplasty has focused on achieving maximum coverage without malrotation. However, the concept of maximizing coverage has not been well defined or researched and yet biased results are often produced. This study aimed to evaluate the effect of a prioritizing maximum coverage positioning strategy on the rotational alignment by using a strict computer algorithm. Computed tomographic scans of 103 tibial specimens were used to reconstruct three-dimensional tibia models. A virtual surgery was performed to generate the resection plane with a posterior slope of 7° on the proximal tibia. Symmetrical and anatomical tibial components were placed and analyzed with an automated program designed for approximating the maximum coverage based on the coherent point drift algorithm. We found that the average tibial coverage achieved across all specimens and implants was 85.62 ± 3.65%, ranging from 83.64 ± 4.10% to 86.69 ± 3.07%. When placed for maximal tibial coverage, the mean degree of rotation related to the Insall line was − 0.73° ± 4.53° for all subjects, 23% of the tibial components were malrotated. The average percentage position of the baseplate anteroposterior axis over the patellar tendon was 26.95 ± 14.71% from the medial edge. These results suggest that with specific design and proper placement of the component, approximating the maximum tibial coverage in total knee arthroplasty does not necessarily result in implant malrotation. The current tibial baseplates have shown good performance on the coverage when aligned parallel to the Insall line with the anteroposterior axis positioned between the medial 1/3 and medial 1/6 of the patella tendon.

Assessment and application of the coherent point drift algorithm to augmented reality surgical navigation for laparoscopic partial nephrectomy.

The surface-based registration approach to laparoscopic augmented reality (AR) has clear advantages. Nonrigid point-set registration paves the way for surface-based registration. Among current non-rigid point set registration methods, the coherent point drift (CPD) algorithm is rarely used because of two challenges: (1) volumetric deformation is difficult to predict, and (2) registration from intraoperative visible tissue surface to whole anatomical preoperative model is a "part-to-whole" registration that CPD cannot be applied directly to. We preliminarily applied CPD on surgical navigation for laparoscopic partial nephrectomy (LPN). However, it introduces normalization errors and lacks navigation robustness. This paper presents important advances for more effectively applying CPD to LPN surgical navigation while attempting to quantitatively evaluate the accuracy of CPD-based surgical navigation. First, an optimized volumetric deformation (Op-VD) algorithm is proposed to achieve accurate prediction of volume deformation. Then, a projection-based partial selection method is presented to conveniently and robustly apply the CPD to LPN surgical navigation. Finally, kidneys with different deformations in vitro, phantom and in vivo experiments are performed to evaluate the accuracy and effectiveness of our approach. The average root-mean-square error of volume deformation was refined to 0.84 mm. The mean target registration error (TRE) of the surface and inside markers in the in vitro experiments decreased to 1.51 mm and 1.29 mm, respectively. The robustness and precision of CPD-based navigation were validated in phantom and in vivo experiments, and the mean navigation TRE of the phantom experiments was found to be [Formula: see text] mm. Accurate volumetric deformation and robust navigation results can be achieved in AR navigation of LPN by using surface-based registration with CPD. Evaluation results demonstrate the effectiveness of our proposed methods while showing the clinical application potential of CPD. This work has important guiding significance for the application of the CPD in laparoscopic AR.

Three-dimensional facial volume analysis using algorithm-based personalized aesthetic templates

Three-dimensional stereophotogrammetry is commonly used to assess volumetric changes after facial procedures. A lack of clear landmarks in aesthetic regions complicates the reproduction of selected areas in sequential images. A three-dimensional volumetric analysis was developed based on a personalized aesthetic template. The accuracy and reproducibility of this method were assessed. Six female volunteers were photographed using the 3dMDtrio system according to a clinical protocol, twice at baseline (T1) and twice after 1year (T2). A styrofoam head was used as control. A standardized aesthetic template was morphed over the baseline images of the volunteers using a coherent point drift algorithm. The resulting personalized template was projected over all sequential images to assess surface area differences, volume differences, and root mean square errors. In 12 well-defined aesthetic areas, mean average surface area and volume differences between the two T1 images ranged from −7.6mm2 to 10.1mm2 and −0.11cm3 to 0.13cm3, respectively. T1 root mean square errors ranged between 0.24mm and 0.62mm (standard deviation 0.18–0.73mm). Comparable differences were found between the T2 images. An increase in volume between T1 and T2 was only observed for volunteers who gained in body weight. Personalized aesthetic templates are an accurate and reproducible method to assess changes in aesthetic areas.

Automatic Reproduction of Natural Head Position Using a Portable 3D Scanner Based on Immediate Calibration

This paper developed a new method to easily record and automatically reproduce the 3D natural head position (NHP) of patients using a portable 3D scanner based on immediate calibration. We first optically scanned the patient’s face using a portable 3D scanner, and the scanned model was easily aligned with the global horizon based on an immediate calibration procedure using a developed calibration plate. The 3D patient NHP Computed Tomography(CT) model was reproduced automatically by performing registration between the CT model and the optically scanned model in the NHP using a modified coherent point drift (CPD) algorithm. In a phantom experiment, we evaluated the developed method’s accuracy using the error between the true and the calculated orientations in roll, pitch, and yaw directions. The mean difference was −0.05 ± 0.13°, 0.08 ± 0.22°, and −0.05 ± 0.18° in the roll, pitch, and yaw directions, respectively. The measured roll, pitch, and yaw directions were not significantly different from the true directions (p > 0.05). The calibration procedure for aligning the scanner coordinate system was easy enough for an inexperienced user to operate, and the 3D NHP CT model could be reproduced automatically. The developed method could be used for diagnosing and treating orthognathic patients with facial asymmetry accurately and conveniently in dental clinics.