Scanning Device Research Articles

On the conjugate symmetry and sparsity of the harmonic decomposition of parametric surfaces with the randomised Kaczmarz method

The downside of increasing the resolution of surface scanning devices is that the amount of acquired data makes the morphological analysis of the scanned surfaces computationally challenging. This limitation is circumvented by using scalable and non–memory–intensive harmonic expansions. In this paper, the projection of parametric surfaces onto disk and spherical harmonics bases is investigated, and three novel computationally efficient algorithms are proposed based on the randomised Kaczmarz (RK). To boost the computational performance and convergence of the root mean square error (RMSE) of the reconstructed surfaces we exploited the conjugate symmetry property of the harmonic basis functions. Further, the sparsity of the signals is used for estimating the projection coefficients from an undersampled surface. The first algorithm only takes into consideration the conjugate symmetry property for enhancing the convergence of the RMSE. The second algorithm endows the sparse version of the RK algorithm with the conjugate symmetry property. The third algorithm combines the previous two to further accelerate the convergence of the RMSE. The performance of the developed algorithms is tested on three surfaces where we demonstrate that they outperform conventional reconstruction techniques in terms of processing time with comparable precision.

A Comparative Study of Shade-Matching Reproducibility Using an Intraoral Scanner and a Spectrophotometer.

We compared the repeatability of the shade determination of resin composite restorations and acrylic teeth in light and darker shades at baseline and after an aging process through two digital tooth color-matching methods: using a Trios 3Shape intraoral scanner and using a Vita Easyshade Compact spectrophotometer. Forty upper central incisor acrylic teeth in the shade A1 (n = 10) and A3 (n = 10) were randomly assigned to be restored with Filtek Bulk Fill in the shade A1 (n = 10) or A3 (n = 10). Subsequently, 20 Class V cavities were prepared in a standardized manner (mesio-distal = 3.0 mm, cervical-occlusal = 2.0 mm, depth = 1.5 mm). Cavities were restored using an universal adhesive system and resin composite in two increments and were light-cured. The shade difference between the resin composite Class V restorations in acrylic teeth of the A1 and A3 shades was evaluated at baseline and after aging. Aging was simulated using ultraviolet light for 120 h. An Easyshade device and an intraoral scanner were used under D65 illumination. Measurements were taken five times, on top of the restoration and on the acrylic teeth, in a randomized manner. Data analysis was on the calculation of the arithmetic mean for the percentage of repeatability conducted by the Trios scanner and the Easyshade device. There was no statistically significant comparison between the shade measurement devices (p > 0.05). At baseline, the repeatability for both the Trios intraoral scanner and the Vita Easyshade Compact device for artificial teeth in the shades A1 and A3 was 100%. After aging, the trueness recorded by the intraoral scanner and the Easyshade device for artificial teeth in the shade A1 was 80%. For Class V restoration with shade A1, the intraoral scanner recorded 80% trueness and the Easyshade device recorded 60% trueness at baseline. For shade A3, the intraoral scanner recorded 60% trueness and the Easyshade device recorded 60% trueness. The intraoral scanner and Easyshade device are reliable for baseline shade selection, but their accuracy decreases after aging, particularly for darker shades.

Semantic segmentation of raw multispectral laser scanning data from urban environments with deep neural networks

Real-time semantic segmentation of point clouds has increasing importance in applications related to 3D city modelling and mapping, automated inventory of forests, autonomous driving and mobile robotics. Current state-of-the-art point cloud semantic segmentation methods rely heavily on the availability of 3D laser scanning data. This is problematic in regards of low-latency, real-time applications that use data from high-precision mobile laser scanners, as those are typically 2D line scanning devices. In this study, we experiment with real-time semantic segmentation of high-density multispectral point clouds collected from 2D line scanners in urban environments using encoder - decoder convolutional neural network architectures. We introduce a rasterized multi-scan input format that can be constructed exclusively from the raw (non-georeferenced profiles) 2D laser scanner measurement stream without odometry information. In addition, we investigate the impact of multispectral data on the segmentation accuracy. The dataset used for training, validation and testing was collected with multispectral FGI AkhkaR4-DW backpack laser scanning system operating at the wavelengths of 905 nm and 1550 nm, and consists in total of 228 million points (39 583 scans). The data was divided into 13 classes that represent various targets in urban environments. The results show that the increased spatial context of the multi-scan format improves the segmentation performance on the single-wavelength lidar dataset from 45.4 mIoU (a single scan) to 62.1 mIoU (24 consecutive scans). In the multispectral point cloud experiments we achieved a 71 % and 28 % relative increase in the segmentation mIoU (43.5 mIoU) as compared to the purely single-wavelength reference experiments, in which we achieved 25.4 mIoU (905 nm) and 34.1 mIoU (1550 nm). Our findings show that it is possible to semantically segment 2D line scanner data with good results by combining consecutive scans without the need for odometry information. The results also serve as motivation for developing multispectral mobile laser scanning systems that can be used in challenging urban surveys.

Bone age assessment based on three-dimensional ultrasound and artificial intelligence compared with paediatrician-read radiographic bone age: protocol for a prospective, diagnostic accuracy study

IntroductionRadiographic bone age (BA) assessment is widely used to evaluate children’s growth disorders and predict their future height. Moreover, children are more sensitive and vulnerable to X-ray radiation exposure than...

A Mathematical Algorithm for Dried Blood Spot Quality Assessment and Results concerning Quality from a Newborn Screening Program.

In addition to newborn screening, dried blood spots (DBSs) are used for a wide variety of analytes for clinical, epidemiological, and research purposes. Guidelines on DBS collection, storage, and transport are available, but it is suggested that each laboratory should establish its own acceptance criteria. An optical scanning device was developed to assess the quality of DBSs received in the newborn screening laboratory from 11 maternity wards between 2013 and 2018. The algorithm was adjusted to agree with the visual examination consensus of experienced laboratory personnel. Once validated, the algorithm was used to categorize DBS specimens as either proper or improper. Improper DBS specimens were further divided based on 4 types of specimen defects. In total, 27 301 DBSs were analyzed. Compared with an annual DBS rejection rate of about 1%, automated scanning rejected 26.96% of the specimens as having at least one defect. The most common specimen defect was multi-spotting (ragged DBS, 19.13%). Among maternity wards, improper specimen rates varied greatly between 5.70% and 49.92%. Improper specimen rates, as well as the dominant type of defect(s), are mainly institution-dependent, with various maternity wards consistently showing specific patterns of both parameters over time. Although validated in agreement with experienced laboratory personnel consensus, automated analysis rejects significantly more specimens. While continuous staff training, specimen quality monitoring, and problem-reporting to maternities is recommended, a thorough quality assessment strategy should also be implemented by every newborn screening laboratory. An important role in this regard may be played by automation in the form of optical scanning devices.

Efficient shrub modelling based on terrestrial laser scanning (TLS) point clouds

ABSTRACT This paper presents a methodology for automatically generating 3D models of individual shrubs from point clouds acquired by Terrestrial Laser Scanning (TLS) devices. The creation of accurate 3D models for shrubs holds significant value for ecological studies. The shrub modelling is more challenging compared to other tree modelling tasks, due to the presence of substantial noise and fuzzy branch structures. To address these challenges, our approach first partitions the input point cloud into two categories: branches and others. This point segmentation process effectively eliminates interference of leaves and noise, thereby enhancing the visibility of branch skeletons. Then, we construct a triangulation network using the branch points and establish a minimum spanning tree (MST) based on this network. Serving as the initial skeleton representation of the plant, the MST preserves the essential topological structures of the branches. Within the extracted MST, we implement a recursive trimming technique to eliminate redundant branches by merging adjacent points and edges, ultimately consolidating the skeleton structure. Finally, we employ an adaptive cylinder fitting algorithm that relies on robust principal component analysis (RPCA) to generate the model. The effectiveness and robustness of the proposed method are demonstrated via experiments on different datasets, with an average fitting error of 1.13 cm, indicating that our approach can achieve high accuracy in modelling individual shrubs.

A scanning device for spatial quantum efficiency measurements of photomultipliers tubes

This paper presents a quantum efficiency measurement setup based on a 2D motorized stage, a wide spectrum xenon lamp, a beam splitter system, and two calibrated photo-diodes for measuring the quantum efficiency (QE) of photomultiplier tubes (1 to 10 inches). We will demonstrate the effectiveness of technical refinements on the measurements procedures over some existing setups already shown in literature. The large area covered by the 2D stages permit to study the quantum efficiency of PMTs with diameter up to ten inches. The results obtained will show the high precision and accuracy in characterizing the quantum efficiency versus wavelength over the range of 250 nm to 1100 nm and along the photo-catode surface. The setup monitors the light intensity synchronously with the output current yield from photosensors under test. This ensures the accuracy and repeatability of the measurements. The motorized stage allows precise positioning of the light source with respect to the active area. The emission spectrum of the xenon lamp provides a broad range of illumination in terms of dynamics and wavelength span.

Switchable Diffraction Pattern Based on Cascaded Metasurfaces

AbstractMetasurfaces have great advantages in the field of laser detection because of their ability to generate large amounts of point sources or various diffraction patterns. Here,cascaded metasurfaces (CM) for generating switchable spatial light distributions is utilized, which can be used for optical scanning and identifications. The light distribution is composed of several distinct diffraction patterns, and the scanning of the diffraction space is realized by multiple pixel‐scale alignment strategies between two pieces of CM. Through combining Dammann optimization and the gradient descent method, various diffraction patterns under different matching situations can be guaranteed by phase superposition. Various diffraction conditions with highly identifiable intensity distribution are guaranteed by using the optimization method. Such optical scanning devices based on CM have small footprints, high integration, flexible composition, and stable diffractive combination features. By manipulating CM to generate changeable spatial diffraction phenomenon may provide flexible modulation properties to optical scanning, measurement, and other applications.

Application of laser scanning thermography and regression analysis to determine characteristics of defects in polymer composite materials

The method of laser point scanning thermography is highly sensitive and allows for reliable detection of surface and subsurface defects in products made of polymer composite materials. When implementing this method, the use of robotic manipulators as a scanning device makes it possible to inspect small objects with a curved surface or to further examine questionable areas identified by other methods. The article provides information about the layout of a robotic complex for laser scanning thermography based on a five-axis robotic manipulator, laser power up to 3 W and wavelength 405 nm, as well as a COX CG640 thermal imager. A technique for processing experimental data has been proposed and regression models have been developed to make it possible to measure the size of defects along the trajectory and determine their location. To test the protocol, a control sample was made from fiberglass laminate, including artificial defects of the “delamination” type, in the form of squares of various sizes. The coefficient of determination R2 of regression models turned out to be no worse than 0.94, the root mean square error of the defect model and the transverse size were no worse than ±0.2 and ±1.5 mm2, respectively.

Strategies for deep learning-based attenuation and scatter correction of brain 18 F-FDG PET images in the image domain.

Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging. In this study, different input settings were considered in the model training to investigate deep learning-based AC in the image space. Three different deep learning methods were developed for direct AC in the image space: (i) use of non-attenuation-corrected PET images as input (NonAC-PET), (ii) use of attenuation-corrected PET images with a simple two-class AC map (composed of soft-tissue and background air) obtained from NonAC-PET images (PET segmentation-based AC [SegAC-PET]), and (iii) use of both NonAC-PET and SegAC-PET images in a Double-Channel fashion to predict ground truth attenuation corrected PET images with Computed Tomography images (CTAC-PET). Since a simple two-class AC map (generated from NonAC-PET images) can easily be generated, this work assessed the added value of incorporating SegAC-PET images into direct AC in the image space. A 4-fold cross-validation scheme was adopted to train and evaluate the different models based using 80 brain 18 F-Fluorodeoxyglucose PET/CT images. The voxel-wise and region-wise accuracy of the models were examined via measuring the standardized uptake value (SUV) quantification bias in different regions of the brain. The overall root mean square error (RMSE) for the Double-Channel setting was 0.157±0.08 SUV in the whole brain region, while RMSEs of 0.214±0.07 and 0.189±0.14 SUV were observed in NonAC-PET and SegAC-PET models, respectively. A mean SUV bias of 0.01±0.26% was achieved by the Double-Channel model regarding the activity concentration in cerebellum region, as opposed to 0.08±0.28% and 0.05±0.28% SUV biases for the network that uniquely used NonAC-PET or SegAC-PET as input, respectively. SegAC-PET images with an SUV bias of -1.15±0.54%, served as a benchmark for clinically accepted errors. In general, the Double-Channel network, relying on both SegAC-PET and NonAC-PET images, outperformed the other AC models. Since the generation of two-class AC maps from non-AC PET images is straightforward, the current study investigated the potential added value of incorporating SegAC-PET images into a deep learning-based direct AC approach. Altogether, compared with models that use only NonAC-PET and SegAC-PET images, the Double-Channel deep learning network exhibited superior attenuation correction accuracy.

Rapid Geometric Evaluation of Transportation Infrastructure Based on a Proposed Low-Cost Portable Mobile Laser Scanning System.

Efficient geometric evaluation of roads and tunnels is crucial to traffic management, especially in post-disaster situations. This paper reports on a study of the geometric feature detection method based on multi-sensor mobile laser scanning (MLS) system data. A portable, low-cost system that can be mounted on vehicles and utilizes integrated laser scanning devices was developed. Coordinate systems and timestamps from numerous devices were merged to create 3D point clouds of objects being measured. Feature points reflecting the geometric information of measuring objects were retrieved based on changes in the point cloud's shape, which contributed to measuring the road width, vertical clearance, and tunnel cross section. Self-developed software was used to conduct the measuring procedure, and a real-time online visualized platform was designed to reconstruct 3D models of the measured objects, forming a 3D digital map carrying the obtained geometric information. Finally, a case study was carried out. The measurement results of several representative nodes are discussed here, verifying the robustness of the proposed system. In addition, the main sources of interference are also discussed.

A Study on Refraction Error Compensation Method for Underwater Spinning Laser Scanning Three-Dimensional Imaging.

Laser scanning 3D imaging technology, because it can obtain accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater spinning laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object's surface. To solve this problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging, and a dynamic refraction mathematical model is established based on the motion of the imaging device. The results of the experiment on error analysis in an optimal underwater environment indicate that the error in reconstructing the radius is decreased by 60% (from 2.5 mm to around 1 mm) when compensating for the measurement data of a standard sphere with a radius of 20 mm. Moreover, the compensated point cloud data exhibit a higher degree of correspondence with the model of the standard spherical point cloud. Furthermore, we examine the impact of physical noise, measurement distance, and partial occlusion of the object on the imaging system inside an authentic underwater setting. This study is a good starting point for looking at the refractive error of an underwater laser scanning imaging system. It also provides to us some ideas for future research on the refractive error of other scanning imaging methods.

A review of rigid point cloud registration based on deep learning.

With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.

Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning with Neural Architecture Search.

Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model. While a centralized training model is effective in mitigating this problem, it raises concerns about privacy protection. Federated learning is a distributed training paradigm that can utilize multi-institutional data for collaborative training without sharing data. However, existing federated learning MR image reconstruction methods rely on models designed manually by experts, which are complex and computationally expensive, suffering from performance degradation when facing heterogeneous data distributions. In addition, these methods give inadequate consideration to fairness issues, namely ensuring that the model's training does not introduce bias towards any specific dataset's distribution. To this end, this paper proposes a generalizable federated neural architecture search framework for accelerating MR imaging (GAutoMRI). Specifically, automatic neural architecture search is investigated for effective and efficient neural network representation learning of MR images from different centers. Furthermore, we design a fairness adjustment approach that can enable the model to learn features fairly from inconsistent distributions of different devices and centers, and thus facilitate the model to generalize well to the unseen center. Extensive experiments show that our proposed GAutoMRI has better performances and generalization ability compared with seven state-of-the-art federated learning methods. Moreover, the GAutoMRI model is significantly more lightweight, making it an efficient choice for MR image reconstruction tasks. The code will be made available at https://github.com/ternencewu123/GAutoMRI.

Comparative Analysis of Algorithms for Laser Line Identification for 3D Scanning Devices

A comparative analysis of algorithms for laser line center determination and recognition has been conducted. The issues in this area are described, outlining the principles, pros, and cons of each method. Additionally, the possibilities of their application using programming code are demonstrated. Algorithm testing was performed using Python language tools and the OpenCV library. It is shown that the quality of the result in the extremum method significantly depends on the quality of the selected parameters for the Butterworth filter. In contrast, the gray gravity method substantially relies on the accuracy of determining the laser incidence angle. The computational efficiency and accuracy for each algorithm are also analyzed.

A Borehole Electromagnetic Scanning Device With Ferrite Core Coil Antenna: Numerical Simulations and Field Experiments

A Borehole Electromagnetic Scanning Device With Ferrite Core Coil Antenna: Numerical Simulations and Field Experiments

Comparative evaluation of low-cost 3D scanning devices for ear acquisition

Autologous ear reconstruction is a surgical procedure performed in the case of defects of the outer ear in which the malformed anatomy is reconstructed with autologous cartilage tissue and often involves the use of surgical guides modelled from a digital reconstruction of the ear anatomy. To obtain such three-dimensional anatomy, traditional imaging methods, which are expensive and invasive, can be replaced by professional 3D scanners or low-cost commercial devices. In this context, this paper focuses on the evaluation of two devices for the acquisition of the outer ear, the Intel® RealSense D405™ (stereo camera) and the TrueDepth camera of the iPhone® 13 (structured light camera), proposing a comparison based on four parameters: accuracy, precision, deviation range and point-to-point distance, in order to assess their usability in the medical field, and in particular in the context of autologous ear reconstruction. The results show that, despite significantly different handling of the raw data, the performance of the two devices is comparable: average accuracy is 0.76 mm for the D405 and 0.95 mm for the iPhone 13, average precision is 0.071 mm for the D405 and 0.065 mm for the iPhone 13, average range of deviation is 3.12 mm for the D405 and 3.64 mm for the iPhone 13.

Color Standardization and Stain Intensity Calibration for Whole Slide Image-Based Immunohistochemistry Assessment.

Automated quantification of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) using whole slide imaging (WSI) is expected to eliminate subjectivity in visual assessment. However, the color intensity in WSI varies depending on the staining process and scanner device. Such variations affect the image analysis results. This paper presents methods to diminish the influence of color variation produced in the staining process using a calibrator slide consisting of peptide-coated microbeads. The calibrator slide is stained along with tissue sample slides, and the 3,3'-diaminobenzidine (DAB) color intensities of the microbeads are used for calibrating the color variation of the sample slides. An off-the-shelf image analysis tool is employed for the automated assessment, in which cells are classified by the thresholds for the membrane staining. We have adopted two methods for calibrating the color variation based on the DAB color intensities obtained from the calibrator slide: (1) thresholds for classifying the DAB membranous intensity are adjusted, and (2) the color intensity of WSI is corrected. In the experiment, the calibrator slides and tissue of breast cancer slides were stained together on different days and used to test our protocol. With the proposed protocol, the discordance in the HER2 evaluation was reduced to one slide out of 120 slides.

Design and application of a bar-code scanning device for nucleic acid testing

Design and application of a bar-code scanning device for nucleic acid testing

Revolutionizing Footwear Recommendations: A Data-Driven Approach Harnessing Advanced Machine Learning Techniques

The footwear landscape is evolving. Individuals seek a personalized shoe and insole fit for enhanced comfort and health. Historically, footwear sizes were measured manually. This traditional method faced challenges in scalability and precision. The study leveraged big data and machine learning to refine shoe size recommendations. Data was sourced from online platforms, foot scanning devices, and user feedback. Rigorous preprocessing ensured the data's consistency and normalization. Multiple machine learning models were evaluated, with the Random Forest algorithm emerging as the most effective. The findings highlighted an improvement in recommendation accuracy.The research indicates that the integration of technology and data holds the potential to transform the footwear industry, prioritizing comfort and health.