Scan Data Research Articles

Optic Nerve Sheath Diameter on Computed Tomography Scans Reflects Elevated Intracranial Pressure in Patients with Craniosynostosis.

Assessment for elevated intracranial pressure (ICP) helps guide interventional decision-making to treat craniosynostosis. However, non-invasive techniques for measuring ICP are limited. This study assesses whether optic nerve sheath diameter (ONSD) on low-dose computed tomography (CT) scans is associated with ICP in patients with craniosynostosis. Pediatric patients treated between 2014 and 2023 with craniosynostosis, intraoperative ICP measurements by direct subdural catheterization, and spectral domain-optical coherent tomography (SD-OCT) data were included. ONSD was retrospectively assessed on pre-operative CT scans by a masked neuroradiologist and compared to measures and proxies of ICP. Among 132 patients included, median age was 6.9 years (IQR 4.7-9.5) and 41 (31.1%) had a syndromic diagnosis. Maximum ONSD (ONSDmax) was increased in patients with ICP≥15mmHg (6.1mm vs. 5.5mm, p<0.01) and ICP≥20mmHg (6.3mm vs. 5.6mm, p<0.01). Maximum (r=0.32, p<0.001), minimum (r=0.26, p=0.003), and average (r=0.29, p<0.001) ONSD correlated with direct ICP measurements. ONSD and SD-OCT measurements were also correlated (RNFLmax: r=0.21, p=0.04; RTmax: r=0.24, p=0.02). An ONSDmax threshold of 5.75mm demonstrated 65% sensitivity and 64% specificity for detecting ICP≥15mmHg on optimized receiver operating characteristic curve analysis. Multivariable logistic regression generated an algorithm incorporating ONSDmax and age to detect ICP≥20mmHg with 64% sensitivity and 80% specificity. Optic nerve sheath diameter measured in low-dose CT scans detects elevated intracranial pressure with moderate accuracy, and precision increases when patient age is taken into consideration. Given the ease of accessing CT scan data, this may be a helpful ICP proxy for clinical decision-making.

Wide & deep learning for predicting relative mineral compositions of sediment cores solely based on XRF scans, a case study from Pleistocene Paleolake Olduvai, Tanzania

This study develops a method to use deep learning models to predict the mineral assemblages and their relative abundances in paleolake cores using high-resolution XRF core scan elemental data and X-ray diffraction (XRD) mineralogical results from the same core taken at coarser resolution. It uses the XRF core scan data along with published mineralogical information from the Olduvai Gorge Coring Project (OGCP) 2014 sediment cores 1A, 2A, and 3A from Paleolake Olduvai, Tanzania. Both regression and classification models were developed using a Keras deep learning framework to assess the predictability of mineral assemblages with their relative abundances (in regression models) or at least the mineral assemblages (in classification models) using XRF core scan data. Models were created using the Sequential class and Functional API with different model architectures. The correlation matrix of element ratios calculated from XRF element intensity records from the cores and XRD-derived mineralogical information was used to select the most useful features to train the models. 1057 training data records were used for the models. Lithological classes were also used for some models using Wide & Deep neural networks since those combine the benefits of memorization and generalization for mineral prediction. The results were validated using 265 validation data records unseen by the model and discuss the accuracy of models using six test records. The optimized Deep Neural Network (DNN) classification model achieved over 86% binary accuracy while the regression models were also able to predict the relative mineral abundances of samples with high accuracies. Overall, the study shows the efficacy of a carefully crafted Deep Learning (DL) model for predicting mineral assemblages and abundances using high-resolution XRF core scan data.

Nutritional implications of substituting plant-based proteins for meat: evidence from home scan data

There is growing concern among policymakers and researchers about the negative health and climate impacts of meat consumption. Consumers are encouraged to re-evaluate their dietary choices to preserve our ecosystem and reduce the burden of diet-related diseases. However, limited information is available about how price changes in animal protein sources affect plant-based protein demand and the consequences for nutrient intake and/or diet quality. The goal of the present paper is to fill this gap by explaining how consumers react to price changes in animal protein types and to present the implications for nutrition or diet quality. This paper applied the exact affine stone index implicit (EASI) Marshallian demand system to 2021 home scan panel data collated by the Kantar Worldpanel to estimate both price and expenditure elasticities. Twelve food groups of seven animal-based protein products and five plant-based protein products were considered. The results revealed that dairy and eggs are daily necessities for the people of Scotland. The demand for fish and non-dairy milk are the most sensitive to price. Estimates based on expenditure elasticities show that beef is considered a luxury and a highly substitutable product in the Scottish diet. Peas are relatively basic, essential foodstuffs. In general, increasing the price of animal protein sources will shift demand towards plant protein. On the positive side, there will be a significant reduction in cholesterol and fat purchases. However, there would also be a significant reduction in the total amount of protein, carbohydrates, and healthy fats, such as unsaturated fatty acids, purchased by the average household. This shows that increases in plant-based protein are not enough to compensate for the reductions in essential macro- and micronutrient purchases from animal protein. From the climate perspective, reductions in meat purchases could reduce emissions from production and consumption.

Reproducibility of a single-volume dynamic CT myocardial blood flow measurement technique: validation in a swine model

BackgroundWe prospectively assessed the reproducibility of a novel low-dose single-volume dynamic computed tomography (CT) myocardial blood flow measurement technique.MethodsThirty-four pairs of measurements were made under rest and stress conditions in 13 swine (54.3 ± 12.3 kg). One or two acquisition pairs were acquired in each animal with a 10-min delay between each pair. Contrast (370 mgI/mL; 0.5 mL/kg) and a diluted contrast/saline chaser (0.5 mL/kg; 30:70 contrast/saline) were injected peripherally at 5 mL/s, followed by bolus tracking and acquisition of a single volume scan (100 kVp; 200 mA) with a 320-slice CT scanner. Bolus tracking and single volume scan data were used to derive perfusion in mL/min/g using a first-pass analysis model; the coronary perfusion territories of the left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA) were automatically assigned using a previously validated minimum-cost path technique. The reproducibility of CT myocardial perfusion measurement within the LAD, LCx, RCA, and the whole myocardium was assessed via regression analysis. The average CT dose index (CTDI) of perfusion measurement was recorded.ResultsThe repeated first (Pmyo1) and second (Pmyo2) single-volume CT perfusion measurements were related by Pmyo2 = 1.01Pmyo1 − 0.03(ρ = 0.96; RMSE = 0.08 mL/min/g; RMSE = 0.07 mL/min/g) for the whole myocardium, and by Preg2 = 0.86Preg1 + 0.13(ρ = 0.87; RMSE = 0.31 mL/min/g; RMSE = 0.29 mL/min/g) for the LAD, LCx, and RCA perfusion territories. The average CTDI of the single-volume CT perfusion measurement was 10.5 mGy.ConclusionThe single-volume CT blood flow measurement technique provides reproducible low-dose myocardial perfusion measurement using only bolus tracking data and a single whole-heart volume scan.Relevance statementThe single-volume CT blood flow measurement technique is a noninvasive tool that reproducibly measures myocardial perfusion and provides coronary CT angiograms, allowing for simultaneous anatomic-physiologic assessment of myocardial ischemia.Key PointsA low-dose single-volume dynamic CT myocardial blood flow measurement technique is reproducible.Motion misregistration artifacts are eliminated using a single-volume CT perfusion technique.This technique enables combined anatomic-physiologic assessment of coronary artery disease.Graphical

Innovative 3D printing and molding process for secondary-skin-collimator fabrication

Purpose. Secondary skin collimation (SSC) is essential for shielding normal tissues near tumors during electron and orthovoltage radiation treatments. Traditional SSC fabrication methods, such as crafting in-house lead sheets, are labor-intensive and produce SSCs with low geometric accuracy. This study introduces a workflow that integrated 3D scanning and 3D printing technologies with an in-house mold process, enabling the production of patient-specific SSCs within six hours. Methods. An anthropomorphic head phantom was scanned with a handheld 3D scanner. The resulting scan data was imported into 3D modeling software for design. The completed model was exported to a 3D printer as a printable file. Subsequently, molten Cerrobend was poured into the mold and allowed to set, completing the SSC production. Geometric accuracy was assessed using CT images, and the shielding effectiveness was evaluated through film dosimetry. Results. The 3D printed mold achieved submillimeter accuracy (0.5 mm) and exhibited high conformity to the phantom surface. It successfully endured the weight and heat of the Cerrobend during pouring and curing. Dosimetric analysis conducted with radiochromic film demonstrated good agreement between the measured and expected attenuation values of the SSC slab, within ±3%. Conclusions. This study presents a proof of concept for novel mold room workflows that produce patient-specific SSCs within six hours, a significant improvement over the traditional SSC fabrication process, which takes 2–3 days. The submillimeter accuracy and versatility of 3D scanning and printing technologies afford greater design freedom and enhanced delivery accuracy for cases involving irregular geometries.

Prototype pipeline modelling using interval scanning point clouds

AbstractWith the aid of computer aided design (CAD) and building information modelling (BIM), as-built to as-designed comparison has seen many developments in improving the workflow of manufacturing and construction tasks. Recently, evolution has been centred around automation of scene interpretation from three-dimensional (3D) scan data. The scope of this paper is to assess assemblies as the installation process progresses and inferring if arising deviations are problematic (complex task). The adequacy of utilising unorganised point clouds to compliance check are trialled with a real life down-scaled prototype model in conjunction with a synthetic dataset. This work aims to highlight areas where large rework could be avoided, in part by the detection of potential clashes of components early in the pipeline installation process. With the help of an extracted model in the form of a point cloud generated from a scanned physical model and a 3D CAD model representing the nominal geometry, an operator can be made visually aware of potential deviations and component clashes during a pipeline assembly process.

Exploring synthetic datasets for computer-aided detection: a case study using phantom scan data for enhanced lung nodule false positive reduction.

Synthetic datasets hold the potential to offer cost-effective alternatives to clinical data, ensuring privacy protections and potentially addressing biases in clinical data. We present a method leveraging such datasets to train a machine learning algorithm applied as part of a computer-aided detection (CADe) system. Our proposed approach utilizes clinically acquired computed tomography (CT) scans of a physical anthropomorphic phantom into which manufactured lesions were inserted to train a machine learning algorithm. We treated the training database obtained from the anthropomorphic phantom as a simplified representation of clinical data and increased the variability in this dataset using a set of randomized and parameterized augmentations. Furthermore, to mitigate the inherent differences between phantom and clinical datasets, we investigated adding unlabeled clinical data into the training pipeline. We apply our proposed method to the false positive reduction stage of a lung nodule CADe system in CT scans, in which regions of interest containing potential lesions are classified as nodule or non-nodule regions. Experimental results demonstrate the effectiveness of the proposed method; the system trained on labeled data from physical phantom scans and unlabeled clinical data achieves a sensitivity of 90% at eight false positives per scan. Furthermore, the experimental results demonstrate the benefit of the physical phantom in which the performance in terms of competitive performance metric increased by 6% when a training set consisting of 50 clinical CT scans was enlarged by the scans obtained from the physical phantom. The scalability of synthetic datasets can lead to improved CADe performance, particularly in scenarios in which the size of the labeled clinical data is limited or subject to inherent bias. Our proposed approach demonstrates an effective utilization of synthetic datasets for training machine learning algorithms.

Utilizing Deep Learning to Detect Kidney Stones from CT Scan Data

Utilizing Deep Learning to Detect Kidney Stones from CT Scan Data

Three-Dimensional Analysis of the Maxillary Sinuses in Ancient Crania Dated to the V-VI Centuries BCE from Opi (Italy): Volumetric Measurements in Ancient Skulls from the Necropolis of Opi, Abruzzi, Italy.

Cone beam computed tomography (CBCT) can provide precise information about complex anatomical structures as it is characterised by rapid volumetric image acquisition with high resolution. The aim of this study was to provide measurements for 20 ancient skulls of the Samnite people found in the necropolis of Opi, a small and isolated mountain village in Abruzzo, a region in central Italy. All the images (left and right) of the 20 ancient skulls from Opi were acquired. All the data are the property of the Department of Innovative Technologies in Medicine and Dentistry of G. D'Annunzio University, Chieti, and different parameters (area and volume) were measured and evaluated. The mean and standard deviation of the facial measurements were also calculated. All the data were subjected to statistical analysis. CBCT scan data of 20 fossil skulls did not show significant values regarding the MS area and the volume between the right and left sides. In the ancient skulls, no difference was found between the right and left sides.

Leveraging Distributed Brain Signal at Rest to Predict Internalizing Symptoms in Youth: Deriving a Polyneuro Risk Score From the ABCD Study Cohort

BackgroundThe prevalence of internalizing psychopathology rises precipitously from early to mid-adolescence, yet the underlying neural phenotypes that give rise to depression and anxiety during this developmental period remain unclear. MethodsYouths from the Adolescent Brain Cognitive Development (ABCD) Study (ages 9–10 years at baseline) with a resting-state functional magnetic resonance imaging scan and mental health data were eligible for inclusion. Internalizing subscale scores from the Brief Problem Monitor-Youth Form were combined across 2 years of follow-up to generate a cumulative measure of internalizing symptoms. The total sample (N = 6521) was split into a large discovery dataset and a smaller validation dataset. Brain-behavior associations of resting-state functional connectivity with internalizing symptoms were estimated in the discovery dataset. The weighted contributions of each functional connection were aggregated using multivariate statistics to generate a polyneuro risk score (PNRS). The predictive power of the PNRS was evaluated in the validation dataset. ResultsThe PNRS explained 10.73% of the observed variance in internalizing symptom scores in the validation dataset. Model performance peaked when the top 2% functional connections identified in the discovery dataset (ranked by absolute β weight) were retained. The resting-state functional connectivity networks that were implicated most prominently were the default mode, dorsal attention, and cingulo-parietal networks. These findings were significant (p < 1 × 10−6) as accounted for by permutation testing (n = 7000). ConclusionsThese results suggest that the neural phenotype associated with internalizing symptoms during adolescence is functionally distributed. The PNRS approach is a novel method for capturing relationships between resting-state functional connectivity and behavior.

Single-center outcomes of artificial intelligence in management of pulmonary embolism and pulmonary embolism response team activation.

Multidisciplinary pulmonary embolism response teams (PERTs) have shown that timely triage expedites treatment. The use of artificial intelligence (AI) may help improve pulmonary embolism (PE) management with early CT pulmonary angiogram (CTPA) screening and accelerate PERT coordination. This study aimed to test the clinical validity of an FDA-approved PE AI algorithm. CTPA scan data of 200 patients referred due to automated AI detection of suspected PE were retrospectively reviewed. In our institution, all patients suspected of PE received a CTPA. The AI app was then used to analyze CTPA for the presence of PE and calculate the right-ventricle/left-ventricle (RV/LV) ratio. We compared the AI's output with the radiologists' report. Inclusion criteria included segmental PE with and without RV dysfunction and high-risk PE. The primary endpoint was false positive rate. Secondary end points included clinical outcomes according to the therapy selected, including catheter-directed interventions, systemic thrombolytics, and anticoagulation. Fifty-seven of 200 exams (28.5%) were correctly identified as positive for PE by the algorithm. A total of 143 exams (71.5%) were incorrectly reported as positive. In 8% of cases, PERT was consulted. Four patients (7%) received systemic thrombolytics without any complications. There were six patients (10.5%) who developed high-risk PE and underwent thrombectomy, one of whom died. Among 46 patients with acute PE without right heart strain, 44 (95%) survived. The false positive rate of our AI algorithm was 71.5%, higher than what was reported in the AI's prior clinical validity study (91% sensitivity, 100% specificity). A high rate of discordant AI auto-detection of suspected PE raises concerns about its diagnostic accuracy. This can lead to increased workloads for PERT consultants, alarm/notification fatigue, and automation bias. The AI direct notification process to the PERT team did not improve PERT triage efficacy.

Development of assessment criteria for elderly lateral torso deformity for 3D scan anthropometry

PurposeWe aimed to establish criteria for determining the deformed lateral torso type to identify individuals requiring measurement methods different from standard methods before extracting dimensions from three-dimensional (3D) scan data.Design/methodology/approachWe collected the 3D body scan data of 119 women aged 70–85 years collected in the 6th Size Korea. Three axes were defined to determine the deformation of the lateral shape, and the angle of each reference axis was used for the analysis. Additionally, to classify the lateral torso shape, 14 experts made visual judgments on the side-view images of the participants.FindingsTo identify the axis that best represented the lateral torso shape, we used each angle value of the three reference axes as an independent variable and the expert’s visual classification as a dependent variable. Each discriminant function was obtained and accuracy calculated. The whole torso axis exhibited the highest accuracy. Next, an assessment scale was developed to determine the shape of the lateral torso using the angular value of the whole torso axis.Originality/valueThe scale developed in this study has the potential to reduce measurement errors arising from elderly deformed torso shapes, thereby enhancing data reliability.

Computer-Assisted Implant Planning: A Review of Data Registration Techniques.

Computer-assisted implant planning allows for a comprehensive treatment plan by combining radiographic data provided by a Cone Beam Computed Tomography (CBCT) with surface optical scan (IOs) data that includes patient intraoral situation and the intended restorative planning. Integrating a tailored restorative design with the patient's anatomical conditions through virtual implant planning allows for an ideal bio-restorative treatment planning to maximize biological, functional, and esthetic outcomes. This article discusses dataset registration techniques that combine radiographic CBCT data with restorative information as the main path to create a virtual patient. The described techniques include the use of removable radiographic templates with radiopaque markers, dual scan technique, and direct digital file registration of intra-oral scans using anatomical references. Depending on the individual clinical situation, different factors must be considered to appropriately select methods that achieve an optimal registration of diverse datasets. An inherent challenge lies in the presence of scattering artifacts in CBCT scans. Two approaches are proposed for these situations - the use of chairside-fabricated composite resin markers or adhesive spot-markers fabricated for the use with CBCT scans. Both techniques exhibit limitations that need to be taken into consideration. Further approaches should be developed for situations involving scattering in CBCT.

Cosmology Large Angular Scale Surveyor (CLASS): 90 GHz Telescope Pointing, Beam Profile, Window Function, and Polarization Performance

The Cosmology Large Angular Scale Surveyor (CLASS) is a telescope array that observes the cosmic microwave background (CMB) over ∼75% of the sky from the Atacama Desert, Chile, at frequency bands centered near 40, 90, 150, and 220 GHz. CLASS measures the large angular scale CMB polarization to constrain the tensor-to-scalar ratio and the optical depth to last scattering. This paper presents the optical characterization of the 90 GHz telescope. Observations of the Moon establish the pointing while dedicated observations of Jupiter are used for beam calibration. The standard deviations of the pointing error in azimuth, elevation, and boresight angle are 1.′3, 2.′1, and 2.′0, respectively, over the first 3 yr of observations. This corresponds to a pointing uncertainty ∼7% of the beam’s full width at half-maximum (FWHM). The effective azimuthally symmetrized instrument 1D beam estimated at 90 GHz has an FWHM of 0.°620 ± 0.°003 and a solid angle of 138.7 ± 0.6(stats.) ± 1.1(sys.) μsr integrated to a radius of 4°. The corresponding beam window function drops to bℓ2=0.93,0.71,0.14 at ℓ = 30, 100, 300, respectively. Far-sidelobes are studied using detector-centered intensity maps of the Moon and measured to be at a level of 10−3 or below relative to the peak. The polarization angle of Tau A estimated from preliminary survey maps is 149°.6 ± 0°.2(stats.) in equatorial coordinates. The instrumental temperature-to-polarization (T → P) leakage fraction, inferred from per-detector demodulated Jupiter scan data, has a monopole component at the level of 1.7 × 10−3, a dipole component with an amplitude of 4.3 × 10−3, with no evidence of quadrupolar leakage.

Association between psychological stress and mandibular condyle structure: an analytical cross-sectional study

ObjectiveThe potential influence of psychological factors on temporomandibular joint disorders has been clinically documented. To date, all research examining the impact of psychological stress on the temporomandibular joint has been conducted on animals. This study aims to explore the relationship between psychological stress and the structure of the human mandibular condyle.MethodsThis cross-sectional study was performed on individuals, who were referred to the radiology division of 5th Azar Hospital for head and neck Computed Tomography (CT) scans. All participants completed a perceived stress questionnaire to determine their level of stress. Bone density and cortical bone thickness were measured as indicators of mandibular condyle structure. Based on multi-slice CT scan data, bone density was calculated in the anterior, middle, and posterior mandibular condyle. The cortical bone thickness was also measured at the anterior and posterior mandibular condyle. Statistical analysis was performed in R 4.0.2 software.ResultsSeventy individuals, aged 18–59 years, participated in this study. The CT scans revealed a decrease in Hounsfield units (HU) and bone mineral density (BMD) in both the anterior and posterior regions. However, in the high-stress group, there was no significant difference in cortical bone thickness in the anterior and posterior regions of the condyle, nor in HU and BMD in the middle region of the condyle. An inverse correlation was observed between BMD and perceived stress in the anterior, middle, and posterior regions of both condyles.ConclusionThe current findings indicate that recent psychological stress is associated with changes in the structure of the condyle.

Simulation-based data reduction and data processing for sheet metal forming in the hybrid twin framework

In sheet metal forming, the interaction between virtual models and the real world remains challenging. Process simulations can exhibit significant errors, and reliable measurements are often scarce during early production stages. This study presents a hybrid twin framework that systematically unifies computer-aided design, simulation, and measurement data in an adaptive manner. Central to this framework is a reverse engineering algorithm that reconstructs and transforms the geometry of deep-drawn components from optical scan data into B-spline surfaces. The algorithm demonstrated high precision, indicating its suitability for process control and geometric analysis. The hybrid twin framework integrates virtual data from simulations and real-world data, as evidenced by a sensor concept for inline surface measurement. The framework ensures robust and redundant measurement concepts by estimating complete geometries from a few systematically preselected measuring points. This adaptive approach permits continuous updates and extensions to the database, accommodating both sparse inline signals and offline inspection data. This framework provides a conceptual model for integrating direct feedback interactions between virtual and physical environments, thereby enhancing the precision of analytical and predictive models in sheet metal forming processes.

Computational comparison of paratropicity trends in antiaromatic s‐indacene derivatives: Does the functional “make all the difference”?

AbstractA recent publication by Wu and co‐workers demonstrated that the workhorse functional B3LYP tends to overemphasize the delocalization in antiaromatic molecules, leading to poor agreement between the calculated proton NMR values and the experimentally obtained numbers. Rather, they showed that the M11 functional affords the best agreement between theory and experiment. We have computationally re‐examined our previously published NICS‐XY scan data using M11‐determined geometries and find that, aside from the placement of s‐indacene, the antiaromaticity trends for both sets of data are essentially identical.

Novel Methods for Cost-Effectively Generating a Heterogeneous Core Model Based on Scale Change of Nuclear Magnetic Resonance and X-ray Computed Tomography Data

Summary In response to the constraint on model size imposed by computational capabilities and the inability to capture the heterogeneity within the core and its dynamic oil displacement characteristics, this paper proposes two novel methods for cost-effectively modeling heterogeneous core models based on scale changes of nuclear magnetic resonance (NMR) and X-ray computed tomography (X-CT) data, respectively. By utilizing NMR and X-CT techniques to characterize information at the subcore scale, we establish a more realistic model at the core scale. First, by using a method of setting up inactive grids, a homogeneous model is established to better represent the actual cross-section of the core. By fitting the core water displacement experimental data, a random heterogeneous core model based on the NMR-T2 spectrum is established by using the modified Schlumberger-Doll Research (SDR) model and complementarity principle. The numerical simulation results show that the random heterogeneous core model partially reflect the heterogeneity of the core, but the simulation results are unstable. Building on this, a deterministic homogeneous core model is established based on X-CT scan data by using the modified Kozeny-Carman model and pore extraction method. Sensitivity analysis results suggest that higher grid accuracy leads to a better fitting effect, with the axial plane grid accuracy impacting the model water-drive process more significantly than that of the end plane. The study paves the way for the rapid and accurate establishment of core models.

Semantic enrichment of BIM with IndoorGML for quadruped robot navigation and automated 3D scanning

Planning scan routes with prior knowledge can improve scan data quality and completeness. This paper presents a BIM-enabled approach to optimize quadruped robot navigation for automated 3D scanning. The BIM data schema is enriched with IndoorGML, integrating building geometry with spatial data to establish an indoor navigation model describing multi-scale spatial topological networks. This navigation model, which includes an enhanced greedy algorithm, optimizes quadruped robot scanning positions and traversal sequences. The scan planning optimization outperforms existing heuristic algorithms in computational efficiency, coverage, and scan point count. The BIM-enabled approach is validated on ROS and in real-world conditions with a 3D LiDAR sensor integrated with a quadruped robot. The robotic scans achieve visible coverage of 70–90% of the structure, with a fluctuation of 0.006–0.021 mm compared to traditional laser scans. The findings demonstrate robotic scans as a viable way of obtaining complete and accurate point clouds, reducing human effort in traditional scanning.

Advances in personalized modelling and virtual display of ethnic clothing for intelligent customization

Abstract To enhance the intelligence of the ethnic clothing customization industry and provide online solutions for clothing ordering and fashion shows, this study analyses the latest developments in the fields of digital human models, 3D clothing models, and virtual clothing displays in recent years. This study focuses on analysing the categories and processes of three related technologies in clothing virtual display and compares the advantages and disadvantages of four human body modelling methods. Reshaping the facial features of a 3D body scan model and binding the bones of each part is key to providing a personalized body model. In addition, 3D clothing modelling technology is classified into three methods. The 3D fabric scan data are used to establish realistic seam information and process details simulation and use hybrid modelling methods to virtually sew 2D samples, which can enhance the realism of clothing models. The virtual display platform for ethnic clothing is constructed through layered design based on the Unity3D engine, matching versions and virtual display. The above research ideas can solve bottleneck problems in clothing customization. This has important guiding significance for opening up the ethnic clothing customization market, promoting the intelligence of the customization industry, and promoting ethnic clothing culture.