Three-dimensional Data Research Articles

Knowledge Distillation for Traversable Region Detection of LiDAR Scan in Off-Road Environments.

In this study, we propose a knowledge distillation (KD) method for segmenting off-road environment range images. Unlike urban environments, off-road terrains are irregular and pose a higher risk to hardware. Therefore, off-road self-driving systems are required to be computationally efficient. We used LiDAR point cloud range images to address this challenge. The three-dimensional (3D) point cloud data, which are rich in detail, require substantial computational resources. To mitigate this problem, we employ a projection method to convert the image into a two-dimensional (2D) image format using depth information. Our soft label-based knowledge distillation (SLKD) effectively transfers knowledge from a large teacher network to a lightweight student network. We evaluated SLKD using the RELLIS-3D off-road environment dataset, measuring the performance with respect to the mean intersection of union (mIoU) and GPU floating point operations per second (GFLOPS). The experimental results demonstrate that SLKD achieves a favorable trade-off between mIoU and GFLOPS when comparing teacher and student networks. This approach shows promise for enabling efficient off-road autonomous systems with reduced computational costs.

Effects of Twin-Block with an expanding device on the upper airway in growing children with skeletal class II malocclusion-a retrospective study based on the consistency of three-dimensional and two-dimensional data.

Skeletal class II malocclusion is one of the most common malocclusions. Among the functional appliances for skeletal class II malocclusion, the Twin-Block appliance with a maxillary expander is effective in repositioning the mandible forward. In this study, we focused our efforts on investigating the effects of Twin-Block appliances with maxillary expanders on the upper airway in growing children with skeletal class II malocclusion by tracing and measuring lateral cephalograms after evaluating the consistency of three-dimensional CBCT data and two-dimensional lateral cephalogram data. A total of 102 patients ranging from 9 to 15years old (11.37 ± 2.80, male/female ratio = 1:1) with skeletal class II malocclusion were selected to evaluate the consistency of CBCT data and lateral cephalogram data. The strongly and moderately correlated segments were then selected to study the effects of Twin-Block with a maxillary expander on the upper airway in 66 growing children with skeletal class II malocclusion (11.31 ± 1.23years old, male/female ratio = 1:1) by lateral cephalograms. The results showed a strong significant correlation in the nasopharynx (r = 0.708) and moderate significant correlations in the overall upper airway (r = 0.641), palatopharynx (r = 0.553), and glossopharynx (r = 0.575) but a weak correlation in the hypopharynx (r = 0.323). The corresponding determination coefficient (R2) was also determined by scatter plot analysis. Moreover, compared with the pretreatment data (T1), the total area of the upper airway and the areas of the nasopharynx, palatopharynx, and glossopharynx after functional treatment (T2) increased statistically and significantly. Lateral cephalograms can reflect the volume of the nasopharynx and oropharynx in skeletal class II children to a certain extent, while Twin-Block appliances with maxillary expanders can widen the volume of the nasopharynx and oropharynx significantly. The lateral cephalogram is reliable for analyzing the nasopharynx, palatopharynx, and glossopharynx in orthodontic clinical practice. Twin-Block appliances with maxillary expanders have a positive effect on skeletal class II patients with airway stenosis.

CottonSense: A high-throughput field phenotyping system for cotton fruit segmentation and enumeration on edge devices

High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton (Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R2 value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R2 value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense.

Study on the Digital Reassembly of Yoni Monument Excavated from Cella of Hong Nang Sida, Lao PDR

During the dismantling of the cella of the Hong Nang Sida temple in Laos, 100 highly significant Yoni-monument members were recovered as fragmented individual stones. To restore the Hong Nang Sida temple to its former glory, it is necessary to restore the fragmented Yoni-monument members to their original positions. However, the high weight and low mobility of the stones make it difficult to physically reassemble the story members. Therefore, this study utilized three-dimensional (3D) scanning, modeling, and printing technologies to digitally reassemble the Yoni monument. To achieve this goal, optimization modeling was performed to connect the three-dimensional (3D) scanning data of individual fragmented stones, after which each story member was reduced to a 1/5 of its original size and printed by sand printing to evaluate the feasibility of physical assembly. Finally, 3D scans of the Yoni monument assembled using the sand-printing outputs were taken, and all the story members were virtually aligned based on the scan data to complete the digital reassembly. We believe that the results of this study will improve the feasibility and completeness of the physical reassembly by allowing for a full preliminary examination of stone blocks, which would otherwise be difficult to achieve because of the risk of secondary damage or weight limitation. In particular, it is possible to objectively and visually illustrate the geometric relationship between the members of each story, which can be extended to similar stone-based cultural heritage sites. To broaden the utility of the digital-reassembly model, it must be evolved into a historic building information model that links the unstructured data, including documents, photos, and images, obtained throughout the process life cycle.

Reconstruction and Intelligent Evaluation of Three-Dimensional Texture of Stone Matrix Asphalt-13 Pavement for Skid Resistance

To examine the three-dimensional texture structure of SMA-13 asphalt pavement and assess its anti-skid performance, a light gradient-boosting machine evaluation model was developed using non-contact three-dimensional laser-scanning technology. The study focused on collecting three-dimensional texture data from newly laid SMA-13 asphalt pavement. Subsequently, wavelet transform was employed to reconstruct the pavement’s three-dimensional texture, and discrete Fourier transform was utilized to separate macro- and microtextures, enabling the calculation of their characteristics. The macro- and micro-characteristics of the three-dimensional texture and friction coefficient were input into the model. A comparative analysis with linear regression and a random forest model revealed superior accuracy and efficiency in the model. The training set R2 is 0.948, and the testing set R2 is 0.842, effectively enabling the evaluation of pavement anti-skid performance. An analysis of parameter importance indicated that Rku and MPD are still effective indicators for evaluating skid resistance. Furthermore, diverse texture indexes exhibited varying effects on the anti-skid performance. The established asphalt pavement anti-skid evaluation model serves as a theoretical foundation for understanding the actual influence on pavement anti-skid performance.

Improvement of density inversion efficiency with projected gradient method: Application to FTG data

With the development of instruments for measuring Earth's gravity gradient tensor, full tensor gravity (FTG) data can be obtained validly and accurately. The highly precise performance of FTG data ensures that they can play an important role in many domains. Generally, FTG data can be acquired from airborne platform, and large-scale data can be obtained by long term observations. For this type of data, research methods for large data processing are very important. Three-dimensional data inversion is an effective and significant method for the quantitative interpretation of FTG data, which has received much attention. Large-scale data inversion is inevitable, and may consume large amounts of time with increase in the amount of data and model parameters. The main aim of this study is to improve the calculation efficiency of FTG data inversion compared with general conjugate gradient (CG) method. Accordingly, an improved gradient method with projections onto a convex set (the projected gradient method) was applied to accelerate the recovery rate of the sub-surface rock density, and a corresponding preconditioning factor was introduced to further improve the calculation efficiency of inversion. This new projected gradient method can improve the calculation efficiency of large amounts of FTG data, which is very necessary for practical application of FTG data. The application of synthetic FTG data with Gaussian noise and the real data demonstrated the feasibility and application value of the proposed inversion method.

3DMASC: Accessible, explainable 3D point clouds classification. Application to bi-spectral topo-bathymetric lidar data

Three-dimensional data have become increasingly present in earth observation over the last decades. However, many 3D surveys are still underexploited due to the lack of accessible and explainable automatic classification methods, for example, new topo-bathymetric lidar data. In this work, we introduce explainable machine learning for 3D data classification using Multiple Attributes, Scales, and Clouds under 3DMASC, a new workflow. This workflow introduces multi-cloud classification through dual-cloud features, encrypting local spectral and geometrical ratios and differences. 3DMASC uses classical multi-scale descriptors adapted to all types of 3D point clouds and new ones based on their spatial variations. In this paper, we present the performances of 3DMASC for multi-class classification of topo-bathymetric lidar data in coastal and fluvial environments. We show how multivariate and embedded feature selection allows the building of optimized predictor sets of reduced complexity, and we identify features particularly relevant for coastal and riverine scene descriptions. Our results show the importance of dual-cloud features, lidar return-based attributes averaged over specific scales, and of statistics of dimensionality-based and spectral features. Additionally, they indicate that small to medium spherical neighbourhood diameters (<7 m) are sufficient to build effective classifiers, namely when combined with distance-to-ground or distance-to-water-surface features. Without using optional RGB information, and with a maximum of 37 descriptors, we obtain classification accuracies between 91 % for complex multi-class tasks and 98 % for lower-level processing using models trained on less than 2000 samples per class. Comparisons with classical point cloud classification methods show that 3DMASC features have a significantly improved descriptive power. Our contributions are made available through a plugin in the CloudCompare software, allowing non-specialist users to create classifiers for any type of 3D data characterized by 1 or 2 point clouds (airborne or terrestrial lidar, structure from motion), and two labelled topo-bathymetric lidar datasets, available on https://opentopography.org/.

Form-finding for free-curved reinforced concrete shell structure considering structural performance and construction formwork

A computational approach for free-curved reinforced concrete (RC) shells that simultaneously considers structural performance and associated construction strategy is proposed. First, for the rapid design and construction process, a unit–formwork construction system (UFS), which uses multiple construction methods dependent on curvature, is developed, and a mock-up test is conducted to validate its effectiveness of UFS. Although the construction time was reduced, the construction costs were confirmed to be approximately twice those of conventional methods. Furthermore, the error between the measured surface coordinates of the plywood form board (PFB) and corresponding three-dimensional (3D) data is computed, and the study confirmed that the error in construction was within half of the required level. Second, a multi-objective optimization problem is formulated to obtain a shell configuration and a suitable distribution of curvatures on the PFB by minimizing the squared normal curvatures of a PFB surface and the strain energy of the completed shell structure under distributed loads. To assess the manufacturability of PFB, the normal curvatures on the surface of the PFB, which are represented as quadrilateral meshes, are calculated. Finally, numerical examples demonstrate the efficiency of the proposed method for designing a free-curved RC shell from the perspectives of structural design and constructability.

Volumetric sonographic depiction of the fallopian tube lumen for the early detection of tubal abnormalities: A work in progress

ObjectivesThe fallopian tube (FT) has emerged as an important imaging target in the detection of early ovarian cancer, which has remained an evasive challenge in the field of medicine. Recent understanding of ovarian cancer has identified the distal FT as the site of early tumorigenesis. Evaluation of the FT poses challenges due to the small size and variable anatomic position. This study outlines image acquisition and analysis techniques for volumetric depiction of the FT lumen. MethodsWomen with BRCA+ ​mutations undergoing risk reduction salpingo-ophorectomy were imaged using contrast-enhanced 3D transvaginal ultrasound (n ​= ​7). Sono-salpingography was conducted with intraluminal contrast instilled through the cervix for improved imaging of the FT lumen. Three-dimensional volumetric data was collected for qualitative and quantitative image analysis. Pathologic correlation was conducted using the Sectioning and Extensively Examining the Fimbriated End Protocol (SEE-FIM). ResultsStudy subjects underwent 3D imaging with trans-cervical FT lumen contrast enhancement at the time of our interim reporting. None of the subjects in our initial study had serous tubal intraepithelial carcinoma (STIC) lesions, however one had acute inflammation of the FT. The 3D quantitative image analysis demonstrated areas of luminal irregularity corresponding to the regions of inflammation on pathology. ConclusionsOur study shows promise in the detailed evaluation of the FT lumen for the detection of luminal abnormalities. We continue to optimize image acquisition and analysis techniques for the detection of STIC lesions as we enroll additional patients.

Direct Numerical Simulation of Atomization Characteristics of ECN Spray-G Injector: In-Nozzle Fluid Flow and Breakup Processes

AbstractIn order to sustain applications dealing with various liquid fuels in internal combustion engine (ICE), it is essential to make available prediction methodologies that allow an early evaluation of the potential usefulness of such fuels in terms of favourable mixture preparation process already in realistic configurations. Since the air-mixture formation and subsequent processes are predominantly governed by the fuel injection, a DNS based numerical investigation coupled with VOF as an interface tracking method is carried out in this paper to gain better insight on the fuel injection from an industrial injector "Spray G" configuration. Chosen from Engine Combustion Network (ECN), this is a gasoline direct injector (GDI) featuring 8-holes orifices and operating with high injection pressure (200 bar). Under consideration of the required computational cost associated with DNS, only the 1/8 of the nozzle geometry including one orifice is used. The numerical simulation is accomplished for the quasi-steady injection condition with nozzle needle fully opened. The obtained results are first validated with available experimental data for nozzle mass flow rate and spray spread angle showing a good agreement. Then, a detailed numerical analysis is provided for the in/near nozzle flow evolution especially for flow turbulence, primary and secondary atomization. Furthermore, droplet statistics in terms of droplet shape, and droplet size-velocity distribution together with a breakup regime map are reported. Finally, a 2-D data curation technique is proposed to extract the droplet statistics along selected planes and evaluated by direct comparison with three-dimensional droplet data, which may allow handling of the DNS data in more feasible and economical way especially for time series data with higher frequency. The comprehensive DNS data generated by this DNS-VOF approach enable not only to carry out detailed numerical analysis of in- and near-nozzle physical phenomena for which experimental data are still scarce, but also to provide a hint of more reliable injector boundary conditions useful for lower order spray injection method based on Lagrangian particle tracking.

EVALUATION OF A LOW-COST PHOTOGRAMMETRIC SYSTEM FOR THE RETRIEVAL OF 3D TREE ARCHITECTURE

Abstract. Reconstruction of major branches of a tree is an important first step for the monitoring of tree sway and assessment of structural stability. Photogrammetric systems can provide a low-cost alternative for the acquisition of three-dimensional data, while also enabling long-term monitoring of a tree of interest. This study introduces a low-cost photogrammetric system based on two Raspberry Pi cameras, which is used to reconstruct the tree architecture for the purpose of stability monitoring. Images of five trees are taken at a range of distances and the resulting point clouds are evaluated in terms of point density and distribution with the reference to TLS. While the photogrammetric point clouds are sparse, it was found that they are capable of reconstructing the tree trunk and lower-order branches, which are most relevant for sway monitoring and tree stability assessment. The most optimal distance for the reconstruction of the relevant branches was found to be 9–10 m, with a baseline of 120 cm.

Three-dimensional vector ECG analysis in the diagnosis of the left bundle branch block

The complete left bundle branch block (CLBBB) leads to the development of severe heart failure and requires resynchronization therapy(CRT). It is known that one third of patients do not respond to CRT. In this regard, it is necessary to search new diagnostic criteria for patient selection for CRT.Aim. Determination of the criteria for CLBBB using three-dimensional vectorcardiography data with analysis of the rate of vector loop formation.Material and research methods. The study included 137 patients. The main group consisted of 57 patients with CLBBB, which was based on D. G. Strauss et al. (2011) criteria. Causes of CLBBB and comorbidities were not considered. The control group included 80 healthy individuals. All patients underwent synchronous registration of 12 standard ECG leads and reconstruction of a three-dimensional vectorcardiogram (VCG) using the McFee-Parungao system.Research results. The following signs are characteristic for CLBBB according to three-dimensional VCG data: • fracture of the spatial vector loop due to a change in the direction of the excitation front in its middle part; • reduction in planarity index (PI) less than 80%; • a sharp, by a factor of 2 or more, decrease in the speed of tracing a spatial vector loop in its middle part with the formation of a “claw-shaped” curve with two peaks R and L.Conclusion. Three-dimensional VCG allows you to reliably determine the «true» CLBBB, based on the “fracture” of the spatial vector loop with a reduction in PI less than 80% and a significant decrease in the speed of tracing the vector loop in its middle part with the formation of a “clawshaped” curve and carry out a differential diagnosis with “false” СLBBB, in which changes on the ECG are associated with a combination of left ventricular hypertrophy and left anterior fascicular block, when there is no “fracture” of the loop, a reduction in PI, and the routing speed decreases at the end of the loop without forming a “claw-shaped” curve.

Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter.

The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

High-resolution measurements of swordfish skin surface roughness

The three-dimensional morphology of swordfish skin roughness remains poorly understood. Subsequently, its importance to the overall physiology and hydrodynamic performance of the swordfish is yet to be determined. This is at least partly attributable to the inherent difficulty in making the required measurements of these complex biological surfaces. To address this, here two sets of novel high-resolution measurements of swordfish skin, obtained using a modular optical coherence tomography system and a gel-based stereo-profilometer, are reported and compared. Both techniques are shown to provide three-dimensional morphological data at micron-scale resolution. The results indicate that the skin surface is populated with spiny roughness elements, typically elongated in the streamwise direction, in groups of up to six, and in good agreement with previously reported information based on coarser measurements. In addition, our data also provide new information on the spatial distribution and variability of these roughness features. Two approaches, one continuous and another discrete, are used to derive various topographical metrics that characterize the surface texture of the skin. The information provided here can be used to develop statistically representative synthetic models of swordfish skin roughness.

Multidimensional characterization of particle morphology and mineralogical composition using CT data and R-vine copulas

Computed tomography (CT) can capture volumes large enough to measure a statistically meaningful number of micron-sized particles with a sufficiently good resolution to allow for the analysis of individual particles. However, the development of methods to efficiently investigate such image data and interpretably model the observed particle features is still an active field of research. When image data of particles exhibiting a wide range of shapes and sizes is considered, traditional image segmentation methods, such as the classic watershed algorithm, struggle to recognize particles with satisfying accuracy. Thus, more advanced methods of machine learning must be utilized for image segmentation to improve the validity of subsequent analyses. Moreover, CT data does not include information about the mineralogical composition of particles and, therefore, additional SEM-EDS image data has to be acquired. In this paper, micro-CT image data of a particle system mostly consisting of zinnwaldite–quartz composites is considered. First, an image segmentation method is applied which uses deep convolutional neural networks, in particular an adaptation of the U-net architecture. This has the advantage of requiring less hand-labeling than other machine learning methods, while also being more flexible with the possibility of transfer learning. Then, fully parameterized models based on vine copulas are designed to determine multivariate probability distributions of descriptor vectors for the size, shape, texture and composition of particles—allowing for the estimation and interpretable characterization of interdependencies between particle descriptors. For model fitting, the segmented three-dimensional CT data and co-registered two-dimensional SEM-EDS data are used. The models are applied to predict the mineralogical composition of particles, solely on the basis of particle descriptors observed in CT data.

A Universal Data Format for Wellbore Logs

This paper describes a proposed high-specification standard format that is ideally suited for the data management of definitive records of wellbore logs. For this reason, it is a good standard for data exchange between applications. The format is suitable for complex three-dimensional (3D) data, including those generated by deep azimuthal resistivity (DAR) and ultradeep azimuthal resistivity (UDAR) tools, acoustic borehole reflection images, vertical seismic profiles (VSP), borehole imaging tools, multifingered caliper logs, and array data with multiple depths of investigation. It is applicable for use with logging-while-drilling (LWD) and wireline-conveyed logging tools. The format also naturally collapses down when utilized to store simple conventional logs that contain one value per depth in the wellbore. The proposed format provides spatial details of every data point collected by or interpreted from a wellbore-logging tool. The position of each data point is defined by reference back to the measure point of the sonde, which in turn is defined by the wellbore deviation survey and its coordinate reference system (CRS). Each data point in space may have an unrestricted number of parameters. An example might be most likely horizontal and vertical resistivity, maximum value based on uncertainty, minimum value based on uncertainty, and flags indicating the data position with respect to depth of detection (DOD). The new proposed format is so versatile. It is suitable as an Open Group Open Subsurface Data UniverseTM (OSDUTM) standard to store and exchange all data measured by logging tools in a wellbore and can possibly be extended to include all well data (for example, core, cuttings, and more). The proposed format requires a detailed definition so that computer scientists can implement it in applications used for subsurface modeling. The OSDU will also require this detailed definition in order to adopt it as a standard.

Research on Multi-dimensional and High-resolution Stereo Detection Technology of Urban Solid Waste Integrated With Open Space

The evolution law, waste type, volume and spatial distribution characteristics of a municipal solid waste landfill were studied by using multi-disciplinary comprehensive technologies such as surveying and mapping, geophysics, geological mapping, engineering drilling and three-dimensional modeling. Through the analysis of historical remote sensing and three-dimensional tilt photography data, the evolution law of solid waste is clarified, and the investigation scheme is formulated. With the help of GOCAD three-dimensional modeling software, the high-precision engineering borehole vertical layered positioning and high-resolution horizontal geophysical horizon tracking are organically combined to realize the high-precision and high-resolution intelligent analysis of spatial distribution and volume calculation of solid waste, The three-dimensional exploration system of eco-environmental geological survey is constructed.

BSR Characteristics and Gas Hydrate Accumulation Mechanism in Qiongdongnan Basin

This paper makes full use of the high-precision three-dimensional seismic data in this area, combined with the differences in the source supply, migration and accumulation mode and occurrence characteristics of hydrate gas, analyzes and summarizes the gas hydrate migration and accumulation reservoir forming model in Qiongdongnan Basin. The hydrate migration and accumulation reservoir forming models in Qiongdongnan Basin are preliminarily divided into three categories: lower-generation and upper-storage type, self-generation and self-storage type and compound mixed type. The first two categories are further divided into five sub categories, of which the lower-generation and upper-storage types are divided into four sub categories: gas chimney dominated type, mud diapir dominated type, micro-fracture dominated type and fault dominated type. The self-generation and self-storage type can be regarded as: shallow in-situ migration and accumulation type.

IBC-Oxford University2023_BJSOral_1Dynamic Magnetic Resonance Imaging (Dy-MRI) Evaluation of the Impact of Endoscopic Sleeve Gastroplasty (ESG) on Gastric Structure and Function in Morbid Obese Patients

Abstract Introduction Endoscopic sleeve gastroplasty (ESG) is a restrictive endoluminal bariatric procedure consisting in the application of multiple full-thickness sutures to reduce gastric capacity (GC) and slow down gastric emptying (GE). Although results are promising, a large variation in the weight loss outcomes is reported. Dynamic resonance imaging (Dy-MRI) has been successfully applied for the assessment and analysis of gastrointestinal (GI) function. It permits to acquire large field-of-view, dynamic, high- resolution, three-dimensional (3D) image data with excellent soft-tissue contrast in less than a second3. In addition, our group previously demonstrated a significant correlation between weight loss and gastric anatomy changes assessed by Dy-MRI in laparoscopic sleeve gastrectomy (LSG) patients. The aim of this study is to use Dy-MRI to objectively assess gastric structural and functional changes 6 months after ESG and their impact on weight loss. Material and methods Prospective single centre study. Patients underwent preoperative and postoperative (after 6 months) Dy-MRI to evaluate total GC and GE changes, immediately after the ingestion of 400 ml of liquid meal and over time up to 120 minutes after ingestion (T120). Only patients with a complete follow up at and 6 months (M6) were included. Demographic data, pre- and intra-operative data, and weight loss outcomes were collected. Continuous variables were reported as median and interquartile rank (IQR), while categorical variables as numbers and percentages. Results From July 2021 to June 2023, 16 ESG patients participated in this study. Patients had a median age of 41.5 years (IQR:35-49.7) with a median BMI (kg/m2) of 36.9 (IQR: 34.08-42.18). A median number of 6 (IQR: 5.2-6) purse string sutures were placed during the ESG. There were no complications. At the follow-up gastroscopy at M6 all patients presented an intact ESG with all the suture still present. Dy- MRI showed a median GC reduction of 34.1% of the initial volume (p&amp;lt;0.0001) and GE decreased by 0.43 mL/min (p=0.01). As a whole, the study population had a 16.8% of total weight loss (TWL) at M6 (p&amp;lt;0.0001). Similarly, the median BMI decreased from 36.9 to 29.9 (p=0.01). %TWL varied from 15.5 at M3 to 18.3 at M6. During the same time-elapse, %EWL increased from 46.4 to 57.1. Discussion and Conclusion Dy- MRI is confirmed to be a non-invasive method capable of objectively quantify gastric volume reduction and GE changes after ESG. It is likely that these two factors play a synergic role in the weight loss journey of patients undergoing ESG. Further investigations in a larger sample are needed to support these initial findings and to support an evidence based, patient specific approach to endoscopic gastric remodelling.

Volumetric B-mode ultrasound and Doppler Imaging: Automatic Tracking With One Single Camera.

Vascular diseases may occur in the upper extremities, and the lesions can span the entire length of the blood vessel. One of the most popular methods to identify vascular disorders is ultrasound Doppler imaging. However, traditional two-dimensional (2D) ultrasound Doppler imaging cannot capture the entire length of a long vessel in one image. Medical professionals often have to painstakingly reconstruct three-dimensional (3D) data using 2D ultrasound images to locate the lesions, especially for large blood vessels. 3D ultrasound Doppler imaging can display the morphological structure of blood vessels and the distribution of lesions more directly, providing a more comprehensive view compared to 2D imaging. In this work, we propose a wide-range 3D volumetric ultrasound Doppler imaging system with dual modality, in which a high-definition camera is adopted to automatically track the movement of the ultrasound transducer, simultaneously capturing a corresponding sequence of 2D ultrasound Doppler images. We conducted experiments on human arms using our proposed system and separately with X-ray computerized tomography (X-CT). The comparison results prove the potential value of our proposed system in the diagnosis of arm vascular diseases.