Sparse-view neutron CT 3D image reconstruction algorithm based on split Bregman method

As a complement to X-ray computed tomography(CT),neutron tomography has been extensively used in nuclear engineer-ing,materials science,cultural heritage,and industrial applications.Reconstruction of the attenuation matrix for neutron tomography with a traditional analytical algorithm requires hundreds of projection views in the range of 0°to 180°and typically takes several hours to complete.Such a low time-resolved resolution degrades the quality of neutron imaging.Decreasing the number of projection acquisitions is an important approach to improve the time resolution of images;however,this requires efficient reconstruction algorithms.Therefore,sparse-view reconstruction algorithms in neutron tomography need to be investigated.In this study,we investigated the three-dimensional reconstruction algorithm for sparse-view neu-tron CT scans.To enhance the reconstructed image quality of neutron CT,we propose an algorithm that uses OS-SART to reconstruct images and a split Bregman to solve for the total variation(SBTV).A comparative analysis of the performances of each reconstruction algorithm was performed using simulated and actual experimental data.According to the analyzed results,OS-SART-SBTV is superior to the other algorithms in terms of denoising,suppressing artifacts,and preserving detailed structural information of images.

Development of Multi-Agent-Based Indoor 3D Reconstruction

Large-scale indoor 3D reconstruction with multiple robots faces challenges in core enabling technologies.This work contributes to a framework addressing localization,coordination,and vision processing for multi-agent reconstruction.A system architecture fusing visible light positioning,multi-agent path finding via reinforcement learning,and 360°camera techniques for 3D reconstruction is proposed.Our visible light positioning algorithm leverages existing lighting for centimeter-level localization without additional infrastructure.Meanwhile,a decentralized reinforcement learning approach is developed to solve the multi-agent path finding problem,with communications among agents optimized.Our 3D reconstruction pipeline utilizes equirectangular projection from 360°cameras to facilitate depth-independent reconstruction from posed monocular images using neural networks.Experimental validation demonstrates centimeter-level indoor navigation and 3D scene reconstruction capabilities of our framework.The challenges and limitations stemming from the above enabling technologies are discussed at the end of each corresponding section.In summary,this research advances fundamental techniques for multi-robot indoor 3D modeling,contributing to automated,data-driven applications through coordinated robot navigation,perception,and modeling.

SAR regional all-azimuth observation orbit design for target 3D reconstruction

Three-dimensional(3D) synthetic aperture radar(SAR)extends the conventional 2D images into 3D features by several acquisitions in different aspects. Compared with 3D techniques via multiple observations in elevation, e.g. SAR interferometry(InSAR) and SAR tomography(TomoSAR), holographic SAR can retrieve 3D structure by observations in azimuth. This paper focuses on designing a novel type of orbit to achieve SAR regional all-azimuth observation(AAO) for embedded targets detection and holographic 3D reconstruction. The ground tracks of the AAO orbit separate the earth surface into grids. Target in these grids can be accessed with an azimuth angle span of360°, which is similar to the flight path of airborne circular SAR(CSAR). Inspired from the successive coverage orbits of optical sensors, several optimizations are made in the proposed method to ensure favorable grazing angles, the performance of 3D reconstruction, and long-term supervision for SAR sensors. Simulation experiments show the regional AAO can be completed within five hours. In addition, a second AAO of the same area can be duplicated in two days. Finally, an airborne SAR data process result is presented to illustrate the significance of AAO in 3D reconstruction.

Efficacy of laparoscopic low anterior resection for colorectal cancer patients with 3D-vascular reconstruction for left coronary artery preservation

BACKGROUND Laparoscopic low anterior resection(LLAR)has become a mainstream surgical method for the treatment of colorectal cancer,which has shown many advantages in the aspects of surgical trauma and postoperative rehabilitation.However,the effect of surgery on patients'left coronary artery and its vascular reconstruction have not been deeply discussed.With the development of medical imaging technology,3D vascular reconstruction has become an effective means to evaluate the curative effect of surgery.AIM To investigate the clinical value of preoperative 3D vascular reconstruction in LLAR of rectal cancer with the left colic artery(LCA)preserved.METHODS A retrospective cohort study was performed to analyze the clinical data of 146 patients who underwent LLAR for rectal cancer with LCA preservation from January to December 2023 in our hospital.All patients underwent LLAR of rectal cancer with the LCA preserved,and the intraoperative and postoperative data were complete.The patients were divided into a reconstruction group(72 patients)and a nonreconstruction group(74 patients)according to whether 3D vascular reconstruction was performed before surgery.The clinical features,operation conditions,complications,pathological results and postoperative recovery of the two groups were collected and compared.RESULTS A total of 146 patients with rectal cancer were included in the study,including 72 patients in the reconstruction group and 74 patients in the nonreconstruction group.There were 47 males and 25 females in the reconstruction group,aged(59.75±6.2)years,with a body mass index(BMI)(24.1±2.2)kg/m^(2),and 51 males and 23 females in the nonreconstruction group,aged(58.77±6.1)years,with a BMI(23.6±2.7)kg/m^(2).There was no significant difference in the baseline data between the two groups(P>0.05).In the submesenteric artery reconstruction group,35 patients were type Ⅰ,25 patients were type Ⅱ,11 patients were type Ⅲ,and 1 patient was type Ⅳ.There were 37 type Ⅰ patients,24 type Ⅱ patients,12 type Ⅲ patients,and 1 type Ⅳ patient in the nonreconstruction group.There was no significant difference in arterial typing between the two groups(P>0.05).The operation time of the reconstruction group was 162.2±10.8 min,and that of the nonreconstruction group was 197.9±19.1 min.Compared with that of the reconstruction group,the operation time of the two groups was shorter,and the difference was statistically significant(t=13.840,P<0.05).The amount of intraoperative blood loss was 30.4±20.0 mL in the reconstruction group and 61.2±26.4 mL in the nonreconstruction group.The amount of blood loss in the reconstruction group was less than that in the control group,and the difference was statistically significant(t=-7.930,P<0.05).The rates of anastomotic leakage(1.4%vs 1.4%,P=0.984),anastomotic hemorrhage(2.8%vs 4.1%,P=0.672),and postoperative hospital stay(6.8±0.7 d vs 7.0±0.7 d,P=0.141)were not significantly different between the two groups.CONCLUSION Preoperative 3D vascular reconstruction technology can shorten the operation time and reduce the amount of intraoperative blood loss.Preoperative 3D vascular reconstruction is recommended to provide an intraoperative reference for laparoscopic low anterior resection with LCA preservation.

Multi-View Image-Based 3D Reconstruction in Indoor Scenes:A Survey

Three-dimensional reconstruction technology plays an important role in indoor scenes by converting objects and structures in indoor environments into accurate 3D models using multi-view RGB images.It offers a wide range of applications in fields such as virtual reality,augmented reality,indoor navigation,and game development.Existing methods based on multi-view RGB images have made significant progress in 3D reconstruction.These image-based reconstruction methods not only possess good expressive power and generalization performance,but also handle complex geometric shapes and textures effectively.Despite facing challenges such as lighting variations,occlusion,and texture loss in indoor scenes,these challenges can be effectively addressed through deep neural networks,neural implicit surface representations,and other techniques.The technology of indoor 3D reconstruction based on multi-view RGB images has a promising future.It not only provides immersive and interactive virtual experiences but also brings convenience and innovation to indoor navigation,interior design,and virtual tours.As the technology evolves,these image-based reconstruction methods will be further improved to provide higher quality and more accurate solutions to indoor scene reconstruction.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

Drishti Paint 3.2:a new open-source tool for both 2D and 3D segmentation

X-ray computed tomography(CT)has been an important technology in paleontology for several decades.It helps researchers to acquire detailed anatomical structures of fossils non-destructively.Despite its widespread application,developing an efficient and user-friendly method for segmenting CT data continues to be a formidable challenge in the field.Most CT data segmentation software operates on 2D interfaces,which limits flexibility for real-time adjustments in 3D segmentation.Here,we introduce Curves Mode in Drishti Paint 3.2,an open-source tool for CT data segmentation.Drishti Paint 3.2 allows users to manually or semi-automatically segment the CT data in both 2D and 3D environments,providing a novel solution for revisualizing CT data in paleontological studies.

Generating animatable 3D cartoon faces from single portraits

Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D avatars.However,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being modeled.This study presents a novel framework for generating animatable 3D cartoon faces from a single portrait image.Methods First,we transferred an input real-world portrait to a stylized cartoon image using StyleGAN.We then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed texture.Our two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark supervision.Finally,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation transfer.Conclusions Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity criteria.Furthermore,we demonstrated the capability of the proposed 3D model for real-time facial animation.

Accurate resection of hilar cholangiocarcinoma using eOrganmap 3D reconstruction and full quantization technique

BACKGROUND For treatment of hilar cholangiocarcinoma(HCCA),the rate of radical resection is low and prognosis is poor,and preoperative evaluation is not sufficiently accurate.3D visualization has the advantage of giving a stereoscopic view,which makes accurate resection of HCCA possible.AIM To establish precise resection of HCCA based on eOrganmap 3D reconstruction and full quantification technology.METHODS We retrospectively analyzed the clinical data of 73 patients who underwent HCCA surgery.All patients were assigned to two groups.The traditional group received traditional 2D imaging planning before surgery(n=35).The eOrganmap group underwent 3D reconstruction and full quantitative technical planning before surgery(n=38).The preoperative evaluation,anatomical classification of hilar hepatic vessels,indicators associated with surgery,postoperative complications,liver function,and stress response indexes were compared between the groups.RESULTS Compared with the traditional group,the amount of intraoperative blood loss in the eOrganmap group was lower,the operating time and postoperative intestinal ventilation time were shorter,and R0 resection rate and lymph node dissection number were higher(P<0.05).The total complication rate in the eOrganmap group was 21.05%compared with 25.71%in the traditional group(P>0.05).The levels of total bilirubin,Albumin(ALB),aspartate transaminase,and alanine transaminase in the eOrganmap group were significantly different from those in the traditional group(intergroup effect:F=450.400,79.120,95.730,and 13.240,respectively;all P<0.001).Total bilirubin,aspartate transaminase,and alanine transaminase in both groups showed a decreasing trend with time(time effect:F=30.270,17.340,and 13.380,respectively;all P<0.001).There was an interaction between patient group and time(interaction effect:F=3.072,2.965,and 2.703,respectively;P=0.0282,0.032,and 0.046,respectively);ALB levels in both groups tended to increase with time(time effect:F=22.490,P<0.001),and there was an interaction effect between groups and time(interaction effect:F=4.607,P=0.004).In the eOrganmap group,there was a high correlation between the actual volume of intraoperative liver specimen resection and the volume of preoperative virtual liver resection(t=0.916,P<0.001).CONCLUSION The establishment of accurate laparoscopic resection of hilar cholangiocarcinoma based on preoperative eOrganmap 3D reconstruction and full quantization technology can make laparoscopic resection of hilar cholangiocarcinoma more accurate and safe.

Preoperative 3D reconstruction and fluorescent indocyanine green for laparoscopic duodenum preserving pancreatic head resection:A case report

BACKGROUND Duodenum-preserving pancreatic head resection(DPPHR)is the choice of surgery for benign or low-grade malignant tumors of the pancreatic head.Laparoscopic DPPHR(LDPPHR)procedure can be improved by preoperative 3D model reconstruction and the use of intravenous indocyanine green fluorescent before surgery for real-time navigation with fluorescent display to guide the surgical dissection and prevention of from injury to vessels and biliary tract.CASE SUMMARY Here we report the successful short-and long-term outcomes after one year following LDPPHR for a 60-year lady who had an uneventful recovery and was discharged home one week after the surgery.CONCLUSION There was no bile leakage or pancreatic leakage or delayed gastric emptying.The histopathology report showed multiple cysts in the pancreatic head and localized pancreatic intraepithelial tumor lesions.The resected margin was free of tumor.

Fast Estimation of Loader’s Shovel Load Volume by 3D Reconstruction of Material Piles

Fast and accurate measurement of the volume of earthmoving materials is of great signifcance for the real-time evaluation of loader operation efciency and the realization of autonomous operation. Existing methods for volume measurement, such as total station-based methods, cannot measure the volume in real time, while the bucket-based method also has the disadvantage of poor universality. In this study, a fast estimation method for a loader’s shovel load volume by 3D reconstruction of material piles is proposed. First, a dense stereo matching method (QORB–MAPM) was proposed by integrating the improved quadtree ORB algorithm (QORB) and the maximum a posteriori probability model (MAPM), which achieves fast matching of feature points and dense 3D reconstruction of material piles. Second, the 3D point cloud model of the material piles before and after shoveling was registered and segmented to obtain the 3D point cloud model of the shoveling area, and the Alpha-shape algorithm of Delaunay triangulation was used to estimate the volume of the 3D point cloud model. Finally, a shovel loading volume measurement experiment was conducted under loose-soil working conditions. The results show that the shovel loading volume estimation method (QORB–MAPM VE) proposed in this study has higher estimation accuracy and less calculation time in volume estimation and bucket fll factor estimation, and it has signifcant theoretical research and engineering application value.

3D Echocardiogram Reconstruction Employing a Flip Directional Texture Pyramid

Three dimensional(3D)echocardiogram enables cardiologists to visua-lize suspicious cardiac structures in detail.In recent years,this three-dimensional echocardiogram carries important clinical value in virtual surgical simulation.However,this 3D echocardiogram involves a trade-off difficulty between accu-racy and efficient computation in clinical diagnosis.This paper presents a novel Flip Directional 3D Volume Reconstruction(FD-3DVR)method for the recon-struction of echocardiogram images.The proposed method consists of two main steps:multiplanar volumetric imaging and 3D volume reconstruction.In the crea-tion of multiplanar volumetric imaging,two-dimensional(2D)image pixels are mapped into voxels of the volumetric grid.As the obtained slices are discontin-uous,there are some missing voxels in the volume data.To restore the structural and textural information of 3D ultrasound volume,the proposed method creates a volume pyramid in parallel with theflip directional texture pyramid.Initially,the nearest neighbors of missing voxels in the multiplanar volumetric imaging are identified by 3D ANN(Approximate Nearest Neighbor)patch matching method.Furthermore,aflip directional texture pyramid is proposed and aggregated with distance in patch matching tofind out the most similar neighbors.In the recon-struction step,structural and textural information obtained from differentflip angle directions can reconstruct 3D volume well with the desired accuracy.Com-pared with existing 3D reconstruction methods,the proposed Flip Directional 3D Volume Reconstruction(FD-3DVR)method provides superior performance for the mean peak signal-to-noise ratio(40.538 for the proposed method I and 39.626 for the proposed method II).Experimental results performed on the cardi-ac datasets demonstrate the efficiency of the proposed method for the reconstruc-tion of echocardiogram images.

Application of generalized regression neural network on fast 3D reconstruction

In robot-assisted surgery projects,researchers should be able to make fast 3D reconstruction. Usually 2D images acquired with common diagnostic equipments such as UT, CT and MRI are not enough and complete for an accurate 3D reconstruction. There are some interpolation methods for approximating non value voxels which consume large execution time. A novel algorithm is introduced based on generalized regression neural network (GRNN) which can interpolate unknown voxles fast and reliable. The GRNN interpolation is used to produce new 2D images between each two succeeding ultrasonic images. It is shown that the composition of GRNN with image distance transformation can produce higher quality 3D shapes. The results of this method are compared with other interpolation methods practically. It shows this method can decrease overall time consumption on online 3D reconstruction.

The effect of slow shot speed and casting pressure on the 3D microstructure of high pressure die casting AE44 magnesium alloy

3D reconstruction and 2D observation were conducted to characterize the microstructure of the castings produced through high pressure die casting with different parameters.Our results indicate that shrinkage pores generally co-existed with externally solidified crystals(ESCs).In specimen produced without fast slow shot speed,big net-shrinkage pores accompanied with ESCs were found in the center of the specimen.When the casting pressurization was introduced,the shrinkage pores gathered to the specimen center and became much less due to the optimization of melt feeding.Much more porosity was found near the gate rather than in the middle of the rod bar,especially gas pores.Thefilling process simulation reveals that the middle position of the bars wasfirstlyfilled and followed by the near gate position accompanied with one intense turbulentflow.

The effect of different high pressure die casting parameters on 3D microstructure and mechanical properties of AE44 magnesium alloy

To understand the relationship between the process-microstructure-mechanical properties of the high-pressure die-casting(HPDC) AE44 magnesium alloy, 3D reconstruction and 2D characterization were carried out on the HPDC castings produced with different process parameters(low slow-shot speed, fast slow-shot speed, solidification pressure). Microstructural characterization revealed that the formation of shrinkage pores are closely related to ESCs, which were mainly controlled by the low slow-shot speed in shot sleeve(ESCs growth time) and fast slow-shot speed into the die cavity(distribution of ESCs). In addition, solidification pressure can significantly reduce the shrinkage porosity in the center by improving the feeding capacity of liquid metal. Tensile fracture revealed that the tearing ridge is mainly evolved from the slip band of ESCs. The quantity and distribution of ESCs determine the fracture mode of castings. The relationship between mechanical properties of castings and the morphology of ESCs and porosity is also statistically discussed.

Fast Mesh Reconstruction from Single View Based on GCN and Topology Modification

3D reconstruction based on single view aims to reconstruct the entire 3D shape of an object from one perspective.When existing methods reconstruct the mesh surface of complex objects,the surface details are difficult to predict and the reconstruction visual effect is poor because the mesh representation is not easily integrated into the deep learning framework;the 3D topology is easily limited by predefined templates and inflexible,and unnecessary mesh self-intersections and connections will be generated when reconstructing complex topology,thus destroying the surface details;the training of the reconstruction network is limited by the large amount of information attached to the mesh vertices,and the training time of the reconstructed network is too long.In this paper,we propose a method for fast mesh reconstruction from single view based on Graph Convolutional Network(GCN)and topology modification.We use GCN to ensure the generation of high-quality mesh surfaces and use topology modification to improve the flexibility of the topology.Meanwhile,a feature fusion method is proposed to make full use of the features of each stage of the image hierarchically.We use 3D open dataset ShapeNet to train our network and add a new weight parameter to speed up the training process.Extensive experiments demonstrate that our method can not only reconstruct object meshes on complex topological surfaces,but also has better qualitative and quantitative results.

3D Reconstruction for Motion Blurred Images Using Deep Learning-Based Intelligent Systems

The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.

3D reconstruction of worn parts for flexible remanufacture based on robotic arc welding

3D reconstruction of worn parts is the foundation for remanufacturing system based on robotic arc welding, because it can provide 3D geometric information for robot task plan. In this investigation, a novel 3D reconstruction system based on linear structured light vision sensing is developed. This system hardware consists of a MTC368-CB CCD camera, a MLH-645 laser projector and a DH-CG300 image grabbing card. This system software is developed to control the image data capture. In order to reconstruct the 3D geometric information from the captured image, a two steps rapid calibration algorithm is proposed. The 3D reconstruction experiment shows a satisfactory result.

3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model

Structure reconstruction of 3 D anatomy from biplanar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3 D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh.Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field.Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and1.2 mm, separately. The average reconstruction time is 3 minutes.

Image processing based three-dimensional model reconstruction for cross-platform numerical simulation

Numerical simulation is the most powerful computational and analysis tool for a large variety of engineering and physical problems.For a complex problem relating to multi-field,multi-process and multi-scale,different computing tools have to be developed so as to solve particular fields at different scales and for different processes.Therefore,the integration of different types of software is inevitable.However,it is difficult to perform the transfer of the meshes and simulated results among software packages because of the lack of shared data formats or encrypted data formats.An image processing based method for three-dimensional model reconstruction for numerical simulation was proposed,which presents a solution to the integration problem by a series of slice or projection images obtained by the post-processing modules of the numerical simulation software.By means of mapping image pixels to meshes of either finite difference or finite element models,the geometry contour can be extracted to export the stereolithography model.The values of results,represented by color,can be deduced and assigned to the meshes.All the models with data can be directly or indirectly integrated into other software as a continued or new numerical simulation.The three-dimensional reconstruction method has been validated in numerical simulation of castings and case studies were provided in this study.