The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological d...The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological data of these particles has became a key focus in wear debris analysis.Herein,we develop a novel multi-view polarization-sensitive optical coherence tomography(PS-OCT)method to achieve accurate 3D morphology detection and reconstruction of aero-engine lubricant wear particles,effectively resolving occlusion-induced information loss while enabling material-specific characterization.The particle morphology is captured by multi-view imaging,followed by filtering,sharpening,and contour recognition.The method integrates advanced registration algorithms with Poisson reconstruction to generate high-precision 3D models.This approach not only provides accurate 3D morphological reconstruction but also mitigates information loss caused by particle occlusion,ensuring model completeness.Furthermore,by collecting polarization characteristics of typical metals and their oxides in aero-engine lubricants,this work comprehensively characterizes and comparatively analyzes particle polarization properties using Stokes vectors,polarization uniformity,and cumulative phase retardation,and obtains a three-dimensional model containing polarization information.Ultimately,the proposed method enables multidimensional information acquisition for the reliable identification of abrasive particle types.展开更多
The impact of heavy reduction on dendritic morphology was explored by combining experimental research and numerical simulation in metallurgy,including a detailed three-dimensional(3D)analysis and reconstruction of den...The impact of heavy reduction on dendritic morphology was explored by combining experimental research and numerical simulation in metallurgy,including a detailed three-dimensional(3D)analysis and reconstruction of dendritic solidification structures.Combining scanning electron microscopy and energy-dispersive scanning analysis and ANSYS simulation,the high-precision image processing software Mimics Research was utilized to conduct the extraction of dendritic morphologies.Reverse engineering software NX Imageware was employed for the 3D reconstruction of two-dimensional dendritic morphologies,restoring the dendritic characteristics in three-dimensional space.The results demonstrate that in a two-dimensional plane,dendrites connect with each other to form irregularly shaped“ring-like”structures.These dendrites have a thickness greater than 0.1 mm along the Z-axis direction,leading to the envelopment of molten steel by dendrites in a 3D space of at least 0.1 mm.This results in obstructed flow,confirming the“bridging”of dendrites in three-dimensional space,resulting in a tendency for central segregation.Dense and dispersed tiny dendrites,under the influence of heat flow direction,interconnect and continuously grow,gradually forming primary and secondary dendrites in three-dimensional space.After the completion of dendritic solidification and growth,these microdendrites appear dense and dispersed on the two-dimensional plane,providing the nuclei for the formation of new dendrites.When reduction occurs at a solid fraction of 0.46,there is a noticeable decrease in dendritic spacing,resulting in improved central segregation.展开更多
This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low ...This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.展开更多
This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D n...This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery.The approach leverages advanced image processing techniques,3D Morphable Models(3DMM),and deformation techniques to overcome the limita-tions of deep learning models,particularly addressing the interpretability issues commonly encountered in medical applications.The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm.Sub-landmarks are extracted through image processing techniques and interpolation.The initial surface is generated using a 3DMM,though its accuracy remains limited.To enhance precision,deformation techniques are applied,utilizing the coordinates of 76 identified landmarks and sub-landmarks.The resulting 3D nose model is constructed based on algorithmic methods and pre-marked landmarks.Evaluation of the 3D model is conducted by comparing landmark distances and shape similarity with expert-determined ground truth on 30 Vietnamese volunteers aged 18 to 47,all of whom were either preparing for or required nasal surgery.Experimental results demonstrate a strong agreement between the reconstructed 3D model and the ground truth.The method achieved a mean landmark distance error of 0.631 mm and a shape error of 1.738 mm,demonstrating its potential for medical applications.展开更多
The Unmanned Aerial Vehicle(UAV)-assisted sensing-transmission--computing integrated system plays a vital role in emergency rescue scenarios involving damaged infrastructure.To tackle the challenges of data transmissi...The Unmanned Aerial Vehicle(UAV)-assisted sensing-transmission--computing integrated system plays a vital role in emergency rescue scenarios involving damaged infrastructure.To tackle the challenges of data transmission and enable timely rescue decision-making,we propose DWT-3DRec-an efficient wireless transmission model for 3D scene reconstruction.This model leverages MobileNetV2 to extract image and pose features,which are transmitted through a Dual-path Adaptive Noise Modulation network(DANM).Moreover,we introduce the Gumbel Channel Masking Module(GCMM),which enhances feature extraction and improves reconstruction reliability by mitigating the effects of dynamic noise.At the ground receiver,the Multi-scale Deep Source-Channel Coding for 3D Reconstruction(MDS-3DRecon)framework integrates Deep Joint Source-Channel Coding(DeepJSCC)with Cityscale Neural Radiance Fields(CityNeRF).It adopts a progressive close-view training strategy and incorporates an Adaptive Fusion Module(AFM)to achieve high-precision scene reconstruction.Experimental results demonstrate that DWT-3DRec significantly outperforms the Joint Photographic Experts Group(JPEG)standard in transmitting image and pose data,achieving an average loss as low as 0.0323 and exhibiting strong robustness across a Signal-to-Noise Ratio(SNR)range of 5--20 dB.In large-scale 3D scene reconstruction tasks,MDS-3DRecon surpasses Multum in Parvo Neural Radiance Fields(Mip-NeRF)and Bungee Neural Radiance Field(BungeeNeRF),achieving a Peak Signal-to-Noise Ratio(PSNR)of 24.921 dB and a reconstruction loss of 0.188.Ablation studies further confirm the essential roles of GCMM,DANM,and AFM in enabling highfidelity 3D reconstruction.展开更多
Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propos...Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction.Our results demonstrate high accuracy,evaluated by the geometric loss function and various statistical measures.To showcase the effectiveness of the approach,we used 3D printing to create a model that covers facial wounds.The findings indicate that our method produces a model that fits well and achieves comprehensive 3D facial reconstruction.This technique has the potential to aid doctors in treating patients with facial injuries.展开更多
Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a n...Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a novel method for efficient and lightweight 3D building reconstruction from drone imagery using line clouds and sparse point clouds.Our approach eliminates the need to generate dense point clouds,and thus significantly reduces the computational burden by reconstructing 3D models directly from sparse data.We addressed the limitations of line clouds for plane detection and reconstruction by using a new algorithm.This algorithm projects 3D line clouds onto a 2D plane,clusters the projections to identify potential planes,and refines them using sparse point clouds to ensure an accurate and efficient model reconstruction.Extensive qualitative and quantitative experiments demonstrated the effectiveness of our method,demonstrating its superiority over existing techniques in terms of simplicity and efficiency.展开更多
BACKGROUND Gastric cancer(GC)remains a significant global health challenge,with high incidence and mortality rates.Neoadjuvant chemotherapy is increasingly used to improve surgical outcomes and long-term survival in a...BACKGROUND Gastric cancer(GC)remains a significant global health challenge,with high incidence and mortality rates.Neoadjuvant chemotherapy is increasingly used to improve surgical outcomes and long-term survival in advanced cases.However,individual responses to treatment vary widely,and current imaging methods often fall short in accurately predicting efficacy.Advanced imaging techniques,such as computed tomography(CT)3D reconstruction and texture analysis,offer potential for more precise assessment of therapeutic response.AIM To explore the application value of CT 3D reconstruction volume change rate,texture feature analysis,and visual features in assessing the efficacy of neoadjuvant chemotherapy for advanced GC.METHODS A retrospective analysis was conducted on the clinical and imaging data of 97 patients with advanced GC who received S-1 plus Oxaliplatin combined chemotherapy regimen neoadjuvant chemotherapy from January 2022 to March 2024.CT texture feature analysis was performed using MaZda software,and ITK-snap software was used to measure the tumor volume change rate before and after chemotherapy.CT visual features were also evaluated.Using postoperative pathological tumor regression grade(TRG)as the gold standard,the correlation between various indicators and chemotherapy efficacy was analyzed,and a predictive model was constructed and internally validated.RESULTS The minimum misclassification rate of texture features in venous phase CT images(7.85%)was lower than in the arterial phase(13.92%).The volume change rate in the effective chemotherapy group(75.20%)was significantly higher than in the ineffective group(41.75%).There was a strong correlation between volume change rate and TRG grade(r=-0.886,P<0.001).Multivariate analysis showed that gastric wall peristalsis(OR=0.286)and thickness change rate≥40%(OR=0.265)were independent predictive factors.Receiver operating characteristic curve analysis indicated that the volume change rate[area under the curve(AUC)=0.885]was superior to the CT visual feature model(AUC=0.795).When the cutoff value was 82.56%,the sensitivity and specificity were 85.62%and 96.45%,respectively.CONCLUSION The CT 3D reconstruction volume change rate can serve as a preferred quantitative indicator for evaluating the efficacy of neoadjuvant chemotherapy in GC.Combining it with a CT visual feature predictive model can further improve the accuracy of efficacy evaluation.展开更多
Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned a...Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.展开更多
We present a grid-growth method to reconstruct 3D rock joints with arbitrary joint roughness and persistence.In the first step of this workflow,the joint model is divided into uniform grids.Then by adjusting the posit...We present a grid-growth method to reconstruct 3D rock joints with arbitrary joint roughness and persistence.In the first step of this workflow,the joint model is divided into uniform grids.Then by adjusting the positions of the grids,the joint morphology can be modified to construct models with desired joint roughness and persistence.Accordingly,numerous joint models with different joint roughness and persistence were built.The effects of relevant parameters(such as the number,height,slope of asperities,and the number,area of rock bridges)on the joint roughness coefficient(JRC)and joint persistence were investigated.Finally,an artificially split joint was reconstructed using the method,and the method's accuracy was evaluated by comparing the JRC of the models with that of the artificially split joint.The results showed that the proposed method can effectively control the JRC of joint models by adjusting the number,height,and slope of asperities.The method can also modify the joint persistence of joint models by adjusting the number and area of rock bridges.Additionally,the JRC of models obtained by our method agrees with that of the artificially split surface.Overall,the method demonstrated high accuracy for 3D rock joint reconstruction.展开更多
Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrat...Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.展开更多
3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safe...3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safer and faster,poses challenges for accurate volumetric reconstruction due to limited spatial information.This study proposes a 3D reconstruction neural network based on adaptive weight fusion(AdapFusionNet)to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images.To address the issue of spatial inconsistency in multi-angle image reconstruction,an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion,thereby improving the final reconstruction quality.The reconstruction network is built on an autoencoder(AE)framework and uses orthogonal-angle X-ray images(frontal and lateral projections)as inputs.The encoder extracts 2D features,which the decoder maps into 3D space.This study utilizes a lung CT dataset to obtain complete three-dimensional volumetric data,from which digitally reconstructed radiographs(DRR)are generated at various angles to simulate X-ray images.Since real-world clinical X-ray images rarely come with perfectly corresponding 3D“ground truth,”using CT scans as the three-dimensional reference effectively supports the training and evaluation of deep networks for sparse-angle X-ray 3D reconstruction.Experiments conducted on the LIDC-IDRI dataset with simulated X-ray images(DRR images)as training data demonstrate the superior performance of AdapFusionNet compared to other fusion methods.Quantitative results show that AdapFusionNet achieves SSIM,PSNR,and MAE values of 0.332,13.404,and 0.163,respectively,outperforming other methods(SingleViewNet:0.289,12.363,0.182;AvgFusionNet:0.306,13.384,0.159).Qualitative analysis further confirms that AdapFusionNet significantly enhances the reconstruction of lung and chest contours while effectively reducing noise during the reconstruction process.The findings demonstrate that AdapFusionNet offers significant advantages in 3D reconstruction of sparse-angle X-ray images.展开更多
This paper explores the key techniques and challenges in dynamic scene reconstruction with neural radiance fields(NeRF).As an emerging computer vision method,the NeRF has wide application potential,especially in excel...This paper explores the key techniques and challenges in dynamic scene reconstruction with neural radiance fields(NeRF).As an emerging computer vision method,the NeRF has wide application potential,especially in excelling at 3D reconstruction.We first introduce the basic principles and working mechanisms of NeRFs,followed by an in-depth discussion of the technical challenges faced by 3D reconstruction in dynamic scenes,including problems in perspective and illumination changes of moving objects,recognition and modeling of dynamic objects,real-time requirements,data acquisition and calibration,motion estimation,and evaluation mechanisms.We also summarize current state-of-theart approaches to address these challenges,as well as future research trends.The goal is to provide researchers with an in-depth understanding of the application of NeRFs in dynamic scene reconstruction,as well as insights into the key issues faced and future directions.展开更多
This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are ob...This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are obtained.Pore structures are segmented by the U-shaped network(U-Net)neural network integrated with the Canny edge detection operator,ensuring accurate pore delineation and edge extraction.The trained U-Net achieves 98.55%accuracy.The 2D data are superimposed and processed into 3D point clouds,enabling reconstruction of the pore structure and aluminum skeleton.Analysis of pore 01 shows the cross-sectional area initially increases,and then decreases with milling depth,with a uniform point distribution of 40 per layer.The reconstructed model exhibits a porosity of 77.5%,with section overlap rates between the 2D pore segmentation and the reconstructed model exceeding 96%,confirming high fidelity.Equivalent sphere diameters decrease with size,averaging 1.95 mm.Compression simulations reveal that the stress-strain curve of the 3D reconstruction model of aluminum foam exhibits fluctuations,and the stresses in the reconstruction model concentrate on thin cell walls,leading to localized deformations.This method accurately restores the aluminum foam’s complex internal structure,improving reconstruction preci-sion and simulation reliability.The approach offers a cost-efficient,high-precision technique for optimizing material performance in engineering applications.展开更多
Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the thr...Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the three-dimensional(3D)surface reconstruction of coarse aggregate particles using occlusion-free multi-view imaging.The system captures synchronized images of particles in free fall,employing a matte sphere and a nonlinear optimization approach to estimate the camera projection matrices.A pre-trained segmentation model is utilized to eliminate the background of the images.The Shape from Silhouettes(SfS)algorithm is then applied to generate 3D voxel data,followed by the Marching Cubes algorithm to construct the 3D surface contour.Validation against standard parts and diverse coarse aggregate particles confirms the method's high accuracy,with an average measurement precision of 0.434 mm and a significant increase in scanning and reconstruction efficiency.展开更多
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 ran...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.展开更多
To better understand the biological structure of bigeye tuna(Thunnus obesus),albacore tuna(Thunnus alalunga),and longtail tuna(Thunnus tonggol),computed tomography(CT)was used to scan their bodies,and the data are pro...To better understand the biological structure of bigeye tuna(Thunnus obesus),albacore tuna(Thunnus alalunga),and longtail tuna(Thunnus tonggol),computed tomography(CT)was used to scan their bodies,and the data are processed by Mimics software.The skeleton,swim bladder,and muscle of the three tuna species are reconstructed in three dimensions.The surface area and volume of the corresponding parts are measured.The results show that the surface areas of the skeleton of longtail tuna,bigeye tuna,albacore tuna accounted for 28.18%,37.34%,33.45%of their whole body surface areas respectively;the surface areas of swim bladder accounted for 0,2.06%,2.72% of their whole body surface area respectively;and the surface areas of muscle accounted for 71.82%,60.6%,63.83%of their whole body surface areas respectively.And the volumes of skeleton accounted for 28.18%,8.05%,3.84%,the volumes of swim bladder accounted for 0,3.44%,0.92%,and the volumes of muscle accounted for 94.84%,88.51%,95.24%of their body volumes respectively.The swim bladder of the longtail tuna has degenerated,while that of the bigeye tuna is conical,exhibiting the highest volume proportion among the three species.In contrast,the swim bladder of the albacore tuna is both flat and elongated,resembling an arc.Additionally,the surface area and the volume of the bigeye tuna’s swim bladder differ signifi-cantly from those of the albacore tuna.Regarding skeletal and muscular structures,the bigeye tuna has the highest skeletal volume proportion(8.05%),whereas the albacore tuna exhibits the highest muscle volume proportion(95.24%).These morphological differences are closely associated with their respective habitats.This study demonstrates the potential of CT technology in fish morphological research,providing a reliable,non-invasive method for analyzing internal structures,quantifying organ characteristics and improving the accuracy of acoustic stock assessment.展开更多
With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image...With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image sets perform well in monocular 3D face reconstruction tasks,they tend to rely on the constraints of the a priori model or the appearance conditions of the input images,fundamentally because of the inability to propose an effective method to reduce the effects of two-dimensional(2D)ambiguity.To solve this problem,we developed an unsupervised training framework for monocular face 3D reconstruction using rotational cycle consistency.Specifically,to learn more accurate facial information,we first used an autoencoder to factor the input images and applied these factors to generate normalized frontal views.We then proceeded through a differentiable renderer to use rotational consistency to continuously perceive refinement.Our method provided implicit multi-view consistency constraints on the pose and depth information estimation of the input face,and the performance was accurate and robust in the presence of large variations in expression and pose.In the benchmark tests,our method performed more stably and realistically than other methods that used 3D face reconstruction in monocular 2D images.展开更多
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 re...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.展开更多
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 m...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.展开更多
文摘The morphological description of wear particles in lubricating oil is crucial for wear state monitoring and fault diagnosis in aero-engines.Accurately and comprehensively acquiring three-dimensional(3D)morphological data of these particles has became a key focus in wear debris analysis.Herein,we develop a novel multi-view polarization-sensitive optical coherence tomography(PS-OCT)method to achieve accurate 3D morphology detection and reconstruction of aero-engine lubricant wear particles,effectively resolving occlusion-induced information loss while enabling material-specific characterization.The particle morphology is captured by multi-view imaging,followed by filtering,sharpening,and contour recognition.The method integrates advanced registration algorithms with Poisson reconstruction to generate high-precision 3D models.This approach not only provides accurate 3D morphological reconstruction but also mitigates information loss caused by particle occlusion,ensuring model completeness.Furthermore,by collecting polarization characteristics of typical metals and their oxides in aero-engine lubricants,this work comprehensively characterizes and comparatively analyzes particle polarization properties using Stokes vectors,polarization uniformity,and cumulative phase retardation,and obtains a three-dimensional model containing polarization information.Ultimately,the proposed method enables multidimensional information acquisition for the reliable identification of abrasive particle types.
基金supported by Open Foundation of the State Key Laboratory of Refractories and Metallurgy(No.G201711)the National Natural Science Foundation of China(Nos.52104317 and 51874001).
文摘The impact of heavy reduction on dendritic morphology was explored by combining experimental research and numerical simulation in metallurgy,including a detailed three-dimensional(3D)analysis and reconstruction of dendritic solidification structures.Combining scanning electron microscopy and energy-dispersive scanning analysis and ANSYS simulation,the high-precision image processing software Mimics Research was utilized to conduct the extraction of dendritic morphologies.Reverse engineering software NX Imageware was employed for the 3D reconstruction of two-dimensional dendritic morphologies,restoring the dendritic characteristics in three-dimensional space.The results demonstrate that in a two-dimensional plane,dendrites connect with each other to form irregularly shaped“ring-like”structures.These dendrites have a thickness greater than 0.1 mm along the Z-axis direction,leading to the envelopment of molten steel by dendrites in a 3D space of at least 0.1 mm.This results in obstructed flow,confirming the“bridging”of dendrites in three-dimensional space,resulting in a tendency for central segregation.Dense and dispersed tiny dendrites,under the influence of heat flow direction,interconnect and continuously grow,gradually forming primary and secondary dendrites in three-dimensional space.After the completion of dendritic solidification and growth,these microdendrites appear dense and dispersed on the two-dimensional plane,providing the nuclei for the formation of new dendrites.When reduction occurs at a solid fraction of 0.46,there is a noticeable decrease in dendritic spacing,resulting in improved central segregation.
基金Support by the Fundamental Research Funds for the Central Universities(2024300443)the National Natural Science Foundation of China(NSFC)Young Scientists Fund(62405131)。
文摘This article proposes a three-dimensional light field reconstruction method based on neural radiation field(NeRF)called Infrared NeRF for low resolution thermal infrared scenes.Based on the characteristics of the low resolution thermal infrared imaging,various optimizations have been carried out to improve the speed and accuracy of thermal infrared 3D reconstruction.Firstly,inspired by Boltzmann's law of thermal radiation,distance is incorporated into the NeRF model for the first time,resulting in a nonlinear propagation of a single ray and a more accurate description of the physical property that infrared radiation intensity decreases with increasing distance.Secondly,in terms of improving inference speed,based on the phenomenon of high and low frequency distribution of foreground and background in infrared images,a multi ray non-uniform light synthesis strategy is proposed to make the model pay more attention to foreground objects in the scene,reduce the distribution of light in the background,and significantly reduce training time without reducing accuracy.In addition,compared to visible light scenes,infrared images only have a single channel,so fewer network parameters are required.Experiments using the same training data and data filtering method showed that,compared to the original NeRF,the improved network achieved an average improvement of 13.8%and 4.62%in PSNR and SSIM,respectively,while an average decreases of 46%in LPIPS.And thanks to the optimization of network layers and data filtering methods,training only takes about 25%of the original method's time to achieve convergence.Finally,for scenes with weak backgrounds,this article improves the inference speed of the model by 4-6 times compared to the original NeRF by limiting the query interval of the model.
文摘This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery.The approach leverages advanced image processing techniques,3D Morphable Models(3DMM),and deformation techniques to overcome the limita-tions of deep learning models,particularly addressing the interpretability issues commonly encountered in medical applications.The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm.Sub-landmarks are extracted through image processing techniques and interpolation.The initial surface is generated using a 3DMM,though its accuracy remains limited.To enhance precision,deformation techniques are applied,utilizing the coordinates of 76 identified landmarks and sub-landmarks.The resulting 3D nose model is constructed based on algorithmic methods and pre-marked landmarks.Evaluation of the 3D model is conducted by comparing landmark distances and shape similarity with expert-determined ground truth on 30 Vietnamese volunteers aged 18 to 47,all of whom were either preparing for or required nasal surgery.Experimental results demonstrate a strong agreement between the reconstructed 3D model and the ground truth.The method achieved a mean landmark distance error of 0.631 mm and a shape error of 1.738 mm,demonstrating its potential for medical applications.
基金supported by the National Key Research and Development Program of China(2022YFB4500800)the Applied Basic Research Program Project of Liaoning Province(2023JH2/101300192)+2 种基金the National Natural Science Foundation of China(62032013,62072094)the Fundamental Research Funds for the Central Universities(N2416006,N2416016)Shenyang Science and Technology Plan Project(ZX20250050).
文摘The Unmanned Aerial Vehicle(UAV)-assisted sensing-transmission--computing integrated system plays a vital role in emergency rescue scenarios involving damaged infrastructure.To tackle the challenges of data transmission and enable timely rescue decision-making,we propose DWT-3DRec-an efficient wireless transmission model for 3D scene reconstruction.This model leverages MobileNetV2 to extract image and pose features,which are transmitted through a Dual-path Adaptive Noise Modulation network(DANM).Moreover,we introduce the Gumbel Channel Masking Module(GCMM),which enhances feature extraction and improves reconstruction reliability by mitigating the effects of dynamic noise.At the ground receiver,the Multi-scale Deep Source-Channel Coding for 3D Reconstruction(MDS-3DRecon)framework integrates Deep Joint Source-Channel Coding(DeepJSCC)with Cityscale Neural Radiance Fields(CityNeRF).It adopts a progressive close-view training strategy and incorporates an Adaptive Fusion Module(AFM)to achieve high-precision scene reconstruction.Experimental results demonstrate that DWT-3DRec significantly outperforms the Joint Photographic Experts Group(JPEG)standard in transmitting image and pose data,achieving an average loss as low as 0.0323 and exhibiting strong robustness across a Signal-to-Noise Ratio(SNR)range of 5--20 dB.In large-scale 3D scene reconstruction tasks,MDS-3DRecon surpasses Multum in Parvo Neural Radiance Fields(Mip-NeRF)and Bungee Neural Radiance Field(BungeeNeRF),achieving a Peak Signal-to-Noise Ratio(PSNR)of 24.921 dB and a reconstruction loss of 0.188.Ablation studies further confirm the essential roles of GCMM,DANM,and AFM in enabling highfidelity 3D reconstruction.
文摘Research on reconstructing imperfect faces is a challenging task.In this study,we explore a data-driven approach using a pre-trained MICA(MetrIC fAce)model combined with 3D printing to address this challenge.We propose a training strategy that utilizes the pre-trained MICA model and self-supervised learning techniques to improve accuracy and reduce the time needed for 3D facial structure reconstruction.Our results demonstrate high accuracy,evaluated by the geometric loss function and various statistical measures.To showcase the effectiveness of the approach,we used 3D printing to create a model that covers facial wounds.The findings indicate that our method produces a model that fits well and achieves comprehensive 3D facial reconstruction.This technique has the potential to aid doctors in treating patients with facial injuries.
基金Supported by the Guangdong Major Project of Basic and Applied Basic Research (2023B0303000016)the National Natural Science Foundation of China (U21A20515)。
文摘Efficient three-dimensional(3D)building reconstruction from drone imagery often faces data acquisition,storage,and computational challenges because of its reliance on dense point clouds.In this study,we introduced a novel method for efficient and lightweight 3D building reconstruction from drone imagery using line clouds and sparse point clouds.Our approach eliminates the need to generate dense point clouds,and thus significantly reduces the computational burden by reconstructing 3D models directly from sparse data.We addressed the limitations of line clouds for plane detection and reconstruction by using a new algorithm.This algorithm projects 3D line clouds onto a 2D plane,clusters the projections to identify potential planes,and refines them using sparse point clouds to ensure an accurate and efficient model reconstruction.Extensive qualitative and quantitative experiments demonstrated the effectiveness of our method,demonstrating its superiority over existing techniques in terms of simplicity and efficiency.
文摘BACKGROUND Gastric cancer(GC)remains a significant global health challenge,with high incidence and mortality rates.Neoadjuvant chemotherapy is increasingly used to improve surgical outcomes and long-term survival in advanced cases.However,individual responses to treatment vary widely,and current imaging methods often fall short in accurately predicting efficacy.Advanced imaging techniques,such as computed tomography(CT)3D reconstruction and texture analysis,offer potential for more precise assessment of therapeutic response.AIM To explore the application value of CT 3D reconstruction volume change rate,texture feature analysis,and visual features in assessing the efficacy of neoadjuvant chemotherapy for advanced GC.METHODS A retrospective analysis was conducted on the clinical and imaging data of 97 patients with advanced GC who received S-1 plus Oxaliplatin combined chemotherapy regimen neoadjuvant chemotherapy from January 2022 to March 2024.CT texture feature analysis was performed using MaZda software,and ITK-snap software was used to measure the tumor volume change rate before and after chemotherapy.CT visual features were also evaluated.Using postoperative pathological tumor regression grade(TRG)as the gold standard,the correlation between various indicators and chemotherapy efficacy was analyzed,and a predictive model was constructed and internally validated.RESULTS The minimum misclassification rate of texture features in venous phase CT images(7.85%)was lower than in the arterial phase(13.92%).The volume change rate in the effective chemotherapy group(75.20%)was significantly higher than in the ineffective group(41.75%).There was a strong correlation between volume change rate and TRG grade(r=-0.886,P<0.001).Multivariate analysis showed that gastric wall peristalsis(OR=0.286)and thickness change rate≥40%(OR=0.265)were independent predictive factors.Receiver operating characteristic curve analysis indicated that the volume change rate[area under the curve(AUC)=0.885]was superior to the CT visual feature model(AUC=0.795).When the cutoff value was 82.56%,the sensitivity and specificity were 85.62%and 96.45%,respectively.CONCLUSION The CT 3D reconstruction volume change rate can serve as a preferred quantitative indicator for evaluating the efficacy of neoadjuvant chemotherapy in GC.Combining it with a CT visual feature predictive model can further improve the accuracy of efficacy evaluation.
基金supported by the Hunan Provin〓〓cial Natural Science Foundation for Excellent Young Scholars(Grant No.2023JJ20045)the National Natural Science Foundation of China(Grant No.12372189)。
文摘Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.
基金supported by the National Natural Science Foundation of China(Nos.12172019 and 42477210).
文摘We present a grid-growth method to reconstruct 3D rock joints with arbitrary joint roughness and persistence.In the first step of this workflow,the joint model is divided into uniform grids.Then by adjusting the positions of the grids,the joint morphology can be modified to construct models with desired joint roughness and persistence.Accordingly,numerous joint models with different joint roughness and persistence were built.The effects of relevant parameters(such as the number,height,slope of asperities,and the number,area of rock bridges)on the joint roughness coefficient(JRC)and joint persistence were investigated.Finally,an artificially split joint was reconstructed using the method,and the method's accuracy was evaluated by comparing the JRC of the models with that of the artificially split joint.The results showed that the proposed method can effectively control the JRC of joint models by adjusting the number,height,and slope of asperities.The method can also modify the joint persistence of joint models by adjusting the number and area of rock bridges.Additionally,the JRC of models obtained by our method agrees with that of the artificially split surface.Overall,the method demonstrated high accuracy for 3D rock joint reconstruction.
基金supported by the National Key R&D Program of China(Grant No.2021YFA1001000)the National Natural Science Foundation of China(Grant Nos.82111530212,U23A20282,and 61971255)+2 种基金the Natural Science Founda-tion of Guangdong Province(Grant No.2021B1515020092)the Shenzhen Bay Laboratory Fund(Grant No.SZBL2020090501014)the Shenzhen Science,Technology and Innovation Commission(Grant Nos.KJZD20231023094659002,JCYJ20220530142809022,and WDZC20220811170401001).
文摘Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004).
文摘3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safer and faster,poses challenges for accurate volumetric reconstruction due to limited spatial information.This study proposes a 3D reconstruction neural network based on adaptive weight fusion(AdapFusionNet)to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images.To address the issue of spatial inconsistency in multi-angle image reconstruction,an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion,thereby improving the final reconstruction quality.The reconstruction network is built on an autoencoder(AE)framework and uses orthogonal-angle X-ray images(frontal and lateral projections)as inputs.The encoder extracts 2D features,which the decoder maps into 3D space.This study utilizes a lung CT dataset to obtain complete three-dimensional volumetric data,from which digitally reconstructed radiographs(DRR)are generated at various angles to simulate X-ray images.Since real-world clinical X-ray images rarely come with perfectly corresponding 3D“ground truth,”using CT scans as the three-dimensional reference effectively supports the training and evaluation of deep networks for sparse-angle X-ray 3D reconstruction.Experiments conducted on the LIDC-IDRI dataset with simulated X-ray images(DRR images)as training data demonstrate the superior performance of AdapFusionNet compared to other fusion methods.Quantitative results show that AdapFusionNet achieves SSIM,PSNR,and MAE values of 0.332,13.404,and 0.163,respectively,outperforming other methods(SingleViewNet:0.289,12.363,0.182;AvgFusionNet:0.306,13.384,0.159).Qualitative analysis further confirms that AdapFusionNet significantly enhances the reconstruction of lung and chest contours while effectively reducing noise during the reconstruction process.The findings demonstrate that AdapFusionNet offers significant advantages in 3D reconstruction of sparse-angle X-ray images.
基金supported by ZTE Industry-UniversityInstitute Cooperation Funds under Grant No.2023ZTE03-04.
文摘This paper explores the key techniques and challenges in dynamic scene reconstruction with neural radiance fields(NeRF).As an emerging computer vision method,the NeRF has wide application potential,especially in excelling at 3D reconstruction.We first introduce the basic principles and working mechanisms of NeRFs,followed by an in-depth discussion of the technical challenges faced by 3D reconstruction in dynamic scenes,including problems in perspective and illumination changes of moving objects,recognition and modeling of dynamic objects,real-time requirements,data acquisition and calibration,motion estimation,and evaluation mechanisms.We also summarize current state-of-theart approaches to address these challenges,as well as future research trends.The goal is to provide researchers with an in-depth understanding of the application of NeRFs in dynamic scene reconstruction,as well as insights into the key issues faced and future directions.
基金supported by the Key Research and DevelopmentPlan in Shanxi Province of China(No.201803D421045)the Natural Science Foundation of Shanxi Province(No.2021-0302-123104)。
文摘This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are obtained.Pore structures are segmented by the U-shaped network(U-Net)neural network integrated with the Canny edge detection operator,ensuring accurate pore delineation and edge extraction.The trained U-Net achieves 98.55%accuracy.The 2D data are superimposed and processed into 3D point clouds,enabling reconstruction of the pore structure and aluminum skeleton.Analysis of pore 01 shows the cross-sectional area initially increases,and then decreases with milling depth,with a uniform point distribution of 40 per layer.The reconstructed model exhibits a porosity of 77.5%,with section overlap rates between the 2D pore segmentation and the reconstructed model exceeding 96%,confirming high fidelity.Equivalent sphere diameters decrease with size,averaging 1.95 mm.Compression simulations reveal that the stress-strain curve of the 3D reconstruction model of aluminum foam exhibits fluctuations,and the stresses in the reconstruction model concentrate on thin cell walls,leading to localized deformations.This method accurately restores the aluminum foam’s complex internal structure,improving reconstruction preci-sion and simulation reliability.The approach offers a cost-efficient,high-precision technique for optimizing material performance in engineering applications.
基金Supported by the Key R&D Projects in Shaanxi Province(2022JBGS3-08)。
文摘Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering.This article introduces an innovative system for the three-dimensional(3D)surface reconstruction of coarse aggregate particles using occlusion-free multi-view imaging.The system captures synchronized images of particles in free fall,employing a matte sphere and a nonlinear optimization approach to estimate the camera projection matrices.A pre-trained segmentation model is utilized to eliminate the background of the images.The Shape from Silhouettes(SfS)algorithm is then applied to generate 3D voxel data,followed by the Marching Cubes algorithm to construct the 3D surface contour.Validation against standard parts and diverse coarse aggregate particles confirms the method's high accuracy,with an average measurement precision of 0.434 mm and a significant increase in scanning and reconstruction efficiency.
基金supported by ZTE Industry University Institute Cooperation Funds under Grant No.HCCN20221102002.
文摘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.
基金funded by the National Key R&D Pro-gram(No.2023YFD2401301)the R&D Program of CNFC Overseas Fishery Co.,Ltd.(No.COFC-C-F-2024-004).
文摘To better understand the biological structure of bigeye tuna(Thunnus obesus),albacore tuna(Thunnus alalunga),and longtail tuna(Thunnus tonggol),computed tomography(CT)was used to scan their bodies,and the data are processed by Mimics software.The skeleton,swim bladder,and muscle of the three tuna species are reconstructed in three dimensions.The surface area and volume of the corresponding parts are measured.The results show that the surface areas of the skeleton of longtail tuna,bigeye tuna,albacore tuna accounted for 28.18%,37.34%,33.45%of their whole body surface areas respectively;the surface areas of swim bladder accounted for 0,2.06%,2.72% of their whole body surface area respectively;and the surface areas of muscle accounted for 71.82%,60.6%,63.83%of their whole body surface areas respectively.And the volumes of skeleton accounted for 28.18%,8.05%,3.84%,the volumes of swim bladder accounted for 0,3.44%,0.92%,and the volumes of muscle accounted for 94.84%,88.51%,95.24%of their body volumes respectively.The swim bladder of the longtail tuna has degenerated,while that of the bigeye tuna is conical,exhibiting the highest volume proportion among the three species.In contrast,the swim bladder of the albacore tuna is both flat and elongated,resembling an arc.Additionally,the surface area and the volume of the bigeye tuna’s swim bladder differ signifi-cantly from those of the albacore tuna.Regarding skeletal and muscular structures,the bigeye tuna has the highest skeletal volume proportion(8.05%),whereas the albacore tuna exhibits the highest muscle volume proportion(95.24%).These morphological differences are closely associated with their respective habitats.This study demonstrates the potential of CT technology in fish morphological research,providing a reliable,non-invasive method for analyzing internal structures,quantifying organ characteristics and improving the accuracy of acoustic stock assessment.
基金Supported by Science and Technology Department Major Innovation Special Fund of Hubei Province of China(2020BAB116)。
文摘With the popularity of the digital human body,monocular three-dimensional(3D)face reconstruction is widely used in fields such as animation and face recognition.Although current methods trained using single-view image sets perform well in monocular 3D face reconstruction tasks,they tend to rely on the constraints of the a priori model or the appearance conditions of the input images,fundamentally because of the inability to propose an effective method to reduce the effects of two-dimensional(2D)ambiguity.To solve this problem,we developed an unsupervised training framework for monocular face 3D reconstruction using rotational cycle consistency.Specifically,to learn more accurate facial information,we first used an autoencoder to factor the input images and applied these factors to generate normalized frontal views.We then proceeded through a differentiable renderer to use rotational consistency to continuously perceive refinement.Our method provided implicit multi-view consistency constraints on the pose and depth information estimation of the input face,and the performance was accurate and robust in the presence of large variations in expression and pose.In the benchmark tests,our method performed more stably and realistically than other methods that used 3D face reconstruction in monocular 2D images.
文摘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.
基金supported by the National Key Research and Development Program of China(No.2022YFB1902700)the National Natural Science Foundation of China(No.11875129)+3 种基金the Fund of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect(No.SKLIPR1810)the Fund of Innovation Center of Radiation Application(No.KFZC2020020402)the Fund of the State Key Laboratory of Nuclear Physics and Technology,Peking University(No.NPT2020KFY08)the Joint Innovation Fund of China National Uranium Co.,Ltd.,State Key Laboratory of Nuclear Resources and Environment,East China University of Technology(No.2022NRE-LH-02).
文摘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.
