From Imperfection to Perfection: Advanced 3D Facial Reconstruction Using MICA Models and Self-Supervision Learning

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.

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.

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.

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.

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.

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.

Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors.Therefore,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair work.First,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be conducted.Second,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes.The experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,respectively.The technique can significantly reduce the error while maintaining the high-precision repair effect.This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration.It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

Improvement of photogrammetric joint roughness coefficient value by integrating automatic shooting parameter selection and composite error model

In order to improve the accuracy of the photogrammetric joint roughness coefficient(JRC)value,the present study proposed a novel method combining an autonomous shooting parameter selection algorithm with a composite error model.Firstly,according to the depth map-based photogrammetric theory,the estimation of JRC from a three-dimensional(3D)digital surface model of rock discontinuities was presented.Secondly,an automatic shooting parameter selection algorithm was novelly proposed to establish the 3D model dataset of rock discontinuities with varying shooting parameters and target sizes.Meanwhile,the photogrammetric tests were performed with custom-built equipment capable of adjusting baseline lengths,and a total of 36 sets of JRC data was gathered via a combination of laboratory and field tests.Then,by combining the theory of point cloud coordinate computation error with the equation of JRC calculation,a composite error model controlled by the shooting parameters was proposed.This newly proposed model was validated via the 3D model dataset,demonstrating the capability to correct initially obtained JRC values solely based on shooting parameters.Furthermore,the implementation of this correction can significantly reduce errors in JRC values obtained via photographic measurement.Subsequently,our proposed error model was integrated into the shooting parameter selection algorithm,thus improving the rationality and convenience of selecting suitable shooting parameter combinations when dealing with target rock masses with different sizes.Moreover,the optimal combination of three shooting parameters was offered.JRC values resulting from various combinations of shooting parameters were verified by comparing them with 3D laser scan data.Finally,the application scope and limitations of the newly proposed approach were further addressed.

Concrete Defects Inspection and 3D Mapping Using City Flyer Quadrotor Robot

The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive. Conventional inspection approaches for concrete inspection, using RGB imagebased thresholding methods, are not able to determine metric information as well as accurate location information for assessed defects for conditions. To address this challenge, we propose a deep neural network(DNN) based concrete inspection system using a quadrotor flying robot(referred to as City Flyer) mounted with an RGB-D camera. The inspection system introduces several novel modules. Firstly, a visual-inertial fusion approach is introduced to perform camera and robot positioning and structure 3 D metric reconstruction. The reconstructed map is used to retrieve the location and metric information of the defects.Secondly, we introduce a DNN model, namely Ada Net, to detect concrete spalling and cracking, with the capability of maintaining robustness under various distances between the camera and concrete surface. In order to train the model, we craft a new dataset, i.e., the concrete structure spalling and cracking(CSSC)dataset, which is released publicly to the research community.Finally, we introduce a 3 D semantic mapping method using the annotated framework to reconstruct the concrete structure for visualization. We performed comparative studies and demonstrated that our Ada Net can achieve 8.41% higher detection accuracy than Res Nets and VGGs. Moreover, we conducted five field tests, of which three are manual hand-held tests and two are drone-based field tests. These results indicate that our system is capable of performing metric field inspection,and can serve as an effective tool for civil engineers.

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.

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.

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.

Uses of a dedicated 3D reconstruction software with augmented and mixed reality in planning and performing advanced liver surgery and living donor liver transplantation(with videos)

The development of digital intelligent diagnostic and treatment technology has opened countless new opportunities for liver surgery from the era of digital anatomy to a new era of digital diagnostics,virtual surgery simulation and using the created scenarios in real-time surgery using mixed reality.In this article,we described our experience on developing a dedicated 3 dimensional visualization and reconstruction software for surgeons to be used in advanced liver surgery and living donor liver transplantation.Furthermore,we shared the recent developments in the field by explaining the outreach of the software from virtual reality to augmented reality and mixed reality.

Characterization of 3D pore nanostructure and stress-dependent permeability of organic-rich shales in northern Guizhou Depression,China

The three-dimensional(3D)pore structures and permeability of shale are critical for forecasting gas production capacity and guiding pressure differential control in practical reservoir extraction.However,few investigations have analyzed the effects of microscopic organic matter(OM)morphology and 3D pore nanostructures on the stress sensitivity,which are precisely the most unique and controlling factors of reservoir quality in shales.In this study,ultra-high nanoscale-resolution imaging experiments,i.e.focused ion beam-scanning electron microscopy(FIB-SEMs),were conducted on two organic-rich shale samples from Longmaxi and Wufeng Formations in northern Guizhou Depression,China.Pore morphology,porosity of 3D pore nanostructures,pore size distribution,and connectivity of the six selected regions of interest(including clump-shaped OMs,interstitial OMs,framboidal pyrite,and microfractures)were qualitatively and quantitatively characterized.Pulse decay permeability(PDP)measurement was used to investigate the variation patterns of stress-dependent permeability and stress sensitivity of shales under different confining pressures and pore pressures,and the results were then used to calculate the Biot coefficients for the two shale formations.The results showed that the samples have high OM porosity and 85%of the OM pores have the radius of less than 40 nm.The OM morphology and pore structure characteristics of the Longmaxi and Wufeng Formations were distinctly different.In particular,the OM in the Wufeng Formation samples developed some OM pores with radius larger than500 nm,which significantly improved the connectivity.The macroscopic permeability strongly depends on the permeability of OM pores.The stress sensitivity of permeability of Wufeng Formation was significantly lower than that of Longmaxi Formation,due to the differences in OM morphology and pore structures.The Biot coefficients of 0.729 and 0.697 were obtained for the Longmaxi and Wufeng Formations,respectively.

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.

Deep-Learning-Empowered 3D Reconstruction for Dehazed Images in IoT-Enhanced Smart Cities

With increasingly more smart cameras deployed in infrastructure and commercial buildings,3D reconstruction can quickly obtain cities’information and improve the efficiency of government services.Images collected in outdoor hazy environments are prone to color distortion and low contrast;thus,the desired visual effect cannot be achieved and the difficulty of target detection is increased.Artificial intelligence(AI)solutions provide great help for dehazy images,which can automatically identify patterns or monitor the environment.Therefore,we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning.First,we propose a fine transmission image deep convolutional regression network(FT-DCRN)dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image.The DCRN is used to obtain the coarse transmission image,which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network.The fine transmission image is obtained by refining the coarse transmission image using a guided filter.The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image.Second,we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction.An advanced relaxed iterative fine matching based on the structure from motion(ARI-SFM)algorithm is proposed.The ARISFM algorithm,which obtains the fine matching corner pairs and reduces the number of iterations,establishes an accurate one-to-one matching corner relationship.The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms.In addition,the ARI-SFM algorithm guarantees the precision and improves the efficiency.

Application of computer tomography-based 3D reconstruction technique in hernia repair surgery

BACKGROUND Hernia is a common condition requiring abdominal surgery.The current standard treatment for hernia is tension-free repair using meshes.Globally,more than 200 new types of meshes are licensed each year.However,their clinical applications are associated with a series of complications,such as recurrence(10%-24%)and infection(0.5%-9.0%).In contrast,3D-printed meshes have significantly reduced the postoperative complications in patients.They have also shortened operating time and minimized the loss of mesh materials.In this study,we used the myopectineal orifice(MPO)data obtained from preoperative computer tomography(CT)-based 3D reconstruction for the production of 3D-printed biologic meshes.AIM To investigate the application of multislice spiral CT-based 3D reconstruction technique in 3D-printed biologic mesh for hernia repair surgery.METHODS We retrospectively analyzed 60 patients who underwent laparoscopic tension-free repair for inguinal hernia in the Department of General Surgery of the First Hospital of Shanxi Medical University from September 2019 to December 2019.This study included 30 males and 30 females,with a mean age of 40±5.6 years.Data on the MPO were obtained from preoperative CT-based 3D reconstruction as well as from real-world intraoperative measurements for all patients.Anatomic points were set for the purpose of measurement based on the definition of MPO:A:The pubic tubercle;B:Intersection of the horizontal line extending from the summit of the inferior edge of the internal oblique and transversus abdominis and the outer edge of the rectus abdominis,C:Intersection of the horizontal line extending from the summit of the inferior edge of the internal oblique and transversus abdominis and the inguinal ligament,D:Intersection of the iliopsoas muscle and the inguinal ligament,and E:Intersection of the iliopsoas muscle and the superior pubic ramus.The distance between the points was measured.All preoperative and intraoperative data were analyzed using the t test.Differences with P<0.05 were considered significant in comparative analysis.RESULTS The distance between points AB,AC,BC,DE,and AE based on preoperative and intraoperative data was 7.576±0.212 cm vs 7.573±0.266 cm,7.627±0.212 cm vs 7.627±0.212 cm,7.677±0.229 cm vs 7.567±0.786 cm,7.589±0.204 cm vs 7.512±0.21 cm,and 7.617±0.231 cm vs 7.582±0.189 cm,respectively.All differences were not statistically significant(P>0.05).CONCLUSION The use of multislice spiral CT-based 3D reconstruction technique before hernia repair surgery allows accurate measurement of data and relationships of different anatomic sites in the MPO region.This technique can provide precise data for the production of 3D-printed biologic meshes.