2D Projection Research Articles

Systematic review of various 3D volume construction algorithms adopted in isocentric C-arm devices

C-arm systems are well established in clinical routines which produces two- dimensional (2D) projection images, which are not sufficient to provide the depth information required by surgeons during interventional procedures. Due to this, the procedural time and radiation dosage are significantly higher which leads to increased procedural cost. In order to extract depth information from 2D projection images acquired with stepwise rotation between 0 to 180 degrees, various 3D volume construction algorithms are developed. The purpose of this paper is to systematically review the various 3D volume construction algorithms in order to develop a cost-effective solution to be incorporated in the iso-centric C-arm machines without 3D volume construction algorithms. The proposed review process is done in the following aspects: i. State-of-the-art 3D volume construction algorithms ii. Comparing the different aspects of commercially available iso- centric C-arm machines incorporated with 3D volume construction algorithms. The impact of various 3D construction algorithms in improving different aspects such as image quality, resolution, procedure time, radiation dose, and accuracy of the final 3D model is discussed. Based on the findings we recommend a procedure to develop and validate a 3D volume construction algorithm. 3D volume construction from C-arm images helps to construct Multi Planar Reconstruction (MPR) views during interventional procedures which will provide in-depth information and minimize the procedure time, cost, and dosage to the patient.

Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants

Accurate lung segmentation plays a vital role in generating 3D projections of lung infections, which contribute to the diagnosis and treatment planning of various lung diseases, including cases like COVID-19. This study capitalizes on the capabilities of deep learning techniques to reconstruct 3D lung projections from CT-scans. In this pursuit, we employ well-established 2D architectural frameworks like UNet, LinkNet, Attention UNet, UNet 3+, and TransUNet. The dataset used comprises 20 3D CT-scans from COVID-19 patients, resulting in over 2900 raw 2D slices. Following preprocessing, the dataset is refined to encompass 2560 2D slices tailored for modeling. Preprocessing procedures involve mask refinement, image resizing, contrast limited adaptive histogram equalization (CLAHE), and image augmentation to enhance the data quality and diversity. Evaluation metrics, including Intersection over Union (IoU) and dice scores, are used to assess the models’ performance. Among the models tested, Attention UNet stands out, demonstrating the highest performance. Its key trait of harnessing attention mechanisms enhances its ability to focus on crucial features. This translates to exceptional results, with an IoU score of 85.36% and dice score of 91.49%. These findings provide valuable insights into guiding the selection of an appropriate architecture tailored to specific requirements, considering factors such as segmentation accuracy and computational resources, in the context of 3D lung projection reconstruction.

A Virtual Simulation Experimental Platform for Smart Home based on Unity3D

Smart home provides people with a more convenient and intelligent way of living and it is one of the most widely used application of Internet of Things (IoT). However, building a smart home laboratory for students to visit and learn is labor intensive and time consuming. In order to meet the demand of experimental teaching in higher education, this paper designed and developed a Virtual Simulation Experimental Platform (VSEP) for smart home. Firstly, we introduced goals, platform architecture and functional modules of the system. Then, the development process was discussed. Specifically, the model of smart home was quickly built in SketchUp and imported into Unity 3D project, and both the scene roaming and interactive functions were achieved by coding C# scripts. Finally, we described the key methods in details and accomplished the development work. The proposed platform provides an immersive virtual environment for students to learn concepts and principles of related theoretical knowledge, and offers a new method for experimental teaching.

Optical soliton solutions of the nonlinear Schrödinger equation in the presence of chromatic dispersion with cubic-quintic-septic-nonicnonlinearities

In this study, one of our main subjects is the examination of optical solitons of the nonlinear Schrödinger equation having cubic-quintic-septic-nonic nonlinearities via the modified F-expansion method. The other subject is also the analysis of the impacts of some parameters in the model on the soliton shape, which is examined for the first time in this study. According to the modified F-expansion method, we select the suitable transformation to gain the nonlinear ordinary differential equation for the nonlinear Schrödinger equation having cubic-quintic-septic-nonic nonlinearities in the first stage. Then, we get a system consisting of linear equations in polynomial form with the aid of the modified F-expansion method. Various solution sets consisting of the parameters of the nonlinear Schrödinger equation having cubic-quintic-septic-nonic nonlinearities are achieved. Inserting the selected sets and transformations into the serial form of the presented method and utilizing the solutions of the auxiliary equation in the presented method, the optical soliton solutions of the model are derived. Furthermore, varied optical soliton solutions, such as anti-kink, singular, and bright, are achieved, and 3D and 2D projections of the generated soliton solutions have been illustrated. The impact of some parameters on each soliton behavior has also been examined. It is found that these parameters have a significant impact on the soliton structure.

ACCURATE REGISTRATION BETWEEN ULTRA-WIDE-FIELD AND NARROW ANGLE RETINA IMAGES WITH 3D EYEBALL SHAPE OPTIMIZATION.

The Ultra-Wide-Field (UWF) retina images have attracted wide attentions in recent years in the study of retina. However, accurate registration between the UWF images and the other types of retina images could be challenging due to the distortion in the peripheral areas of an UWF image, which a 2D warping can not handle. In this paper, we propose a novel 3D distortion correction method which sets up a 3D projection model and optimizes a dense 3D retina mesh to correct the distortion in the UWF image. The corrected UWF image can then be accurately aligned to the target image using 2D alignment methods. The experimental results show that our proposed method outperforms the state-of-the-art method by 30%.

Readiness for Change in the Implementation of a 3D Printing Initiative in a Catalan Tertiary Hospital Using the Normalization Process Theory: Survey Study.

The high failure rate of innovation projects motivates us to understand the perceptions about resistances and barriers of the main stakeholders to improving success rates. This study aims to analyze the readiness for change in the implementation of a 3D printing project in a Catalan tertiary hospital prior to its implementation. We used a web-based, voluntary, and anonymous survey using the Normalization Measurement Development questionnaire (NoMAD) to gather views and perceptions from a selected group of health care professionals at Germans Trias i Pujol University Hospital. In this study, 58 professionals, including heads of service (n=30, 51%), doctors (n=18, 31%), nurses (n=7, 12%), and support staff (n=3, 5%), responded to the questionnaire. All groups saw the value of the project and were willing to enroll and support it. Respondents reported the highest scores (out of 5) in cognitive participation (mean 4.45, SD 0.04), coherence (mean 3.72, SD 0.13), and reflective monitoring (mean 3.80, SD 0.25). The weakest score was in collective action (mean 3.52, SD 0.12). There were no statistically significant differences in scores among professions in the survey. The 3D printing project implementation should pay attention to preparing, defining, sharing, and supporting the operational work involved in its use and implementation. It should also understand, assess, and communicate the ways in which the new set of practices can affect the users and others around them. We suggest that health officers and politicians consider this experience as a solid ground toward the development of a more efficient health innovation system and as a catalyst for transformation.

El diseño como mediación comunicativa para el desarrollo de proyectos gráficos colaborativos

Collaborative graphic design is a form of teamwork that allows the creation of visual communication projects with a broader and multidisciplinary vision. This article shows the concept of design and communication as communicative mediation and its importance in the development of collaborative graphic projects for the improvement of projects. It analyzes how design acts as a means for communication and interaction between members of multidisciplinary teams, and how it encourages creativity, understanding and effective management of the project to be developed. In addition, the relevance of design as a tool for collaboration and teamwork in the graphic field is highlighted, where the communications messages exposed in a collaborative way are projected to transmit explicit assertive messages in the projects.

Deciphering viscoelastic cell manipulation in rectangular microchannels.

Viscoelastic focusing has emerged as a promising method for label-free and passive manipulation of micro and nanoscale bioparticles. However, the design of microfluidic devices for viscoelastic particle focusing requires a thorough comprehensive understanding of the flow condition and operational parameters that lead to the desired behavior of microparticles. While recent advancements have been made, viscoelastic focusing is not fully understood, particularly in straight microchannels with rectangular cross sections. In this work, we delve into inertial, elastic, and viscoelastic focusing of biological cells in rectangular cross-section microchannels. By systematically varying degrees of fluid elasticity and inertia, we investigate the underlying mechanisms behind cell focusing. Our approach involves injecting cells into devices with a fixed, non-unity aspect ratio and capturing their images from two orientations, enabling the extrapolation of cross-sectional equilibrium positions from two dimensional (2D) projections. We characterized the changes in hydrodynamic focusing behaviors of cells based on factors, such as cell size, flow rate, and fluid characteristics. These findings provide insights into the flow characteristics driving changes in equilibrium positions. Furthermore, they indicate that viscoelastic focusing can enhance the detection accuracy in flow cytometry and the sorting resolution for size-based particle sorting applications. By contributing to the advancement of understanding viscoelastic focusing in rectangular microchannels, this work provides valuable insight and design guidelines for the development of devices that harness viscoelastic focusing. The knowledge gained from this study can aid in the advancement of viscoelastic particle manipulation technique and their application in various fields.

Polarized light vision pose measurement method by fusing the constraints provided between control points for hand-eye calibration

The accuracy of hand-eye calibration is determined by the pose measurement accuracy of the calibration target. In the hand-eye calibration of industrial robots, the pose measurement can be performed based on the polarization information to filter out high-brightness areas of the image, optimize image processing, and increase effective control points. However, it is hard to decide on an adequate polarization angle. An inadequate polarization angle will affect the image contrast and ultimately affect the extraction accuracy of the 2D projections of the control points. In addition, the existing non-iterative pose measurement methods only consider the constraints provided by the single control points. The constraints provided between the control points are not in consideration. This results in reduced pose measurement accuracy in noisy environments. Aiming at the above problems, a polarized light vision pose measurement method by fusing the constraints provided between control points for hand-eye calibration is proposed: the adequate polarization angle was determined based on the cross-ratio invariance, and the target image collected under the adequate polarization angle was used for subsequent pose measurement to reduce the high-brightness areas on the premise of ensuring the image contrast. We introduced the plane created by two control points and the optical center to represent the constraints provided between these two control points. In this way, the constraints provided by any two control points can be fused with the constraints provided by the single control points to solve the pose non-iteratively. Compared to other current methods, there are more significant increases in our method's pose measurement accuracy and stability. The experimental results show that the angle measurement error is less than 0.052° over the measuring range of -60°-+60°, and the displacement measurement error is less than 0.01 mm over the measuring range of 0-20 mm.

Pressure Dependence of Mechanical and Thermodynamic Properties of MAX‐Phase M2GaC (M = Nb and Ta) from First‐Principles Calculations

Herein, the high‐pressure impacts on the electronic, mechanical, and thermodynamic properties of MAX‐phase M2GaC (M = Nb and Ta) are calculated through the first principles. The phonon dispersion results indicate that M2GaC (M = Nb and Ta) is dynamically stable. The elastic constants and elastic modulus of Nb2GaC and Ta2GaC increase with the pressure increase, while the elastic anisotropic 3D surface structure and projection diagram show that bulk modulus, shear modulus, and Young's modulus all show anisotropy. M2GaC (M = Nb and Ta) has metallic, covalent, and ionic bonds. In addition, based on the quasiharmonic Debye model, the effects of high pressure (0–50 GPa) and temperature (0–2000 K) on the thermodynamic properties of Nb2GaC and Ta2GaC are systematically studied. The constant pressure heat capacity (CP) and thermal expansion coefficient (α) of Nb2GaC and Ta2GaC decrease with the increase of pressure, while the internal energy (U) and Gibbs free energy (G) of Nb2GaC and Ta2GaC increase with the increase of pressure. The sound velocity and kmin increase with the pressure increase, and Nb2GaC has a higher thermal conductivity than Ta2GaC.

Three-dimensional Anatomy of the Velopharyngeal Muscles in the Cleft Palate.

We have used micro-computed tomography (CT) to elucidate the relationship between the muscle fibers in specimens with cleft palate. These findings could be useful for muscle reconstruction in cleft palate repair and to better understand cleft palate speech. Cadaveric anatomical study. This study included three specimens with cleft palate. The specimens were stained with phosphomolybdic acid and scanned by Micro-CT. The anatomy of the muscles. Using 2D projection images and 3D reconstruction models, subtle anatomical structures could be observed in the muscles. The attachment of the levator veli palatini (LVP) was not at the posterior edge of the hard palate or palatine aponeurosis (PA), but at the anterior 21.71-44.2% of the cleft edge. The palatopharyngeal (PP) was composed of two bundles: inferior and superior heads, which clasped the LVP. The uvularis was unevenly distributed, and located on both sides of the cleft edge, originating at the edge. The palatoglossus, superior constrictor of pharynx and anatomical structure around the pterygoid hamulus, were normal. The PA, PP and LVP were attached to the cleft edge from front to back, in that order. The position of the uvularis was not fixed. With the help of Micro-CT technology, detailed anatomical features and the relationship between muscles could be visualized. In the specimen with cleft palate, muscles in the soft palate were associated with the pharyngeal muscles, which formed the 3D "velopharyngeal muscles complex." These findings provide anatomical evidence for muscle reconstruction in cleft palate repair.

Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections.

Using 3D CNNs on high-resolution medical volumes is very computationally demanding, especially for large datasets like UK Biobank, which aims to scan 100,000 subjects. Here, we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of 3D volumes leads to reasonable test accuracy (mean absolute error of about 3.5 years) when predicting age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20-50 s using a single GPU, which is two orders of magnitude faster than a small 3D CNN. This speedup is explained by the fact that 3D brain volumes contain a lot of redundant information, which can be efficiently compressed using 2D projections. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.

A general image misalignment correction method for tomography experiments

Tomography experiments generate three-dimensional (3D) reconstructed slices from a series of two-dimensional (2D) projection images. However, the mechanical system generates joint offsets that result in unaligned 2D projections. This misalignment affects the reconstructed images and reduces their actual spatial resolution. In this study, we present a novel method called outer contour-based misalignment correction (OCMC) for correcting image misalignments in tomography. We use the sample's outer contour structure as auxiliary information to estimate the extent of misalignment in each image. This method is generic and can be used with various tomography imaging techniques. We validated our method with five datasets collected from different samples and across various tomography techniques. The OCMC method demonstrated significant advantages in terms alignment accuracy and time efficiency. As an end-to-end correction method, OCMC can be easily integrated into an online tomography data processing pipeline and facilitate feedback control in future synchrotron tomography experiments.

Generation of fluoroscopy-alike radiographs as alternative datasets for deep learning in interventional radiology.

In fluoroscopy-guided interventions (FGIs), obtaining large quantities of labelled data for deep learning (DL) can be difficult. Synthetic labelled data can serve as an alternative, generated via pseudo 2D projections of CT volumetric data. However, contrasted vessels have low visibility in simple 2D projections of contrasted CT data. To overcome this, we propose an alternative method to generate fluoroscopy-like radiographs from contrasted head CT Angiography (CTA) volumetric data. The technique involves segmentation of brain tissue, bone, and contrasted vessels from CTA volumetric data, followed by an algorithm to adjust HU values, and finally, a standard ray-based projection is applied to generate the 2D image. The resulting synthetic images were compared to clinical fluoroscopy images for perceptual similarity and subject contrast measurements. Good perceptual similarity was demonstrated on vessel-enhanced synthetic images as compared to the clinical fluoroscopic images. Statistical tests of equivalence show that enhanced synthetic and clinical images have statistically equivalent mean subject contrast within 25% bounds. Furthermore, validation experiments confirmed that the proposed method for generating synthetic images improved the performance of DL models in certain regression tasks, such as localizing anatomical landmarks in clinical fluoroscopy images. Through enhanced pseudo 2D projection of CTA volume data, synthetic images with similar features to real clinical fluoroscopic images can be generated. The use of synthetic images as an alternative source for DL datasets represents a potential solution to the application of DL in FGIs procedures.

Reconstructing Geometrical Models of Indoor Environments Based on Point Clouds

In this paper, we present a workflow that combines supervised and unsupervised methods for the reconstruction of geometric models with architectural information from unordered 3D data. Our method uses a downsampling strategy to enrich features to provide scalability for large datasets, increase robustness, and be independent of the sensor used. A Neural Network is then used to segment the resulting point cloud into basic structures. This removes furniture and clutter and preserves the relevant walls, ceilings, floors, and openings. A 2D projection combined with a graph structure is used to find a Region of Interest within the cleaned point cloud, indicating a potential room. Each detected region is projected back into a 3D data patch to refine the room candidates and allow for more complex room structures. The resulting patches are fitted with a polygon using geometric approaches. In addition, architectural features, such as windows and doors, are added to the polygon. To demonstrate that the presented approach works and that the network provides usable results, even with changing data sources, we tested the approach in different real-world scenarios with different sensor systems.

What cannot be seen correctly in 2D visualizations of single-cell 'omics data?

A common strategy for exploring single-cell 'omics data is visualizing 2D nonlinear projections that aim to preserve high-dimensional data properties such as neighborhoods. Alternatively, mathematical theory and other computational tools can directly describe data geometry, while also showing that neighborhoods and other properties cannot be well-preserved in any 2D projection.

Introducing a novel fast algebraic reconstruction technique and advancing 3D image reconstruction in a specialized bioimaging system

In this study, our primary goal was to remarkably reduce computation time and enhance the efficiency of 3D image reconstruction in bioimaging applications by focusing on iterative image reconstruction methods, particularly algebraic reconstruction technique (ART). To achieve this, we introduced a novel fast algebraic reconstruction technique called the mining-ART, consisting of two versions. We validated our proposed method using the mini-Opto tomography device, a specialized bioimaging system, and a synthetic biological phantom. This phantom, developed in our laboratory for bioimaging experiments, was composed of polydimethylsiloxane (PDMS) and had dimensions of 8 mm × 8 mm × 500 µm. We acquired two-dimensional (2D) projections of the phantom from 11 different angles using the bioimaging device, and then reconstructed these projections in 3D using both ART and the mining-ART. The dimensions of the 3D reconstructed images ranged from 100 × 100 × 50 to 800 × 800 × 50, and voxel resolutions varied correspondingly from 80 × 80 × 10 µm to 10 × 10 × 10 µm. Our experimental results demonstrated that the proposed mining-ART outperformed ART in terms of superior 3D image reconstruction speed across various sizes, while maintaining similar image quality. The mining-ART achieved a significant acceleration in computation time, ranging from 5.89 to 92.77 times faster than ART, depending on the dimensions. Furthermore, we extensively explored the feasibility of integrating compressed sensing-based three-dimensional total variation (3DTV) into the mining-ART. In conclusion, our proposed mining-ART demonstrated its potential in dramatically enhancing the computational performance of image reconstruction methods in bioimaging and made a significant contribution to advancing 3D image reconstruction in various research fields.

Enhancing Occlusion Handling in Real-Time Tracking Systems through Geometric Mapping and 3D Reconstruction Validation

Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching and its 3D projection obtained. The developed algorithm has been tested using sample data and the results are presented.

Projection of realistic three-dimensional photogrammetry models using stereoscopic display: A technical note.

The 3D stereoscopic technique consists in providing the illusional perception of depth of a given object using two different images mimicking how the right and left eyes capture the object. Both images are slightly different and when overlapped gives a three-dimensional (3D) experience. Considering the limitations for establishing surgical laboratories and dissections courses in some educational institutions, techniques such as stereoscopy and photogrammetry seem to play an important role in neuroanatomy and neurosurgical education. The aim of this study was to describe how to combine and set up realistic models acquired with photogrammetry scans in 3D stereoscopic projections. Three donors, one dry skull, embalmed brain and head, were scanned using photogrammetry. The software used for displaying the final realistic 3D models (Blender, Amsterdam, the Netherlands) is a free software and allows stereoscopic projection without compromising the interactivity of each model. By default, the model was exported and immediately displayed as a red cyan 3D mode. The 3D projector used in the manuscript required a side-by-side 3D mode which was set up with simple commands on the software. The final stereoscopy projection offered depth perception and a visualization in 360° of each donor; this perception was noted especially when visualizing donors with different cavities and fossae. The combination of 3D techniques is of paramount importance for neuroanatomy education. Stereoscopic projections could provide a valuable tool for neuroanatomy instruction directed at clinical trainees and could be especially useful when access to laboratory-based learning is limited.

Indoor Clutter Object Removal Method for an As-Built Building Information Model Using a Two-Dimensional Projection Approach

Point cloud data are used to create an as-built building information model (as-built BIM) that reflects the actual status of any building, whether being constructed or already completed. However, indoor clutter objects in the point cloud data, such as people, tools, and materials, should be effectively eliminated to create the as-built BIM. In this study, the authors proposed a novel method to automatically remove indoor clutter objects based on the Manhattan World assumption and object characteristics. Our method adopts a two-dimensional (2D) projection of a 3D point cloud approach and utilizes different properties of indoor clutter objects and structural elements in the point cloud. Voxel-grid downsampling, density-based spatial clustering (DBSCAN), the statistical outlier removal (SOR) filter, and the unsupervised radius-based nearest neighbor search algorithm were applied to our method. Based on the evaluation of our proposed method using six actual scan datasets, we found that our method achieved a higher mean accuracy (0.94), precision (0.97), recall (0.90), and F1 core (0.93) than the commercial point cloud processing software. Our method shows better results than commercial point cloud processing software in classifying and removing indoor clutter objects in complex indoor environments acquired from construction sites. As a result, assumptions about the different properties of indoor clutter objects and structural elements are being used to identify indoor clutter objects. Additionally, it is confirmed that the parameters used in the proposed method could be determined by the voxel size once it is decided during the downsampling process.