3D Reconstruction Research Articles

3D Model Simulation in Preoperative Planning of Complex and Revision Hip Replacement

Complex and revision hip arthroplasty has remained one of the most discussed orthopedic topics in recent years. With the advancement of printing technologies 3D preoperative planning and live model simulation provides us with “hands on” procedures without any risk for the patient. Modern computer technologies present the possibility for preoperative planning of Total Hip Arthroplasty (THA) in digital environment. 3D printing of anatomical models in real size allows accurate assessment of the pathological deviations of the anatomy, precise preoperative planning of the reconstruction and simulation of the operative intervention ex-vivo. We present a series of 3D simulations for planning hip replacement, illustrating the technique, discussing its advantages and disadvantages, in complex primary and revision hip arthroplasty. In the complex and revision surgery of the hip joint, a correct assessment of bone defects is mandatory. Reconstructing the biomechanical parameters – centre of rotation, femoral offset and leg length discrepancy, is also of great significance. The 3D reconstruction with biomodel simulations offers exceptional accuracy in this respect. The method can be an excellent tool for education of surgeons in training and residents.

A Star‐Nose‐Inspired Bionic Soft Robot for Nonvisual Spatial Detection and Reconstruction

The star‐nosed mole is recognized as a tactilely sensitive mammal due to its unique nose, which facilitates spatial detection in dark environments through touch using its appendages enveloped in numerous sensory receptors. This article introduces a bionic soft robot inspired by the star‐nosed mole, which combines a pneumatic soft platform with a polydimethylsiloxane–polyethylene terephthalate cylindrical tactile sensor array based on bilayer single‐electrode triboelectric nanogenerators, mimicking the muscle tissue of the mole's nose and the cylindrical appendages surrounded by Eimer's organs. The cylindrical sensor array enables multiangle spatial detection without an external power supply and remains unaffected by external materials. By implementing a constant curvature model for robot motion control, positional information is provided for the contact points between the cylindrical sensor array and the external environment. The robot effectively discriminates the distance and shape of various objects and achieves nonvisual 3D spatial detection and reconstruction in real‐world scenarios. This work presents a novel bionic approach for 3D spatial detection in nonvisual environments.

3D Seismic Data Reconstruction based on Weighted Fast Iterative Shrinkage Thresholding algorithm

3D Seismic Data Reconstruction based on Weighted Fast Iterative Shrinkage Thresholding algorithm

Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction

In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.

Neural Surfel Reconstruction: Addressing Loop Closure Challenges in Large-Scale 3D Neural Scene Mapping.

Efficiently reconstructing complex and intricate surfaces at scale remains a significant challenge in 3D surface reconstruction. Recently, implicit neural representations have become a popular topic in 3D surface reconstruction. However, how to handle loop closure and bundle adjustment is a tricky problem for neural methods, because they learn the neural parameters globally. We present an algorithm that leverages the concept of surfels and expands relevant definitions to address such challenges. By integrating neural descriptors with surfels and framing surfel association as a deformation graph optimization problem, our method is able to effectively perform loop closure detection and loop correction in challenging scenarios. Furthermore, the surfel-level representation simplifies the complexity of 3D neural reconstruction. Meanwhile, the binding of neural descriptors to corresponding surfels produces a dense volumetric signed distance function (SDF), enabling the mesh reconstruction. Our approach demonstrates a significant improvement in reconstruction accuracy, reducing the average error by 16.9% compared to previous methods, while also generating modeling files that are up to 90% smaller than those produced by traditional methods.

3D reconstruction from focus for lensless imaging

The lensless camera is an ultra-thin imaging system that utilizes encoding elements instead of lenses to perceive the light field and reconstruct it through computational methods. Early studies have demonstrated that lensless cameras can encode 3D scenes at various depths in caustic patterns with varying sizes, known as point spread functions (PSFs). By deconvolving measurements with these PSFs, the reconstruction exhibits distinct focusing effects: objects in the focal plane appear sharp, while objects in other planes become blurred. Building upon this feature, we propose a feedforward network based on depth from focus to generate the depth map and the all-in-focus image by reconstructing the focal stack and deriving the probability of pixel clarity. Using our optimization framework, we present superior and more stable depth estimation than previous methods in both simulated data and real measurements captured by our lensless camera.

Histological assessment of the shared vascular wall in anomalous aortic origin of coronary arteries with an interarterial course

Abstract Background In anomalous aortic origin of a coronary artery (AAOCA) an intramural segment is present if the proximal AAOCA courses through the aortic wall. This is defined as a shared tunica media of the aorta and AAOCA in the ‘intramural septum’. This definition is based on autopsy studies of selected AAOCA patients who suffered sudden cardiac death and is difficult to apprehend on imaging modalities. Therefore, the aim was to identify the histological features and variants of the interarterial vascular wall in living adult patients, and correlate this to CT-angiography (CTA). Methods This prospective multicenter study included consecutive adult AAOCA patients who underwent surgical unroofing between 2021 to 2024. The excised interatrial vascular wall tissue was embedded, sectioned, and immunohistochemically stained for physiological components, e.g. smooth muscle cells and connective tissue, and pathological components such as fibrosis. Microscopic examination and quantification was performed by two independent observers and findings were correlated with CTA. Results Fifteen patients (mean age 42.9±14.0 years, 60% female) and 1 postmortem specimen were included. Fourteen (93%) had a right-AAOCA and 1 (7%) a left-AAOCA, with a mean intramural length of 8.4±4.1mm. Histopathologically, disorganized elastic lamellae with fragmentation, presence of fibrosis and loss of nuclei were observed. No vasa vasorum or nervi vasorum were identified. Post-sectioning virtual 3D reconstructions enhanced the spatial insight in correlation of histological sections with CTA. Conclusion This first study of the intramural septum in adults with AAOCA reveals the histological structure of the shared vascular wall between the aorta and AAOCA. Marked vascular wall abnormalities such as lack of vasa and nervi vasorum, fragmentation of elastic lamellae and fibrotic changes were observed. These findings support a propensity to local vascular wall pathology in patients with AAOCA. 3D reconstruction supported the translation of histological data to clinical imaging techniques.Central Figure

In-depth morphological and functional analysis of ATTR-CA myocardial cells and niche by electron microscopic 3D reconstruction

Abstract Background Cell-cell interactions and interplay with extracellular matrix are known to be important in the steady state and pathological conditions, including wild-type transthyretin cardiac amyloidosis (wtATTR-CA). However, alterations of the microstructure of cardiomyocytes (CMs) and cardiac niches have not been well analyzed. Normal histological staining is usually limited in resolution, and 2D electron microscopy cannot capture stereoscopic images or the distribution of target particles and organelles in intra- and extracellular 3D space, so novel methods are being attempted. Methods and Results Using Serial Block Face-Scanning Electron Microscopy (SBF-SEM) and a 3D structural reconstruction system, we for the first time investigated the microstructure of human myocardial tissue from control hypertrophied heart (hypertensive heart disease) and wtATTR-CA patients. The microstructure of myocardial niche, cell morphology/numbers, and the distribution of intracellular organelles such as intercalated discs (ICDs) were compared to control endomyocardial biopsy (EMB) samples (2 controls vs 5 wtATTR-CA patients). In all wtATTR-CA samples, amyloid fibers were deposited originating around the capillary structures with various electron densities, compressing myocardial cells not only from the periphery but also dividing them from the center of the cell body, and CMs showed degeneration and dwarfing according to the severity of amyloid fiber deposition (Figure 1). The capillary structures showed degeneration and narrowing with stratified basement membrane, and rarefaction (3378±216.5 vs 1448±116.8/mm2, p<0.0001). Notably, the stromal cells (pericytes, fibroblasts, macrophages) were more likely buried by amyloid fibers than CMs, and their numbers were also reduced, indicating a loss of adjacent and intercellular interaction with capillaries and CMs (Fig. 2, 1892±194.4 vs 1189±107.4 mm2, p=0.0043). This method can also be used to analyze intracellular organelles and microstructures in CMs. In ATTR-CA, morphological abnormalities were observed in ICDs (Figure 3) and mitochondria. Conclusion In the ATTR-CM patients, there was a significant decrease in the number of capillaries in accordance with the deposition of myocardial amyloid fibers, as well as a decrease in stromal cells. These results support the hypothesis that amyloid fibril deposition not only causes chronic myocardial ischemia, but also reduces the number of niche cells, which play a supportive role for CM function and entirely exacerbate the damage. The SBF-SEM is an innovative tool for 3D imaging and quantitative analysis of myocardial ultrastructure and functions.Methods & Figure 1Figure 2 and Figure 3

Enhancing computational fluid dynamics simulation for coronary artery disease analysis through spatial iterative modeling

Abstract Background Endothelial shear stress (ESS) critically influences the pathogenesis of coronary artery disease (CAD) by modulating the formation and progression of atherosclerotic lesions. Despite the use of 3D modeling in computational fluid dynamics (CFD) simulations to calculate ESS, the precision of these models significantly depends on the method of reconstruction. Spatial Iterative Method (SIM) extends the iterative approach in computer science to spatial data, using recurring algorithms to refine 3D models and analyses. Iterative approach executes a sequence of instructions repeatedly until a certain condition is met, enabling progressive problem-solving. Purpose In this study, we investigated the application of the SIM to estimate ESS in coronary arteries and explored Atherosclerotic Plaque Areas (APA) in patients with CAD. Methods We analyzed fifteen patients (9 males, mean age of 63 ± 10.56 years) with atherosclerotic plaque on coronary arteries. The eighteen images captured from an anterior viewpoint in 10-degree increments of the coronary artery obtained from Coronary Computed Tomography Angiography at the best diastolic phase for each patient were used. We implemented the SIM for 3D model reconstruction, facilitating ninety comparisons across all iterations to evaluate ESS estimations accurately. (Figure 1.) Results In our study, we evaluated ESS contours from CFD simulations for a total of 282 APA (200 calcified and 82 soft plaques) across ninety spatial iterations. We categorized ESS into three groups: low (162 APA cases with ESS of less than 1 Pascal (Pa)), medium (67 APA cases with ESS between 1 and 2 Pa), and high (53 APA cases with ESS greater than 2 Pa). There was a significant association between ESS categories and plaque types (p < 0.0001, Figure 2.). The calcified plaques were formed in areas with low ESS (%80 of calcified plaques) and soft plaques in areas with medium and high ESS (%97 percent of soft plaques). Conclusion The significant association between ESS categories and plaque types shows the potential of SIM in hemodynamic analysis. This finding highlights the potential of SIM in enhancing the understanding of hemodynamic influences on plaque formation, offering a promising direction for future cardiovascular research.Figure 1.Sample Iteration of the SIMFigure 2.Heatmap of ESS vs. APA

Three-Dimensional Reconstruction of Retaining Structure Defects from Crosshole Ground Penetrating Radar Data Using a Generative Adversarial Network

Crosshole ground penetrating radar (GPR) is an efficient method for ensuring the quality of retaining structures without the need for excavation. However, interpreting crosshole GPR data is time-consuming and prone to inaccuracies. To address this challenge, we proposed a novel three-dimensional (3D) reconstruction method based on a generative adversarial network (GAN) to recover 3D permittivity distributions from crosshole GPR images. The established framework, named CGPR2VOX, integrates a fully connected layer, a residual network, and a specialized 3D decoder in the generator to effectively translate crosshole GPR data into 3D permittivity voxels. The discriminator was designed to enhance the generator’s performance by ensuring the physical plausibility and accuracy of the reconstructed models. This adversarial training mechanism enables the network to learn non-linear relationships between crosshole GPR data and subsurface permittivity distributions. CGPR2VOX was trained using a dataset generated through finite-difference time-domain (FDTD) simulations, achieving precision, recall and F1-score of 91.43%, 96.97% and 94.12%, respectively. Model experiments validate that the relative errors of the estimated positions of the defects were 1.67%, 1.65%, and 1.30% in the X-, Y-, and Z-direction, respectively. Meanwhile, the method exhibits noteworthy generalization capabilities under complex conditions, including condition variations, heterogeneous materials and electromagnetic noise, highlighting its reliability and effectiveness for practical quality assurance of retaining structures.

Single centre experience using vessel Fractional Flow Reserve (vFFR) and diagnostic performance compared with invasive Fractional Flow Reserve (FFR)

Abstract Background Fractional flow reserve (FFR) is a diagnostic gold standard in the functional assessment of coronary artery stenoses (1). Alternative invasive and non-invasive methods have been developed in an attempt to avoid limitations associated with FFR including added procedural risk, patient discomfort, time and cost. Vessel fractional flow reserve (vFFR) is a novel software using two angiographic projections to generate a 3D vessel reconstruction which has shown high diagnostic accuracy in predicting a positive FFR (2-4). Methods All patients undergoing coronary angiography between 01/10/2022 and 08/03/2023, where FFR was performed, were assessed. Patients were included where images met requirements necessary to use vFFR software. vFFR measurements were taken retrospectively by a single investigator and not used to inform patient care. Data were expressed as mean +/- standard deviation and %. The correlation of vFFR to FFR was compared using Pearson’s correlation coefficient. Receiving operator characteristic (ROC) area under the curve (AUC) analysis was used to determine diagnostic performance. Statistical analysis was performed using SPSS v27. Results 28 of 37 patients and 32 of 44 vessels were included and analysed with vFFR software. Excluded vessels were unsuitable due to insufficient frame rate or projection angulation during angiography. Mean age was 63 +/- 3.8 years and 75% of patients were male. Of 32 vessels assessed, 20 were LAD, 5 RCA, 5 LCx and 2 LMS. 29 of 32 vFFR values agreed with positive or negative FFR. Positive vFFR values were seen in all 15 vessels with positive FFR. 3 vessels received a positive vFFR where FFR values were negative. Mean vFFR was 0.76 +/- 0.1 and mean FFR was 0.81 +/- 0.09. vFFR correlated significantly with FFR (R=0.777, p<0.001). vFFR demonstrated good diagnostic accuracy in discriminating "positive" (>0.8) and "negative" (≤0.8) FFR with an AUC of 0.758 (95% CI 0.61-0.91, p<0.01). Conclusion vFFR correlated significantly with FFR and all positive FFR values also received positive vFFR values. vFFR demonstrated good diagnostic accuracy in predicting a positive FFR result. Thus, these data support a strategy of using a negative vFFR to avoid further testing.Pearson correlation of vFFR to invFFRROC curve: vFFR predicting invFFR

Impact of adjacent thoracic structures in negative left atrial remodelling in atrial fibrillation

Abstract Background Previous work has suggested that compression from external structures can promote negative atrial remodelling in atrial fibrillation (AF) [1]. This is mechanistically plausible, with many recognised risk factors in developing AF (age, hypertension, obesity, obstructive sleep apnoea, etc.) related through activation of the autonomic nervous system with an associated increase in atrial pressure and chronic elevation in wall stress [2,3]. Such negative remodelling contributes to the existence of arrhythmogenic atrial substrate [4]. This is highly significant when we consider catheter ablation of extrapulmonary vein targets. Purpose This study aims to investigate the relationship between abnormal conduction patterns indicative of potential AF drivers and their proximity to structures adjacent to the left atrium (LA). Methods Eight patients with paroxysmal or persistent AF, scheduled for an elective catheter ablation procedure using a charge density mapping (CDM) system [5] underwent pre-procedural cardiac MRI. 3D surface mesh reconstructions of individuals’ LA and adjacent structures (ascending aorta [AoA], descending aorta [AoD], and spine) were produced from 2D MRI slices using a proprietary graphical interface tool developed in MATLAB (Figure 1A & B) [6]. MRI derived LA geometries were registered with their CDM counterparts by the Iterative Closest Point algorithm (Figure 1C); this enabled integration of MRI LA geometries into the CDM system. Subsequently, non-contact intrachamber voltage measurements from individuals’ procedures were used to derive cardiac activation directly onto MRI LA geometries. Abnormal conduction patterns representing possible AF drivers (focal firing, rotational propagation, and pivoting propagation) [5] were quantified as occurrences per second at each vertex of MRI LA geometries (Figure 1D). These conduction patterns could then be visualised and assessed in the context of adjacent structures (Figure 1E), with the Euclidean distance between each vertex of the MRI LA geometries and the closest point to their adjacent structures being calculated. Results Mean age was 65±9 years; 4 patients (50%) were male; AF was paroxysmal in 3 (37%) and persistent in 5 (63%); median time since AF diagnosis was 1 (1-3.8) years; ablation type was de novo in 5 (63%) and retreatment in 3 (38%). Frequencies of pivoting and rotational propagation were notably higher in regions of the LA closer to the AoA, and lower near the AoD and spine (Figure 2A & B). Conversely, focal firing showed a less prominent trend, being more frequent in LA regions nearer the AoD and distant from the AoA (Figure 2C). Conclusions Compression from the ascending aorta may promote anterior LA remodelling in AF, leading to increased pivoting and rotational AF drivers. Clinically, this suggests a potential target for ablation therapy with an anterior seatbelt line connecting the right superior pulmonary vein to the mitral annulus.

Assessing aortic morphology post aortic valve replacement using unsupervised hierarchical clustering and statistical shape modelling

Abstract Introduction There are different surgical procedures used to treat aortic valve disease such as aortic valve replacement (AVR), the Ozaki procedure, the Ross procedure, and the valve-sparing procedure. There may be postoperative side effects associated with aortic morphology. Computational analyses that assess morphology are thus necessary in such scenario. Statistical shape modelling is used to assess three-dimensional morphology, discover unique shape features, create mean shapes, and create shape modes that display morphological variability. Hierarchical cluster analysis is a machine learning method used to classify a population into subgroups. Both of these statistical methods were applied to aortic valve replacement patients to evaluate morphological variability after aortic valve replacement (AVR) surgery. Purpose The purpose of this study is to assess the aortic morphology of AVR patients, identify morphological differences between different surgical procedures, identify subgroups postoperatively using statistical shape modelling and hierarchical cluster analysis, and assessing possible correlations between the resultant clusters and function i.e. ejection fraction. Methods Computed tomography and cardiac magnetic resonance images were used to reconstruct aortas into 3D models using segmentation and 3D reconstruction software. For each patient, two models were created (i.e. ascending aorta only and aorta including the arch and the descending aorta). N=35 patients were included in the study. Statistical shape analysis was run to create templates and shape modes. Statistical environment and language software was used to run hierarchical cluster analysis. Results Overall, the hierarchical cluster analysis did not classify the aortas based on type of surgery, but most patients in the Ross group demonstrated morphological differences from the other surgical groups and many of the aortas belonging to the Ross group were clustered together in both clustering analyses. No significant differences between clusters were seen (p=0.47 for ascending, p=0.19 for whole aorta). No correlation was found between clusters and ejection fraction (p=0.75, r2<0.01), possibly indicating that post-AVR aortic morphology in this sample does not reflect overt functional changes. Conclusion Both statistical shape modelling and hierarchical cluster analyses were successful in assessing morphological variability and identifying subgroups in this population. This framework could therefore be extended to larger samples to inform on surgical decision-making allowing surgeons to choose the appropriate surgery for the patient or design prosthetic valves that are more customised to the patient’s anatomy.Cluster-Ejection Fraction Correlation

A quantitative analysis of bestrophin 1 cellular localization in mouse cerebral cortex.

Calcium-activated ligand-gated chloride channels, beyond their role in maintaining anion homeostasis, modulate neuronal excitability by facilitating nonvesicular neurotransmitter release. BEST1, a key member of this family, is permeable to γ-aminobutyric acid (GABA) and glutamate. While astrocytic BEST1 is well-studied and known to regulate neurotransmitter levels, its distribution and role in other brain cell types remain unclear. This study aimed to reassess the localization of BEST1 in the mouse cerebral cortex. We examined the localization and distribution of BEST1 in the mouse parietal cortex using light microscopy, confocal double-labeling with markers for astrocytes, neurons, microglia, and oligodendrocyte precursor cells, and 3D reconstruction techniques. In the cerebral cortex, BEST1 is more broadly distributed than previously thought. Neurons are the second most abundant BEST1+ cell type in the cerebral cortex, following astrocytes. BEST1 is diffusely expressed in neuronal somatic and neuropilar domains and is present at glutamatergic and GABAergic terminals, with a prevalence at GABAergic terminals. We also confirmed that BEST1 is expressed in cortical microglia and identified it in oligodendrocyte precursor cells, albeit to a lesser extent. Together, these findings suggest that BEST1's role in controlling neurotransmission may extend beyond astrocytes to include other brain cells. Understanding BEST1's function in these cells could offer new insights into the molecular mechanisms shaping cortical circuitry. Further research is needed to clarify the diverse roles of BEST1 in both normal and pathophysiological conditions.

Feasibility, accuracy, and reproducibility of device sizing in virtual reality for percutaneous left atrial appendage closure - a single center study

Abstract Background Left atrial appendage closure (LAAC) is a safe and effective procedure for stroke prevention in patient with non-valvular atrial fibrillation. Procedural planning and device sizing remains challenging due to the highly complex and variable three-dimensional (3D) anatomy of the left atrial appendage (LAA). Virtual reality (VR) is a innovative, recent technology allowing a superior spatial orientation visualization of 3D imaging datasets. Purpose Therefore, we aimed to evaluate feasibility and accuracy of LAA measurements in VR for device sizing in patients planned for LAAC. Methods Twenty-one patients (79±7 years, 62% male) with non-valvular atrial fibrillation scheduled for LAAC were randomly included in our study. Preprocedural a multi-slice computed tomography (MSCT) was performed for device sizing in all patients. MSCT-based 3D models were established and visualized in VR using a dedicated software (VMersive, Warsaw, Poland). LAA dimensions (Ostium, landing zone (LZ), and depth) were assessed directly in the virtual environment. Conventional measurements of LAA ostium, landing zone (LZ) and depth using commercially available software were compared to measurements in VR. Results 3D reconstruction of MSCT datasets and visualization in VR were successfully performed automatically in all patients within a few seconds. MSCT and VR demonstrated good correlations for ostium minimum (r = 0.93), maximum (r = 0.8) and mean (r = 0.88, all p<0.001) diameters. Additionally, LZ minimum (r=0.84), maximum (r=0.86) and mean (r=0.9) diameters showed good correlation (all p<0.001). Moderate correlation was observed for depth measurements (r=0.76, p<0.001). Furthermore, intraclass correlation coefficients ranging from 0.88 to 0.98 denoted high inter- and intraobserver agreement. Conclusion Immersive VR allows highly feasible, accurate and reproducible measurements of the LAA dimensions directly in the virtual environment, hence enabling accurate device sizing for LAAC.

Impact of neonatal hyperoxia on the development of the coronary vascular bed: an experimental model for the study of cardiomyopathy associated with premature birth

Abstract Introduction Very preterm birth (PT; <32 weeks of gestation), nearly 2% of all births, leads to short-term complications such as bronchopulmonary dysplasia and retinopathy of prematurity that are driven by compromised organ vascular development. Adults born preterm display an increased risk of ischaemic cardiomyopathy and heart failure. Our group has shown that neonatal rats exposed to hyperoxia, simulating the PT neonatal conditions, develop cardiomyocytes hypertrophy, fibrosis and cardiac dysfunction, or Oxygen-Induced Cardiomyopathy (OIC). Whether cardiac changes also associate with alterations in vascular development in the left ventricle (LV) remains unknown. Purpose We hypothesized that neonatal exposure to hyperoxia impact the development of the LV vascular network. Methods Male pups were exposed to 80% O2 (OIC) or room air (CTRL) from day 3 (P3) to P10 of life. Hearts were collected at P10, 4- and 16 weeks. Arteriolar length and capillary density per cardiomyocyte were measured using stereology (elastin) and immunofluorescence (WGA, α-SMA, CD31). High-resolution 3D reconstruction of the vascular network was achieved by combining fluorescent labelling of coronaries (anti-α-SMA) and capillaries (anti-CD31) with a tissue-cleaning protocol (iDisco+), allowing detailed morphometric characterization of the vascular network. The expression of vascular endothelial growth factors and receptors (VEGFA, VEGFB, VEGFR 1 and 2, NRP1, and PIGF) were quantified by RT-qPCR and Western blots. Differences between groups were analyzed with Student's t-test (P < 0.05) (N = 6 per group). Results At P10, we observed a notable rise in the number of capillaries per cardiomyocyte (0.88 ± 0.05 vs. 0.62 ± 0.02 mm2) and a significant increase in VEGFA (+76%) and NRP1 (+38%) expression in the LV from OIC vs. CTRL rats. By 4 weeks, the OIC group showed a significant reduction in arteriolar length vs. CTRL (6.51 ± 0.76 vs. 9.77 ± 1.12 m/cm3), coupled with an increase in the number of capillaries per cardiomyocyte (1.11 ± 0.05 vs. 0.85 ± 0.03 mm2). VEGFA was decreased (-14%) and PLGF increased (+24%) in the LV of OIC rats. At 16 weeks, the OIC condition displayed a significant increase in the number of capillaries per cardiomyocyte (1.37 ± 0.03 vs. 0.98 ± 0.07 mm2) and a decrease in VEGFA (-23%), along with reduction in its receptors VEGFR1 (-27%), VEGFR2 (-20%) and NRP1 (-17%). Conclusion OIC resulting from neonatal hyperoxia exposure associates with vascular changes in the LV that persist to adulthood. These changes are characterized by a reduction in arteriolar length and an increase in the number of capillaries, accompanied by modulation of the expression of pro-angiogenic VEGFA and receptors. The impact of these vascular alterations on the susceptibility of the LV to ischemic or hypertension-related damage is to be explored. These findings suggest a crucial mechanistic pathway for understanding the risk of cardiac pathologies associated with preterm birth.

PV-LaP: Multi-sensor fusion for 3D Scene Understanding in intelligent transportation systems

Intelligent transportation systems are pivotal in modern urban development, aiming to enhance traffic management efficiency, safety, and sustainability. However, existing 3D Visual Scene Understanding methods often face challenges of robustness and high computational complexity in complex traffic environments. This paper proposes a Multi-Sensor Signal Fusion method based on PV-RCNN and LapDepth (PV-LaP) to improve 3D Visual Scene Understanding. By integrating camera and LiDAR data, the PV-LaP method enhances environmental perception accuracy. Evaluated on the KITTI and WHU-TLS datasets, the PV-LaP framework demonstrated superior performance. On the KITTI dataset, our method achieved an Absolute Relative Error (Abs Rel) of 0.079 and a Root Mean Squared Error (RMSE) of 3.014, outperforming state-of-the-art methods. On the WHU-TLS dataset, the method improved 3D reconstruction precision with a PSNR of 19.15 and an LPIPS of 0.299. Despite its high computational demands, PV-LaP offers significant improvements in accuracy and robustness, providing valuable insights for the future development of intelligent transportation systems.

Comprehensive Review of Tunnel Blasting Evaluation Techniques and Innovative Half Porosity Assessment Using 3D Image Reconstruction

To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents a comprehensive review of the current state of the art in tunnel blasting evaluation, organized into five key areas: Blasting Techniques and Optimization, 3D Reconstruction and Visualization, Monitoring and Assessment Technologies, Automation and Advanced Techniques, and Half Porosity in Tunnel Blasting. Each section provides an indepth analysis of the latest research and developments, offering insights into enhancing blasting efficiency, improving safety, and optimizing tunnel design. Building on this foundation, we introduce a digital identification method for assessing half porosity through 3D image reconstruction. Utilizing the Structure from Motion (SFM) technique, we re-construct the 3D contours of tunnel surfaces and bench faces after blasting. Curvature values are employed as key indicators for extracting 3D point cloud data from boreholes. The acquired postblasting point cloud data is processed using advanced software that incorporates the RANSAC algorithm to accurately project and fit the borehole data, leading to the determination of the target circle and borehole axis. The characteristics of the boreholes are analyzed based on the fitting results, culminating in the calculation of half porosity. Field experiments conducted on the Huangtai Tunnel (AK20 + 970.5 to AK25 + 434), part of the new National Highway 109 project, provided data from shell holes generated during blasting. These data were analyzed and compared with traditional onsite measurements to validate the proposed method’s effectiveness. The computed half porosity value using this technique was 58.7%, showing minimal deviation from the traditional measurement of 60%. This methodology offers significant advantages over conventional measurement techniques, including easier equipment acquisition, non-interference with construction activities, a comprehensive detection range, rapid processing speed, reduced costs, and improved accuracy. The findings demonstrate the method’s potential for broader application in tunnel blasting assessments.

Sparse Annotation is Sufficient for Bootstrapping Dense Segmentation.

Producing dense 3D reconstructions from biological imaging data is a challenging instance segmentation task that requires significant ground-truth training data for effective and accurate deep learning-based models. Generating training data requires intense human effort to annotate each instance of an object across serial section images. Our focus is on the especially complicated brain neuropil, comprising an extensive interdigitation of dendritic, axonal, and glial processes visualized through serial section electron microscopy. We developed a novel deep learning-based method to generate dense 3D segmentations rapidly from sparse 2D annotations of a few objects on single sections. Models trained on the rapidly generated segmentations achieved similar accuracy as those trained on expert dense ground-truth annotations. Human time to generate annotations was reduced by three orders of magnitude and could be produced by non-expert annotators. This capability will democratize generation of training data for large image volumes needed to achieve brain circuits and measures of circuit strengths.

Artificial Skin Based on Visuo‐Tactile Sensing for 3D Shape Reconstruction: Material, Method, and Evaluation

AbstractArtificial skin has shown great potential in robot perception and human healthcare. It provides multifunctional tactile sensing, including 3D shape reconstruction, contact feedback, and temperature perception, where the 3D reconstruction function is indispensable for dexterous hands in tactile cognition and interaction. Vision‐based tactile sensor (VTS) is an innovative bionic tactile sensor and supports high‐resolution, high‐precision, and high‐density tactile reconstruction compared with electronic tactile sensors. Considering the unique contribution of visuo‐tactile sensing to artificial skin, this review focuses on the 3D reconstruction techniques of the VTS. 3D reconstruction methods are classified into five categories based on sensing modalities, hardware categories, and modeling approaches: 1) photometric stereo, 2) binocular depth calibration, 3) optical flow, 4) deep learning, and 5) ToF (time of flight). In addition, the association and difference of reconstruction methods are analyzed from the hardware perspective, and the development and technological details of 3D reconstruction are summarized. On this basis, the challenges and development direction are discussed. This review can be viewed as a technology guide to provide references for interested researchers. Furthermore, it is expected to promote the extensive application of the VTS in artificial skins.