Volumetric Reconstruction Research Articles

Clinically Relevant and Precisely Printable Live Adipose Tissue-Based Bio-Ink for Volumetric Soft Tissue Reconstruction.

Autologous fat is widely used in soft tissue reconstruction; however, significant volume reduction owing to necrosis and degradation of the transplanted adipose tissue (AT) remains a major challenge. To address this issue, a novel live AT micro-fragment-based bio-ink (ATmf bio-ink) compatible with precision 3D printing, is developed. Live AT micro-fragments of ≈280µm in size are prepared using a custom tissue micronizer and they are incorporated into a fibrinogen/gelatin mixture to create the ATmf bio-ink. AT micro-fragments exhibit high viability and preserve the heterogeneous cell population and extracellular matrix of the native AT. The developed bio-ink enables precise micropatterning and provides an excellent adipo-inductive microenvironment. AT grafts produced by co-printing the bio-ink with polycaprolactone demonstrate a 500% improvement in volume retention and a 300% increase in blood vessel infiltration in vivo compared with conventional microfat grafts. In vivo engraftment of AT grafts is further enhanced by using a stem cell-laden ATmf bio-ink. Last, it is successfully demonstrated that the bio-ink is enabled for the creation of clinically relevant and patient-specific AT grafts for patients undergoing partial mastectomy. This novel ATmf bio-ink for volumetric soft tissue reconstruction offers a pioneering solution for addressing the limitations of existing clinical techniques.

Principles of stereo reconstruction of aerial objects using stationary cameras

ABSTRACT An overview is given here of the principles and mathematics of stereo reconstruction of objects in the sky using stationary cameras with an emphasis on meteorological applications. Through its Atmospheric Radiation Measurement program, the Department of Energy has operated stereo-photogrammetric cameras since 2017 as part of an effort to measure the life-cycle properties of clouds. At the core of that technology is stereo reconstruction, which calculates the real-world position of an object from the location of the object’s image in two cameras’ photographs. Here, stereo reconstruction is stripped down to its basic elements and presented using conventions tailored to applications in atmospheric science. In addition, the resulting equations are used to illustrate the high sensitivity of reconstructed cloud positions to errors in the cameras’ Euler angles. The interested reader will find here a self-contained guide to performing stereo reconstructions using distortion-corrected images from a pair of calibrated, stationary cameras, as well as a demonstration of the need for high accuracy in the measurement of camera properties and orientations.

Multi-view high-dynamic-range 3D reconstruction and point cloud quality evaluation based on dual-frame difference images

A high dynamic range can easily lead to image saturation, making it a challenge for structured light 3D reconstruction. The article proposes a multi-view 3D topography measurement system, which consists of dual projectors, a single camera, and a high-precision rotary platform. The system utilizes single-frame images to achieve high-dynamic-range surface adaptive exposure. Subsequently, it proposes a method that combines two-frame differential images with multi-view imaging to identify highly reflective regions and complete point cloud holes. This approach addresses the issue of visual blind spots caused by shadows and local high reflectivity due to the occlusion of the measured object’s geometric features. Finally, the system employs deep learning to evaluate the quality of the high-dynamic-range point cloud data. Comparative experiments show that optimal exposure in single-frame images can achieve better imaging quality and shorten capture time. The dual-frame difference image algorithm can identify high-reflection areas and complete the point cloud data. The point cloud quality evaluation model based on IT-PCQA demonstrates the effectiveness of the proposed method for high-dynamic-range 3D reconstruction.

Computed tomography of acute orbital trauma in children: a retrospective study

INTRODUCTION: Orbital injuries account for 36 to 64% of all blunt trauma to the facial bones. Diagnosis of orbital wall fractures is carried out using conventional radiography in several projections and computed tomography; magnetic resonance imaging is less commonly used. Often, due to the small displacement of orbital bone fragments, X-ray diagnosis is difficult. Multislice CT with reconstruction of sagittal, coronal projections and 3D in the bone window in such cases is the best visualization method. However, there are still no clear indications for the use of each method and there is no complete view of the CT characteristics of orbital damage.OBJECTIVE: To study the diagnostic value of computed tomography of orbital fractures in pediatric patients with acute trauma.MATERIAL AND METHODS: Retro- and prospectively analyzed the results of CT scans of 94 patients with facial fractures from 01/01/2023 to 09/01/2023 to assess combined orbital injuries. Orbital fractures were detected in 63 children (67.0%). There were 44 boys (69.84%), 19 girls (30.15%) aged from 7 months to 17 years 10 months (average age 10.3). Statistics: For statistical analysis, the method of calculating the nominal correlation using the Kramer coefficient (V) using the IBM SPSS Statistics software package was used.RESULTS: Concomitant injury was observed in 30 (47.6%); isolated 33 (52.36%). The number and frequency of fractures observed were as follows: superior wall of the orbit — 39 (61.9%); zygomatic complex — 19 (30.1%); lower wall of the orbit — 43 (68.2%); nasal bone — 17 (26.9%); lower jaw — 6 (9.5%); medial wall of the orbit — 28 (44.4%); upper jaw — 27 (42.8%); alveolar process — 6 (9.5%); isolated zygomatic arch — 14 (22.2%); Le Fort type I — 1 (1.5%); Le Fort type II — 2 (3.1%); and Le Fort type III — 0 (0%). Orbital hematomas were found in 25 patients (39.6%). It was revealed a strong correlation between the presence of contiguous skull fractures and multiple skull fractures (V=0.878, p<0.001), frontal sinus fractures and frontal sinus hemosinus (V=0.69, p<0.001).CONCLUSION: Orbital fractures are a frequent type of facial fracture occurring in children with blunt isolated and combined trauma. In children with blunt trauma, head CT combined with clinical examination is currently the optimal tool for diagnosing clinically significant orbital injuries. There is a strong correlation between orbital hematomas and orbital vault fractures, orbital hematomas and lattice fractures, adjacent skull and orbital vault fractures, and adjacent skull and lattice fractures. CT examination should include multiplanar (in coronal and sagittal planes) and 3D reconstructions, which help to assess the extent of the fracture, the presence of diastasis, the degree of muscle impingement. Based on the data obtained, the clinician determines the need for surgical intervention.

Adaptive infrared patterns for microscopic surface reconstructions.

Multi-zoom microscopic surface reconstructions of operating sites, especially in ENT surgeries, would allow multimodal image fusion for determining the amount of resected tissue, for recognizing critical structures, and novel tools for intraoperative quality assurance. State-of-the-art three-dimensional model creation of the surgical scene is challenged by the surgical environment, illumination, and the homogeneous structures of skin, muscle, bones, etc., that lack invariant features for stereo reconstruction. An adaptive near-infrared pattern projector illuminates the surgical scene with optimized patterns to yield accurate dense multi-zoom stereoscopic surface reconstructions. The approach does not impact the clinical workflow. The new method is compared to state-of-the-art approaches and is validated by determining its reconstruction errors relative to a high-resolution 3D-reconstruction of CT data. 200 surface reconstructions were generated for 5 zoom levels with 10 reconstructions for each object illumination method (standard operating room light, microscope light, random pattern and adaptive NIR pattern). For the adaptive pattern, the surface reconstruction errors ranged from 0.5 to 0.7 mm, as compared to 1-1.9 mm for the other approaches. The local reconstruction differences are visualized in heat maps. Adaptive near-infrared (NIR) pattern projection in microscopic surgery allows dense and accurate microscopic surface reconstructions for variable zoom levels of small and homogeneous surfaces. This could potentially aid in microscopic interventions at the lateral skull base and potentially open up new possibilities for combining quantitative intraoperative surface reconstructions with preoperative radiologic imagery.

A multiple x-ray-source array (MXA) system with a planar two-dimensional source distribution for digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one-dimensional (1D) scan geometry, x-ray source motion during acquisition, and patient motion artifacts from long exam times. A thermionic cathode x-ray system employing two-dimensional (2D, planar) multiple x-ray-source arrays (MXA) is proposed to improve DBT image quality. A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of =23mm. The 11 cathodes were paired with 11 molybdenum 50mm diameter anode disks, mounted on a rotating shaft within a single vacuum enclosure. Image quality was investigated as a function of MXA configuration by integrating the 1D MXA with a 200 × 250 mm2 flat panel detector at a source-to-detector distance of 630mm, resulting in a 20° tomographic arc. To simulate a 2D MXA, the detector (with phantom) was translated orthogonally to the linear array by a distance ( ) ranging from =0mm (conventional 1D) to =57mm. All sources operated at 30kV with 80mA and 4.5 mAs/pulse, yielding ∼100 mAs per DBT dataset. DBT reconstructions involved 22 projections and used filtered backprojection with a ramp and Hann apodization filter. Volumetric reconstructions for each source were weighted by sampling differences between sources, and averaged. Image quality was assessed in terms of contrast-to-noise ratio (CNR), background clutter noise and power spectrum, and slice sensitivity profile (SSP) using a set of physical phantoms, including: (i) contrast-detail signals coupled to spherical clutter (PMMA in air); (ii) an SSP phantom; (iii) a commercial "breast" phantom (CIRS BR3D, Sun Nuclear, Norfolk, VA); and (iv) bovine muscle. Background clutter noise amplitude reduced monotonically from the 1D MXA (σclutter=5.9 A.U., =0mm) and 2D MXA arrays with increasing , with statistical significance between the 1D MXA and 2D MXA with =57mm (σclutter=5.0 A.U., p<0.001). The contrast-detail/clutter phantom demonstrated CNR from the 2D MXA (δ=57mm) outperforming the 1D MXA in all combinations of contrast and detail. 2D power spectrum analysis of clutter demonstrated a pronounced Fourier domain null cone for the 1D MXA in the anterior field-of-view (away from the 1D MXA position), whereas the 2D MXA geometry (δ=57mm) did not exhibit the null cone. The SSP was 15%-50% narrower (FWHM) for the 2D versus the 1D geometry, across all reconstruction setups. The advantages of a 2D source geometry for DBT imaging were demonstrated quantitatively compared to a conventional 1D line of x-ray sources. The improvement in the 2D geometry was attributed both to improved Fourier domain sampling and reduced SSP. We conclude that 2D MXA sources have the potential to substantially improve DBT imaging in comparison to existing commercial DBT systems.

A Scaled Monocular 3D Reconstruction Based on Structure from Motion and Multi-View Stereo

Three-dimensional digital modeling at actual scales is essential for digitally preserving cultural relics. While 3D reconstruction using a monocular camera offers a cost-effective solution, the lack of scale information in the resulting models limits their suitability for geometric measurements. Objects with monotonous textures, such as batteries, pose additional challenges due to insufficient feature points, increasing positional uncertainty. This article proposes a method incorporating point and line features to address the scale ambiguity in multi-view 3D reconstruction using monocular cameras. By pre-measuring the lengths of multiple sets of real line segments, building a lookup table, and associating the line features in different images, the table was input into the improved reconstruction algorithm to further optimize the scale information. Experimental results on real datasets showed that the proposed method outperformed the COLMAP method by 70.82% in reconstruction accuracy, with a scale recovery reaching millimeter-level accuracy. This method is highly generalizable, cost-effective, and supports lightweight computation, making it suitable for real-time operation on a CPU.

Three-dimensional spatiotemporal wind field reconstruction based on LiDAR and multi-scale PINN

In this numerical study, we use a multi-scale version of physics-informed neural network (PINN) for wind field reconstruction from LiDAR measurements. The reference velocity field is obtained from high-fidelity large-eddy simulation of neutral atmospheric boundary layer, and the LiDAR measurement strategy is restricted to that of a real LiDAR. The multi-scale PINN reconstructs the velocity field by minimizing a combination of the L2-error with respect to the LiDAR measurements and residuals of the governing equations. Our most significant finding is that adding a multi-scale layer to PINN enables us to: (i) capture wider range of scales, (ii) reconstruct the flow field outside the LiDAR scanning area, and (iii) reach faster convergence. We also find that for volumetric flow reconstruction and to deal with uncertainty in the wind direction, the reconstruction accuracy can be greatly improved by employing multiple LiDAR devices. Overall, our results show that the normalized errors in the reconstructed wind field are lower than 5% for all the experiments conducted in this study and that the errors do not increase even in the presence of measurement noise levels of typical LiDAR. These findings show the feasibility of three-dimensional dynamic wind field reconstruction across large wind farms using LiDAR devices.

Improved weak texture multi-view 3D reconstruction algorithm based on deformable convolutions networks

Improved weak texture multi-view 3D reconstruction algorithm based on deformable convolutions networks

Neural Impedance Boundary (NeIB): a neural-network based framework for acoustic surface impedance estimation utilizing sparse measurement data

Amidst recent advancements in the 3D digital representation that have significantly enhanced the modeling of geometric attributes of pre-existing environments, accurate estimation of acoustic boundary conditions remains a complex challenge. This paper presents a novel way to determine what we refer to as a neural boundary field, using physics-informed neural networks (PINN). The aim is to estimate the surface impedance measured in-situ by utilizing several points of sound field pressure. This approach couples the Helmholtz equation with automatic differentiation in the PINN framework to estimate accurately the surface impedance using a hybrid modeling approach where measurement data and domain knowledge in the form of equations for the acoustic waves are combined. As a proof-of-concept, we train the neural networks using 2D sound field data obtained from high-fidelity numerical acoustical simulations that incorporate actual surface materials parameters. We discuss the measurement techniques associated with this method and outline our vision for its application to 3D scene reconstructions in the future.

Radially patterned morphogenesis of murine hair follicle placodes ensures robust epithelial budding.

The bending of simple cellular sheets into complex three-dimensional (3D) forms requires developmental patterning cues to specify where deformations occur, but how positional information directs morphological change is poorly understood. Here, we investigate how morphogen signaling and cell fate diversification contribute to the morphogenesis of murine hair placodes, in which collective cell movements transform radially symmetric primordia into bilaterally symmetric tubes. Through live imaging and 3D volumetric reconstructions, we demonstrate that Wnt and Shh establish radial patterns of cell fate, cell morphology, and movement within developing placodes. Cell fate diversity at different radial positions provides unique and essential contributions to placode morphogenesis. Further, we show that downstream of radial patterning, gradients of classical cadherin expression are required for efficient epithelial rearrangements. Given that the transformation of epithelial discs into 3D tubes is a common morphological motif used to shape diverseorgan primordia, mechanisms of radially patterned morphogenesis are likely highly conserved across evolution.

DSEM-NeRF: Multimodal feature fusion and global-local attention for enhanced 3D scene reconstruction

3D scene understanding often faces the problems of insufficient detail capture and poor adaptability to multi-view changes. To this end, we proposed a NeRF-based 3D scene understanding model DSEM-NeRF, which effectively improves the reconstruction quality of complex scenes through multimodal feature fusion and global-local attention mechanism. DSEM-NeRF extracts multimodal features such as color, depth, and semantics from multi-view 2D images, and accurately captures key areas by dynamically adjusting the importance of features. Experimental results show that DSEM-NeRF outperforms many existing models on the LLFF and DTU datasets, with PSNR reaching 20.01, 23.56, and 24.58 respectively, and SSIM reaching 0.834. In particular, it shows strong robustness in complex scenes and multi-view changes, verifying the effectiveness and reliability of the model.

A robust correspondence-based registration method for large-scale outdoor point cloud

ABSTRACT Large-scale outdoor point cloud registration is essential for the 3D reconstruction of outdoor scenes. Its central objective is to achieve accurate point cloud registration by determining accurate spatial transformation parameters. While feature-based methods eliminate the need for initial position estimation, they encounter challenges in handling high outlier rates. Therefore, a method capable of effectively managing outliers is crucial for enhancing the efficiency and accuracy of large-scale outdoor point cloud registration. This paper introduces the maximal clique with adaptive voting (MCAV) method, which leverages graph-based inlier compatibility to optimize potential matches. MCAV employs adaptive parameter voting (APV) to enhance computational efficiency, demonstrating significant speedup characteristics in datasets with a significant number of inliers. To further reduce outliers in potential matches, we integrate Black-Rangarajan Duality (BRD) and graduated non-convexity (GNC) into the truncated least squares (TLS) framework (BG-TLS). Accordingly, we propose the efficient BG-TLS (EBG-TLS) method for computing the registration model. Comparative analyses with traditional and deep learning-based methods across various real-world environments demonstrate that the proposed method outperforms existing algorithms in terms of rotation error, translation error, and efficiency, particularly in complex, high-noise settings. This method finds broad applications in geospatial mapping and surveying, autonomous navigation, and environmental monitoring.

Image response-assisted volumetric reconstruction for simultaneous multi-color light-field microscopy

Light-field microscopy enables real-time volumetric imaging, offering substantial advantages for real-time fluorescence imaging. However, when applied to multi-color imaging, positional cross-talk between different fluorescent signals in the object space reduces reconstruction accuracy. Additionally, imaging each color through sequential excitation of fluorophores significantly compromises imaging speed. In this paper, an image response-assisted volumetric reconstruction method that unmixes multi-color fluorescence light-field images using pixel support derived from the light-field imaging response is proposed. This approach enables simultaneous multi-color imaging with significantly improved volumetric reconstruction accuracy. The correctness and effectiveness of the proposed method are validated through both simulations and experiments. The root-mean-square-error of multi-color volumetric reconstruction is reduced by 37.87 % on average compared with the simultaneous multi-color reconstruction methods obtained by simply combining single-pixel spectrum conversion methods and linear unmixing method in rapid-moving micro-particle observation, showcasing high accuracy simultaneous multi-color imaging performance. Volumetric imaging of motor neurons and whole-body cells of live dual-color zebrafish larvae at 20 Hz demonstrates the ability to be applied to real biomedical imaging.

Analytical form of the refocused images from correlation plenoptic imaging

Correlation plenoptic imaging (CPI) is emerging as a promising approach to light-field imaging (LFI), a technique for concurrently measuring light intensity distribution and propagation direction of light rays from a 3D scene. LFI thus enables single-shot 3D imaging, offering rapid volumetric reconstruction. The optical performance of traditional LFI, however, is limited by a micro-lens array, causing a decline in resolution as 3D capabilities improve. CPI overcomes these limitation by measuring photon number correlations on two photodetectors with spatial resolution, in a lenslet-free design, so that the correlation function can be decoded in post-processing to reconstruct high-resolution images. In this paper, we derive the analytical expression of CPI images reconstructed through refocusing, addressing the previously unknown mathematical relationship between object shape and its final image. We show that refocused images are not limited by numerical aperture-induced blurring, as in conventional imaging. Rather, the image features of CPI can be explained through an analogy with imaging systems illuminated by spatially coherent light.

Enhancing Patient Comprehension in Skull-Base Meningioma Surgery through 3D Volumetric Reconstructions: A Cost-Effective Approach.

Understanding complex neurosurgical procedures and diseases, such as skull-base meningiomas, is challenging for patients due to the intricate anatomy and the involvement of critical neurovascular structures. Enhanced patient comprehension is crucial for satisfaction and improved clinical outcomes. Patient-specific 3D models have demonstrated benefits in patient education, though they are costly and time-intensive to produce. This study investigates whether the use of 3D volumetric reconstructions with anatomical segmentation, widely available via neuronavigation software, can improve patients' understanding of skull-base meningiomas, surgical procedures, and potential complications. This study included twenty patients with skull-base meningiomas. Three-dimensional volume reconstructions and anatomical segmentations were created using preoperative MRI sequences with neuronavigation software. These reconstructions were used during patient consultations where a surgeon explained key aspects of the disease, the surgical intervention, and potential complications. A questionnaire assessed the patients' perceptions of the utility of these 3D reconstructions. The majority of patients (75%) found the 3D volumetric reconstructions and anatomical segmentations to be more beneficial than MRI images for understanding their disease. Similarly, 75% reported improved comprehension of the surgical approach, and 85% felt that the reconstructions enhanced their understanding of potential surgical complications. Overall, 65% of patients considered the 3D reconstructions valuable in medical consultations. Our study indicates that using accessible, cost-effective, and non-time-consuming 3D volumetric reconstructions with anatomical segmentation enhances patient understanding of skull-base meningiomas. Further research is necessary to confirm these findings, compare these reconstructions with physical 3D models and virtual reality models, and evaluate their impact on patient anxiety regarding the surgical procedure.

Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet.

Multi-target single-molecule super-resolution fluorescence microscopy offers a powerful means of understanding the distributions and interplay between multiple subcellular structures at the nanoscale. However, single-molecule super-resolution imaging of whole mammalian cells is often hampered by high fluorescence background and slow acquisition speeds, especially when imaging multiple targets in 3D. In this work, we have mitigated these issues by developing a steerable, dithered, single-objective tilted light sheet for optical sectioning to reduce fluorescence background and a pipeline for 3D nanoprinting microfluidic systems for reflection of the light sheet into the sample. This easily adaptable novel microfluidic fabrication pipeline allows for the incorporation of reflective optics into microfluidic channels without disrupting efficient and automated solution exchange. By combining these innovations with point spread function engineering for nanoscale localization of individual molecules in 3D, deep learning for analysis of overlapping emitters, active 3D stabilization for drift correction and long-term imaging, and Exchange-PAINT for sequential multi-target imaging without chromatic offsets, we demonstrate whole-cell multi-target 3D single-molecule super-resolution imaging with improved precision and imaging speed.

Three-dimensional reconstruction of a shooting crime scene

The crime scene is technically the place where an illegal act took place. The investigation first starts at the crime scene. Documentation of the crime scene is of great importance in order to make correct decisions in the investigation and prosecution stages, With the development of technology; the use of different devices and different technologies in crime scene investigation and crime scene documentation has created a new working area for many forensic scientists and law enforcement officers. Taking a general view of the crime scene without contamination, low margin of error in measurements can be shown as the main reasons for the use of laser scanning devices at the crime scene. A firearm crime scene was staged using different evidence. In the staged crime scene, a laser scanning device was also used along with the use of photography, sketching and note-taking methods from traditional documentation methods. In this study, the adequacy of the traditional methods alone, the adequacy of the laser scanning device alone, and the adequacy of the two methods together for reconstruction were discussed. The aim of this study is to determine the contributions and limitations of documenting the crime scene with a laser scanning device in combination with traditional documentation methods to the three-dimensional reconstruction of the crime scene.

Three-dimensional object reconstruction based on spin–orbit interaction of light

Three-dimensional (3D) reconstruction has received extensive attention in recent years and is of great significance to various application fields. We propose a simple method of 3D object reconstruction based on spin–orbit interactions occurring from light reflection at the air–glass interface. By rotating the object, edge images of the object in various orientations are obtained; we overlap these edge images to ultimately reconstruct the 3D object. This 3D image detection based on spin–orbit interaction of light provides a low energy consumption, low cost, and more algorithmic method compared to traditional 3D image processing. This can be used in fields such as medicine, autonomous vehicles, planetary observation, and other areas.

Three-Dimensional Reconstruction of Forest Scenes with Tree–Shrub–Grass Structure Using Airborne LiDAR Point Cloud

Fine three-dimensional (3D) reconstruction of real forest scenes can provide a reference for forestry digitization and forestry resource management applications. Airborne LiDAR technology can provide valuable data for large-area forest scene reconstruction. This paper proposes a 3D reconstruction method for complex forest scenes with trees, shrubs, and grass, based on airborne LiDAR point clouds. First, forest vertical distribution characteristics are used to segment tree, shrub, and ground–grass points from an airborne LiDAR point cloud. For ground–grass points, a ground–grass grid model is constructed. For tree points, a method based on hierarchical canopy point fitting is proposed to construct a trunk model, and a crown model is constructed with the 3D α-shape algorithm. For shrub points, a shrub model is directly constructed based on the 3D α-shape algorithm. Finally, tree, shrub, and ground–grass models are spatially combined to achieve the reconstruction of real forest scenes. Taking six forest plots located in Hebei, Yunnan, and Guangxi provinces in China and Baden-Württemberg in Germany as study areas, experimental results show that the accuracy of individual tree segmentation reaches 87.32%, the accuracy of shrub segmentation reaches 60.00%, the height accuracy of the grass model is evaluated with an RMSE &lt; 0.15 m, the volume accuracy of shrub and tree models is assessed with R2 &gt; 0.848 and R2 &gt; 0.904, respectively. Furthermore, we compared the model constructed in this study with simplified point cloud and voxel models. The results demonstrate that the proposed modeling approach can meet the demand for the high-accuracy and lightweight modeling of large-area forest scenes.