Accurate Reconstruction Research Articles

Enabling 3D CT-scanning of cultural heritage objects using only in-house 2D X-ray equipment in museums

Visualizing the internal structure of museum objects is a crucial step in acquiring knowledge about the origin, state, and composition of cultural heritage artifacts. Among the most powerful techniques for exposing the interior of museum objects is computed tomography (CT), a technique that computationally forms a 3D image using hundreds of radiographs acquired in a full circular range. However, the lack of affordable and versatile CT equipment in museums, combined with the challenge of transporting precious collection objects, currently keeps this technique out of reach for most cultural heritage applications. We propose an approach for creating accurate CT reconstructions using only standard 2D radiography equipment already available in most larger museums. Specifically, we demonstrate that a combination of basic X-ray imaging equipment, a tailored marker-based image acquisition protocol, and sophisticated data-processing algorithms, can achieve 3D imaging of collection objects without the need for a costly CT imaging system. We implemented this approach in the British Museum (London), the J. Paul Getty Museum (Los Angeles), and the Rijksmuseum (Amsterdam). Our work paves the way for broad facilitation and adoption of CT technology across museums worldwide.

Deep tomography for the three-dimensional atmospheric turbulence wavefront aberration

Multiconjugate adaptive optics (MCAO) can overcome atmospheric anisoplanatism to achieve high-resolution imaging with a large field of view (FOV). Atmospheric tomography is the key technology for MCAO. The commonly used modal tomography approach reconstructs the three-dimensional atmospheric turbulence wavefront aberration based on the wavefront sensor (WFS) detection information from multiple guide star (GS) directions. However, the atmospheric tomography problem is severely ill-posed. The incomplete GS coverage in the FOV coupled with the WFS detection error significantly affects the reconstruction accuracy of the three-dimensional atmospheric turbulence wavefront aberration, leading to a nonuniform aberration detection precision over the whole FOV. We propose an efficient approach for achieving accurate atmospheric tomography to overcome the limitations of the traditional modal tomography approach. We employed a deep-learning-based approach to the tomographic reconstruction of the three-dimensional atmospheric turbulence wavefront aberration. We propose an atmospheric tomography residual network (AT-ResNet) that is specifically designed for this task, which can directly generate wavefronts of multiple turbulence layers based on the Shack-Hartmann (SH) WFS detection images from multiple GS directions. The AT-ResNet was trained under different turbulence intensity conditions to improve its generalization ability. We verified the performance of the proposed approach under different conditions and compared it with the traditional modal tomography approach. The well-trained AT-ResNet demonstrates a superior performance compared to the traditional modal tomography approach under different atmospheric turbulence intensities, various turbulence layer distributions, higher-order turbulence aberrations, detection noise, and reduced GSs conditions. The proposed approach effectively addresses the limitations of the modal tomography approach, leading to a notable improvement in the accuracy of atmospheric tomography. It achieves a highly uniform and high-precision wavefront reconstruction over the whole FOV. This study holds great significance for the development and application of the MCAO technology.

Generation of Virtual Ground Control Points Using a Binocular Camera

This paper presents a methodology for generating virtual ground control points (VGCPs) using a binocular camera mounted on a drone. We compare the measurements of the binocular and monocular cameras between the classical method and the proposed one. This work aims to decrease human processing times while maintaining a reduced root mean square error (RMSE) for 3D reconstruction. Additionally, we propose utilizing COLMAP to enhance reconstruction accuracy by solely utilizing a sparse point cloud. The results demonstrate that implementing COLMAP for pre-processing reduces the RMSE by up to 16.9% in most cases. We prove that VGCPs further reduce the RMSE by up to 61.08%.

Improved 3D human face reconstruction from 2D images using blended hard edges

AbstractThis study reports an effective and robust edge-based scheme for the reconstruction of 3D human faces from input of single images, addressing drawbacks of existing methods in case of large face pose angles or noisy input images. Accurate 3D face reconstruction from 2D images is important, as it can enable a wide range of applications, such as face recognition, animations, games and AR/VR systems. Edge features extracted from 2D images contain wealthy and robust 3D geometric information, which were used together with landmarks for face reconstruction purpose. However, the accurate reconstruction of 3D faces from contour features is a challenging task, since traditional edge or contour detection algorithms introduce a great deal of noise, which would adversely affect the reconstruction. This paper reports on the use of a hard-blended face contour feature from a neural network and a Canny edge extractor for face reconstruction. The quantitative results indicate that our method achieves a notable improvement in face reconstruction with a Euclidean distance error of 1.64 mm and a normal vector distance error of 1.27 mm when compared to the ground truth, outperforming both traditional and other deep learning-based methods. These metrics show particularly significant advancements, especially in face shape reconstruction under large pose angles. The method also achieved higher accuracy and robustness on in-the-wild images under conditions of blurring, makeup, occlusion and poor illumination.

ZH3: Quadratic Zonal Harmonics

Spherical Harmonics (SH) have been used widely to represent lighting in games and film. While the quadratic (SH3) and higher order spherical harmonics represent irradiance well, they are expensive to store and evaluate, requiring 27 coefficients per sample. Linear SH (SH2), requiring only 12 coefficients, are sometimes used, but they do not represent irradiance signals accurately and can have challenges with negative reconstruction. We introduce a new representation (ZH3) that augments linear SH with just the zonal coefficient of quadratic SH, yielding significant visual improvement with just 15 coefficients, and discuss how solving for a luminance zonal axis can significantly improve reconstruction accuracy and reduce color artifacts. We also discuss how, rather than storing the ZH3 coefficients explicitly, we can hallucinate them from the linear SH, improving reconstruction accuracy over linear SH at minimal extra cost.

Triplet Epipolar Images for Spaceborne Three Linear Cameras

Abstract. Epipolar images can reduce conjugate points searching from a two-dimensional to a one-dimensional space, which significantly improves the robustness and efficiency of dense matching. However, two images matched the lack of redundancy and suffered from occlusions. The third image could introduce two more stereos and improve the accuracy and completeness of the three-dimension reconstruction, which were adopted by spaceborne three linear cameras (TLCs), the ALOS PRSIM and ZY-3. In this study, we propose a new triplet epipolar images generation method for spaceborne TLCs. Triplet epipolar images were defined as any two of the three epipolar image pairs. The triplet epipolar geometry requires the imaging rays of any conjugate point to be coplanar. For high-resolution satellite images, parallel projection can be used to approximate the imaging geometry of satellite images. Therefore, a coplanar principal optical axis is a fundamental requirement for spaceborne TLCs. We propose a general workflow to generate triplet epipolar images (TEIs), which includes free-net bundle adjustment of TLCs, building a triplet epipolar geometry, correcting the y-parallaxes, and generating a rational function model (RFM) for TEIs. The ZY3-02 satellite images were used to validate the proposed method. The root means square error (RMSEs) of the free-net bundle adjustment in the image space was 0.185 pixels, which proved the fine intrinsic accuracy. After compensation, the RMSEs of the y parallaxes of the three epipolar image pairs were 0.295, 0.310, and 0.370 pixels. Owing to the simple geometry of the TEIs, the RMSEs of the RFM replacements were within 0.001 pixels.

Three-Way Alignment Improves Multiple Sequence Alignment of Highly Diverged Sequences

The standard approach for constructing a phylogenetic tree from a set of sequences consists of two key stages. First, a multiple sequence alignment (MSA) of the sequences is computed. The aligned data are then used to reconstruct the phylogenetic tree. The accuracy of the resulting tree heavily relies on the quality of the MSA. The quality of the popularly used progressive sequence alignment depends on a guide tree, which determines the order of aligning sequences. Most MSA methods use pairwise comparisons to generate a distance matrix and reconstruct the guide tree. However, when dealing with highly diverged sequences, constructing a good guide tree is challenging. In this work, we propose an alternative approach using three-way dynamic programming alignment to generate the distance matrix and the guide tree. This three-way alignment incorporates information from additional sequences to compute evolutionary distances more accurately. Using simulated datasets on two symmetric and asymmetric trees, we compared MAFFT with its default guide tree with MAFFT with a guide tree produced using the three-way alignment. We found that (1) the three-way alignment can reconstruct better guide trees than those from the most accurate options of MAFFT, and (2) the better guide tree, on average, leads to more accurate phylogenetic reconstruction. However, the improvement over the L-INS-i option of MAFFT is small, attesting to the excellence of the alignment quality of MAFFT. Surprisingly, the two criteria for choosing the best MSA (phylogenetic accuracy and sum-of-pair score) conflict with each other.

Maisonneuve fracture treated with short leg cast: A case report with 41-month follow-up.

Maisonneuve fracture is a specific type of severe ankle injury. To our current knowledge, once a Maisonneuve fracture is diagnosed, the surgery is always recommended for fear of sequelae from inaccurate joint reconstruction. However, in this case, we treated a Maisonneuve fracture with a short leg cast, and the 41-month follow-up showed a favorable outcome with no post-traumatic osteoarthritis, chronic pain, and instability. Therefore, this case provides evidence for the feasibility of conservative treatment of Maisonneuve fracture. A female patient in her early twenties sprained her left ankle while running, suffering regional pain, swelling, and limited mobility. We diagnosed a Maisonneuve fracture with superior fibular fracture and Volkmann tuberosity fracture, a slight separation of inferior tibiofibular syndesmosis (ITS). The patient rejected our surgical recommendations in favor of nonsurgical treatment, in addition to refusing immobilization of the knee. Consequently, we had to treat her with a short leg cast for 8 weeks and asked her to return for regular follow-up visits. At the final follow-up, the radiography showed complete healing of proximal fibula fracture. The patient reported no discernible subjective differences between her bilateral ankles. The range of motion of the left ankle was measured at 22° of dorsiflexion and 40° of plantarflexion. Functional assessments using Olerud-Molander ankle scale and American Orthopedic Foot and Ankle Society Ankle-Hindfoot scale both scored 100 points. Additionally, the radiographic assessment classified arthritis as stage 0 according to Morrey-Wiedeman classification. To avoid missing and misdiagnosing, the physical examination should always extend to 2 neighboring joints. Secondly, if a Maisonneuve fracture is suspected, further computed tomography scans, radiography, and magnetic resonance imaging can help to determine the stability of the ITS and the integrity of the lateral collateral ligaments before making therapeutic decisions. Finally, considering the lateral collateral ligaments may remain intact, we recommend stabilizing ITS by repairing the medial ligaments, which can be conducted arthroscopically and be more minimally invasive, providing an elastic fixation that aligns better with the biomechanics of the ITS which is characterized as a micro-mobile rather than fully fixed joint.

Unraveling generalized parton distributions through Lorentz symmetry and partial DGLAP knowledge

Relying on the polynomiality property of generalized parton distributions, which roots on Lorentz covariance, we prove that it is enough to know them at vanishing and low skewness within the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi region to obtain a unique extension to their entire support up to a D term. We put this idea in practice using two methods: reconstruction using artificial neural networks and finite-elements methods. We benchmark our results against standard models for generalized parton distributions. In agreement with the formal expectation, we obtain very a accurate reconstruction for a maximal value of the skewness as low as 20% of the longitudinal momentum fraction. This result might be relevant for reconstruction of generalized parton distribution from experimental and lattice QCD data, where computations are, for now, restricted in skewness. Published by the American Physical Society 2024

Directional TV algorithm for image reconstruction from sparse-view projections in EPR imaging.

Electron paramagnetic resonance (EPR) imaging is an advanced in vivo oxygen imaging modality. The main drawback of EPR imaging is the long scanning time. Sparse-view projections collection is an effective fast scanning pattern. However, the commonly-used filtered backprojection (FBP) algorithm is not competent to accurately reconstruct images from sparse-view projections because of the severe streak artifacts. The aim of this work is to develop an advanced algorithm for sparse reconstruction of 3D EPR imaging. The optimization based algorithms including the total variation (TV) algorithm have proven to be effective in sparse reconstruction in EPR imaging. To further improve the reconstruction accuracy, we propose the directional TV (DTV) model and derive its Chambolle-Pock (CP) solving algorithm. After the algorithm correctness validation on simulation data, we explore the sparse reconstruction capability of the DTV algorithm via a simulated six-sphere phantom and two real bottle phantoms filled with OX063 trityl solution and scanned by an EPR imager with a magnetic field strength of 250G. Both the simulated and real data experiments show that the DTV algorithm is superior to the existing FBP and TV-type algorithms and a deep learning based method according to visual inspection and quantitative evaluations in sparse reconstruction of EPR imaging. These insights gained in this work may be used in the development of fast EPR imaging workflow of practical significance.

Momentum Fourier ptychographic topography

Reflective Fourier Ptychographic Microscopy (FPM) has demonstrated the potential as a technology for semiconductor metrology and inspection applications, offering both a wide field-of-view (FOV) and high resolution with nanoscale height reconstruction accuracy. However, to enhance resolution, the wavelength of the illumination source has been shortened to the ultraviolet (UV) range, leading to a significant decrease in height calculation accuracy due to low signal-to-noise issues. Additionally, the time-consuming aspect persists as a challenge. To address these issues, we introduce Momentum Fourier Ptychographic Topography (MFPT). MFPT can overcome the problem of getting trapped in local minima due to noise, and it does not require an iterative process of total variant regularization, allowing for significant computational time savings. Using UV illumination with MFPT, we measure a United States Air Force (USAF) 1951 target and two step-height samples, providing evidence that MFPT is robust to noise and accurately reconstructs nanoscale height. As a result, utilizing UV illumination with MFPT allows us to achieve a resolution of 173 nm, and we can rapidly and accurately calculate the height of step-height samples with nominal heights of 30 nm and 70 nm.

Accuracy improvement of two-dimensional shape reconstruction based on OFDR using first-order differential local filtering

The accuracy of two-dimensional (2D) shape reconstruction is highly susceptible to fake peaks in the strain distribution measured by optical frequency domain reflectometry (OFDR). In this paper, a post-processing method using first-order differential local filtering is proposed to suppress fake peaks and further improve the accuracy of shape reconstruction. By analyzing the principles of 2D shape reconstruction, an explanation of how fake peaks lead to shape reconstruction errors is provided, along with the introduction of an error evaluation standard. The principle of first-order differential local filtering is presented, and its feasibility is verified by simulation. An OFDR 2D shape reconstruction system is built, with three groups of 2D shape reconstruction experiments carried out, including up bending, down bending and arch bending. The experimental results show that the end errors of the three groups of shape reconstruction are respectively reduced from 2.33%, 2.97%, and 1.07% to 0.25%, 0.78%, and 0.20%, at the shape reconstruction length of 0.5 m. The research demonstrates that the accuracy of OFDR 2D shape reconstruction can be improved by using first-order differential local filtering.

Anatomy-guided multi-resolution image reconstruction in PET

Objective. In this paper, we propose positron emission tomography image reconstruction using a multi-resolution triangular mesh. The mesh can be adapted based on patient specific anatomical information that can be in the form of a computed tomography or magnetic resonance imaging image in the hybrid imaging systems. The triangular mesh can be adapted to high resolution in localized anatomical regions of interest (ROI) and made coarser in other regions, leading to an imaging model with high resolution in the ROI with clearly reduced number of degrees of freedom compared to a conventional uniformly dense imaging model. Approach. We compare maximum likelihood expectation maximization reconstructions with the multi-resolution model to reconstructions using a uniformly dense mesh, a sparse mesh and regular rectangular pixel mesh. Two simulated cases are used in the comparison, with the first one using the NEMA image quality phantom and the second the XCAT human phantom. Main results. When compared to the results with the uniform imaging models, the locally refined multi-resolution mesh retains the accuracy of the dense mesh reconstruction in the ROI while being faster to compute than the reconstructions with the uniformly dense mesh. The locally dense multi-resolution model leads also to more accurate reconstruction than the pixel-based mesh or the sparse triangular mesh. Significance. The findings suggest that triangular multi-resolution mesh, which can be made patient and application specific, is a potential alternative for pixel-based reconstruction.

Runoff variability in the Truckee-Carson River basin from tree rings and a water balance model

Abstract Regional warming and associated changes in hydrologic systems pose challenges to water supply management in river basins of the western United States, and call for improved understanding of the spatial and temporal variability of runoff. We apply a network of total-width, subannual width and delta blue intensity tree-ring chronologies in combination with a monthly water balance model to identify droughts and their associated precipitation (P) and temperature (T) footprints in the Truckee-Carson River basin (TCRB). Stepwise regression gave reasonably accurate reconstructions, from 1688 to 1999, of seasonal P and T (e.g., R2 = 0.50 for May-Sept T). These were disaggregated to monthly values, which were then routed through a water balance model to generate “indirectly” reconstructed runoff. Reconstructed and observed annual runoff correlate highly (r = 0.80) from 1906 to 1999. The extended runoff record shows that 20th century droughts are unmatched in severity in a 300-year context. Our water balance modeling reconstruction advances the conventional regression-based dendrochronological methods as it allows for multiple hydrologic components (evapotranspiration, snowmelt, etc.) to be evaluated. We found that imposed warming (3 °C and 6 °C) generally exacerbated the runoff deficits in past droughts but that the impact could be lessened and sometimes even reversed in some years by compensating factors, including changes in snow regime. Our results underscore the value of combining multi-proxy tree-ring data with water balance modelling to place past hydrologic droughts in the context of climate change.

The use of the multi-sequential LSTM in electrical tomography for masonry wall moisture detection

The study describes an innovative approach to reconstructing moisture distribution within walls using EIT, extending beyond the conventional single measurement vector methods. The research introduces a unique strategy involving a series of five measurement vectors, each deliberately disrupted by noise, to mirror real-life conditions more accurately. The aim was to compare the efficacy of this sequential approach with traditional single vector techniques in terms of accuracy and robustness in mapping moisture in masonry structures. The effectiveness of these reconstructions was evaluated based on various quality indicators. A multi-branch LSTM network was employed to model the moisture distribution reconstruction. The validation process included a comparative analysis using results from dielectric and gravimetric methods, enhanced with thermal imaging. The findings indicate that the multi-sequential reconstructions provide more detailed and accurate EIT representations than single-vector methods, demonstrating the increased accuracy and reliability of EIT reconstructions using this innovative technique.

Real-time volume rendering with octree-based implicit surface representation

Recent breakthroughs in neural radiance fields have significantly advanced the field of novel view synthesis and 3D reconstruction from multi-view images. However, the prevalent neural volume rendering techniques often suffer from long rendering time and require extensive network training. To address these limitations, recent initiatives have explored explicit voxel representations of scenes to expedite training. Yet, they often fall short in delivering accurate geometric reconstructions due to a lack of effective 3D representation. In this paper, we propose an octree-based approach for the reconstruction of implicit surfaces from multi-view images. Leveraging an explicit, network-free data structure, our method substantially increases rendering speed, achieving real-time performance. Moreover, our reconstruction technique yields surfaces with quality comparable to state-of-the-art network-based learning methods. The source code and data can be downloaded from https://github.com/LaoChui999/Octree-VolSDF.

An Improved Synchrosqueezing S-Transform and Its Application in a GPR Detection Task

The S-transform is a fundamental time–frequency (T-F) domain analysis method in ground penetrating radar (GPR) data processing and can be used for identifying targets, denoising, extracting thin layers, and high-resolution imaging. However, the S-transform spectrum experiences energy leakage near the instantaneous frequency. This phenomenon causes frequency components to erroneously spread over a wider range, impacting the accuracy and precision of GPR data processing. Synchrosqueezing is an effective method to prevent spectrum leakage. In this work, we introduce the synchrosqueezing generalized phase-shifting S-transform (SS-GPST). Initially, it resolves the compatibility issue between the S-transform and the synchrosqueezing strategy through phase-shifting. Subsequently, the SS-GPST accomplishes spectral energy focusing and resolution enhancement via a generalized parameter and synchrosqueezing. A synthetic signal test shows that the SS-GPST excels over other methods at focusing degree, spectral resolution, and signal reconstruction accuracy and speed. In actual GPR tunnel detection data processing, we assess the adaptability of the SS-GPST from three aspects: spectral energy distribution, thin layer identification, and data denoising. The results indicate: (1) compared to other methods, the SS-GPST accurately expresses spectral components with a strong focusing degree and fewer interference components; (2) high-frequency slices of the SS-GPST accurately detect the top and bottom interfaces of a 3.0–3.5 cm reinforcement protection layer; and (3) due to fewer interference components in the SS-GPST spectrum, reconstructing GPR profiles through the SS-GPST inverse transform is an efficient denoising technique. The SS-GPST demonstrates adaptability to different data processing purposes, offers high-resolution T-F spectra, and shows potential to supersede the S-transform.

Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively. Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom. Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97–0.99) and decreasing the RMSE (62–55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02–0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU). Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the potential of applying this CBCT system in more advanced scenarios by comparing the quality against fan-beam CT.

Determination of Mouth Width for Facial Reconstruction Based on Statistical Regression Model.

It is important to generate predictable statistical models by increasing the number of variables on the human skeletal and soft tissue structures on the face to increase the accuracy of human facial reconstructions. The purpose of this study was to determine mouth width 3-dimensionally based on statistical regression model. Cone-beam computed tomography scan data from 130 individuals were used to measure the horizontal and vertical dimensions of orbital and nasal structures and intercanine width. The correlation between these hard tissue variables and the mouth width was evaluated using the statistical regression model. Orbital width, nasal width, and intercanine width were found to be strong predictors of the mouth width determination and were used to generate the regression formulae to find the most approximate position of the mouth. These specific variables may contribute to improving the accuracy of mouth width determination for oral and maxillofacial reconstructions.

A new ECT image reconstruction algorithm based on Vision transformer (ViT)

Aiming at the problem of low accuracy of ECT image reconstruction, this paper proposes an ECT image reconstruction algorithm based on Vision Transformer (ViT). ViT is a method that applies the Transformer to the field of image classification, which is characterized by strong long-distance dependency learning ability and strong multi-modal fusion ability compared to CNN. This paper fully utilizes the characteristics of ViT to transform the image reconstruction process of ECT into the classification process of ViT. Here, a method is proposed to use the object field distribution as the image classification label. This method converts a two-dimensional image of the object field distribution into a one-dimensional vector, which is the label vector of the image classification. These vectors and their corresponding reconstructed images obtained by the Landweber algorithm form the sample set of ViT. Extracting a large number of flow pattern samples with various shapes and distributions through COMSOL is used as a training set. After training ViT, this network model is used to infer the labels of the predicted flow patterns. After post-processing this label, the corresponding reconstructed image can be obtained. Finally, simulation experiments are conducted, and the experiments results show that the image errors and correlation coefficients of the reconstructed images obtained through the algorithm in this paper are better than those of Tikhonov algorithm, Landweber algorithm and Long Short-Term Memory Network(LSTM). And this algorithm has better resistance to noise interference than Tikhonov algorithm, Landweber algorithm, and LSTM. This also provides a new approach and means for ECT image reconstruction.