Efficient Reconstruction Method Research Articles

Phase reconstruction by phase shift estimation with reliable parameter minimization

Abstract Achieving a balance between accuracy and speed in phase reconstruction is a challenging problem. In phase-shifting interferometry, increasing the speed by reducing the number of phase shifts while maintaining high accuracy is highly desirable. We propose an accurate and efficient two-step phase reconstruction method utilizing random phase shift. This method directly estimates the phase shift through reliable parameter minimization, allowing for easy and precise phase reconstruction. Simulations and experiments demonstrate the superior performance of our method across various scenarios, outperforming well-known two-step phase-shifting algorithms. We expect this paper to provide a general and powerful tool for phase reconstruction.

Separation of overlapping non-stationary signals and compressive sensing-based reconstruction using instantaneous frequency estimation

Compressive sensing uses an incomplete sample set to consider signal reconstruction with sparse basis representations over a specific transform domain. This work examines the difficulties encountered in various signal processing applications, such as data overlap with the target signal in time and frequency domains. The conventional filtering and windowing processes fail to promote better results in recovering the desired signal. The non-stationary signal with time-frequency representations is highly significant for certain valuable applications. An efficient signal separation and reconstruction method based on instantaneous frequency estimate is proposed to improve the non-stationary signal outcome. The proposed research includes speech signal data acquisition, signal component decomposition, instantaneous frequency estimation, overlapped signal separation, and reconstruction. Two datasets, Dacon Overlapped Speech and LJ Speech Dataset, are utilized to generate a new overlapped signal dataset. Initially, the generated speech signals are collected and decomposed into several monocomponents using Synchrosqueezing Wavelet Transform (SynWav), Renyi's quadratic entropy (RQuad), Narrow bandpass filtering (NBand) and single frequency filtering (SFreq). The instantaneous frequencies are estimated using the Hilbert-Huang transform (HilHuT) to generate a time-frequency representation of multi-component signals. The overlapped signals are separated and reconstructed to a desired signal using a Compressive sensing-based Discrete Cosine transform with amended intrinsic chirp separation (CDcT_AIChirS). The proposed model is simulated using PYTHON to examine the performances. In comparison, the mean square error is calculated to be 2.71, the root mean square error is calculated to be 1.64, and the signal to noise ratio is calculated to be 32dB.

Computationally efficient dominant load-based local bone microstructure reconstruction method using topology optimization

The bone microstructure of the human proximal femur is clinically crucial for diagnosing skeletal pathologies, such as osteoporosis and bone metastases. The topology optimization-based bone microstructure method obtains these bone microstructures by converting low-resolution (LR) images into high-resolution images. However, this method is inherently computationally inefficient as it requires numerous finite elements, iterative analyses, and parallel computations. Therefore, this study proposes a novel topology optimization-based localised bone microstructure reconstruction method using the dominant load, which highly affects the selected region of interest (ROI), for efficient resolution enhancement. The load dependency of selected ROIs is quantified with a load dependency score. Then, the localised finite element model is constructed based on the local load estimation. Finally, the selected dominant load is applied as an input for the topology optimization-based bone microstructure reconstruction method. The reconstructed bone microstructure was similar to that of the conventional method. The localised finite element model applied by the dominant load effectively and accurately reconstructed the bone morphology and exhibited high computational efficiency. In conclusion, the dominant load-based approach can be used to construct a reasonable trabecular bone structure for ROI with high computational efficiency. The predictive performance of the proposed method was validated and showed promise for accurate trabecular bone structure prediction without additional radiation exposure.

Efficiently improving the performance of noisy quantum computers

Using near-term quantum computers to achieve a quantum advantage requires efficient strategies to improve the performance of the noisy quantum devices presently available. We develop and experimentally validate two efficient error mitigation protocols named ``Noiseless Output Extrapolation" and ``Pauli Error Cancellation" that can drastically enhance the performance of quantum circuits composed of noisy cycles of gates. By combining popular mitigation strategies such as probabilistic error cancellation and noise amplification with efficient noise reconstruction methods, our protocols can mitigate a wide range of noise processes that do not satisfy the assumptions underlying existing mitigation protocols, including non-local and gate-dependent processes. We test our protocols on a four-qubit superconducting processor at the Advanced Quantum Testbed. We observe significant improvements in the performance of both structured and random circuits, with up to 86% improvement in variation distance over the unmitigated outputs. Our experiments demonstrate the effectiveness of our protocols, as well as their practicality for current hardware platforms.

Research and Application of Treatment Measures for Low-Yield and Low-Efficiency Coalbed Methane Wells in Qinshui Basin

China is rich in high-grade coalbed methane resources, accounting for one-third of the total amount of coalbed methane resources. Qinshui Basin is the main high ranking coalbed methane mining basin in China. In the early stage of CBM development, low-production and low-efficiency wells were formed in the process of block development because of an insufficient understanding of reservoir geological conditions. The existence of low-yield and low-efficiency wells with low output and a poor development benefit seriously restricts the efficient development of coalbed methane. In order to improve the overall development efficiency of coalbed methane fields, how to revitalize low-yield and low-efficiency wells is the main problem facing the development process of coalbed methane. With the deepening understanding of the study area geology, the formation of low-yield and low-efficiency wells has been basically identified. With the advancement of development technology, developers have the ability to retrofit some low-producing and inefficient wells. Low-production and low-efficiency wells are widely distributed. It is difficult to find the criteria for classifying low-producing and low-efficiency wells because of the great differences in geological conditions and reservoir physical properties in different blocks. In addition, the causes of a low-production and low-efficiency well are complex, as the same well is often caused by many reasons, and how to identify the causes of low-production and low-efficiency wells is difficult. In recent decades, developers have studied many methods to retrofit low-production wells, but the retrofit results are not satisfactory. How to choose an economical and efficient reservoir reconstruction method to revitalize low-production and low-efficiency wells is particularly important. This paper starts with the definition of low-production and low-efficiency wells in different blocks, combining an economic evaluation and productivity characteristics to judge whether they are low-production and low-efficiency wells, and defines the distribution of low-production and low-efficiency wells in blocks. The reasons for the formation of low-production and low-efficiency wells are analyzed with the geological characteristics, production dynamic performance, and engineering reconstruction effects. This paper makes a comparative analysis of the current relatively mature low-production and low-efficiency well treatment measures, clearly identifies the advantages and disadvantages of different treatment measures, and takes corresponding stimulation measures for different causes of low-production and low-efficiency wells. The research shows that there are 687 low-production and low-efficiency wells in block A, accounting for 69.4% of the total number of wells, and the low-production and low-efficiency wells account for a relatively large proportion; so, it is necessary to treat them. The main causes of low-production and low-efficiency wells are geology, engineering and drainage systems. The geological reason mainly refers to the low gas production of coalbed methane wells influenced by three factors: resource abundance, faults, and collapse columns. According to the different causes, three treatment measures of large-scale secondary fracturing, temporary plugging, and diversion fracturing and foam fracturing are put forward. The research method in this paper is targeted at different geological conditions so it can be used to guide the treatment of low-yield and low-efficiency wells in other CBM blocks, and it has very important significance for revitalizing the existing low-efficiency CBM assets and improving the development efficiency of CBM.

SPLIT: Statistical Positronium Lifetime Image Reconstruction via Time-Thresholding.

Positron emission tomography (PET) is a widely utilized medical imaging modality that uses positron-emitting radiotracers to visualize biochemical processes in a living body. The spatiotemporal distribution of a radiotracer is estimated by detecting the coincidence photon pairs generated through positron annihilations. In human tissue, about 40% of the positrons form positroniums prior to the annihilation. The lifetime of these positroniums is influenced by the microenvironment in the tissue and could provide valuable information for better understanding of disease progression and treatment response. Currently, there are few methods available for reconstructing high-resolution lifetime images in practical applications. This paper presents an efficient statistical image reconstruction method for positronium lifetime imaging (PLI). We also analyze the random triple-coincidence events in PLI and propose a correction method for random events, which is essential for real applications. Both simulation and experimental studies demonstrate that the proposed method can produce lifetime images with high numerical accuracy, low variance, and resolution comparable to that of the activity images generated by a PET scanner with currently available time-of-flight resolution.

Realistic reconstruction of trees from sparse images in volumetric space

The realistic reconstruction of real-world trees is a challenging task in the community of computer graphics because natural trees have complex structures of branches and leaves. Existing terrestrial laser scanning (TLS) system is able to capture dense and precise tree point clouds, yet the TLS system is expensive and not easy to carry around. An alternative low-cost and portable way is the reconstruction of tree point cloud from multiple view images. However, it is usually difficult to reconstruct a complete tree point cloud because of the texture similarity of branches and leaves as well as the lack of a sufficient number of images. Thus, we propose a new approach for reconstructing tree point clouds and geometries from sparse images. We first infer the camera parameters of each image, and then calculate the bounding volume of a tree from the camera parameters. Next, we set the mask of each image and the resolution of voxel, and then project each voxel in 3D space to all the mask images to determine the validity of the voxel. To alleviate the miss deletion of valid voxel, we utilize a boundary threshold and adjust mask resolution for robust point cloud reconstruction. Finally, an efficient tree reconstruction method is proposed to generate plausible tree geometries. We tested 6 different tree species that contain deciduous and evergreen trees, and the results showed that our approach is able to generate complete tree point cloud and realistic tree models even from a few number of images.

Deep learning based event reconstruction for cyclotron radiation emission spectroscopy

The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β −-decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

Complete Neuron Reconstruction Based on Branch Confidence.

In the past few years, significant advancements in microscopic imaging technology have led to the production of numerous high-resolution images capturing brain neurons at the micrometer scale. The reconstructed structure of neurons from neuronal images can serve as a valuable reference for research in brain diseases and neuroscience. Currently, there lacks an accurate and efficient method for neuron reconstruction. Manual reconstruction remains the primary approach, offering high accuracy but requiring significant time investment. While some automatic reconstruction methods are faster, they often sacrifice accuracy and cannot be directly relied upon. Therefore, the primary goal of this paper is to develop a neuron reconstruction tool that is both efficient and accurate. The tool aids users in reconstructing complete neurons by calculating the confidence of branches during the reconstruction process. The method models the neuron reconstruction as multiple Markov chains, and calculates the confidence of the connections between branches by simulating the reconstruction artifacts in the results. Users iteratively modify low-confidence branches to ensure precise and efficient neuron reconstruction. Experiments on both the publicly accessible BigNeuron dataset and a self-created Whole-Brain dataset demonstrate that the tool achieves high accuracy similar to manual reconstruction, while significantly reducing reconstruction time.

Efficient Image Reconstruction Methods Based on Structured Sparsity for Short-Range Radar

Efficient Image Reconstruction Methods Based on Structured Sparsity for Short-Range Radar

An Efficient and High-Quality Mesh Reconstruction Method with Adaptive Visibility and Dynamic Refinement

Image-based 3D reconstruction generates 3D mesh models from images and plays an important role in all walks of life. However, existing methods suffer from poor reconstruction quality and low reconstruction efficiency. To address this issue, we propose an improved optimization-based mesh reconstruction method with adaptive visibility reconstruction and dynamic photo-metric refinement. The adaptive visibility reconstruction adjusts soft visibility based on the observation and geometry structure of points to reconstruct details while suppressing noise in the rough mesh. The dynamic photo-metric refinement tunes the learning rate using historical gradients and stops to optimize converged triangles to speed up the mesh refinement. Experiments on BlendedMVS and real datasets showed that our method found a good balance between reconstruction quality and reconstruction efficiency. Compared with the state-of-the-art methods, OpenMVS and TDR, our method achieved higher reconstruction quality than OpenMVS and obtained competitive reconstruction quality with TDR, but required only one-third of the reconstruction time of OpenMVS and one-tenth of the reconstruction time of TDR. Our method balances reconstruction efficiency and reconstruction quality and can meet real-world application requirements.

Efficient image reconstruction for a small animal PET system with dual-layer-offset detector design.

A compact PET/SPECT/CT system Inliview-3000B has been developed to provide multi-modality information on small animals for biomedical research. Its PET subsystem employed a dual-layer-offset detector design for depth-of-interaction capability and higher detection efficiency, but the irregular design caused some difficulties in calculating the normalization factors and the sensitivity map. Besides, the relatively larger (2mm) crystal cross-section size also posed a challenge to high-resolution image reconstruction. We present an efficient image reconstruction method to achieve high imaging performance for the PET subsystem of Inliview-3000B. List mode reconstruction with efficient system modeling was used for the PET imaging. We adopt an on-the-fly multi-ray tracing method with random crystal sampling to model the solid angle, crystal penetration and object attenuation effect, and modify the system response model during each iteration to improve the reconstruction performance and computational efficiency. We estimate crystal efficiency with a novel iterative approach that combines measured cylinder phantom data with simulated line-of-response (LOR)-based factors for normalization correction before reconstruction. Since it is necessary to calculate normalization factors and the sensitivity map, we stack the two crystal layers together and extend the conventional data organization method here to index all useful LORs. Simulations and experiments were performed to demonstrate the feasibility and advantage of the proposed method. Simulation results showed that the iterative algorithm for crystal efficiency estimation could achieve good accuracy. NEMA image quality phantom studies have demonstrated the superiority of random sampling, which is able to achieve good imaging performance with much less computation than traditional uniform sampling. In the spatial resolution evaluation based on the mini-Derenzo phantom, 1.1mm hot rods could be identified with the proposed reconstruction method. Reconstruction of double mice and a rat showed good spatial resolution and a high signal-to-noise ratio, and organs with higher uptake could be recognized well. The results validated the superiority of introducing randomness into reconstruction, and demonstrated its reliability for high-performance imaging. The Inliview-3000B PET subsystem with the proposed image reconstruction can provide rich and detailed information on small animals for preclinical research.

Efficient physics-based learned reconstruction methods for real-time 3D near-field MIMO radar imaging

Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. These systems generally reconstruct the three-dimensional (3D) complex-valued reflectivity distribution of the scene using sparse measurements. Consequently, imaging quality highly relies on the image reconstruction approach. Existing analytical reconstruction approaches suffer from either high computational cost or low image quality. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field MIMO imaging. The goal is to achieve high image quality with low computational cost at compressive settings. The developed approaches have two stages. In the first approach, physics-based initial stage performs adjoint operation to back-project the measurements to the image-space, and deep neural network (DNN)-based second stage converts the 3D backprojected measurements to a magnitude-only reflectivity image. Since scene reflectivities often have random phase, DNN processes directly the magnitude of the adjoint result. As DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To comparatively evaluate the significance of exploiting physics in a learning-based approach, two additional approaches that replace the physics-based first stage with fully connected layers are also developed as purely learning-based methods. The performance is also analyzed by changing the DNN architecture for the second stage to include complex-valued processing (instead of magnitude-only processing), 2D convolution kernels (instead of 3D), and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer to generate large-scale dataset for training the neural networks with 3D extended targets. We illustrate the performance through experimental data and extensive simulations. The results show the effectiveness of the developed physics-based learned reconstruction approach compared to commonly used approaches in terms of both runtime and image quality at highly compressive settings. Our source codes and dataset are made available at https://github.com/METU-SPACE-Lab/Efficient-Learned-3D-Near-Field-MIMO-Imaging upon publication to advance research in this field.

The Mastoid Skin Graft: A Fast, Efficient Donor Site for Auricle Reconstruction After Mohs Surgery.

Mohs micrographic surgery has been considered the gold standard for treating nonmelanoma skin cancers. Approximately 8% to 10% of skin cancers requiring Mohs surgery occur on the auricle. Skin grafts can be used to achieve optimal functional and cosmetic results. To describe a skin graft technique using the mastoid area as a donor site for auricular reconstruction following Mohs micrographic surgery, to achieve optimal esthetic results in functionally important areas. This retrospective study assessed the outcomes of patients who underwent MMS in an out-patient Mohs surgery clinic from 2010 to 2021. All patients had MMS of the auricle with reconstruction using a split thickness skin graft harvested from the mastoid area. A total of 154 patients were included. The average lesion diameter was 13.7 mm (range 5-30 mm), excised in an average of 1.9 rounds of MMS. Skin graft success rate was 96%. Complications included one patient with a hypertrophic scar. The cosmetic appearance of the recipient site was rated as excellent by 87.5% of patients. All patients rated the cosmetic appearance of the donor area as excellent and well-concealed. Split thickness skin grafts harvested from the mastoid area are a quick and efficient reconstruction method for post-Mohs auricular defects.

Cultural relic reconstruction device based on ORB-SLAM

ORB-SLAM, an advanced visual SLAM technology, has excellent positioning and mapping capabilities [1]. In the reconstruction of cultural relics, ORB-SLAM technology can achieve autonomous perception and modeling of the surrounding environment of cultural relics by fusing and analyzing the images of monocular depth cameras, thereby reconstructing the 3D model of cultural relics, providing new possibilities for the protection and inheritance of cultural relics. Point cloud images are a digital technology used for 3D modeling, which can save point data on object surfaces and their relationships as 3D point clouds [2]. In terms of cultural relic reconstruction, point cloud images can restore and reconstruct the shape, size, texture, and other features of cultural relics by processing the collected point data on the surface of cultural relics into a 3D model. The purpose of this device is to reconstruct cultural relics by generating point cloud images based on ORB-SLAM technology. Combined with the designed handheld pan tilt device, it can achieve multi-angle point cloud mapping of cultural relics scenes in complex terrain. This paper will introduce the handheld gimbal design of the device, the realization of point cloud mapping based on the ORB-SLAM3 algorithm [3], and the application of point cloud scenes. Through the research in this article, it is expected to provide a new and efficient method for cultural relic reconstruction in the field of cultural relic protection and make contributions to the protection and inheritance of cultural heritage.

Time delay-based spectrum reconstruction for nonuniform and sub-Nyquist sampling in blade tip timing

Rotating blades play a functional role during the working process of turbomachinery, but they are prone to failure due to harsh working conditions. Blade tip timing (BTT) is a potential technique for operational blade health monitoring due to its noncontact feature and efficiency. The critical issue in the application of BTT is extracting vibration parameters or reconstructing the spectrum for subsequent fault diagnosis. However, the nonuniform and undersampling characteristics of the BTT signal challenge signal processing. In this work, we developed an effective and efficient time delay-based spectrum reconstruction method. On this basis, we proposed the conditions (minimal probe number and delay sampling criterion) for the spectrum reconstruction. The proposed reconstruction conditions apply to most BTT methods for parameter identification or spectrum reconstruction and provide guidance for BTT measurement. Finally, the effectiveness of the proposed method and the correctness of the reconstruction conditions were demonstrated by a series of simulations and experiments.

Lagrangian displacement field estimators in cosmology

The late-time nonlinear Lagrangian displacement field is highly correlated with the initial field, so reconstructing it could enable us to extract primordial cosmological information. Our previous work [A. Ota et al., Phys. Rev. D 104, 123508 (2021)] carefully studied the displacement field reconstructed from the late-time density field using the iterative method proposed by Schmittfull et al. [Phys. Rev. D 96, 023505 (2017)] and found that it does not fully converge to the true, underlying displacement field (e.g., $\ensuremath{\sim}8%$ offset at $k\ensuremath{\sim}0.2\text{ }\text{ }h\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$ at $z=0.6$). We also constructed the Lagrangian perturbation theory model for the reconstructed field, but the model could not explain the discrepancy between the true and the reconstructed fields in the previous work. The main sources of the discrepancy were speculated to be a numerical artifact in the displacement estimator due to the discreteness of the sample. In this paper, we develop two new estimators of the displacement fields to reduce such a numerical discreteness effect, the normalized momentum estimator and the rescaled resumed estimator. We show that the discrepancy Ota et al. [Phys. Rev. D 104, 123508 (2021)] reported is not due to the numerical artifacts. We conclude that the method from Schmittfull et al. [Phys. Rev. D 96, 023505 (2017)] cannot fully reconstruct the shape of the nonlinear displacement field at the redshift we studied, while it is still an efficient baryon acoustic oscillation reconstruction method. In parallel, by properly accounting for the UV-sensitive term in a reconstruction procedure with an effective field theory approach, we improve the theoretical model for the reconstructed displacement field, by almost 5 times, from $\ensuremath{\sim}15%$ to the level of a few percent at $k\ensuremath{\sim}0.2\text{ }\text{ }h\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$ at the redshift $z=0.6$.

A Multicomponent Iterative Method for Efficient Source Reconstruction Based on Magnitude-Only and Single-Plane Near-Field Scanning

A multicomponent iterative method is proposed for efficient source reconstruction based on magnitude-only and single-plane near field scanning. Compared with the conventional method filling the multicomponent data into a whole transformation matrix, the multicomponent iterative method can save huge calculation time by evenly reducing the size of the transformation matrix into magnetic group and electric group, without losing solution accuracy of the equivalent dipole moments. The accuracy and efficiency of the proposed source reconstruction is verified on a simulated patch antenna model. The proposed method is also applied to efficiently find the equivalent sources and reconstruct the surface electromagnetic field of a practical serpentine line. For a larger transformation matrix (depending on the factors such as the scanned field components, scanned points, and dipole numbers), the proposed iteration method shows higher efficiency.

Surface reconstruction method for particle-based fluids using discrete indicator functions

Particle-based methods are practical for computing level-sets that represent liquid interfaces. However, these methods are computationally expensive to reconstruct the liquid surface when the number of particles increases considerably due to the massive amount of particle approximations. This paper introduces two simple and efficient surface reconstruction methods for particle-based fluids based on discrete indicator functions (DIFs). The first approach provides fast level-set approximation using a DIF defined by counting particles inside grid cells. The second approach generates a high-quality liquid surface using a DIF obtained by the particle distribution inside grid cells. Our DIF-based methods are fast, easy to code, and can be adapted straightforwardly in particle-based fluid solvers, even implemented in GPU. Moreover, we show the effectiveness of our approaches through experiments against prior surface reconstruction methods.

GAN‐LSTM‐3D: An efficient method for lung tumour 3D reconstruction enhanced by attention‐based LSTM

AbstractThree‐dimensional (3D) image reconstruction of tumours can visualise their structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is proposed for 3D reconstruction of lung cancer tumours from 2D CT images. Our method consists of three phases: lung segmentation, tumour segmentation, and tumour 3D reconstruction. Lung segmentation is done using snake optimisation followed by tumour segmentation using Gustafson‐Kessel (GK) clustering method. The outputs of GK (2D lung cancer images) are fed to a pre‐trained Visual Geometry Group (VGG) for feature extraction. The VGG outputs are used as input for an attention‐based LSTM which performs feature unpacking. The output of LSTM units is given to generator network of a Generative Adversarial Networks (GAN) model to carry out 3D reconstruction of (normal/cancerous) images with high quality. During training, the discriminator network of the GAN is used to judge the generator outputs. The authors to the best of their knowledge were the first to use GAN for 3D reconstruction of lung cancer tumours which is the primary contribution of this article. Moreover, existing studies are mostly focused on brain tumours and are not suitable for lung tumour reconstruction. Focusing on lung tumours is the second contribution of this article. Evaluation on LUNA data collection against existing methods like MC, MC + fairing etc. reveals the superiority of our method in terms of Hamming and Euclidean distance metrics. Additionally, the computational complexity of the proposed method is lower compared to evaluated methods.