Fast Reconstruction Algorithm Research Articles

Laminography as a tool for imaging large-size samples with high resolution.

Despite the increased brilliance of the new generation synchrotron sources, there is still a challenge with high-resolution scanning of very thick and absorbing samples, such as a whole mouse brain stained with heavy elements, and, extending further, brains of primates. Samples are typically cut into smaller parts, to ensure a sufficient X-ray transmission, and scanned separately. Compared with the standard tomography setup where the sample would be cut into many pillars, the laminographic geometry operates with slab-shaped sections significantly reducing the number of sample parts to be prepared, the cutting damage and data stitching problems. In this work, a laminography pipeline for imaging large samples (>1 cm) at micrometre resolution is presented. The implementation includes a low-cost instrument setup installed at the 2-BM micro-CT beamline of the Advanced Photon Source. Additionally, sample mounting, scanning techniques, data stitching procedures, a fast reconstruction algorithm with low computational complexity, and accelerated reconstruction on multi-GPU systems for processing large-scale datasets are presented. The applicability of the whole laminography pipeline was demonstrated by imaging four sequential slabs throughout an entire mouse brain sample stained with osmium, in total generating approximately 12 TB of raw data for reconstruction.

Time Transfer Link fusion algorithm based on wavelet multi-resolution analysis

To raise the precision and stability of time transfer links, a wavelet-based multi-resolution data fusion technique is offered. This approach fixes the “Day-Boundary discontinuities” in global positioning system (GPS) precise point positioning (PPP) link and lessens the diurnal in Two-way satellite time and frequency transfer (TWSTFT) link by integrating the model-based dynamic system analysis method with the statistically based multi-scale signal transformation method. It can also handle data from GPS PPP links and TWSTFT links at different sampling rates. More specifically, after applying wavelet transformation to the raw data and Kalman filtering estimation on various scales, this method uses the Mallat fast reconstruction algorithm of wavelet transform to achieve the fused results. The National Time Service Center (NTSC) and the Physikalisch-Technische Bundesanstalt (PTB) handle time transfer results using this method. Experiments show that this strategy greatly improves the link's short-term stability and can manage atypical interruptions while also boosting the link's robustness and reliability, which has a wide range of possible applications.

Convolutional Neural Networks for Spherical Signal Processing via Area-Regular Spherical Haar Tight Framelets.

In this article, we develop a general theoretical framework for constructing Haar-type tight framelets on any compact set with a hierarchical partition. In particular, we construct a novel area-regular hierarchical partition on the two spheres and establish its corresponding spherical Haar tight framelets with directionality. We conclude by evaluating and illustrate the effectiveness of our area-regular spherical Haar tight framelets in several denoising experiments. Furthermore, we propose a convolutional neural network (CNN) model for spherical signal denoising, which employs fast framelet decomposition and reconstruction algorithms. Experiment results show that our proposed CNN model outperforms threshold methods and processes strong generalization and robustness.

CT image reconstruction via industrial CT fast scanning

In automated manufacturing and safety inspection, there is a high demand for fast computed tomography (CT) scanning and image reconstruction. Currently, faster scanning can be achieved by reducing the X-ray exposure time within sparse view CT. The faster scanning strategy introduces significant streak artefacts and noise during the sampling process. Consequently, streak artefacts and noise need to be simultaneously suppressed, which is poses a challenge for existing reconstruction methods. This paper presents a fast iterative reconstruction algorithm that can simultaneously suppress both streak artefacts and noise. This method can not only reconstruct high-fidelity images from rapidly acquired projection data, but also has a faster reconstruction speed than the existing iterative reconstruction algorithms. First, we present a high-order multi-directional total variation (HOM-TV) method that specifically focuses on preserving edge details of the image. Then, we present a fast iterative reconstruction model by incorporating HOM-TV and non-local means into the objective function. Finally, the effectiveness of the presented reconstruction model is validated by simulation and real experiments. The faster scanning method can complete the scan in only 5 seconds, and the structural similarity index (SSIM) of the CT image reconstructed by our method is 0.9755, which is higher than 0.0175 of the Fast Null Space Reconstruction (FNSR) algorithm. The peak signal-to-noise ratio (PSNR) index is 1.656, which is higher than that of the contrast algorithm. In terms of reconstruction time, our algorithm can achieve reconstruction in as little as 36 seconds, outperforming the baseline algorithms.

Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography

Real-time applications in three-dimensional photoacoustic tomography from planar sensors rely on fast reconstruction algorithms that assume the speed of sound (SoS) in the tissue is homogeneous. Moreover, the reconstruction quality depends on the correct choice for the constant SoS. In this study, we discuss the possibility of ameliorating the problem of unknown or heterogeneous SoS distributions by using learned reconstruction methods. This can be done by modelling the uncertainties in the training data. In addition, a correction term can be included in the learned reconstruction method. We investigate the influence of both and while a learned correction component can improve reconstruction quality further, we show that a careful choice of uncertainties in the training data is the primary factor to overcome unknown SoS. We support our findings with simulated and in vivo measurements in 3D.

A Second-Order Generalized Total Variation with Improved Alternating Direction Method of Multipliers Algorithm for Electrical Impedance Tomography Reconstruction

The issue of Electrical Impedance Tomography (EIT) is a well-known inverse problem that presents challenging characteristics. In order to address the difficulties associated with ill-conditioned inverses, regularization methods are typically employed. One commonly used approach is total variation (TV) regularization, which has shown effectiveness in EIT. In order to meet the requirements of real-time tracking, it is essential to acquire fast and reliable algorithms for image reconstruction. Therefore, we present a modified second-order generalized regularization algorithm that enables more-accurate reconstruction of organ boundaries and internal structures, to reduce EIT artifacts, and to overcome the inability of the conventional Tikhonov regularization method in solving the step effect of the medium boundary. The proposed algorithm uses the improved alternating direction method of multipliers (ADMM) to tackle this optimization issue and adopts the second-order generalized total variation (SOGTV) function with strong boundary-preserving features as the regularization generalization function. The experiments are based on simulation data and the physical model of the circular water tank that we developed. The results showed that SOGTV regularization can improve image realism compared with some classic regularization.

Self-Guided Algorithm for Fast Image Reconstruction in Photo-Magnetic Imaging: Artificial Intelligence-Assisted Approach.

Previously, we introduced photomagnetic imaging (PMI) that synergistically utilizes laser light to slightly elevate the tissue temperature and magnetic resonance thermometry (MRT) to measure the induced temperature. The MRT temperature maps are then converted into absorption maps using a dedicated PMI image reconstruction algorithm. In the MRT maps, the presence of abnormalities such as tumors would create a notable high contrast due to their higher hemoglobin levels. In this study, we present a new artificial intelligence-based image reconstruction algorithm that improves the accuracy and spatial resolution of the recovered absorption maps while reducing the recovery time. Technically, a supervised machine learning approach was used to detect and delineate the boundary of tumors directly from the MRT maps based on their temperature contrast to the background. This information was further utilized as a soft functional a priori in the standard PMI algorithm to enhance the absorption recovery. Our new method was evaluated on a tissue-like phantom with two inclusions representing tumors. The reconstructed absorption map showed that the well-trained neural network not only increased the PMI spatial resolution but also improved the accuracy of the recovered absorption to as low as a 2% percentage error, reduced the artifacts by 15%, and accelerated the image reconstruction process approximately 9-fold.

Mobile edge assisted multi-view light field video system: Prototype design and empirical evaluation

Metaverse is recently envisioned as the main driver for immersive multimedia in future networks. Light field video (LFV), considered an intermediate transition scheme towards the metaverse, is conducive to sensing and reconstructing realistic scenes in the digital twins. However, the research on LFV systems still lacks comprehensive investigation, impeding their practical application. This paper addresses major technical challenges involved in LFV delivery through mobile networks, showing a trade-off between equipment cost, calibration effort, and workflow simplicity. It also considers the balance of communications and computations latency when maintaining reconstruction performance. Leveraging edge and cloud infrastructure, a novel Mobile Edge Assisted Multi-view Light-field-video System (MEAMLS) is proposed, integrating LFV collection, volumetric video construction, and an immersive viewing scheme. A custom-made LFV capture array is designed to capture real-world scenes, while employing a learning method-integrated edge server to facilitate adaptive LFV coding. Moreover, a fast sparse reconstruction algorithm is established, leveraging edge–cloud collaboration to minimize computation latency during volumetric video construction. Intelligent service for users is deployed on edge to enable viewport-driven virtual reality viewing. By providing an immersive visual experience, the proposed MEAMLS bridges the physical world and its digital twins. The system is prototyped on a realistic 5G network to empirically validate the performance under static and dynamic circumstances. Experimental results yield fundamental insights for designing mobile edge networks-assisted LFV system from the perspective of transmission resource, adaptability, and deployment.

Fast reconstruction algorithm based on HMC sampling

In Noisy Intermediate-Scale Quantum (NISQ) era, the scarcity of qubit resources has prevented many quantum algorithms from being implemented on quantum devices. Circuit cutting technology has greatly alleviated this problem, which allows us to run larger quantum circuits on real quantum machines with currently limited qubit resources at the cost of additional classical overhead. However, the classical overhead of circuit cutting grows exponentially with the number of cuts and qubits, and the excessive postprocessing overhead makes it difficult to apply circuit cutting to large scale circuits. In this paper, we propose a fast reconstruction algorithm based on Hamiltonian Monte Carlo (HMC) sampling, which samples the high probability solutions by Hamiltonian dynamics from state space with dimension growing exponentially with qubit. Our algorithm avoids excessive computation when reconstructing the original circuit probability distribution, and greatly reduces the circuit cutting post-processing overhead. The improvement is crucial for expanding of circuit cutting to a larger scale on NISQ devices.

A fast and flexible algorithm for microstructure reconstruction combining simulated annealing and deep learning

Microstructural analyses of porous media have considerable research value when studying of macroscopic properties,and the accurate reconstruction of a digital microstructure model is an important component of this research. Computational reconstruction algorithms for microstructures have attracted much attention due to their low cost and excellent performance. However, achieving faster and more efficient reconstruction remains a challenge for computational reconstruction algorithms. The bottleneck lies in the computational reconstruction algorithms themselves, which are either too slow (traditional reconstruction algorithms) or not flexible to the training process (deep learning reconstruction algorithms). To address these limitations, we propose a fast and flexible deep learning algorithm using a neural network based on an improved simulated annealing framework (ISAF-NN). The proposed algorithm adopts structural information of the reference image to guide the network design, and uses the description function to extract feature distribution of the reference image as the objective function to complete the network optimization. Benefit from the network structure is simple and flexible, the proposed algorithm can complete training and reconstruction in a short time. By adjusting the input size, the algorithm can also achieve arbitrary sized reconstruction. The proposed algorithm is experimentally applied to several materials to verify its effectiveness and generalizability.

Fast algorithm for 3D volume reconstruction from light field microscopy datasets.

Light field microscopy can capture 3D volume datasets in a snapshot, making it a valuable tool for high-speed 3D imaging of dynamic biological events. However, subsequent computational reconstruction of the raw data into a human-interpretable 3D+time image is very time-consuming, limiting the technique's utility as a routine imaging tool. Here we derive improved equations for 3D volume reconstruction from light field microscopy datasets, leading to dramatic speedups. We characterize our open-source Python implementation of these algorithms and demonstrate real-world reconstruction speedups of more than an order of magnitude compared with established approaches. The scale of this performance improvement opens up new possibilities for studying large timelapse datasets in light field microscopy.

A Fast Reconstruction Algorithm for Stereolithography File Topology Based on Hash Tables

Aiming at the problem of redundant data and loss of original topological relations in the conversion of 3D models into STL files in the engineering manufacturing process, a hash table structure based on the square-taking method and the chain table method is established based on extracting the information storage characteristics of STL files, and a fast reconstruction algorithm based on the hash table for the topological relations of STL files is proposed. The hash table is used to merge redundant point data, and the topological reconstruction of the model is completed by using the point table and face table structure to store the index value relationships of the vertex and face pieces. Finally, four example models are verified respectively. The verification results show that the algorithm reduces the storage space of STL files by 91% on average, and the topology reconstruction time is reduced by 25%, which improves the efficiency of processing such as layering and region division in subsequent manufacturing.

Deep-MSIM: Fast Image Reconstruction with Deep Learning in Multifocal Structured Illumination Microscopy.

Fast and precise reconstruction algorithm is desired for for multifocal structured illumination microscopy (MSIM) to obtain the super-resolution image. This work proposes a deep convolutional neural network (CNN) to learn a direct mapping from raw MSIM images to super-resolution image, which takes advantage of the computational advances of deep learning to accelerate the reconstruction. The method is validated on diverse biological structures and in vivo imaging of zebrafish at a depth of 100µm. The results show that high-quality, super-resolution images can be reconstructed in one-third of the runtime consumed by conventional MSIM method, without compromising spatial resolution. Last but not least, a fourfold reduction in the number of raw images required for reconstruction is achieved by using the same network architecture, yet with different training data.

Approximately dual frames of vector‐valued nonstationary Gabor frames and reconstruction errors

Nonstationary Gabor (NSG) frames for allow for flexible sampling and varying window functions and have found applications in adaptive signal analysis. Recently, the first author of this paper generalized the notion of NSG frames for to the space of vector‐valued signals and investigated the existence of vector‐valued NSG (VVNSG) frames. In this paper, we consider the reconstruction of any functions in from coefficients obtained from VVNSG frames. We provide different approximately dual frames of VVNSG frames and give the corresponding reconstruction error bounds. In particular, for a special class of VVNSG frames that are related to painless VVNSG frames, we show that the approximately dual frames carry the VVNSG structure, which is very important since there exist fast analysis and reconstruction algorithms for VVNSG frames. We also provide a method to construct the special class of VVNSG Bessel sequences. Finally, a constructive example is given to illustrate the theoretical results.

Streak artifact suppressed back projection for sparse-view photoacoustic computed tomography.

The development of fast and accurate image reconstruction algorithms under constrained data acquisition conditions is important for photoacoustic computed tomography (PACT). Sparse-view measurements have been used to accelerate data acquisition and reduce system complexity; however, reconstructed images suffer from sparsity-induced streak artifacts. In this paper, a modified back-projection (BP) method termed anti-streak BP is proposed to suppress streak artifacts in sparse-view PACT reconstruction. During the reconstruction process, the anti-streak BP finds the back-projection terms contaminated by high-intensity sources with an outlier detection method. Then, the weights of the contaminated back-projection terms are adaptively adjusted to eliminate the effects of high-intensity sources. The proposed anti-streak BP method is compared with the conventional BP method on both simulation and in vivo data. The anti-streak BP method shows substantially fewer artifacts in the reconstructed images, and the streak index is 54% and 20% lower than that of the conventional BP method on simulation and in vivo data, when the transducer number N=128. The anti-streak BP method is a powerful improvement of the BP method with the ability of artifact suppression.

Research on Detection and Recognition Technology of a Visible and Infrared Dim and Small Target Based on Deep Learning

With the increasing trend towards informatization and intelligence in modern warfare, high-intensity and continuous reconnaissance activities are becoming increasingly common in battlefield environments via airborne, vehicle, UAV, satellite and other platforms. Visible and infrared images are preferred due to their high resolution, strong contrast, rich texture details and color features, and strong information expression ability. However, the quality of imaging is easily affected by environmental factors, making it crucial to quickly and accurately filter useful information from massive image data. To this end, super-resolution image preprocessing can improve the detection performance of UAV, and reduce false detection and missed detection of targets. Additionally, super-resolution reconstruction results in high-quality images that can be used to expand UAV datasets and enhance the UAV characteristics, thereby enabling the enhancement of small targets. In response to the challenge of “low-slow small” UAV targets at long distances, we propose a multi-scale fusion super-resolution reconstruction (MFSRCNN) algorithm based on the fast super-resolution reconstruction (FSRCNN) algorithm and multi-scale fusion. Our experiments confirm the feasibility of the algorithm in reconstructing detailed information of the UAV target. On average, the MFSRCNN reconstruction time is 0.028 s, with the average confidence before and after reconstruction being 80.73% and 86.59%, respectively, resulting in an average increase of 6.72%.

Multi-Energy and Fast-Convergence Iterative Reconstruction Algorithm for Organic Material Identification Using X-ray Computed Tomography.

In order to significantly reduce the computing time while, at the same time, keeping the accuracy and precision when determining the local values of the density and effective atomic number necessary for identifying various organic material, including explosives and narcotics, a specialized multi-stage procedure based on a multi-energy computed tomography investigation within the 20-160 keV domain was elaborated. It consisted of a compensation for beam hardening and other non-linear effects that affect the energy dependency of the linear attenuation coefficient (LAC) in the chosen energy domain, followed by a 3D fast reconstruction algorithm capable of reconstructing the local LAC values for 64 energy values from 19.8 to 158.4 keV, and, finally, the creation of a set of algorithms permitting the simultaneous determination of the density and effective atomic number of the investigated materials. This enabled determining both the density and effective atomic number of complex objects in approximately 24 s, with an accuracy and precision of less than 3%, which is a significantly better performance with respect to the reported literature values.

A Radon diffraction theorem for plane wave ultrasound imaging.

The rising demand on high frame rate ultrasound imaging applications necessitates the development of fast algorithms for plane wave image reconstruction. We introduce a new class of plane wave reconstructions that relies on a relation between receive data and image data in the Radon domain. This relation is derived for arbitrary dimensions and validated on multiple two-dimensional plane wave data sets. We further present a mathematical relation between conventional delay-and-sum and Fourier domain reconstruction methods and the method proposed. Our analysis shows that they all rely on the same physical model with slight variations in certain filtering steps and, therefore, the new Radon domain reconstruction yields similar results as other methods in terms of image quality. However, we show that our method offers a huge potential to improve computation time by reducing the number of applied projections and to improve image quality by introducing nonlinear operations in the Radon domain, e.g., for edge enhancement. As the Radon transform retains both angular and temporal information, the relation also provides new insights on the fundamentals of plane wave imaging that can be leveraged for optimizing acquisition schemes or for developing novel compounding strategies in the future.

Full-field x-ray fluorescence imaging using a Fresnel zone plate coded aperture

We present an approach to full-field x-ray fluorescence imaging that uses a Fresnel zone plate as a coded aperture positioned between the object and detector. For this particular coded aperture, the incoherent image formation is identical to the coherent propagation image (inline hologram) of an equivalent absorption object. In the first experiment, we show that prior knowledge of the coded aperture structure and position is no longer required. Fast and robust reconstruction algorithms well known from coherent imaging can be applied. We demonstrate the approach using x-ray fluorescence excited by a collimated Cu- K α beam at an ordinary laboratory x-ray source. Images are recorded with a field of view of 1 m m 2 and resolution of 35 µm. The simple and efficient imaging scheme is well suited for further dissemination of x-ray fluorescence imaging and translation to a broader user community, which could take advantage of full-field imaging with chemical contrast. More generally, the scheme also enables full-field imaging of other incoherent radiation processes for which lenses are either not available at all or not with sufficient numerical apertures. Examples include Compton imaging, imaging of inelastic neutron scattering, or γ -ray imaging in nuclear medicine.

Fast iterative reconstruction for photoacoustic tomography using learned physical model: Theoretical validation

Iterative reconstruction has demonstrated superior performance in medical imaging under compressed, sparse, and limited-view sensing scenarios. However, iterative reconstruction algorithms are slow to converge and rely heavily on hand-crafted parameters to achieve good performance. Many iterations are usually required to reconstruct a high-quality image, which is computationally expensive due to repeated evaluations of the physical model. While learned iterative reconstruction approaches such as model-based learning (MBLr) can reduce the number of iterations through convolutional neural networks, it still requires repeated evaluations of the physical models at each iteration. Therefore, the goal of this study is to develop a Fast Iterative Reconstruction (FIRe) algorithm that incorporates a learned physical model into the learned iterative reconstruction scheme to further reduce the reconstruction time while maintaining robust reconstruction performance. We also propose an efficient training scheme for FIRe, which releases the enormous memory footprint required by learned iterative reconstruction methods through the concept of recursive training. The results of our proposed method demonstrate comparable reconstruction performance to learned iterative reconstruction methods with a 9x reduction in computation time and a 620x reduction in computation time compared to variational reconstruction.