Rapid Reconstruction Research Articles

Amorphous Magnesium Coating for Achieving Functional Changes from Antibacterial to Osteogenic Activities.

Medical devices composed of titanium (Ti) should exhibit antibacterial and osteogenic activities to achieve both infection prevention and rapid bone reconstruction. Here, a Ti surface was modified by performing magnetron sputtering (MS) using pure Mg or Mg-30Ca alloy targets for surface functionalization. MC0, prepared with a pure Mg target, had a crystalline metallic-Mg coating layer, whereas MC30, prepared with an Mg-30Ca alloy target, had an amorphous coating composed of Mg and Ca. Both samples rapidly dissolved when immersed in a cell culture medium and exhibited antibacterial activities against methicillin-resistant Staphylococcus aureus and cytotoxicity against MC3T3-E1 cells. Furthermore, MC30 promoted the proliferation and calcification of MC3T3-E1 cells because of the subsequent deposition of calcite on the surface after rapid dissolution. Our findings are the first to reveal that MS performed by using an Mg-30Ca alloy target endowed Ti surfaces with functional changes from antibacterial to osteogenic activities over time. Our results provide fundamental insights into the surface design of Ti-based medical devices for enhanced bone reconstruction and infection prevention and offer possibilities for biomedical applications of Mg-based coatings.

Flow field reconstruction of compressor blade cascade based on deep learning methods

In the design process of compressors, calculating the S1 flow surface involves solving the Navier-Stokes equations, which results in slow convergence and makes determining cascade characteristics time-consuming. However, deep learning offers significant advantages in flow field reconstruction by not only automatically extracting complex flow features and reducing prediction time but also providing high accuracy in reconstruction. This paper implements the rapid reconstruction of the compressor S1 flow surface cascade flow field using two deep learning models: U-Net and 1D-CNN. Using a double-circular arc airfoil as an example, we selected four key design parameters that define the geometry and position of the airfoil, ultimately designing 5,292 sets of cascade geometries. By performing batch meshing and CFD simulations, we built a cascade flow field dataset. The U-Net neural network uses design parameters as input and outputs the aerodynamic distribution of the cascade flow field. After training, it can directly predict the flow field based on the design parameters. Since the U-Net model cannot directly obtain the aerodynamic parameter distribution and flow field aerodynamic coefficients on the airfoil surface, a 1D-CNN model is used as a complementary approach. The 1D-CNN model takes the design parameters as input and outputs the aerodynamic parameter distribution on the airfoil surface and the flow field aerodynamic coefficients. The prediction results show that the U-Net model achieves an average relative error of less than 1% in cascade flow field reconstruction, while the 1D-CNN model achieves an average relative error of less than 1% in predicting the pressure recovery coefficient and less than 2% in predicting the total pressure loss coefficient. This study presents a method for the rapid reconstruction of compressor blade cascade flow fields, which helps improve design efficiency and shorten the design cycle.

The Corrosive Cl–-Induced Rapid Surface Reconstruction of Amorphous NiFeCoP Enables Efficient Seawater Splitting

The Corrosive Cl^–-Induced Rapid Surface Reconstruction of Amorphous NiFeCoP Enables Efficient Seawater Splitting

Temperature reconstruction in data centers using gappy pod combined with genetic algorithm and optimization of sensor layout

Temperature field data are essential for intelligent environmental control systems. However, acquiring comprehensive temperature distributions in data centers with numerous sensors is not only costly but can also disrupt other equipment layouts. Thus, the capability to rapidly reconstruct temperature fields using a minimal number of sensors is crucial for smart adjustments in these systems. This paper introduces a genetic algorithm (GA) to the gappy proper orthogonal decomposition (POD) method, proposing an objective function to optimize the placement and quantity of sensors. This benchmark for optimization is applied to a case study on environmental control within a data center, where the optimal sensor layouts and the corresponding mean absolute errors were determined for configurations using 5 to 15 sensors, all maintained within 1 °C. Notably, the lowest reconstruction errors of 0.03 °C and 0.05 °C were achieved with a 12-sensor setup. Six uniform sensor layouts acted as control groups. Results demonstrated that error improvements in the optimized layouts were 98.25 % superior to those in uniform layouts. Moreover, transient CFD simulations were conducted to experimentally control the gappy POD algorithm. These simulations confirmed the capability of this approach to compute optimal air supply parameters and maintain temperature control within standard limits, resulting in a 24 % airflow savings compared to fixed ventilation modes under identical conditions. Consequently, this study effectively achieves rapid reconstruction and environmental control of temperature fields in data centers, offering important theoretical and practical insights for real-world engineering applications.

Blip-up blip-down circular EPI (BUDA-cEPI) for distortion-free dMRI with rapid unrolled deep learning reconstruction

Purpose: BUDA-cEPI has been shown to achieve high-quality, high-resolution diffusion magnetic resonance imaging (dMRI) with fast acquisition time, particularly when used in conjunction with S-LORAKS reconstruction. However, this comes at a cost of more complex reconstruction that is computationally prohibitive. In this work we develop rapid reconstruction pipeline for BUDA-cEPI to pave the way for its deployment in routine clinical and neuroscientific applications. The proposed reconstruction includes the development of ML-based unrolled reconstruction as well as rapid ML-based B0 and eddy current estimations that are needed. The architecture of the unroll network was designed so that it can mimic S-LORAKS regularization well, with the addition of virtual coil channels.Methods: BUDA-cEPI RUN-UP – a model-based framework that incorporates off-resonance and eddy current effects was unrolled through an artificial neural network with only six gradient updates. The unrolled network alternates between data consistency (i.e., forward BUDA-cEPI and its adjoint) and regularization steps where U-Net plays a role as the regularizer. To handle the partial Fourier effect, the virtual coil concept was also introduced into the reconstruction to effectively take advantage of the smooth phase prior and trained to predict the ground-truth images obtained by BUDA-cEPI with S-LORAKS.Results: The introduction of the Virtual Coil concept into the unrolled network was shown to be key to achieving high-quality reconstruction for BUDA-cEPI. With the inclusion of an additional non-diffusion image (b-value = 0 s/mm2), a slight improvement was observed, with the normalized root mean square error further reduced by approximately 5 %. The reconstruction times for S-LORAKS and the proposed unrolled networks were approximately 225 and 3 s per slice, respectively.Conclusion: BUDA-cEPI RUN-UP was shown to reduce the reconstruction time by ∼88× when compared to the state-of-the-art technique, while preserving imaging details as demonstrated through DTI application.

Compact speckle spectrometer based on CNN-LSTM denoising.

We propose a compact speckle spectrometer that utilizes micro-nanostructures processed by femtosecond lasers on sapphire surfaces as scattering media. The spectral resolution is 0.5 nm, and the entire system is compact and stable. At the same time, the convolutional long short-term memory network (CNN-LSTM) was introduced into the denoising algorithm. Compared with traditional reconstruction algorithms, this method not only ensures rapid spectral reconstruction but also offers better reconstruction accuracy. It can effectively reduce the reconstruction error caused by the reduction of speckle autocorrelation caused by environmental noise and prolong the stability time of the system.

Rapid parallel reconstruction and specificity screening of hundreds of T cell receptors.

The ability to screen the reactivity of T cell receptors (TCRs) is essential to understanding how antigen-specific T cells drive productive or dysfunctional immune responses during infections, cancer and autoimmune diseases. Methods to profile large numbers of TCRs are critical for characterizing immune responses sustained by diverse T cell clones. Here we provide a medium-throughput approach to reconstruct dozens to hundreds of TCRs in parallel, which can be simultaneously screened against primary human tissues and broad curated panels of antigenic targets. Using Gibson assembly and miniaturized lentiviral transduction, individual TCRs are rapidly cloned and expressed in T cells; before screening, TCR cell lines undergo combinatorial labeling with dilutions of three fluorescent dyes, which allows retrieval of the identity of individual T cell effectors when they are organized and tested in pools using flow cytometry. Upon incubation with target cells, we measure the upregulation of CD137 on T cells as a readout of TCR activation. This approach is scalable and simultaneously captures the reactivity of pooled TCR cell lines, whose activation can be deconvoluted in real time, thus providing a path for screening the reactivity of dozens of TCRs against broad panels of synthetic antigens or against cellular targets, such as human tumor cells. We applied this pipeline to systematically deconvolute the antitumoral and antiviral reactivity and antigenic specificity of TCRs from human tumor-infiltrating lymphocytes. This protocol takes ~2 months, from experimental design to data analysis, and requires standard expertise in cloning, cell culture and flow cytometry.

Optical property discrepancies found between healthy and unhealthy skin cells using digital holographic microscopy with three wavelengths

Cancer and other health disorders can be differentiated by changes in cell optical properties such as their refractive index, thickness, and topology (height and width). Here, we employ three wavelengths simultaneously in digital holographic microscopy (3λ-DHM) to visualize the whole cell topology as 3D images through a numerical reconstruction algorithm applied to a hologram. By identifying the cell state and the changes in its optical properties, it is possible to discern between healthy and unhealthy cells. The simultaneous use of three wavelengths provides a rapid and straightforward quantitative reconstruction of the whole cell without the need for an unwrapping algorithm. This is a benefit over traditional methods, which often require complicated procedures. The performance of the approach was first validated in a known sample, a silicon dioxide thin film, where we were able to corroborate its refractive index with the values reported in the literature. Then the method was applied to fixed skin cells finding a refractive index of 1.3443 for healthy cells and 1.3246 for cells found in tumor tissue. We discuss and highlight differences based on the refractive index to demonstrate that the employed process can provide reliable information to distinguish characteristics between healthy and unhealthy cells.

High-Efficiency Photo-Assisted Large Current-Density Water Splitting with Mott-Schottky Heterojunctions.

The development of bifunctional photogenerated carrier-assisted electrocatalytic (PCA-EC) electrodes that operate with stability at large current-density remains a significant challenge. Herein, we demonstrate a simple sputtering-deposition process to synthesize a novel MnWO4/FeCoNi Mott-Schottky heterojunction coating and deposit it on a pure Ti substrate to prepare high-performance PCA-EC electrodes, which exhibits enhanced light absorption range/intensity and rapidly separated photogenerated electron-hole pairs. This design allows photogenerated electrons to directly participate in the hydrogen evolution reaction (HER), while the strong oxidation of photogenerated holes significantly reduces the defect formation energy of active metals, thereby facilitating the rapid reconstruction of highly active Ni(FeCo)OOH/MnOOH species for the oxygen evolution reaction (OER). As expected, the as-prepared electrode demonstrates the overpotentials of 64 mV for the HER and 204 mV for the OER at 10 mA cm-2 under illumination. Benefiting from the stable interface with Fe/Co/Ni-O-Mn/W bonding units, the dual-electrode photoassisted electrolytic cell achieves long-term stability at current densities of 500 and 1000 mA cm-2. This work provides detailed insights into the enhancement mechanism of PCA-EC and contributes to the development of photo-assisted water splitting electrodes for large current-density applications.

High‐Efficiency Photo‐Assisted Large Current‐Density Water Splitting with Mott‐Schottky Heterojunctions

AbstractThe development of bifunctional photogenerated carrier‐assisted electrocatalytic (PCA‐EC) electrodes that operate with stability at large current‐density remains a significant challenge. Herein, we demonstrate a simple sputtering‐deposition process to synthesize a novel MnWO4/FeCoNi Mott‐Schottky heterojunction coating and deposit it on a pure Ti substrate to prepare high‐performance PCA‐EC electrodes, which exhibits enhanced light absorption range/intensity and rapidly separated photogenerated electron‐hole pairs. This design allows photogenerated electrons to directly participate in the hydrogen evolution reaction (HER), while the strong oxidation of photogenerated holes significantly reduces the defect formation energy of active metals, thereby facilitating the rapid reconstruction of highly active Ni(FeCo)OOH/MnOOH species for the oxygen evolution reaction (OER). As expected, the as‐prepared electrode demonstrates the overpotentials of 64 mV for the HER and 204 mV for the OER at 10 mA cm−2 under illumination. Benefiting from the stable interface with Fe/Co/Ni‐O−Mn/W bonding units, the dual‐electrode photoassisted electrolytic cell achieves long‐term stability at current densities of 500 and 1000 mA cm−2. This work provides detailed insights into the enhancement mechanism of PCA‐EC and contributes to the development of photo‐assisted water splitting electrodes for large current‐density applications.

A deep learning framework for solving the prediction and reconstruction problem of Bingham fluid flow field

As a typical non-Newtonian fluid, Bingham fluid is employed in a multitude of fields, including petroleum, construction, and the chemical industry. However, due to the intricate intrinsic properties of Bingham fluids and the necessity for precision and efficacy in specific engineering applications, the rapid and precise prediction and reconstruction of its flow field information has become a challenge and a focal point of contemporary research. In this paper, we introduce a novel deep-learning approach to address the two-dimensional laminar motion of Bingham fluids. The proposed Papanastasiou Regularization Physics-Informed Neural Network (PR-PINN) framework effectively predicts and reconstructs the flow field of Bingham fluids. Initially, the framework applies Papanastasiou regularization to the governing equations of Bingham fluids, enhancing the network's adaptability to solving the flow field problem by incorporating boundary conditions and an adaptive weight assignment strategy. We consider two scenarios: equal-diameter circular pipe flow and conical pipe flow. The PR-PINN network is utilized for flow field prediction and reconstruction. Our results show that PR-PINN achieves high accuracy in flow field prediction and can reconstruct velocity and pressure fields using limited measurement data. Based on these findings, we explore the impact of boundary constraints, the effect of large intrinsic parameters on prediction accuracy, and the influence of measurement points and boundary constraints on flow field reconstruction. In summary, the PR-PINN network exhibits satisfactory performance and significant potential for predicting and reconstructing Bingham fluid flow fields.

Rapid Surface Reconstruction of Amorphous-Crystalline NiO for Industrial-Scale Electrocatalytic PET Upcycling.

The conversion of plastic waste into valuable chemicals through innovative and selective nano-catalysts offers significant economic benefits and positive environmental impacts. However, our current understanding of catalyst design capable of achieving industrial-grade current densities is limited. Herein, we develop a self-supported amorphous-crystalline NiO electrocatalyst for the electrocatalytic upcycling of polyethylene terephthalate (PET) into formate and hydrogen (H2) fuel. The catalyst achieves an industrial current density of over 1 A cm-2 at 1.5 V vs. RHE, with an 80% Faradaic efficiency and a formate production rate of 7.16 mmol cm-2 h-1. In situ Raman spectroscopy, X-ray absorption spectroscopy, and density functional theory calculations reveal that the rapid transformation of amorphous-crystalline NiO into γ-NiOOH at the amorphous-crystalline interface provides a thermodynamic advantage for formate desorption, leading to the high activity required for industrial applications, which is difficult to achieve for fully crystalline NiO. A techno-economic analysis indicates that recycling waste PET using this catalytic process could generate a profit of $501 per ton. This work presents a cost-effective and highly efficient approach to promoting the sustainable utilization of waste PET.

Ship Power System Network Reconfiguration Based on Swarm Exchange Particle Swarm Optimization Algorithm

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.

Deep Learning-Based Rapid Flow Field Reconstruction Model with Limited Monitoring Point Information

Deep Learning-Based Rapid Flow Field Reconstruction Model with Limited Monitoring Point Information

Nervonic acid as novel therapeutics initiates both neurogenesis and angiogenesis for comprehensive wound repair and healing.

Rapid tissue reconstruction in acute and chronic injuries are challengeable, the inefficient repair mainly due to the difficulty in simultaneous promoting the regeneration of peripheral nerves and vascular, which are closely related. Main clinical medication strategy of tissue repair depends on different cytokines to achieve nerves, blood vessels or granulation tissue regeneration, respectively. However, their effect is still limited to single aspect with biorisk exists upon long-time use. Herein, for the first time, we have demonstrated that NA isolated from Malania oleifera has potential to simultaneously promote both neurogenesis and angiogenesis in vitro and in vivo. First, NA was identified by NMR and FTIR structural characterization analysis. In a model of oxidative stress in neural cells induced by hydrogen peroxide, the cells viability of RSC96 and PC12 were protected from oxidative stress injury by NA. Similarly, based on the rat wound healing model, effective blood vessel formation and wound healing can be observed in tissue staining under NA treatment. In addition, according to the identification of nerve and vascular related markers in the wound tissue, the mechanism of NA promoting nerve regeneration lies in the upregulation of the secretion NGF, NF-200 and S100 protein, and NA treatment was also able to up-regulate VEGF and CD31 to directly promote angiogenesis during wound healing. This study provides an important candidate drug molecules for acute or chronic wound healing and nerve vascular synchronous regeneration.

A Multi-View Real-Time Approach for Rapid Point Cloud Acquisition and Reconstruction in Goats

The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance.

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.

Eigenbin compression for reducing photon-counting CT data size.

Photon-counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring. This study proposes a lossy method to compress photon-counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system. The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon-count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M<N eigenvectors with highest eigenvalues (i.e., the vectors that account for most of the information in the data). Only M coefficients are then transferred per measurement, which we term eigenbin values. After transmission, the original N energy-bin measurements are estimated as a linear combination of the M eigenvectors. Two versions of the eigenbin method were investigated: pixel-specific and pixel-general. The pixel-specific eigenbin method determines eigenvectors for each individual detector pixel, while the more practically realizable pixel-general eigenbin method finds one set of eigenvectors for the entire detector array. The eigenbin method was experimentally evaluated by scanning a 20cm diameter Gammex Multienergy phantom with different material inserts on a clinical silicon-based PCCT prototype. The method was evaluated with the number of eigenbins varied between two and four. In each case, the eigenbins were used to estimate the original 8-bin data, after which material decomposition was performed. The mean, standard deviation, and contrast-to-noise ratio (CNR) of values in the reconstructed basis and virtual monoenergetic images (VMI) were compared for the original 8-bin data and for the eigenbin data. The pixel-specific eigenbin method reduced photon-counting CT data size by a factor of four with<5% change in mean values and a small noise penalty (mean change in noise of<12%, maximum change in noise of 20% for basis images). The pixel-general eigenbin compression method reduced data size by a factor of 2.67 with<5% change in mean values and a less than 10% noise penalty in the basis images (average noise penalty≤5%). The noise penalty and errors were less for the VMIs than for the basis images, resulting in<5% change in CNR in the VMIs. The eigenbin compression method reduced photon-counting CT data size by a factor of two to four with less than 5% change in mean values, noise penalty of less than 10%-20%, and change in CNR ranging from 15% decrease to 24% increase. Eigenbin compression reduces the data transfer time and storage space of photon-counting CT data for applications that require rapid initial reconstructions.

Adaptive stepsize forward–backward pursuit and acoustic emission-based health state assessment of high-speed train bearings

Compressed sensing (CS) is a promising tool for data compression reconstruction. However, fault diagnosis methods for high-speed train bearings based on CS and acoustic emission (AE) technologies have not been reported yet. Notably, the accuracy and speed of CS two-stage reconstruction methods are affected and restricted by prior initial conditions. Therefore, this article proposes adaptive dynamic thresholds applicable to adaptive stepsize forward–backward pursuit (ASFBP), and bearing health state assessment method. First, the adaptive dynamic thresholds for atom selection and deletion are constructed based on the residual feedback mechanism and the atom quality distribution law, which enables ASFBP to realize high-precision rapid reconstruction of signal without any atom priori initial conditions. Second, the initial dictionary length is improved based on the AE hit characteristics. Furthermore, a damage state comprehensive evaluation index (DSCEI) is established using principal component analysis based on AE time-domain hit parameters and compression-domain energy parameter. Compared with the kurtosis index and permutation entropy index, the DSCEI demonstrates better monotonicity and stability in the quantitative evaluation of high-speed train bearing condition. Finally, the validity and stability of the method are verified by testing under complex test conditions resembling actual high-speed train lines, providing valuable insights for the CS-based data-driven bearing fault diagnosis.

3D reconstruction of gas cloud concentration field with high temporal and spatial resolution based on an imaging-type FTIR.

Imaging-type FTIR devices provide numerous benefits for the detection and alarm of hazardous gases. This paper presents an improved algorithm for reconstructing the 3D concentration field of gas clouds, utilizing hypothesis testing and a synchronized algebraic iteration algorithm. Specifically designed for use with imaging-type FTIR devices, this algorithm enables rapid reconstruction of gas cloud concentration fields. Using CFD software, an open-space detection scenario for HFC-152a gas was simulated, and the 3D concentration field was reconstructed from dual-angle column concentration data. The accuracy was confirmed, with a deviation of less than 4.6% in re-projected column concentrations along the center streamline and a maximum deviation of 8.8% between simulated and reconstructed voxel concentrations. Laboratory experiments further validated the algorithm. Two sets of line-of-sight angles yielded similar average total mass results calculated from the continuously reconstructed concentration field, measuring 7285.8 mg and 7310.1 mg, with relative standard deviations of 2.4% and 2.7%, respectively. In an open field, an experimental detection of HFC-152a gas leakage was conducted. The algorithm employed facilitated the 3D reconstruction and precise localization of the gas leak source, which underscores the algorithm's versatility across various environmental contexts and its utility in determining the source of gas leaks. The lab and open field experiments share a same temporal resolution of 2.9 seconds. The algorithm proposed in this article effectively expands the practicality of imaging-type FTIR devices for real-time gas leak monitoring applications.