Image Reconstruction Algorithm Research Articles

Enhanced-resolution reconstruction for the China-France Oceanography Satellite scatterometer

The China-France Oceanography Satellite SCATterometer (CSCAT) can observe radar backscatter values on the same sea surface at multiple incidence angles, and is often used to estimate the ocean near-surface wind. However, CSCAT utilizes a novel scanning mechanism and the wind vector cell has a spatial resolution is 25 km or 12.5 km, which limit the study of high-resolution land and sea ice monitoring. To address this issue, this paper constructs a geometric model of the main lobe-to-ground projection relationship and generates the enhanced-resolution radar images. CSCAT data are applied to three main image reconstruction algorithms (SIR, AART, and MART), and experiments are performed in the Iceland and Hudson Bay, and verified by Sentinel-2 optical remote sensing data. The experiments show the geometric model for CSCAT improves the spatial resolution from traditional 25 km to 5 km, and the SIR-reconstructed images are characterized by higher accuracy and better suppression of noise than are those obtained with the AART and MART methods. Therefore, this study extends the application of domestic remote sensors and provides data support for high-resolution applications, such as land and sea ice monitoring.

Super-Resolution Structured Illumination Microscopy for the Visualization of Interactions between Mitochondria and Lipid Droplets

Visualizing the dynamics of fine structures in cells requires noninvasive, long-duration imaging of the intracellular environment at high spatiotemporal resolution and low background noise. With modularized hardware and polarized laser modulation, we developed a nanoscale super-resolution structured illumination microscope (SR-SIM) imaging technique. Combined with a reliable image reconstruction algorithm and timing synchronization of all devices, the super-resolution (SR) images in hepatocytes reached 134.95 nm spatial resolution and 50 fps temporal resolution. This imaging system was able to maintain the optimal operation state over hundreds of time points due to less exposure and low phototoxicity. In hepatocytes, interactions between mitochondria and lipid droplets (LDs) underpin many crucial physiological processes, ranging from cellular metabolism to signaling. In this study, we pioneered the use of the SIM system for imaging the interaction characteristics between mitochondria and LDs. More than 200 hepatocytes were counted and recorded effectively. We found that LDs in an unstable state were divided under mitochondrial contact and fused without it. Among 200 LDs, more than 69% were surrounded by mitochondria that tended to wrap LDs. The SR-SIM imaging technique was demonstrated to break the limitations of conventional imaging methods in spatial-temporal resolution and imaging duration in the field of the dynamic study between mitochondria and LDs.

Structured Illumination Microscopy of Mitochondrial in Mouse Hepatocytes with an Improved Image Reconstruction Algorithm.

In this paper, a structured illumination microscopy (SIM) image reconstruction algorithm combined with notch function (N-SIM) is proposed. This method suppresses the defocus signal in the imaging process by processing the low-frequency signal of the image. The existing super-resolution image reconstruction algorithm produces streak artifacts caused by defocus signal. The experimental results show that the algorithm proposed in our study can well suppress the streak artifacts caused by defocused signals during the imaging process without losing the effective information of the image. The image reconstruction algorithm is used to analyze the mouse hepatocytes, and the image processing tool developed by MATLAB is applied to identify, detect and count the reconstructed images of mitochondria and lipid droplets, respectively. It is found that the mitochondrial activity in oxidative stress induced growth inhibitor 1 (OSGIN1) overexpressed mouse hepatocytes is higher than that in normal cells, and the interaction with lipid droplets is more obvious. This paper provides a reliable subcellular observation platform, which is very meaningful for biomedical work.

Brain image quality according to beam collimation width and image reconstruction algorithm: A phantom study

PurposeTo compare quantitatively and qualitatively brain image quality acquired in helical and axial modes on two wide collimation CT systems according to the dose level and algorithm used. MethodsAcquisitions were performed on an image quality and an anthropomorphic phantoms at three dose levels (CTDIvol: 45/35/25 mGy) on two wide collimation CT systems (GE Healthcare and Canon Medical Systems) in axial and helical modes. Raw data were reconstructed using iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms. The noise power spectrum (NPS) was computed on both phantoms and the task-based transfer function (TTF) on the image quality phantom. The subjective quality of images from an anthropomorphic brain phantom was evaluated by two radiologists including overall image quality. ResultsFor the GE system, noise magnitude and noise texture (average NPS spatial frequency) were lower with DLR than with IR. For the Canon system, noise magnitude values were lower with DLR than with IR for similar noise texture but the opposite was true for spatial resolution. For both CT systems, noise magnitude was lower with the axial mode than with the helical mode for similar noise texture and spatial resolution. Radiologists rated the overall quality of all brain images as “satisfactory for clinical use”, whatever the dose level, algorithm or acquisition mode. ConclusionsUsing 16-cm axial acquisition reduces image noise without changing the spatial resolution and image texture compared to helical acquisitions. Axial acquisition can be used in clinical routine for brain CT examinations with an explored length of less than 16 cm.

Double total variation (DTV) regularization and Improved adaptive moment estimation (IADAM) optimization method for fast MR image reconstruction

Background and ObjectiveCompressed sensing has been extensively studied as an advanced technique for fast MR image reconstruction. Current reconstruction algorithms often use total variation as the regularization term. Traditional total variation can easily lead to a staircase effect because it only pays attention to the variational information of the horizontal and vertical subbands. MethodsIn this paper, we propose a novel algorithm to reduce the staircase effect by increasing the variational information of the two diagonal subbands, which named Double Total Variation (DTV). We optimize the conjugate gradient algorithm by Improved Adaptive Moment Estimation (IADAM) as the solution algorithm. ResultsMR images of three body parts (head, knee and ankle) were used for simulations under different acceleration factor conditions. The conjugate gradient and fast conjugate gradient series algorithms were selected for comparison experiments. The results showed that the improved adaptive moment estimation conjugate gradient combined with DTV achieves the best reconstruction performance, therefore proved the superiority of DTV. After that, 64 different MR images of the three body parts were further simulated and the results demonstrated the general superiority from the proposed algorithm. ConclusionsThe results of this study support that the proposed method may facilitate the development of the research field of image reconstruction algorithms and provide ideas for other algorithmic improvements.

Unrolled-DOT: an interpretable deep network for diffuse optical tomography.

Imaging through scattering media is critical in many biomedical imaging applications, such as breast tumor detection and functional neuroimaging. Time-of-flight diffuse optical tomography (ToF-DOT) is one of the most promising methods for high-resolution imaging through scattering media. ToF-DOT and many traditional DOT methods require an image reconstruction algorithm. Unfortunately, this algorithm often requires long computational runtimes and may produce lower quality reconstructions in the presence of model mismatch or improper hyperparameter tuning. We used a data-driven unrolled network as our ToF-DOT inverse solver. The unrolled network is faster than traditional inverse solvers and achieves higher reconstruction quality by accounting for model mismatch. Our model "Unrolled-DOT" uses the learned iterative shrinkage thresholding algorithm. In addition, we incorporate a refinement U-Net and Visual Geometry Group (VGG) perceptual loss to further increase the reconstruction quality. We trained and tested our model on simulated and real-world data and benchmarked against physics-based and learning-based inverse solvers. In experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10× reduction in runtime and mean-squared error, compared to traditional physics-based solvers. We demonstrated a learning-based ToF-DOT inverse solver that achieves state-of-the-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging.

Evaluating the effective resolution of enhanced resolution SMAP brightness temperature image products

The MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) includes conventional- and enhanced-resolution radiometer brightness temperature (TB) images on standard, compatible grids from calibrated satellite radiometer measurements collected over a multi-decade period. Recently, the CETB team processed the first 4 years of enhanced resolution Soil Moisture Active Passive (SMAP) L-band (1.41 GHz) radiometer TB images. The CETB processing employs the radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm to create enhanced resolution images, which are posted on fine resolution grids. In this paper, we evaluate the effective resolution of the SMAP TB image products using coastline and island crossings. We similarly evaluate the effective resolution of the SMAP L1C_TB_E enhanced resolution product that is based on Backus-Gilbert processing. We present a comparison of the spatial resolution of the rSIR and L1C_TB_E enhanced resolution products with conventionally-processed (gridded) SMAP data. We find that the effective resolution of daily CETB rSIR SMAP TB images is slightly finer than that of L1C_TB_E and about 30% finer than conventionally processed data.

A New Image Reconstruction Algorithm for CCERT Based on Improved DPC and K-Means

Based on density peaks clustering (DPC) and K-means, this work aims to propose a new image reconstruction algorithm for capacitively coupled electrical resistance tomography (CCERT). To better apply DPC and K-means to CCERT, DPC is improved by automatically selecting the cluster centers and K-means is improved by introducing a post-processing in consider of the non-uniform sensitivity characteristic in the sensing area. With the proposed algorithm, linear back projection (LBP) is adopted to obtain the initial image. With the initial image, the improved DPC is adopted to identify the number of targets and get the region of each target. The improved K-means is adopted to determine the gray level threshold in the region of each target according to the distance between the centroid of the target and the center of the pipe. The final image is obtained by gray level threshold filtering. Image reconstruction experiments are carried out by a 12-electrode CCERT system. The experimental results verify the effectiveness of the proposed image reconstruction algorithm. Results also indicate that the improvements of DPC and K-means are successful. Compared with conventional image reconstruction algorithms, the proposed image reconstruction algorithm could get better image reconstruction results with less manual intervention.

The value of using a deep learning image reconstruction algorithm of thinner slice thickness to balance the image noise and spatial resolution in low-dose abdominal CT.

Traditional reconstruction techniques have certain limitations in balancing image quality and reducing radiation dose. The deep learning image reconstruction (DLIR) algorithm opens the door to a new era of medical image reconstruction. The purpose of the study was to evaluate the DLIR images at 1.25 mm thickness in balancing image noise and spatial resolution in low-dose abdominal computed tomography (CT) in comparison with the conventional adaptive statistical iterative reconstruction-V at 40% strength (ASIR-V40%) at 5 and 1.25 mm. This retrospective study included 89 patients who underwent low-dose abdominal CT. Five sets of images were generated using ASIR-V40% at a 5 mm slice thickness and 1.25 mm (high-resolution) with DLIR at 1.25 mm using 3 strengths: low (DLIR-L), medium (DLIR-M), and high (DLIR-H). Qualitative evaluation was performed for image noise, artifacts, and visualization of small structures, while quantitative evaluation was performed for standard deviation (SD), signal-to-noise ratio (SNR), and spatial resolution (defined as the edge rising slope). At 1.25 mm, DLIR-M and DLIR-H images had significantly lower noise (SD in fat: 14.29±3.37 and 9.65±3.44 HU, respectively), higher SNR for liver (3.70±0.78 and 5.64±1.20, respectively), and higher overall image quality (4.30±0.44 and 4.67±0.40, respectively) than did the respective values in ASIR-V40% images (20.60±4.04 HU, 2.60±0.63, and 3.77±0.43; all P values <0.05). Compared with the 5 mm ASIR-V40% images, the 1.25 mm DLIR-H images had lower noise (SD: 9.65±3.44 vs. 13.63±10.03 HU), higher SNR (5.64±1.20 vs. 4.69±1.28), and higher overall image quality scores (4.67±0.40 vs. 3.94±0.46) (all P values <0.001). In addition, DLIR-L, DLIR-M, and DLIR-H images had a significantly higher spatial resolution in terms of edge rising slope (59.66±21.46, 58.52±17.48, and 59.26±13.33, respectively, vs. 33.79±9.23) and significantly higher image quality scores in the visualization of fine structures (4.43±0.50, 4.41±0.49, and 4.38±0.49, respectively vs. 2.62±0.49) than did the 5 mm ASIR-V40 images. The 1.25 mm DLIR-M and DLIR-H images had significantly reduced image noise and improved SNR and overall image quality compared to the 1.25 mm ASIR-V40% images, and they had significantly improved the spatial resolution and visualization of fine structures compared to the 5 mm ASIR-V40% images. DLIR-H images had further reduced image noise compared with the 5 mm ASIR-V40% images, and DLIR-H was the most effective technique at balancing the image noise and spatial resolution in low-dose abdominal CT.

A model-based image reconstruction algorithm for near real-time transcranial photoacoustic imaging

Photoacoustic imaging is a promising hybrid imaging modality that can visualize both microscopic and macroscopic structures and oxygenation changes in brain function. Phase aberration caused by the skull is a major barrier for high-quality photoacoustic imaging of the brain. Time-reversal methods have been used to address this issue, though they rely on solving the full-wave equation, and is therefore computationally heavy and slow. Herein, a near real-time model-based image reconstruction algorithm is proposed. The acoustic forward model is mathematically described as a model matrix and image reconstructions are performed by employing the pseudoinverse of the model matrix and calculating the initial pressure distribution. The pseudoinverse matrix only needs to be computed once for the same experimental setup and acoustic medium and is obtained offline prior to imaging. The proposed algorithm shows equivalent image quality but considerably faster reconstruction time (&amp;gt;40 times faster), compared to the time-reversal method and therefore could potentially enable near real-time photoacoustic imaging.

Vectorized PoCA algorithm for faster image reconstruction for Muon Tomography

Muon Tomography is a promising technique for detecting the presence of high atomic number (Z) material, using naturally available cosmic muons. The reconstruction algorithms used in Muon Tomography need to process a considerably high number of muon tracks to reconstruct the image of the object under test. This makes the image reconstruction process computation-intensive and time-consuming. The algorithms to process the muon tracks are event-based algorithms, where all the events are independent and can be processed in any order. The traditional form of parallelism tries to distribute a small segment of work to multiple computing cores, but does not utilize the data level parallelism. The event-based algorithm can be processed on vector architecture and can provide a path to better utilize the data level parallelism. This approach provides another level of parallelism on standard computers without the need for specialized hardware. In this paper, we have tried to exploit the vector architecture provided by the current generation of CPUs to implement the vectorized version of Point of Closest Approach (PoCA) reconstruction algorithm for Muon Tomography. Results are presented using different vector instruction sets, for single-precision and double-precision calculations.

Evolutionary Optimization of Multiple Machine-Learned Objectives for PET Image Reconstruction

This article proposes optimizing machine-learned objective functions to estimate a set of images optimal for multiple tasks. Tomographic image reconstruction commonly relies on optimizing an objective function that dictates the goodness of an image estimate. We propose a novel use of deep learning in which we train models to perform blind estimation of the goodness of an image. The trained model that maps an image to a target figure of merit serves as the objective function. Using simulated PET images, we train convolutional neural networks (CNNs) to estimate for a single image the root mean square error (RMSE), signal-to-noise ratio, and full width half maximum (FWHM) of a point source. These figures of merit serve as surrogates for the common medical task performance in terms of quantitation, detection, and spatial resolution, respectively. These multiple objective functions are optimized using our recently proposed multiobjective evolutionary algorithm for PET image reconstruction. The evolutionary algorithm seeks to identify the Pareto-optimal front of image solutions, optimal for multiple objectives. Additionally, we propose a reference point method to focus the evolutionary optimization on regions of the multiobjective space to reduce computation. Likewise, we integrate optimality conditions to estimate how close a solution is to optimal without any prior knowledge about the Pareto-optimal front in an effort to guide termination conditions. The multiobjective approach is compared with conventional maximum-likelihood reconstruction with increasing explicit regularization and maximum a posteriori reconstruction with varying penalty strength. The approaches are tested with simulated PET data from a digital phantom and patient data. Results suggest that the proposed machine-learned objectives can estimate quantification, detection, and spatial resolution performance and that the evolutionary algorithm estimates a set of solutions with a good balance for convergence and diversity across different multiobjective combinations. In general, the evolutionary algorithm estimates images with superior performance compared to conventional techniques. Future work is needed to ensure and improve generalizability of the machine-learned objectives for different imaging conditions and to explore other target figures of merit for training machine-learned objectives. This proof-of-concept effort demonstrates a new use of deep learning for tomographic image reconstruction.

Multistatic MIMO Sparse Imaging Based on FFT and Low-Rank Matrix Recovery Techniques

This article proposes a simple sparse imaging scheme using a linear sparse aperiodic array and a new fast Fourier transform matched filtering (FFTMF) algorithm for a THz multistatic multiple-input and multiple-output (MIMO) imaging system. The simple linear sparse aperiodic array and multipass interferometric synthetic aperture focusing technique are used to achieve fast sampling, low system cost, and high imaging performance. Unlike a traditional generalized synthetic aperture focusing technique (GSAFT) for multistatic MIMO imaging, which is time-consuming and exhibits increased reconstruction time with increased data volume, the proposed FFTMF image reconstruction algorithm is capable of providing comparable image quality but significantly reducing the reconstruction time. For example, we show that for an image of 300 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times320$ </tex-math></inline-formula> mm with a pixel size of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.75\times0.83$ </tex-math></inline-formula> mm, the reconstruction time is reduced from about 1.50 min to 0.25 s in the 220-GHz five-pass synthetic imaging experiments. The proposed imaging algorithm uses internal zero padding, a multipass interferometric synthetic aperture focusing technique, and a wideband imaging technique to improve the imaging performance under a low-cost, sparse sampling scheme. It shows a strong antinoise ability and a high tolerance to target focusing distance. In addition, integrated with an algorithm of principal component pursuit by alternating directions method (PCPADM), sparse imaging is available to further save system cost and sampling data without a loss of image quality while the novel use of an error matrix provides an additional detection capability for imaging systems.

PILN: A posterior information learning network for blind reconstruction of lung CT images

Background and objective:Computer tomography (CT) imaging technology has played significant roles in the diagnosis and treatment of various lung diseases, but the degradations in CT images usually cause the loss of detailed structural information and interrupt the judgement from clinicians. Therefore, reconstructing noise-free, high-resolution CT images with sharp details from degraded ones is of great importance for the computer-assisted diagnosis (CAD) system. However, current image reconstruction methods suffer from unknown parameters of multiple degradations in actual clinical images.Methods:To solve these problems, we propose a unified framework, so called Posterior Information Learning Network (PILN), for blind reconstruction of lung CT images. The framework consists of two stages: Firstly, a noise level learning (NLL) network is proposed to quantify the Gaussian and artifact noise degradations into different levels. Inception-residual modules are designed to extract multi-scale deep features from the noisy image, and residual self-attention structures are proposed to refine deep features to essential representations of noise. Secondly, by taking the estimated noise levels as prior information, a cyclic collaborative super-resolution (CyCoSR) network is proposed to iteratively reconstruct the high-resolution CT image and estimate the blur kernel. Two convolutional modules are designed based on cross-attention transformer structure, named as Reconstructor and Parser. The high-resolution image is restored from the degraded image by the Reconstructor under the guidance of the predicted blur kernel, while the blur kernel is estimated by the Parser according to the reconstructed image and the degraded one. The NLL and CyCoSR networks are formulated as an end-to-end framework to handle multiple degradations simultaneously.Results:The proposed PILN is applied to the Cancer Imaging Archive (TCIA) dataset and the Lung Nodule Analysis 2016 Challenge (LUNA16) dataset to evaluate its ability in reconstructing lung CT images. Compared with the state-of-the-art image reconstruction algorithms, it can provide high-resolution images with less noise and sharper details with respect to quantitative benchmarks.Conclusions:Extensive experimental results demonstrate that our proposed PILN can achieve better performance on blind reconstruction of lung CT images, providing noise-free, detail-sharp and high-resolution images without knowing the parameters of multiple degradation sources.

Deep learning image reconstruction algorithm: impact on image quality in coronary computed tomography angiography

PurposeTo perform a comprehensive intraindividual objective and subjective image quality evaluation of coronary CT angiography (CCTA) reconstructed with deep learning image reconstruction (DLIR) and to assess correlation with routinely applied hybrid iterative reconstruction algorithm (ASiR-V).Material and methodsFifty-one patients (29 males) undergoing clinically indicated CCTA from April to December 2021 were prospectively enrolled. Fourteen datasets were reconstructed for each patient: three DLIR strength levels (DLIR_L, DLIR_M, and DLIR_H), ASiR-V from 10% to 100% in 10%-increment, and filtered back-projection (FBP). Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) determined objective image quality. Subjective image quality was assessed with a 4-point Likert scale. Concordance between reconstruction algorithms was assessed by Pearson correlation coefficient.ResultsDLIR algorithm did not impact vascular attenuation (P ≥ 0.374). DLIR_H showed the lowest noise, comparable with ASiR-V 100% (P = 1) and significantly lower than other reconstructions (P ≤ 0.021).DLIR_H achieved the highest objective quality, with SNR and CNR comparable to ASiR-V 100% (P = 0.139 and 0.075, respectively). DLIR_M obtained comparable objective image quality with ASiR-V 80% and 90% (P ≥ 0.281), while achieved the highest subjective image quality (4, IQR: 4–4; P ≤ 0.001). DLIR and ASiR-V datasets returned a very strong correlation in the assessment of CAD (r = 0.874, P = 0.001).ConclusionDLIR_M significantly improves CCTA image quality and has very strong correlation with routinely applied ASiR-V 50% dataset in the diagnosis of CAD.

High-speed compressive wide-field fluorescence microscopy with an alternant deep denoisers-based image reconstruction algorithm

As an indispensable tool to observe the microcosmic structures and dynamics, wide-field fluorescence microscopy (WFM) has been widely applied in biological and medical areas, but the low imaging speed hinders its applications in high-speed fluorescent dynamics due to the limited frame rate of the camera. Here, we develop a high-speed compressive wide-field fluorescence microscopy (CWFM) to break the imaging speed limitation of conventional WFM. In our CWFM method, a coded aperture temporal compressive imaging technique is introduced into conventional WFM system to accelerate the imaging speed by reconstructing multiple images from every compressed image. An iterative algorithm with alternant deep denoisers of FFDnet and FastDVDnet is designed to reconstruct high-quality sequential images from every compressed image through spatial denoising and temporal constraining. The high-speed imaging ability of CWFM is verified by measuring the movement of fluorescence beads on a slide, and an imaging speed of 2000 Hz is experimentally obtained with an ordinary CMOS camera of 200 Hz, the imaging speed is increased by an order of magnitude. Using our high-speed CWFM system, we successfully observe the hydrodynamics by detecting the flow of fluorescence beads in the water across a corner of microchannel. This technique provides a well-established tool for detecting the high-speed fluid and biological dynamic scene, especially in microflow and cell tracking.

Sampling and Reconstruction Jointly Optimized Model Unfolding Network for Single-Pixel Imaging

In recent years, extensive research has shown that deep learning-based compressed image reconstruction algorithms can achieve faster and better high-quality reconstruction for single-pixel imaging, and that reconstruction quality can be further improved by joint optimization of sampling and reconstruction. However, these network-based models mostly adopt end-to-end learning, and their structures are not interpretable. In this paper, we propose SRMU-Net, a sampling and reconstruction jointly optimized model unfolding network. A fully connected layer or a large convolutional layer that simulates compressed reconstruction is added to the compressed reconstruction network, which is composed of multiple cascaded iterative shrinkage thresholding algorithm (ISTA) unfolding iteration blocks. To achieve joint optimization of sampling and reconstruction, a specially designed network structure is proposed so that the sampling matrix can be input into ISTA unfolding iteration blocks as a learnable parameter. We have shown that the proposed network outperforms the existing algorithms by extensive simulations and experiments.

Synthetic hyperspectral array video database withapplications to cross-spectral reconstruction andhyperspectral video coding.

In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground-truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pixel in all spatial dimensions as well as the reflectance in spectral dimension. Two novel algorithms for two different applications are proposed to prove the diversity of applications that can be addressed by this novel database. First, a cross-spectral image reconstruction algorithm is extended to exploit the temporal correlation between two consecutive frames. The evaluation using this hyperspectral database shows an increase in peak signal-to-noise ratio (PSNR) of up to 5.6dB dependent on the scene. Second, a hyperspectral video coder is introduced, which extends an existing hyperspectral image coder by exploiting temporal correlation. The evaluation shows rate savings of up to 10% depending on the scene.

Quantifying Emissions from Fugitive Area Sources Using a Hybrid Method of Multi-Path Optical Remote Sensing and Tomographic Inverse-Dispersion Techniques

Reducing methane (CH4) emissions from anthropogenic activities is critical to climate change mitigation efforts. However, there is still considerable uncertainty over the amount of fugitive CH4 emissions due to large-scale area sources and heterogeneous emission distributions. To reduce the uncertainty and improve the spatial and temporal resolutions, a new hybrid method was developed combining optical remote sensing (ORS), computed tomography (CT), and inverse-dispersion modeling techniques on the basis of which a multi-path scanning system was developed. It uses a horizontal radial plume mapping path configuration and adapts a Lagrangian stochastic dispersion mode into CT reconstruction. The emission map is finally calculated by using a minimal curvature tomographic reconstruction algorithm, which introduces smooth constraints at each pixel. Two controlled-release experiments of CH4 were conducted with different configurations, showing relative errors of only 2% and 3%. Compared with results from the single-path inverse-dispersion method (5–175%), the new method can not only derive the emission distribution but also obtain a more accurate emission rate. The outcome of this research would bring broad application of the ORS-CT and inverse-dispersion techniques to other gases and sources.

ECT for flow imaging: total least squares for image reconstruction algorithm

ECT for flow imaging: total least squares for image reconstruction algorithm