Acoustic tomography as a remote sensing method inside the atmospheric surface layer

Acoustic travel-time tomography of the atmosphere is a nonlinear inverse problem which attempts to reconstruct temperature and wind velocity fields in the atmospheric surface layer using the dependence of sound speed on temperature and wind velocity fields along the propagation path. This paper presents a statistical-based acoustic travel-time tomography algorithm based on dual state-parameter unscented Kalman filter (UKF) which is capable of reconstructing and tracking, in time, temperature, and wind velocity fields (state variables) as well as the dynamic model parameters within a specified investigation area. An adaptive 3-D spatial-temporal autoregressive model is used to capture the state evolution in the UKF. The observations used in the dual state-parameter UKF process consist of the acoustic time of arrivals measured for every pair of transmitter/receiver nodes deployed in the investigation area. The proposed method is then applied to the data set collected at the Meteorological Observatory Lindenberg, Germany, as part of the STINHO experiment, and the reconstruction results are presented.

Acoustic travel time tomography uses the dependence of sound speed on temperature and flow properties along the propagation path to measure these parameters. An algorithm is introduced that is capable of resolving the two-dimensional flow field within a specified measuring area. Different flow fields have been simulated to explore the reconstruction properties of the algorithm. Furthermore, an experiment within the Barrel of Ilmenau has been carried out, which demonstrates that the acoustic tomographic method is able to detect flow fields in a closed convection chamber. The simulations reveal that the vector tomographic algorithm can resolve the flow field characteristics speed and direction. Furthermore it is demonstrated that the distribution of sound sources and receivers has to be homogeneous around the measuring site. For the reconstruction of flow distributions, this requirement is even more important compared to temperature reconstructions, since information along inclined sound ray paths within each grid cell are needed to reconstruct the flow vector properly.

Convergence rate calculation of simultaneous iterative reconstruction technique algorithm for diffuse optical tomography image reconstruction: A feasibility study

Optical projection tomography (OPT) is an advanced three-dimensional (3D) imaging technology, which uses the filtered backprojection (FBP) algorithm to recover the 3D volume with sufficient number of projections. As to in vivo imaging, it is urgent to reduce the number of projections because the acquisition time could be minimized. However, reconstructing from undersampled OPT data can lead to artifacts and the decline in image quality. The simultaneous iterative reconstruction technique (SIRT) is introduced to remove artifacts and improve the image quality. The image qualities reconstructed from FBP and SIRT separately are compared, and the structural similarity and peak signal-to-noise ratio are calculated. Through simulated phantoms and OPT data of in vivo zebrafish embryo, SIRT consistently outperforms FBP in terms of reduced artifacts and enhanced image contrast especially when the projection numbers are reduced. SIRT method can provide high-quality reconstruction with 50 or fewer projections, thereby significantly reducing the minimum acquisition time and light dose while maintaining reconstruction quality. Through optimization and GPU acceleration, the SIRT algorithm can converge faster so as to reduce the image processing time. To our knowledge, this is the first time one SIRT algorithm is used for reconstruction of sparse OPT data. The experimental results show that SIRT algorithm outperform FBP especially when the number of projections is reduced. In addition, SIRT performs better in the preservation of vascular signal, which is significant for the monitoring of angiogenesis.

On the basis of the stream function field computed using a diagnostic model in the South China Sea in the summer 1998, the acoustic ray travel time tomography algorithm for reconstruction of two-dimensional flow field was tested by the numerical simulation in the region east to Vietnam. Error or noise contaminates the coefficients of both transfer matrix and measure data of the inverse equation of ocean acoustic tomography (OAT). Previous work on ill-posed problem in OAT commonly used Tikhonov regularization technology considering error only in observation data. Here, the truncated total least squares method (TTLS), which simultaneously considers error and noise on both sides, is applied for inverse solutions and its performances is compared with conventional Tikhonov method. The investigation suggests that the truncated total least squares method can substantially improve reconstruction of stream function field and is a promising alternative method for the solution of ocean acoustic tomography inverse problem.

Acoustic travel-time tomography of the atmosphere is a nonlinear inverse problem which attemps to reconstruct temperature and wind velocity fields in the atmospheric surface layer (ASL) using the dependence of sound speed on temperature and wind velocity fields along the propagation path. A new statistical-based algorithm is introduced in this paper based on fixed-point unscented Kalman filter (UKF) which is capable of reconstructing and tracking temperature and wind velocity fields within a specified investigation area.

In the spring of 1981, an acoustic tomography experiment consisting of 4 moorings with low‐frequency (224 Hz) sources and 5 moorings carrying receivers was deployed as an array about 300 km on a side centered at approximately 26 N, 70 W. Subsurface moorings were used in order to minimize motion, so that the acoustic instruments were placed at about 2100‐m depth, well below the sound channel. The array was sited in a region of moderate mesoscale activity, overlapping the MODE region somewhat, and CTD surveys during the experiment showed a cold eddy in the experimental area, with 2‐ to 3‐degree amplitude at 500‐decibars depth. Rays traveling from a source to a receiver within a volume of ocean sample the velocity and sound speed of the water along their propagation paths. The fields of dynamic significance are coupled to the acoustic fluctuations primarily through sound speed changes, so that the ability of a tomographic array to directly measure a given feature is partly limited by the degree to which the feature affects the sound speed. At present, acoustic travel times from source to receiver are the primary data to be used for the estimation of the mesoscale fields. Estimates are produced using both statistical optimal estimation and deterministic linear inversion. These are similar techniques with somewhat different limitations and capabilities. The performance of the inverse methods can be evaluated relative to each other and to the CTD data to yield checks of resolution, accuracy, and noise sensitivity. This array thus provides a theoretical and experimental test of tomography as a system for studying the mesoscale variability.

Acoustic tomography inside the atmospheric boundary layer

The use of transmission electron tomography for three dimensional morphology analysis of nanostructured materials has grown significantly in recent years due to the improved robustness of automated image collection, alignment and reconstruction routines. Although the amount of published 3D electron tomography data has increased accordingly, in many cases the data is used purely for visualization purposes, due to the difficulty of making quantitative measurements from the as-reconstructed data. This is due to the traditionally difficult step of segmentation of the 3D image volume, which has been hindered due to noise, artifacts and lack of necessary image detail or sharpness. In this work the Simultaneous Iterative Reconstruction Technique (SIRT) is used to reconstruct High Angle Annular Dark Field-Scanning Transmission Electron Microscopy (HAADF-STEM) tomography data. The number of SIRT iterations is varied to optimize the reconstruction for segmentation. In particular, signal to noise ratio, signal to artifact ratio, and image blur are identified as parameters which each contribute to the suitability of the reconstruction for segmentation. The Expectation Maximization/ Maximization of Posterior Marginals image segmentation algorithm is applied to the SIRT reconstructions, and the resulting Average Intensity Projection (AIP) of the segmented image stack is compared to the raw tomography projection data.

The 2010–12 Acoustic Technology for Observing the Interior of the Arctic Ocean (ACOBAR) experiment provided acoustic tomography data along three 167–301-km-long sections in Fram Strait between Greenland and Spitsbergen. Ocean sound speed data were assimilated into a regional numerical ocean model using the Massachusetts Institute of Technology General Circulation Model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system. The resulting state estimate matched the assimilated sound speed time series; the root-mean-squared (RMS) error of the sound speed estimate (∼0.4 m s−1) is smaller than the uncertainty of the measurement (∼0.8 m s−1). Data assimilation improved modeled range- and depth-averaged ocean temperatures at the 78°50′N oceanographic mooring section in Fram Strait. The RMS error of the state estimate (0.21°C) is comparable to the uncertainty of the interpolated mooring section (0.23°C). Lack of depth information in the assimilated ocean sound speed measurements caused an increased temperature bias in the upper ocean (0–500 m). The correlations with the mooring section were not improved as short-term variations in the mooring measurements and the ocean state estimate do not always coincide in time. This is likely due to the small-scale eddying and nonlinearity of the ocean circulation in Fram Strait. Furthermore, the horizontal resolution of the state estimate (4.5 km) is eddy permitting, rather than eddy resolving. Thus, the state estimate cannot represent the full ocean dynamics of the region. This study is the first to demonstrate the usefulness of large-scale acoustic measurements for improving ocean state estimates at high latitudes. Significance Statement Acoustic tomography measurements allow one to observe ocean temperature in large ocean volumes under the Arctic sea ice by measuring sound speed, which is hard to synoptically observe by other methods. This study has established methods for assimilation of depth- and range-averaged ocean sound speed from an acoustic tomography experiment in Fram Strait. For the first time, a 2-yr time series of ocean sound from acoustic tomography has been assimilated into an ocean state estimate. The results highlight the use of ocean tomography in ice-covered regions to improve state estimates of ocean temperature.

OA158] Noise equivalent number of quanta (NEQ): A tool for choosing the optimal reconstruction algorithm in computed tomography (CT)

Renal fibrosis is strongly correlated with renal functional outcomes. Therefore, this is a significant finding in determining renal prognosis. There are various reports on the imaging evaluation of renal fibrosis, but these are not well established. Scanning acoustic microscopy (SAM) uses ultra-high-frequency ultrasound to visualize tissues in just over a minute. SAM can simultaneously measure acoustic data such as speed of sound (SOS). SOS indicates the elasticity (stiffness) of a material. In this study, we aimed to compare and evaluate SAM acoustic intensity images and SOS data with light microscopy images of renal lesions, especially renal fibrosis. Renal specimens containing fibrosis were selected. The acoustic intensity images were compared to PAS-stained images. SOS data of the tubulointerstitium were compared with Masson's trichrome (MT)-stained images. The blue intensity of MT staining, which indicates fibrosis, was numerically valued using image-processing software. Furthermore, the correlations between it and the SOS values were evaluated. The acoustic intensity images suggested tubular atrophy and interstitial expansion in the same areas as in the PAS staining. SOS values of interstitial expansion with fibrosis were higher than normal area, interstitial expansion without fibrosis. A weak positive correlation was observed between the SOS values and the blue intensity of MT staining. SOS data can be used to evaluate renal fibrosis. The combination of SOS data and MT-stained images enables a more detailed evaluation of renal fibrosis. This study can contribute to the evaluation of renal fibrosis and has potential clinical applications in the future.

In this paper, we developed an accelerated algorithm for the classical simultaneous iterative reconstruction technique (SIRT) method applied in CT image reconstruction. The proposed algorithm possesses the following two features. First, it can flexibly handle the image reconstruction problem where projection data is contaminated by Poisson noise. This property makes it successful in compensating the disadvantage of the typical algebraic reconstruction technique (ART) method. Second, we utilize Passty's proximal splitting framework to construct a row-action type accelerated iterative algorithm to minimize the cost function. The accelerating strategy makes it successful in compensating the disadvantage of the famous SIRT method. We proved that the new algorithm can achieve significant image quality with noise reduction in less than 10 iterations, while SIRT needs more than 200 iterations. Both digital phantom and clinical abdominal CT image were reconstructed for demonstrating the efficiency of the proposed method.

This letter describes an attenuation tomography method via variations in the envelope peak instantaneous frequencies (EPIFs) of seismic signals. For a linear frequency-attenuation model, assuming that the propagating wavelet can be modeled by a Gaussian function with constant phase, an imaging formula using EPIF variation is derived. In tomography, the simultaneous iterative reconstruction technique (SIRT) with travel-time weighting is proposed for tomographic inversion. Numerical simulations of both noise-free and noise-added data show the validity of attenuation tomography from EPIF variation. SIRT with travel-time weighting yields a better reconstruction and lower residual.

Algebraic Reconstruction Technique (ART) is an age-old method used for solving the problem of three-dimensional (3-D) reconstruction from projections in electron microscopy and radiology. In medical applications, direct 3-D reconstruction is at the forefront of investigation. The simultaneous iterative reconstruction technique (SIRT) is an ART-type algorithm with the potential of generating in a few iterations tomographic images of a quality comparable to that of convolution backprojection (CBP) methods. Pixel-based reconstruction (PBR) is similar to SIRT reconstruction, and it has been shown that PBR algorithms give better quality pictures compared to those produced by SIRT algorithms. In this work, we propose a few modifications to the PBR algorithms. The modified algorithms are shown to give better quality pictures compared to PBR algorithms. The PBR algorithm and the modified PBR algorithms are highly compute intensive, Not many attempts have been made to reconstruct objects in the true 3-D sense because of the high computational overhead. In this study, we have developed parallel two-dimensional (2-D) and 3-D reconstruction algorithms based on modified PBR. We attempt to solve the two problems encountered by the PBR and modified PBR algorithms, i.e., the long computational time and the large memory requirements, by parallelizing the algorithm on a multiprocessor system. We investigate the possible task and data partitioning schemes by exploiting the potential parallelism in the PBR algorithm subject to minimizing the memory requirement. We have implemented an extended hypercube (EH) architecture for the high-speed execution of the 3-D reconstruction algorithm using the commercially available fast floating point digital signal processor (DSP) chips as the processing elements (PEs) and dual-port random access memories (DPR) as channels between the PEs. We discuss and compare the performances of the PBR algorithm on an IBM 6000 RISC workstation, on a Silicon Graphics Indigo 2 workstation, and on an EH system. The results show that an EH(3,1) using DSP chips as PEs executes the modified PBR algorithm about 100 times faster than an LBM 6000 RISC workstation. We have executed the algorithms on a 4-node IBM SP2 parallel computer. The results show that execution time of the algorithm on an EH(3,1) is better than that of a 4-node IBM SP2 system. The speed-up of an EH(3,1) system with eight PEs and one network controller is approximately 7.85.