Seismic Attenuation Tomography by Envelope Peak Instantaneous Frequency

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.

As a result of the similarity between velocity and attenuation imaging, we have implemented both using the same 3-D tomography software, with simple variable changes. The resulting sets of linear equations are solved by the Simultaneous Iterative Reconstruction Technique (SIRT). The algorithm is applied to 3-D estimation of near‐surface velocity and attenuation distributions from 3-D surface‐survey field data from the Ouachita frontal thrust zone in southeastern Oklahoma; the images obtained correlate well with the known surface geology. Resolution analysis by computation of point spread functions indicates highest resolution in the direction parallel to the densest distribution of survey points (the receiver lines).

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.

Seismic attenuation tomography and its application to rock mass evaluation

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

Seismic tomography is a new geophysical technique being developed to meet the needs of mining and engineering projects. Attenuation tomography, as one kind of seismic tomography technique, is a powerful prospecting tool for determining structure defaults such as fault, broken zone, and hidden voids, but it has been little discussed up to now. Its principle and applications are presented in detail. Attenuation tomography technology includes the following main points. First, on the basis of spectrum analysis to each seismic record, geometric correction is conducted, then common source parameters are introduced into common shot records in order to keep absorption effect of the geological media separate from source spectrum influence. Finally, an equation set is obtained which consists of absorption coefficients, common source parameters and energy field data observed. This technique can be employed together with velocity tomography to provide more evidence for engineering and geological diagnosis. Its applications to exploration of mineral and engineering have led to satisfactory results.

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.

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.

A methodology for finding the optimal iteration number of the SIRT algorithm for quantitative Electron Tomography

Two high-resolution multi-offset vertical seismic profile (VSP) surveys were carried out in a permafrost mound near Umiujaq in northern Quebec, Canada, while performing seismic cone penetration tests (SCPT) to study the cryostratigraphy and assess the body waves velocities and the dynamic properties of warm permafrost. Penetrometer-mounted triaxial accelerometers were used as the VSP receivers, and a swept impact seismic technique (SIST) source generating both compressional and shear waves was moved near the surface following a cross configuration of 40 seismic shot-point locations surrounding each of the two SCPTs. The inversion of travel times based on a simultaneous iterative reconstruction technique (SIRT) provided tomographic images of the distribution of seismic velocities in permafrost. The Young's and shear moduli at low strains were then calculated from the seismic velocities and the permafrost density measured on core samples. The combination of multi-offset VSP survey, SCPT, SIST, and SIRT for tomographic imaging led to new insights in the dynamic properties of permafrost at temperatures close to 0 °C. The P- and S-wave velocities in permafrost vary from 2400 to 3200 m/s and from 900 to 1750 m/s, respectively, for a temperature range between –0.2 and –2.0 °C. The Young's modulus varies from 2.15 to 13.65 GPa, and the shear modulus varies from 1.00 to 4.75 GPa over the same range of temperature.Key words: permafrost, seismic cone penetration test, vertical seismic profiling, seismic tomography, dynamic properties.

Characterization of a landslide geometry using 3D seismic refraction traveltime tomography: The La Valette landslide case history

Limited-data tomography, to which electromagnetic geotomography belongs, is analyzed in this paper. In this technique, a discrete forward projection model may be expressed by a rank-deficient system of linear equations whose the nullspace is non-trivial. This means that some image components may fall into the nullspace, and hence the minimal-norm least-square solution, to which many image reconstructions methods converge, may be different from the true one. The Algebraic Reconstruction Technique (ART), Simultaneous Iterative Reconstruction Technique (SIRT), or Conjugate Gradients Least Squares (CGLS) are examples of such methods. In this paper, we deal with the question of how to partially recover the missing image components. First, we analyze the advantages of using the iterative Tikhonov regularization and the Maximum A Posteriori (MAP) algorithm with Gibbs prior. Then, we conclude that the missing (nullspace) image components can be partially recovered if the MAP algorithm is implemented through a multigrid technique. The results, which are presented for synthetic noise-free and noisy data, demonstrate the validity of our assumption. The problem of estimating the regularization and scaling parameters in the MAP algorithm is also addressed.

Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm3 IC (≃6 × 108 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

ABSTRACTA hybrid algorithm, combining Monte‐Carlo optimization with simultaneous iterative reconstructive technique (SIRT) tomography, is used to invert first arrival traveltimes from seismic data for building a velocity model. Stochastic algorithms may localize a point around the global minimum of the misfit function but are not suitable for identifying the precise solution. On the other hand, a tomographic model reconstruction, based on a local linearization, will only be successful if an initial model already close to the best solution is available. To overcome these problems, in the method proposed here, a first model obtained using a classical Monte Carlo‐based optimization is used as a good initial guess for starting the local search with the SIRT tomographic reconstruction. In the forward problem, the first‐break times are calculated by solving the eikonal equation through a velocity model with a fast finite‐difference method instead of the traditional slow ray‐tracing technique. In addition, for the SIRT tomography the seismic energy from sources to receivers is propagated by applying a fast Fresnel volume approach which when combined with turning rays can handle models with both positive and negative velocity gradients. The performance of this two‐step optimization scheme has been tested on synthetic and field data for building a geologically plausible velocity model.This is an efficient and fast search mechanism, which permits insertion of geophysical, geological and geodynamic a priori constraints into the grid model and ray path is completed avoided. Extension of the technique to 3D data and also to the solution of ‘static correction’ problems is easily feasible.