Snehlata Shakya

Computerized Tomography Application in Oil Industry Using KT-2 Signature

Oil industry situations incorporate analysis of the performance of three-phase pumps used in pumping of oil under various conditions. The very popular method of nondestructive evaluation of solid materials, X-ray tomography, has been found suitable also for such flow applications under certain restrictions. Convolution back-projection (CBP) is the most commonly used algorithm that utilizes the projection data (obtained from a CT scanner) to reconstruct the phase distributions in multiphase flows. The practical implementation of this algorithm utilizes the filter functions that are user dependent, and it incorporates the finite Fourier cutoff frequency. It leads to certain errors in reconstruction. Present study concerns with one of the inherent errors arising in CBP implementation leading to characterization of the flow cross-sections. Kanpur Theorem-2 (KT-2) utilizes different Fourier cutoff frequencies (equivalent to different numbers of data-rays) in CBP reconstructions with a fixed filter function. This KT-2 signature provides interesting information about the distribution of different phase fractions in a three-phase bubble column reactor. The variations in these signatures for different flow conditions provide the information about the flow patterns. The performance of three-phase pumps can be characterized by this technique, thus providing useful input for maintenance activities.

Multi-Fiber Reconstruction Using Probabilistic Mixture Models for Diffusion MRI Examinations of the Brain

In the field of MRI brain image analysis, Diffusion tensor imaging ( DTI) provides a description of the diffusion of water through tissue and makes it possible to trace fiber connectivity in the brain, yielding a map of how the brain is wired. DTI employs a second order diffusion tensor model based on the assumption of Gaussian diffusion. The Gaussian assumption, however, limits the use of DTI in solving intra-voxel fiber heterogeneity as the diffusion can be non-Gaussian in several biological tissues including human brain. Several approaches to modeling the non-Gaussian diffusion and intra-voxel fiber heterogeneity reconstruction have been proposed in the last decades. Among such approaches are the multi-compartmental probabilistic mixture models. These models include the discrete or continuous mixtures of probability distributions such as Gaussian, Wishart or von Mises-Fisher distributions. Given the diffusion weighted MRI data, the problem of resolving multiple fibers within a single voxel boils down to estimating the parameters of such models.

Optimal Spatial Filtering Schemes and Compact Tomography Setups

ABSTRACT Three compact computerized tomography (CT) scanner prototypes are established and tested for acceptable reconstruction results. Performance of conventional iterative reconstruction algorithm is enhanced via incorporating a spatial filtering/masking step. Generally, these masking strategies incorporate an arbitrary (3 3 or 2 2) size of square averaging mask to subdue the ill-posedness. Three different spatial filtering schemes are tested in this work. The objective is to remove any dependency on a user for deciding an appropriate masking parameter. The outcome of the simulation study is successfully verified for three real data situations using three specimens with pre-assigned/known inner profile. Such austere scanning situations arise in real-time environment especially for undetachable/fixed small size objects situated in inaccessible locations. The present study encourages the development of low budget CT setups.

Error analysis of tomographic reconstructions in the absence of projection data

Error estimates for tomographic reconstructions (using Fourier transform-based algorithm) are available for cases where projection data are available. These data are used for reconstructions with different filter functions and the reliability of these reconstructions can be checked as per guidelines of those error estimates. There are cases where projection data are large (in gigabytes or terabytes) so storage of these data becomes an issue. It leads to storing of only the reconstructed images. Error estimation in such cases is presented here. Second-level projection data are calculated from the given reconstructed images (‘first-level’ images). These ‘second-level’ data are now used to generate ‘second-level’ reconstructed images. Different filter functions are employed to check the fidelity of these ‘second-level’ images. This inference is extended to first-level images in view of the characteristics of the convolution operator. This approach is validated with experimental data obtained by the X-ray micro-CT scanner installed at IIT Kanpur. Five specimens (of same material) have been scanned. Data are available in this case thus we have performed a comparative error estimate analysis for the ‘first-level’ reconstructions (data obtained from CT machine) and second-level reconstructions (data generated from first-level reconstructions). We observe that both approaches show similar outcome. It indicates that error estimates can also be applied to images when data are not available.

Adaptive Discretization for Computerized Tomography

abstract Two adaptive discretization frameworks are tested for computerized tomography (CT) data reconstruction. Removal of inactive pixels is primary motivation. Efficient and user independent entropy optimized masking is employed for spatial filtering purposes. Density of nodes at high gradient of reconstructed physical property is used as adaptation criterion. An alternative option, independent from noisy projection data and nature of the physical properties, is also discussed. Sensitivity analysis between the uniform and nonuniform (evolved via adaptive route) reconstruction grid reveals the utility of nonuniform grids. Iterative and transform based reconstruction techniques are used. Outcomes are tested successfully on three real world projection data from two different compact CT setups and one commercial high-resolution micro-CT scanner.

Multi-fiber Estimation and Tractography for Diffusion MRI using mixture of Non-central Wishart Distributions.

Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model ...