Marina V. Chukalina

Effective regularized algebraic reconstruction technique for computed tomography

A new fast version of the reconstruction algorithm for computed tomography based on the simultaneous algebraic reconstruction technique (SART) is proposed. The algorithm iteration is asymptotically accelerated using the fast Hough transform from O(n3) to O(n2logn). Similarly to the algebraic reconstruction technique (RegART), which was proposed by us previously, the regularization operator is applied after each iteration. A bilateral filter plays the role of this operator. The algorithm behavior is investigated using the model experiment.

CT metal artifact reduction by soft inequality constraints

The artifacts (known as metal-like artifacts) arising from incorrect reconstruction may obscure or simulate pathology in medical applications, hide or mimic cracks and cavities in the scanned objects in industrial tomographic scans. One of the main reasons caused such artifacts is photon starvation on the rays which go through highly absorbing regions. We indroduce a way to suppress such artifacts in the reconstructions using soft penalty mimicing linear inequalities on the photon starved rays. An efficient algorithm to use such information is provided and the effect of those inequalities on the reconstruction quality is studied.

A computationally efficient version of the algebraic method for computer tomography

We propose a new fast version of the Simultaneous Algebraic Reconstruction Technique (SART) for computer tomography. The algorithm’s iteration has been made asymptotically faster with a fast Hough transform. The algorithm’s behavior has been studied with a model experiment.

Efficient and effective regularised ART for computed tomography

Algebraic Reconstruction Technique (ART) is a widely employed method in computed tomography since it has certain advantages, such as allowing reconstruction of data with missing projections in some angle ranges, over other techniques such as Filtered Back Projection (FBP). Recently, a regularisation technique for ART, RegART, was introduced which provides greatly reduced noise levels. However, a serious drawback of both ART and RegART is the computational complexity of the methods. In this paper, we present a fast version of RegART, which makes use of nVidias CUDA technology, and show that this approach performs favourably compared to FBP.

Contrast formation by a laboratory microtomograph in the scheme with analyzer crystal in the asymmetric bragg geometry

A method of numerical simulation of X-ray optical systems is presented. This method has been used to analyze the operation of a unit of a laboratory microtomograph in which monochromator crystals in asymmetric Bragg geometry are applied as optical elements. Based on the analysis, it is concluded that this scheme provides in principle submicron resolution.

Comparison of the data of X-ray microtomography and fluorescence analysis in the study of bone-tissue structure

The possibility of localizing clusters of heavy atoms is substantiated by comparing the data of X-ray microtomography at different wavelengths, scanning electron microscopy, and X-ray fluorescence analysis. The proximal tail vertebrae of Turner’s thick-toed gecko (Chondrodactylus turneri) have been investigated for the first time by both histological and physical methods, including X-ray microtomography at different wavelengths and elemental analysis. This complex methodology of study made it possible to reveal the regions of accumulation of heavy elements in the aforementioned bones of Turner’s thick-toed gecko.

Blur kernel estimation with algebraic tomography technique and intensity profiles of object boundaries

Motion blur caused by camera vibration is a common source of degradation in photographs. In this paper we study the problem of finding the point spread function (PSF) of a blurred image using the tomography technique. The PSF reconstruction result strongly depends on the particular tomography technique used. We present a tomography algorithm with regularization adapted specifically for this task. We use the algebraic reconstruction technique (ART algorithm) as the starting algorithm and introduce regularization. We use the conjugate gradient method for numerical implementation of the proposed approach. The algorithm is tested using a dataset which contains 9 kernels extracted from real photographs by the Adobe corporation where the point spread function is known. We also investigate influence of noise on the quality of image reconstruction and investigate how the number of projections influence the magnitude change of the reconstruction error.

Overview of machine vision methods in x-ray imaging and microtomography

Digital X-ray imaging became widely used in science, medicine, non-destructive testing. This allows using modern digital images analysis for automatic information extraction and interpretation. We give short review of scientific applications of machine vision in scientific X-ray imaging and microtomography, including image processing, feature detection and extraction, images compression to increase camera throughput, microtomography reconstruction, visualization and setup adjustment.

Analysis of computer images in the presence of metals

Artifacts caused by intensely absorbing inclusions are encountered in computed tomography via polychromatic scanning and may obscure or simulate pathologies in medical applications. Тo improve the quality of reconstruction if high-Z inclusions in presence, previously we proposed and tested with synthetic data an iterative technique with soft penalty mimicking linear inequalities on the photon-starved rays. This note reports a test at the tomographic laboratory set-up at the Institute of Crystallography FSRC “Crystallography and Photonics” RAS in which tomographic scans were successfully made of temporary tooth without inclusion and with Pb inclusion.

To image analysis in computed tomography

The presence of errors in tomographic image may lead to misdiagnosis when computed tomography (CT) is used in medicine, or the wrong decision about parameters of technological processes when CT is used in the industrial applications. Two main reasons produce these errors. First, the errors occur on the step corresponding to the measurement, e.g. incorrect calibration and estimation of geometric parameters of the set-up. The second reason is the nature of the tomography reconstruction step. At the stage a mathematical model to calculate the projection data is created. Applied optimization and regularization methods along with their numerical implementations of the method chosen have their own specific errors. Nowadays, a lot of research teams try to analyze these errors and construct the relations between error sources. In this paper, we do not analyze the nature of the final error, but present a new approach for the calculation of its distribution in the reconstructed volume. We hope that the visualization of the error distribution will allow experts to clarify the medical report impression or expert summary given by them after analyzing of CT results. To illustrate the efficiency of the proposed approach we present both the simulation and real data processing results.