Dr. Marina Chukalina Profile

Dr. Marina Chukalina

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Proceedings Article | 3 April 2024 Paper

CT metal artifacts simulation under x-ray total absorption

Mikhail Shutov, Marat Gilmanov, Dmitry Polevoy, Alexey Buzmakov, Anastasia Ingacheva, Marina Chukalina, Dmitriy Nikolaev

Proceedings Volume 13072, 130720Z (2024) https://doi.org/10.1117/12.3023491

KEYWORDS: Sensors, Image restoration, X-rays, Absorption, Dark current, Metals, Visualization, Quantum noise, Tomography, Signal detection

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Computed tomography (CT) is a powerful tool for reconstruction and analysis of inner structure of objects applied in various fields. Although many classes of objects of interest may have highly absorbent inclusions, leading to a certain type of distortions on reconstructed volume images (metal-like artifacts). The correction of this type of artifacts can’t be considered a solved task, despite all the efforts in this direction. The development and research of methods for suppressing CT artifacts require high-quality synthetic data which allow for numerical assessment of the accuracy of the metal-like artifacts reduction methods and training of neural networks. Although simplified methods considering only beam hardening and Poisson photon distribution are commonly used to simulate the data with type of distortions. In present work we design experiments using the tomographic scanner of the Federal Research Center “Crystallography and Photonics” of the Russian Academy of Sciences to demonstrate that in some cases beam hardening may not be the dominant reason for the arising of metal-like artifacts. These experiments are closely analyzed and modeled within different approaches. The problems in both simplified and state of the art approaches are emphasized and discussed. The provided results show the importance of paying attention to the dark current modeling for synthesized data generation under the conditions of total photon absorption.

Proceedings Article | 7 June 2023 Paper

Mean projection image application to the automatic rotation axis alignment in cone-beam CT

Danil Kazimirov, Anastasia Ingacheva, Alexey Buzmakov, Marina Chukalina, Smart Engines Service LLC Nikolaev

Proceedings Volume 12701, 127011G (2023) https://doi.org/10.1117/12.2680000

KEYWORDS: Automatic alignment, X-ray computed tomography, Image processing, Data modeling, Calibration

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In X-ray computed tomography (CT) the real rotation axis position often does not coincide with the assumed one: technical imperfections of the tomographic setup, the fast speed of movement of the gantry and goniometer cause rotation axis displacements and inclinations. At the same time the use of incorrect axis location parameters during reconstruction leads to the appearance of so-called tuning-fork artifacts in the form of stripes and blurs at the object boundary. The existing rotation axis alignment methods for cone-beam CT require a large amount of computing resources, are laborious in implementation, are not able to accurately determine several axis location parameters at once, or are based on the processing of additional equipped with reference markers object post-scans and shots that are not always available. Thus the rotation axis alignment methods development in the cone-beam CT still seems to be relevant. In this paper, the developed model for parameterizing the rotation axis position is described and justified. The novel several-stage automatic method for rotation axis parameters determination is described. The proposed method is based on usage of mean projection image and tested both on synthetic and real data in parallel-beam and cone-beam geometric schemes. The absolute error of that method on the simulated data is no more than 1 pixel and 1 degree, respectively for shift and slope.

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