The evaluations have shown that the best result can be achieved by the method which makes the reconstructions step as a part a neural network. The evaluations were based on three different error measures. We tested the performance of four convolutional networks-based methods for correcting the distorted data at various stages of the tomographic imaging pipeline. Pelvic Beam-Hardening Artifacts in Dual-Energy CT Image Reconstructions: Occurrence and Impact on Image Quality. For these purposes we produced a large heterogeneous simulated dataset. Moreover, we investigated various ways of fusing deep neural networks with the process of computer tomographic imaging. Thus, the attempt to utilize a single CT number range to identify and quantify the extent of a particular material can become problematic. Various algorithms have been proposed to. Beam hardening can be a pernicious artifact because it changes the CT value of a material (or void) depending upon its location in an image. Our goal in this paper was to reduce beam hardening artifact using deep convolutional neural networks and to develop a model, which can improve the quality of the beam hardening distorted image with the presence of high amount of electrical noise. In practice, beam hardening can contribute to cupping and streaking artifacts in the reconstructed images 27. Deep learning is a set of techniques that proved to be useful in various fields, and it is more and more commonly used in computer tomography as well. Numerous factors contribute to these streaks: under sampling, photon starvation, motion, beam hardening, and Compton scatter. bone or iodinated contrast) may result in characteristic artifacts. In CT, beam hardening from a very dense target (e.g. ring artifact tube arcing out of field artifact air bubble artifact helical and multichannel artifact. Beam hardening is the phenomenon that occurs when an x-ray beam comprised of polychromatic energies passes through an object, resulting in selective attenuation of lower energy photons. As the beam passes through an object, the beam hardens (i.e. beam hardening cupping artifact streak and dark bands metal artifact / high-density foreign material artifact partial volume averaging quantum mottle photon starvation aliasing truncation artifact Hardware-based artifacts. There are many possibilities to manage beam hardening artifacts. Physics Based Artifacts Beam Hardening The X-ray beam is composed of a spectrum of energies. Beam hardening caused cupping and light or dark streaks are one of the major problems in computer tomographic imaging.
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