Coronary Artery Disease (CAD) is the leading cause of death globally [1]. Modern cardiac computed tomography angiography (CCTA) is highly effective at identifying and assessing coronary blockages associated with CAD. The diagnostic value of this anatomical information can be substantially increased in combination with a non-invasive, low-dose, correlative, quantitative measure of blood supply to the myocardium. While CT perfusion has shown promise of providing such indications of ischemia, artifacts due to motion, beam hardening, and other factors confound clinical findings and can limit quantitative accuracy. In this paper, we investigate the impact of applying a novel motion correction algorithm to correct for motion in the myocardium. This motion compensation algorithm (originally designed to correct for the motion of the coronary arteries in order to improve CCTA images) has been shown to provide substantial improvements in both overall image quality and diagnostic accuracy of CCTA. We have adapted this technique for application beyond the coronary arteries and present an assessment of its impact on image quality and quantitative accuracy within the context of dual-energy CT perfusion imaging. We conclude that motion correction is a promising technique that can help foster the routine clinical use of dual-energy CT perfusion. When combined, the anatomical information of CCTA and the hemodynamic information from dual-energy CT perfusion should facilitate better clinical decisions about which patients would benefit from treatments such as stent placement, drug therapy, or surgery and help other patients avoid the risks and costs associated with unnecessary, invasive, diagnostic coronary angiography procedures.
Image restoration using Wiener and geometric mean filtering is one of the commonly used techniques in image processing applications. In this paper we propose the use of discrete fractional Fourier transform in place of conventional discrete Fourier transform (DFT) in the Wiener and geometric mean filters. The use of discrete fractional
Fourier transform (DFrFT) provides us additional degree of freedom in terms of the angle parameter of the transforms
which can be exploited for the purpose of image restoration. The proposed restoration filters are applied on both colored and grey images and the simulation results of the proposed technique are presented. The effect of variation of parameters of the transforms and filters are also studied under the presence of noise. It is observed that the results of the conventional Wiener and geometric mean filters are better than the filters using DFrFT except for a specific value of the angle parameter about 0.8.
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