Liver cirrhosis is the end stage of Chronic Liver Disease (CLD) for which hepatitis viral disease, nonalcoholic fatty liver disease, alcoholic liver disease, and autoimmune liver disease are common causes. Liver cirrhosis is considered irreversible, and CLD affects more than 800 million individuals and causes an estimated two million deaths per year worldwide. Meanwhile, in Sweden between 2005 and 2019, the incidence of alcohol-related and non-alcoholic fatty liver disease and unspecified liver cirrhosis increased by 47%, 217% and 87% respectively, reaching a prevalence of 13.1, 24.7 and 44.8/100,000 inhabitants. Ultrasound (US)-guided liver biopsy is the gold-standard for liver cirrhosis diagnosis, but it suffers from sampling errors, subjective interpretation, semi-quantitativeness, invasiveness, etc. Non-invasive alternatives utilizing US and Magnetic Resonance Imaging (MRI) are becoming increasingly popular. These imaging modalities employ elastography techniques to assess the stiffness and elasticity of soft tissues. Unfortunately, neither of these modalities are suitable for all patients as the reliability of US elastography diminishes in obese patients, while MRI based techniques are unsuitable for individuals with metal implants. Computed Tomography (CT) is generally cheaper and more accessible than MRI machines, and is very commonly used for liver examinations. However, CT elastography remains an underexplored area, and to our knowledge pure software-based solution has not been proposed. In this study, we aim to develop a multi-phase computed tomography elastography for liver cirrhosis evaluation by analyzing inter-phase tissue displacements as the indicator of tissue elasticity based on openly accessible abdominal CT scans, comparing patients with control subjects. We hypothesize that breathing and involuntary movements of organs and would lead to displacements of soft-tissue organs during multi-phase CT acquisition after contrast media injection. These displacements should be quantifiable using 3D image registration algorithms and the magnitude of displacements can be assumed linearly correlated with tissue elasticity. By false color-coding the displacement via cross-phase CT image registration and simultaneously automatically segmenting regions of interests using the state-of-the art open source segmentation model TotalSegmentator, we would be able to semi-quantitatively describe and visualize the tissue elasticity of a subject. Our method is expected to enable CT image-based elasticity assessment for liver cirrhosis in comparison with control subjects and can likely be adapted for evaluating stiffness of other soft tissues which would be affected by breadthing or involuntary movements.