Electrical Impedance Tomography (EIT) reconstruction is an ill-posed inverse problem, meaning that small amounts of noise or model errors can cause larger artifacts in reconstructed images. One of the largest sources of error is shape change of the imaged object with respect to a reference condition. Systematic shape change artefacts occur in static imaging due to inaccurate assumption of object. Regular shape changes can occur during a time series measurement, such as during the respiratory cycle. We modeled simplified boundary shape changes between circular and elliptic profiles in 2D using the Joukowski transformation. We compared truncated Singular Value Decomposition (tSVD) and Tikhonov regularized reconstruction methods with respect to this shape change in terms of its effect on image quality and Quantity Index (QI) using a single anomaly at various locations within the image plane. During our investigation, we defined a new criterion to choose a suitable regularization parameter for use in quantitative image analysis. The results show that QI is stable over a large range of elliptic distortions, even though quality is not similarly well preserved.