Strain Encoded Magnetic Resonance Imaging (SENC-MRI) is a new technique that allows real-time quantification of tissue deformation. The technique is based on initially modulating the magnetization of the imaged object with sinusoidal pattern (MR-tagging) in the z-direction (through-plane direction). Compression is then applied to the object resulting in a change of the frequency of the sinusoidal tagging depending, in part, on tissue stiffness; e.g. the softer the material the higher the resulting frequency. By determining the changes in frequency, regional deformations can be determined and quantified. In SENC MRI, this is achieved by acquiring several images (typically 8 images), each with different phase-encoding, which we call tunings, in the z-direction. For each tuning, the intensity of pixels whose tagging frequency coincides with the tuning frequency is higher than other pixels. Since the number of the acquired images is limited, only a limited range of frequencies can be covered and, hence, the accuracy of the estimates may be inefficient if the tuning are not selected carefully. However, in this paper, we show that deformation maps can be obtained with good accuracy from the limited number of tunings. In this regard, we propose three methods and compare between them for maximum achievable accuracy. The methods are 1) center-of-mass, 2) curve fitting, and 3) clustering-based method. The methods are applied to simulated data and MR images obtained from a gel phantom experiment. The results of comparisons shows that good estimates of deformation can be obtained even if the sampled data is distorted by noise or MR artifacts.