Prostate cancer detection at early stage is very critical for desirable treatment outcome. In fact prostate cancer can be cured, if it is detected at early stage. Among imaging modalities used for cancer assessment, ultrasound elastography is emerging as an effective clinical tool for prostate and breast cancer diagnosis. Current clinical ultrasound elastography systems utilize strain imaging where tissue strain images are generated to approximate the tissue elastic modulus distribution. While strain images can be generated in real-time fashion, they lack the accuracy necessary for having high sensitivity and specificity. To improve strain imaging, researchers have developed full inversion based elastography techniques. These techniques are not based on simplifying assumptions such as tissue stress uniformity leading to accurate elastic modulus reconstruction. The drawback of these techniques, however, is that they are computationally intensive, hence are not suitable for real-time imaging. Among these techniques, a constrained elastography technique was developed which showed promising results as long as the tumor geometry can be obtained accurately from the imaging modality used in conjunction with elastography. This requirement is not easy to fulfill, especially with ultrasound imaging. To address this issue, we present an unconstrained full inversion ultrasound elastography method for prostate cancer imaging where knowledge of tissue geometry is not necessary. Tissue elastic modulus reconstruction in the proposed elastography technique is iterative, where each iteration involves tissue stress computation using Finite Element Method (FEM) followed by Young’s modulus updating using Hooke’s law. The method was validated using in silico and tissue mimicking prostate phantom studies. Results obtained from these studies indicate that the technique is reasonably accurate and robust.