Multi-parametric magnetic resonance imaging (mpMRI) has been shown to be a powerful tool in the detection, treatment, and prognosis of prostate cancer. The pre-processing of MR images prior to any type of quantitative analysis is crucial to the quality, reproducibility, and reliability of the results. Co-registration of mpMRI data, in particular, plays an important role in combining the imaging data from each MR modality (i.e., T2, diffusion- weighted imaging (DWI), dynamic contrast enhanced (DCE)) for use in machine learning tools and quantitative analyses. The aim of intensity-based deformable registration is to determine a transformation that relates one image to another. The ideal transformation is both biologically and mathematically sound (smooth and regular transformation) and provides the highest degree of alignment that can be achieved with the least amount of inference or loss of information. Despite the necessity for accurate co-registration of prostate mpMRI, there is no standardization for an optimal approach. Furthermore, once a method has been chosen, an accurate assessment of registration quality can be difficult to establish using many of the standard techniques. These methods are typically only surrogate measures of accuracy that may produce misleading results. Thus, the determination of a strategy for consistent alignment of prostate imaging data, as well as a more strategic choice of registration quality evaluation, could prove to be particularly useful in the emerging context of radiomics analysis for lesion characterization. We aim to accomplish this by investigating the quality of state-of-the-art co-registration obtainable by commonly used open source toolkits.