We describe evaluations of our newly developed HYPR-OSEM algorithm using experimental phantom and clinical patient data. HYPR-OSEM is an iterative reconstruction method which incorporates HighlY constrained back-PRojection (HYPR) de-noising directly within the widely used OSEM algorithm. Our previous work demonstrated that HYPR-OSEM can achieve noise reduction without degrading accuracy in terms of resolution and contrast, and it can attain better precision than OSEM with similar accuracy and better accuracy than filtered OSEM with similar precision based on simulation results. Furthermore, the proposed composite does not require any prior information. In this work, further evaluations have been conducted using experimental phantom and clinical patient data acquired on the High Resolution Research Tomograph (HRRT). The regional contrast recovery coefficient (CRC) as a function of image voxel noise within uniform background, coefficient of variation (COv) in CRC vs bias in CRC, and root-mean- squared-error (RMSE) in CRC for various sizes of hot and cold regions (based on 50 realizations of both high and low count experimental phantom data) were compared across all forms of HYPR-OSEM and OSEM with and without a post reconstruction filter. In contrast to our previous simulation results, higher noise reduction was achieved by HYPR-OSEM for the HRRT data. HYPR-AU-OSEM showed the lowest noise-induced bias at low count level, the lowest RMSE in CRC, and the most stable performance in COV or reproducibility of CRC (i.e. the least sensitive to the number of iterations). Similar results were also observed from the patient data.