One of the main challenges in low dose x-ray computed tomography (CT) is the presence of highly structured noise. Model based iterative reconstruction methods (MBIR) have shown great potential to overcome this problem; however, they have also introduced an additional challenge: highly nonlinear behavior. One example is the noise variance vs. dose power-law, σ2 α (dose)−β, for which quasilinear FBP-based systems have a β value equal to 1, while MBIR methods have values in the range 0.4-0.6.1 This nonlinearity is attributed mainly to the regularization term of the objective function rather than the data fidelity term. Therefore, if statistical iterative reconstruction was performed in the absence of the regularization term, it could be possible to minimize the nonlinear imaging performance of these methods, while still taking advantage of the benefits from the data fidelity term. Once the image is reconstructed, an additional shift-invariant filter could be implemented to reduce the overall noise magnitude. In this work, the potential benefits of performing (I) unregularized statistical iterative reconstruction with additional image domain denoising are explored and compared against (II) regularized statistical iterative reconstruction using a total variation (TV) regularizer. Rigorous repeated phantom studies were performed at 5 exposure levels to assess the imaging performance in terms of noise and spatial resolution. Results regarding the power-law showed that for FBP reconstruction and for paradigm I, β= 1, while for paradigm II β= 0.6. Additionally, noise was independent of contrast in paradigm I, but was contrast dependent in paradigm II.