Optimal noise control is critical for dose reduction in CT. In this work, we investigated the use of a locally-adaptive method for noise reduction in low-dose CT. This method is based upon bilateral filtering, which smoothes the projection data using a weighted average in a local neighborhood, where the weights are determined according to both the spatial proximity and intensity similarity between the center pixel and the neighboring pixels. This filtering is locally adaptive and can preserve important edge information in the sinogram, thus without significantly sacrificing the spatial resolution. It is closely related to anisotropic diffusion, but is significantly faster. More importantly, a CT noise model can be readily incorporated in the filtering and denoising process. We have evaluated the noise-resolution properties of the bilateral filtering in a phantom study and a preliminary patient study with contrast-enhanced abdominal CT exams. The results demonstrated that bilateral filtering can achieve a better noise-resolution tradeoff than a series of commercial reconstruction kernels. This improvement on noise-resolution properties can be used for improving the image quality in low-dose CT and can also be translated to substantial dose reduction.