Accurate prediction of reconstructed noise in computed tomography (CT) images is important for purposes of system design, optimization and evaluation. A large body of work describes noise prediction methods for CT, the vast majority of which assume stationarity of both noise and signal processes. Consequently, these methods are usually applied to and evaluated using simple phantoms, and only a portion of the image is scrutinized. In this work, we derive a practical method for reconstructing CT noise variance maps for arbitrary objects. Photon Poisson noise and system electronic noise are considered. The final formula has the same structure as that of the filtered backprojection (FBP) formula, but with different weighting factors and convolution kernels. The algorithm is verified using computer simulations of the Shepp-Logan phantom, and a good match is found between the predicted noise map from one single noisy scan and the measured noise using 128 noisy scans. As compared to other proposed noise models, our complementary work provides a method of noise prediction by simple adaptation of FBP reconstruction algorithms. The result is a tool that can be useful for system optimization and evaluation tasks as well as the design of reconstruction filters.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.