Image-domain insertion of spatially correlated, locally varying noise in CT images

Noise simulation methods for computed tomography (CT) scans are powerful tools for assessing image quality at a range of doses without compromising patient care. Current state of the art methods to simulate lower-dose images from standard-dose images insert Poisson or Gaussian noise in the raw projection data; however, these methods are not always feasible. The objective of this work was to develop an efficient tool to insert realistic, spatially correlated, locally varying noise to CT images in the image-domain utilizing information from the image to estimate the local noise power spectrum (NPS) and variance map. In this approach, normally distributed noise is filtered using the inverse Fourier transform of the square root of the estimated NPS to generate noise with the appropriate spatial correlation. The noise is element-wise multiplied by the standard deviation map to produce locally varying noise and is added to the noiseless or high-dose image. Results comparing the insertion of noise in the projection-domain versus the proposed insertion of noise in the image-domain demonstrate excellent agreement. While this image-domain method will never replace projection-domain methods, it shows promise as an alternative for tasks where projection-domain methods are not practical, such as the case for conducting large-scale studies utilizing hundreds of noise realizations or when the raw data is not available.