A method for quantification of noise non-uniformity in computed tomography images: A computational study

Abstract

In computed tomography (CT), the noise is sometimes non-uniform, i.e. the noise magnitude may vary with the gradient level within the image. The purpose of this study was to quantify the noise non-uniformity in CT images using appropriate 1D and 2D computational phantoms, and to validate the effectiveness of the proposed concept in images filtered by the bilateral filter (BF), as an example of a non-linear filter. We first developed 1D and 2D computational phantoms, and Gaussian noises with several noise levels were then added to the phantoms. In addition, to simulate the real form of noise from images obtained in a real CT scanner, a homogeneous water phantom image was used. These noise levels were referred to as ground truth noise (σG). The phantoms were then filtered by the bilateral filter with various pixel value spreads (σ) to produce non-uniform noise. The original gradient phantoms (G) were subtracted from both the noisy phantoms (IN) and the filtered noisy phantoms (IBF), and the magnitudes of the resulting noise for each gradient were computed. The noise-gradient dependency (NGD) curve was used to display the dependency of noise magnitude on image gradient in the non-uniform noise. It is found that for uniform noise, the magnitude of noise was constant for all gradients. However, for non-uniform noise, the measured noise was dependent on the gradient levels and on the strength of the BF for every ground truth noise (σG). It was found that the noise magnitude was large for the large gradients and decreased with the magnitude of the image gradient.