Image quality evaluation of iterative CT reconstruction algorithms: a perspective from spatial domain noise texture measures

Abstract

Non-linear iterative reconstruction (IR) algorithms have shown promising improvements in image quality at reduced dose levels. However, IR images sometimes may be perceived as having different image noise texture than traditional filtered back projection (FBP) reconstruction. Standard linear-systems-based image quality evaluation metrics are limited in characterizing such textural differences and non-linear image-quality vs. dose trade-off behavior, hence limited in predicting potential impact of such texture differences in diagnostic task. In an attempt to objectively characterize and measure dose dependent image noise texture and statistical properties of IR and FBP images, we have investigated higher order moments and Haralicks Gray Level Co-occurrence Matrices (GLCM) based texture features on phantom images reconstructed by an iterative and a traditional FBP method. In this study, the first 4 central order moments, and multiple texture features from Haralick GLCM in 4 directions at 6 different ROI sizes and four dose levels were computed. For resolution, noise and texture trade-off analysis, spatial frequency domain NPS and contrastdependent MTF were also computed. Preliminary results of the study indicate that higher order moments, along with spatial domain measures of energy, contrast, correlation, homogeneity, and entropy consistently capture the textural differences between FBP and IR as dose changes. These metrics may be useful in describing the perceptual differences in randomness, coarseness, contrast, and smoothness of images reconstructed by non-linear algorithms.