MO‐A‐BRD‐02: Noise Suppression for Dual‐Energy CT Through Entropy Minimization

Abstract

Purpose:In dual energy CT (DECT), noise amplification during signal decomposition significantly limits the utility of basis material images. Since clinically relevant objects contain a limited number of materials, we propose to suppress noise in decomposed images through entropy minimization within a 2D transformation space. Distinct from other noise suppression techniques, the entropy minimization method does not estimate and suppress noise based on spatial variations of signals and thus maximally preserves image spatial resolution.Methods:From decomposed images, we first generate a 2D plot of scattered data points, using basis material densities as coordinates. Data points representing the same material generate a cluster with a highly asymmetric shape. We orient an axis by minimizing the entropy in a 1D histogram of these points projected onto the axis. To suppress noise, we replace the pixel values of decomposed images with center‐of‐mass values in the direction perpendicular to the optimized axis. The proposed method's performance is assessed using a Catphan 600 phantom and an anthropomorphic head phantom. Electron density calculations are used to quantify its accuracy. Our results are compared to those without noise suppression, with a filtering method, and with a recently developed iterative method.Results:On both phantoms, the proposed method reduces noise standard deviations of the decomposed images by at least on order of magnitude. In the Catphan study, this method retains the spatial resolution of the CT images and increases the accuracy of electron density calculations. In the head phantom study, the proposed method outperforms the others in retaining fine, intricate structures.Conclusion:This work shows that the proposed method of noise suppression through entropy minimization for DECT suppresses noise without loss of spatial resolution while increasing electron density calculation accuracy. Future investigations will analyze possible bias and incorporate spatial information into the technique for further noise reduction.The work is partially supported by the NIH under the grant number R21EB012700.