An Adaptive Optimum Contrast CT Image Acquisition for Improved Spectrum Estimation-guided DECT reconstruction

Dual energy computed tomography (DECT) expands applications of CT imaging in its capability to acquire two datasets, one at high and the other at low energies, and produces decomposed material images. Instead of being viewed simultaneously, the produced two series images are most often fused into a single optimum contrast image for disease diagnosis using a blending technique with a ratio of the two datasets. However, current existing methods apply blending technique on the individual reconstructed CT images, which suffer from beam hardening artifacts. Furthermore, how to select a reasonable ratio to improve the contrast resolution remains a practical problem in current DECT application. In order to alleviate the above two issues, we proposed an adaptive optimum contrast image acquisition strategy and demonstrated its success in improving our previous spectrum estimation-guided DECT (SEG-DECT) reconstruction. Specifically, the proposed strategy takes x-ray spectrum information into consideration to reconstruct the basis material density images by the SEG-DECT method to eliminate the effect of beam hardening. Furthermore, based on the statistical properties of the decomposed material images, an adaptive nonlinear blending technique is incorporated in the reconstruction of an optimum contrast CT image. Numerical experiments with the XCAT phantom containing a lesion showed that the proposed method significantly improves both the contrast-to-noise ratio and quality of fused CT images.