Simulation and optimization of system configuration for the stationary head CT using CNT x-ray source array: reconstruction and quality evaluation

Abstract

Purpose: The invention of carbon nanotube (CNT) x-ray source array has allowed development of many novel imaging systems including stationary tomosynthesis devices for breast, chest and dental imaging. This technology enables stationary computed tomography with potentially a fast data acquisition rate and a mechanically robust structure by eliminating the rotating gantry. It reduces the image blur caused by the mechanical motion. The purpose of this work is to explore possible system configurations of stationary head CT (s-HCT) using fixed-position linear CNT x-ray source arrays and detector arrays. Methods: Sinogram coverage is used for qualitative evaluation on the CT projection data collection efficiency for a given configuration. Accordingly, the configuration is optimized based on the coverage in sinogram space. To evaluate the system feasibility on imaging low-contrast brain tissues, a modified low-contrast Shepp-Logan phantom is implemented for quality assessment using quantitative metrics. Different Iterative Reconstruction methods are compared for both qualitative and quantitative assessment as well. Results: The sinogram coverage of s-HCT configurations changes significantly with different number of CNT source arrays used, as well as the layout of the geometry. Preliminary results suggest that a s-HCT configuration with three planes gives a nearly completed sinogram coverage which provides enough information to reconstruct image with good quality. Different reconstruction techniques are used for such configuration with a low-contrast head phantom. High-quality images are reconstructed for the proposed configuration. Conclusion: An optimized s-HCT system configuration can be built with few linear CNT x-ray source arrays. Given such configuration, Iterative Reconstruction algorithms in conjunction with Total-Variation Regularization provides highquality images even for low-contrast objects.