Characterizing CT Reconstruction of Pre-log Transmission Data toward Ultra-low Dose Imaging by Texture Measures

Tremendous research efforts have been devoted to minimizing the radiation exposure to patients by acquiring the X-ray computed tomography (CT) transmission data at as low radiation exposure as reasonably practical (ALARP) and developing the corresponding image reconstruction methods. To address the ALARP radiation, this study aims to develop texture-enhancing image reconstruction algorithms and texture-based image quality evaluation strategies because image textures play an essential role for many clinical tasks. The image reconstruction is based on the maximum a posteriori probability given the acquired data, where the a priori knowledge is learnt tissue textures from the existing diagnostic full-dose CT image, and the transmission data fidelity is modeled by a shift Poisson statistic considering both the X-ray quanta fluctuation and the system electronic background noise. The image evaluation is based on the regional gray-scale co-occurrence texture measures. Evaluation of the developed methodologies was performed on patient data acquired with 120kVp and 100 mAs settings, followed on simulated data at 20, 10, 5 and 1mAs. The image texture measures showed a monotonic drop as the dose level decreased from 20 to 1 mAs. The most striking observation is a critical turning point on the plot of the relative change of texture measure vs. the mAs levels. This critical turning point indicates the minimum dose level that a CT scanner hardware configuration and image reconstruction software can achieve with a reasonable image quality. The effect of the background noise is also evaluated through the simulated data in this study.