(A) novel method of improving image quality in spectral CT by use of a hybrid half-fan mode

Abstract

CT imaging is widely used in various clinical applications for its rich image information and its fast data acquisition time. However, CT produces relatively low contrast in the soft tissues and CT number variation exists among systems due to polychromatic x-ray spectrum. These limitations are also partly coupled to the energy integrating detectors which are mostly used in the CT systems. Photon counting detectors, however, have the capability to discriminate the x-ray energies thus mitigating the aforementioned limitations and making it possible to decompose the materials of similar attenuation coefficients such as contrast agent and calcium plaque. In this research, we proposed a hybrid half-fan scan mode, which enables to obtain both conventional CT image and photon counting CT image from a single scan. Since photon counting detector suffers from pulse pileup effect which results from high flux of incident photons, a filter was placed in front of the photon counting detector to reduce the x-ray flux dramatically. The image quality, e.g., signal to noise ratio (SNR) related to the square root of the number of photons, would be degraded due to decreased number of incident photons and increased energy bins. The degraded SNR of the photon counting CT image can be improved by use of the gradient magnitude image (GMI) from the conventional CT image. GMI represents the edge image information which is highly correlated between the two CT images in our proposed scanning scheme. By using the difference between GMI of CT image and that of photon counting CT image as a regularization term in the objective function, image is reconstructed by an iterative algorithm based on image total variation minimization algorithm. For a numerical simulation, water phantom composed of contrast and calcium plaque was scanned. We have shown that the proposed method can improve the signal to noise ratio (SNR) of the spectral CT by a factor of 2.64 compared with the reconstruction technique that does not use a GMI regularization and can decompose the materials more accurately.