Spectral CT Reconstruction Based on PICCS and Dictionary Learning

Photon-counting detector based spectral computed tomography (CT) can obtain energy-discriminative attenuation map of an object in different energy channels, extending the conventional volumetric image along a spectral dimension. However, compared with the full spectrum data, the noise in a narrower energy channel is significantly increased. In order to improve image quality of spectral CT images, this paper proposes an iterative reconstruction algorithm based on the prior image constrained compressed sensing (PICCS) and dictionary learning (DL) theories, which is called PICCS-DL. The PICCS-DL utilizes the correlation of the images reconstructed from different energy channels by taking the broad spectrum image as a prior constraint, and it utilizes the sparse of the images by taking the total variation (TV) and DL as prior constraints. The alternating minimization, Split-Bregman and the steepest descent (SD) methods are used to solve the objective function. The effectiveness of the proposed method is validated with numerical simulations and preclinical applications. The results demonstrate that the proposed algorithm generally produces superior image quality, especially for noisy and sparse projection data.