Work

Spectral CT Reconstruction Based on PICCS and Dictionary Learning

Year: 2020

Type: article

Abstract: 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.

Source: IEEE Access

DOI: 10.1109/access.2020.3010228

Authors: Huihua Kong, Xiaoxue Lei, Lei Lei, Yanbo Zhang, Hengyong Yu

Institutions: North University of China, Ping An (China), University of Massachusetts Lowell

Language: English

Topic: Advanced X-ray and CT Imaging

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,750

Funder: National Natural Science Foundation of China