Spectral CT Reconstruction Based on PICCS and Dictionary Learning

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

doi:10.1109/access.2020.3010228

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.

Highlights

As a nondestructive imaging technology, X-ray computed tomography (CT) has been widely used in clinical diagnosis, industrial testing and safety inspection since it was innovated in 1971 [1]–[3]
MATHEXPERIMENT RESULTS AND ANALYSIS The major goal of this paper is to evaluate the performance of the prior image constrained compressed sensing (PICCS)-dictionary learning (DL) for spectral CT
The algorithms of FBP, PICCS and DL are implemented for comparison

Summary

Introduction

As a nondestructive imaging technology, X-ray computed tomography (CT) has been widely used in clinical diagnosis, industrial testing and safety inspection since it was innovated in 1971 [1]–[3]. The traditional CT detectors integrate photon energies in the range of whole spectrum, and they ignore energy dependent information. This makes it difficult to distinguish materials with similar attenuation coefficients [4]. The theoretical model of traditional CT system does not match the actual situation, and the reconstructed images suffer from beam hardening artifacts [5]. The spectral CT with a photon-counting detector (PCD) has gained considerable interests because of its energy-resolution in identifying and discriminating materials [6]–[8]. In the PCD based spectral CT, the number of photons can be counted in each energy channel for those whose energies are above a given threshold. Different photon energies can be distinguished to generate multiple projection datasets simultaneously with appropriate post

Objectives

Results

Conclusion