Guided Image Filtering Reconstruction Based on Total Variation and Prior Image for Limited-Angle CT

Zhaoqiang Shen,Wei Yu,Li Zeng,Changcheng Gong

doi:10.1109/access.2020.3016332

Abstract

For limited-angle computed tomography (CT) image reconstruction, the classical total variation (TV) based algorithms suffer from the limited-angle artifacts, because TV only used the gradient information of the image. The priori image constrained compressed sensing (PICCS) based reconstruction algorithms can reduce the limited-angle artifacts by using a priori image consistent with the target image. However, it is difficult to ensure the consistency of priori image and target image in practice. In order to reconstruct high quality image when the prior image is inconsistent with the target image, we proposed a guided image filter reconstruction based on TV and prior image (TVPI-G). In each iteration phase, our algorithm first performs a TV step (include simultaneous algebra reconstruction technique (SART) and TV) to get initiatory reconstruction image; Then, the results of TV iteration are combined with prior images to form an intermediate result; Finally, we use the guided image filter to modify the intermediate results with the TV result as the guide image. Numerical reconstruction results on simulation phantom with different intensities Poisson noise illustrates that our proposed TVPI-G algorithm is better than other comparison algorithms in both qualitative and quantitative aspects, including TV, PICCS, and SART guided image filtering.

Highlights

X-ray Computed Tomography (CT) has been successfully applied in many aspects, for example, in medical diagnosis [1], industrial nondestructive testing [2], safety inspection [3]
Inspired by their work [27], in this paper, we propose a guided image filter reconstruction algorithm based on total variation and prior image (TVPI-G) for limited-angle CT
We propose a guided image filter reconstruction based on TV and prior image (TVPI-G) for limited-angle CT

Summary

INTRODUCTION

X-ray Computed Tomography (CT) has been successfully applied in many aspects, for example, in medical diagnosis [1], industrial nondestructive testing [2], safety inspection [3]. Chen et al proposed a prior image constrained compressed sensing (PICCS) algorithm [19], which is more robust to incomplete projection data reconstruction problems than traditional compressed sensing (CS) based technology [20]. For CT images with prior images and simple structure, the SART-G algorithm can reconstruct high-quality images from sparse angle projection data [27]. SART-G algorithm iteration consists of two steps: The first is SART step, which uses the SART algorithm to solve the CT discrete linear system; a guided image filtering step is used to reduce noise and retain the detailed structure of reconstructed image. Inspired by their work [27], in this paper, we propose a guided image filter reconstruction algorithm based on total variation and prior image (TVPI-G) for limited-angle CT. The TV-based regularization algorithm is summarized as following optimization problem: min TV (f ) s.t

PICCS MODEL

GUIDED IMAGE FILTERING

PROPOSED TVPI-G ALGORITHM

NCAT EXPERIMENTS WITHOUT NOISE

DISCUSS AND CONCLUSION