Gaussian diffusion sinogram inpainting for X-ray CT metal artifact reduction

Chengtao Peng,Bensheng Qiu,Zhongyi Wu,Ming Li,Cheng Zhang,Yihui Guan,Jian Zheng

doi:10.1186/s12938-016-0292-9

Abstract

BackgroundMetal objects implanted in the bodies of patients usually generate severe streaking artifacts in reconstructed images of X-ray computed tomography, which degrade the image quality and affect the diagnosis of disease. Therefore, it is essential to reduce these artifacts to meet the clinical demands.MethodsIn this work, we propose a Gaussian diffusion sinogram inpainting metal artifact reduction algorithm based on prior images to reduce these artifacts for fan-beam computed tomography reconstruction. In this algorithm, prior information that originated from a tissue-classified prior image is used for the inpainting of metal-corrupted projections, and it is incorporated into a Gaussian diffusion function. The prior knowledge is particularly designed to locate the diffusion position and improve the sparsity of the subtraction sinogram, which is obtained by subtracting the prior sinogram of the metal regions from the original sinogram. The sinogram inpainting algorithm is implemented through an approach of diffusing prior energy and is then solved by gradient descent. The performance of the proposed metal artifact reduction algorithm is compared with two conventional metal artifact reduction algorithms, namely the interpolation metal artifact reduction algorithm and normalized metal artifact reduction algorithm. The experimental datasets used included both simulated and clinical datasets.ResultsBy evaluating the results subjectively, the proposed metal artifact reduction algorithm causes fewer secondary artifacts than the two conventional metal artifact reduction algorithms, which lead to severe secondary artifacts resulting from impertinent interpolation and normalization. Additionally, the objective evaluation shows the proposed approach has the smallest normalized mean absolute deviation and the highest signal-to-noise ratio, indicating that the proposed method has produced the image with the best quality.ConclusionsNo matter for the simulated datasets or the clinical datasets, the proposed algorithm has reduced the metal artifacts apparently.

Highlights

Metal objects implanted in the bodies of patients usually generate severe streaking artifacts in reconstructed images of X-ray computed tomography, which degrade the image quality and affect the diagnosis of disease
Based on the forward sinogram inpainting metal artifact reduction (MAR) framework, in this paper we propose a Gaussian diffusion sinogram inpainting technique based on a prior image to reduce metal artifacts in a Bayesian framework
Results the results corrected by the linear interpolation (LI), normalized metal artifact reduction (NMAR) and proposed algorithms for the simulation and clinical datasets are presented

Summary

Introduction

Metal objects implanted in the bodies of patients usually generate severe streaking artifacts in reconstructed images of X-ray computed tomography, which degrade the image quality and affect the diagnosis of disease. It is essential to reduce these artifacts to meet the clinical demands. The use of X-ray computed tomography (CT) in the clinical setting has lasted for several decades and has considerably helped disease diagnosis and therapy. Metallic implants in the bodies of patients usually produce severe streaking artifacts, Peng et al BioMed Eng OnLine (2017) 16:1 which will obscure crucial information and reduce image quality. That is to say, when an X-ray passes through body of a patient containing metal, the low energy photons are absorbed and output mostly contains high-energy photons, the high-energy photons detected by detector become richer. To solve the problem of severe streaking artifacts, many metal artifact reduction (MAR) algorithms have been proposed

Methods

Results

Conclusion