A Limited-View CT Reconstruction Framework Based on Hybrid Domains and Spatial Correlation.

Ken Deng,Hongwen Yang,Yitong Liu,Wuxuan Gong,Chang Sun

doi:10.3390/s22041446

Abstract

Limited-view Computed Tomography (CT) can be used to efficaciously reduce radiation dose in clinical diagnosis, it is also adopted when encountering inevitable mechanical and physical limitation in industrial inspection. Nevertheless, limited-view CT leads to severe artifacts in its imaging, which turns out to be a major issue in the low dose protocol. Thus, how to exploit the limited prior information to obtain high-quality CT images becomes a crucial issue. We notice that almost all existing methods solely focus on a single CT image while neglecting the solid fact that, the scanned objects are always highly spatially correlated. Consequently, there lies bountiful spatial information between these acquired consecutive CT images, which is still largely left to be exploited. In this paper, we propose a novel hybrid-domain structure composed of fully convolutional networks that groundbreakingly explores the three-dimensional neighborhood and works in a “coarse-to-fine” manner. We first conduct data completion in the Radon domain, and transform the obtained full-view Radon data into images through FBP. Subsequently, we employ the spatial correlation between continuous CT images to productively restore them and then refine the image texture to finally receive the ideal high-quality CT images, achieving PSNR of 40.209 and SSIM of 0.943. Besides, unlike other current limited-view CT reconstruction methods, we adopt FBP (and implement it on GPUs) instead of SART-TV to significantly accelerate the overall procedure and realize it in an end-to-end manner.

Highlights

Computed Tomography (CT) [1] is diffusely known as an approach to exhibit precise details inside the scanned object [2], is applied to a wide range of applications including clinical diagnosis, industrial inspection, material science and biomedicine [3,4]
These CT images are concatenated into groups and sent into our proposed Spatial Adversarial Autoencoder (Spatial-AAE) to perform image inpainting based on strong spatial correlation between consecutive CT images, which can manage to eliminate almost all the artifacts from the original limited-view CT images
Images that contains severe artifacts, we propose a hybrid-domain structure that efficaciously utilizes the spatial information between consecutive CT images, and utilizes the idea of “coarse to fine” to refine the image texture

Summary

Introduction

Computed Tomography (CT) [1] is diffusely known as an approach to exhibit precise details inside the scanned object [2], is applied to a wide range of applications including clinical diagnosis, industrial inspection, material science and biomedicine [3,4]. The associated x-ray radiation dose brings potential risk of cancers [5], which has drawn wide attention. Low-dose Computed Tomography (LDCT) can be realized through two strategies including current (or voltage) reduction [11,12] and projection reduction [13,14,15]. The second strategy can avoid the above problem and realize the additional benefit of accelerated scanning and calculation, it gives rise to severe image quality deterioration of increased artifacts due to its lack of projections

Methods

Discussion

Conclusion