Citation: Deng, K.; Sun, C.; Gong, W.;
Liu, Y.; Yang, H. A Limited-View CT
Reconstruction Framework Based on
Hybrid Domains and Spatial
Correlation. Sensors 2022, 22, 1446.
https://doi.org/10.3390/s22041446
Academic Editors: M. Jamal Deen,
Subhas Mukhopadhyay, Yangquan
Chen, Simone Morais, Nunzio
Cennamo and Junseop Lee
Received: 7 December 2021
Accepted: 9 February 2022
Published: 13 February 2022
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Article
A Limited-View CT Reconstruction Framework Based on
Hybrid Domains and Spatial Correlation
Ken Deng , Chang Sun , Wuxuan Gong , Yitong Liu * and Hongwen Yang
Institute of Wireless Theories and Technologies Laboratory, Beijing University of Posts and Telecommunications,
Haidian, Beijing 100876, China; arieldeng@bupt.edu.cn (K.D.); sc1998@bupt.edu.cn (C.S.);
gongwuxuan@bupt.edu.cn (W.G.); yanghong@bupt.edu.cn (H.Y.)
* Correspondence: liuyitong@bupt.edu.cn
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.
Keywords:
CT image reconstruction; low dose protocol; adversarial autoencoder; deep learning;
hybrid domain; spatial correlation; inverse problems
1. Introduction
Computed Tomography (CT) [
1
] is diffusely known as an approach to exhibit precise
details inside the scanned object [
2
], thus is applied to a wide range of applications including
clinical diagnosis, industrial inspection, material science and biomedicine [
3
,
4
]. In addition,
the raging epidemic caused by the Corona Virus Disease 2019 (COVID-19) has made CT
known to the public as an efficacious auxiliary technology. Nevertheless, the associated
x-ray radiation dose brings potential risk of cancers [
5
], which has drawn wide attention.
Consequently, the demand of radiation dose reduction is becoming more and more acute
under the principle of ALARA (as low as reasonably achievable) [6–10].
Generally, Low-dose Computed Tomography (LDCT) can be realized through two
strategies including current (or voltage) reduction [
11
,
12
] and projection reduction [
13
–
15
].
The first strategy aims to lower the x-ray exposure in each view, while it greatly suffers
from the increased noise in projections. Although 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.
In this paper, we will focus on obtaining high-quality CT images from limited-view CT
with inadequate scanning angle.
Researchers have proposed various CT image reconstruction algorithms in the past
few decades, but when it comes to LDCT reconstruction, the problem becomes challenging.
Sensors 2022, 22, 1446. https://doi.org/10.3390/s22041446 https://www.mdpi.com/journal/sensors
