Deep learning based image reconstruction algorithm for limited-angle translational computed tomography

Autoři: Jiaxi Wang aff001;  Jun Liang aff003;  Jingye Cheng aff004;  Yumeng Guo aff005;  Li Zeng aff001
Působiště autorů: Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, China aff001;  Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, China aff002;  College of Computer Science, Civil Aviation Flight University of China, Guanghan Sichuan, China aff003;  College of Mathematics and Statistics, Chongqing University, Chongqing, China aff004;  College of Mathematics and Statistics, Chongqing Technology and Business University, Chongqing, China aff005
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article


As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the inconsistency of the detector for the large angle scanning, we use the limited-angle TCT scanning mode to scan an object within a limited angular range. However, this scanning mode introduces some additional noise and limited-angle artifacts that seriously degrade the imaging quality and affect the diagnosis accuracy. To reconstruct a high-quality image for the limited-angle TCT scanning mode, we develop a limited-angle TCT image reconstruction algorithm based on a U-net convolutional neural network (CNN). First, we use the SART method to the limited-angle TCT projection data, then we import the image reconstructed by SART method to a well-trained CNN which can suppress the artifacts and preserve the structures to obtain a better reconstructed image. Some simulation experiments are implemented to demonstrate the performance of the developed algorithm for the limited-angle TCT scanning mode. Compared with some state-of-the-art methods, the developed algorithm can effectively suppress the noise and the limited-angle artifacts while preserving the image structures.

Klíčová slova:

Abdomen – Algorithms – Computed axial tomography – Data acquisition – Deep learning – Image processing – Imaging techniques – X-ray radiography


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