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

Jiaxi Wang,Yumeng Guo,Li Zeng,Jingye Cheng,Jun Liang,Huafeng Liu

doi:10.1371/journal.pone.0226963

Abstract

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.

Highlights

Translational computed tomography (TCT) as a new low-end CT system, which can obtain the interior image without destroying the scanned object by using the projection data obtained from the detector, is created for developing countries [1]
The red arrows point to some obvious artifacts which are enlarged in the Figs 8 and 9
Decrease X-ray radiation or scan some long objects, to avoid the inconsistency of the detector for the large angle scanning in translational scanning scheme, we use a limited-angle TCT scanning mode which introduces some additional noise and artifacts that seriously degrade the imaging quality and affect the accuracy, due to it is short of the continuous angular projection data

Summary

Introduction

Translational computed tomography (TCT) as a new low-end CT system, which can obtain the interior image without destroying the scanned object by using the projection data obtained from the detector, is created for developing countries [1]. In some practical TCT applications, in order to reduce the scan time, decrease the X-ray radiation which may cause potential risks to patients, or scan some long objects within a limited angular range, to avoid the inconsistency of the detector for the large angle scanning in the translational scanning scheme, the obtained projection data of the scanned object are usually incomplete. In this circumstance, some artifacts are presented in the image reconstructed by the FBPtype method. If the available projection data are incomplete, these methods cannot obtain satisfactory reconstructed image

Methods

Results

Discussion

Conclusion