Accelerating an Ordered-Subset Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction with a Power Factor and Total Variation Minimization.

Hsuan-Ming Huang,Ing-Tsung Hsiao,Chin-Tu Chen

doi:10.1371/journal.pone.0153421

Hsuan-Ming Huang, Ing-Tsung Hsiao + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0153421

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Journal: PloS one	Publication Date: Apr 13, 2016
Citations: 12	License type: CC BY 4.0

Affiliation: Chang Gung Memorial Hospital, Chang Gung University

Abstract

In recent years, there has been increased interest in low-dose X-ray cone beam computed tomography (CBCT) in many fields, including dentistry, guided radiotherapy and small animal imaging. Despite reducing the radiation dose, low-dose CBCT has not gained widespread acceptance in routine clinical practice. In addition to performing more evaluation studies, developing a fast and high-quality reconstruction algorithm is required. In this work, we propose an iterative reconstruction method that accelerates ordered-subsets (OS) reconstruction using a power factor. Furthermore, we combine it with the total-variation (TV) minimization method. Both simulation and phantom studies were conducted to evaluate the performance of the proposed method. Results show that the proposed method can accelerate conventional OS methods, greatly increase the convergence speed in early iterations. Moreover, applying the TV minimization to the power acceleration scheme can further improve the image quality while preserving the fast convergence rate.

Highlights

Due to radiation-induced cancer risks and biological perturbations, the use of low-dose X-ray cone beam computed tomography (CBCT) has been gradually gaining attention in many fields including dentistry [1,2], breast imaging [3,4], image-guided radiation therapy [5,6], small animal imaging [7,8] and phase-contrast imaging [9,10]
Comparing the results of AOSTR with those of ordered subsets transmission (OSTR), it can be seen that the present AOSTR algorithm can provide faster convergence rate since its relative root mean square error (RRMSE) values decrease rapidly
Despite using different values of α, both the iteration-level OSTR-TV (α = 0.001) and the subiteration-level OSTR-TV (α = 0.00003) algorithms have almost the same convergence rate. This result indicates that the implementation of the iteration-level AOSTR-TV could be feasible and efficient

Summary

Introduction

Due to radiation-induced cancer risks and biological perturbations, the use of low-dose X-ray cone beam computed tomography (CBCT) has been gradually gaining attention in many fields including dentistry [1,2], breast imaging [3,4], image-guided radiation therapy [5,6], small animal imaging [7,8] and phase-contrast imaging [9,10]. The low-dose CBCT data acquisition can be achieved by decreasing the milliampere seconds (mAs) per projection view or acquiring a small number of projection data (i.e. sparse views) per rotation [11,12] These dose reduction strategies lead to degradation of image quality, which may directly affect diagnostic accuracy. Total-variation (TV) minimization methods were primarily used to suppress streak artifacts and noise in sparse-view CBCT [18,19,20,21,22] Both high computational load and slow convergence make them impractical for routine use. Recent studies showed that combing OS-type IR methods with other techniques such as spatially nonuniform optimization transfer [31] and Nesterov’s momentum [32] could improve the initial convergence speed [33,34] These techniques require a couple of relaxation parameters. Tuning the relaxation parameters is inconvenient and tedious since optimizing parameters for ensuring a faster convergence rate remains a challenging issue [33]

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