北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (9): 1757-1764.doi: 10.13700/j.bh.1001-5965.2018.0775

• 论文 • 上一篇    下一篇

基于可变阶变分模型的医用低剂量CT图像去噪

王娜1,2, 张权1,2, 刘祎1,2, 贾丽娜1,2, 桂志国1,2   

  1. 1. 中北大学 生物医学成像与影像大数据山西省重点实验室, 太原 030051;
    2. 中北大学 信息与通信工程学院, 太原 030051
  • 收稿日期:2019-01-02 出版日期:2019-09-20 发布日期:2019-09-29
  • 通讯作者: 桂志国 E-mail:gzgtg@163.com
  • 作者简介:王娜,女,博士研究生。主要研究方向:图像处理;桂志国,男,博士,教授,博士生导师。主要研究方向:信号与信息处理、图像处理和识别、图像重建。
  • 基金资助:
    国家自然科学基金(61671413,61801438);国家重大科学仪器设备开发专项(2014YQ24044508);山西省归国学者基金(2016-085)

Medical low-dose CT image denoising based on variable order variational model

WANG Na1,2, ZHANG Quan1,2, LIU Yi1,2, JIA Lina1,2, GUI Zhiguo1,2   

  1. 1. Shanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, North University of China, Taiyuan 030051, China;
    2. School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
  • Received:2019-01-02 Online:2019-09-20 Published:2019-09-29
  • Supported by:
    National Natural Science Foundation of China (61671413,61801438); National Key Scientific Instrument and Equipment Development Project of China (2014YQ24044508); Shanxi Provincial Foundation for Returned Scholars (Main Program), China (2016-085)

摘要: 为了降低患者的辐射风险,低剂量CT(LDCT)广泛用于临床诊断,但辐射剂量的减少在重建的LDCT图像中引入了斑点噪声和条纹伪影。为了提高LDCT图像的质量,提出了一种基于可变阶变分模型的后处理技术。所提出的变分模型使用边缘指示器控制变分阶数,根据图像的特征在一阶全变分(TV)正则项和二阶有界Hessian(BH)正则项之间交替变换。采用基于快速傅里叶变换(FFT)的分裂Bregman算法求解所提出的变分模型。该模型在保留高剂量CT(HDCT)图像相应结构的同时,有效抑制了斑点噪声和条纹伪影。重建的图像和实验数据表明,所提出的变分模型比现有的先进模型具有更好的质量。

关键词: 低剂量CT (LDCT), 图像降噪, 边缘指示器, 全变分(TV), 有界Hessian (BH), 快速傅里叶变换(FFT), 分裂Bregman算法

Abstract: Low-dose CT (LDCT) is widely used for clinical diagnosis to reduce radiation risk to patients. However, the radiation dose reduction introduces mottle noise and streak artifacts into the reconstructed LDCT images. In this paper, a post-processing technique is proposed based on variable order variational model to improve the LDCT image quality. The proposed variational model employs the edge indicator to control the order of variation, which can alternate between the first order total variation (TV) regularizer and second order bounded Hessian(BH) regularizer based on the image feature. Moreover, the proposed model is solved by split Bregman algorithm based on fast Fourier transform (FFT). The proposed model effectively suppresses mottle noise and streak artifacts, meanwhile preserving structure in reference to high-dose CT (HDCT) images. The reconstructed images and experimental data indicate that the proposed model has better quality than some existing state-of-the-art models.

Key words: low-dose CT (LDCT), image denoising, edge indicator, total variation (TV), bounded Hessian (BH), fast Fourier transform (FFT), split Bregman algorithm

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