混合高斯/泊松最大似然函数下的CBCT图像重建

郑蓉珍; 赵芳; 李波

doi:10.3788/OPE.20202802.0457

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混合高斯/泊松最大似然函数下的CBCT图像重建

信息科学 | 更新时间：2020-07-16

- 混合高斯/泊松最大似然函数下的CBCT图像重建
- CBCT image reconstruction using a mixed Poisson-Gaussian maximum likelihood function
- 光学精密工程 2020年28卷第2期页码：457-464
- 作者机构：
  
  1.武汉大学电子信息学院，湖北武汉 430072
  2.武汉大学中南医院心血管内科，湖北武汉 430071
  3.武汉大学口腔医院放射科，湖北武汉 430079
- 作者简介：
  
  [ "郑蓉珍 (1991—)，女，宁夏固原人，硕士研究生，主要从事数字图像处理方面的研究。Email：zrzwh@foxmail.com" ]
- 基金信息：
  
  中央高校基本科研业务费(2042018kf1009);湖北省自然科学基金资助项目(2018CFB435)
- DOI：10.3788/OPE.20202802.0457
  中图分类号： R816.98;TP391.41
- 收稿日期：2019-07-29，
  
  修回日期：2019-09-06，
  
  录用日期：2019-9-6，
  
  纸质出版日期：2020-02-25
- 稿件说明：
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郑蓉珍, 赵芳, 李波. 混合高斯/泊松最大似然函数下的CBCT图像重建[J]. 光学精密工程, 2020,28(2):457-464.

Rong-zhen ZHENG, Fang ZHAO, Bo LI. CBCT image reconstruction using a mixed Poisson-Gaussian maximum likelihood function[J]. Optics and precision engineering, 2020, 28(2): 457-464.
郑蓉珍, 赵芳, 李波. 混合高斯/泊松最大似然函数下的CBCT图像重建[J]. 光学精密工程, 2020,28(2):457-464. DOI： 10.3788/OPE.20202802.0457.

Rong-zhen ZHENG, Fang ZHAO, Bo LI. CBCT image reconstruction using a mixed Poisson-Gaussian maximum likelihood function[J]. Optics and precision engineering, 2020, 28(2): 457-464. DOI： 10.3788/OPE.20202802.0457.

摘要

如何在复杂噪声条件下提升锥形束计算机断层扫描成像(Cone Beam Computed Tomography

CBCT)图像重建质量，对于CBCT系统而言是非常重要的。本文提出了一种混合高斯/泊松最大似然函数下的CBCT图像重建方法。首先研究了适宜于描述混合高斯/泊松噪声环境下的CBCT图像重建模型，它包含一个基于混合高斯/泊松最大似然函数的保真项和一个基于三维全变分正则化方法的约束项。保真项用于约束在混合噪声模型下重建结果与观测值尽可能的相近，约束项用于噪声去除并要求尽可能较好地保留图像的边缘与细节信息。进一步通过可分离近似方法和扩展拉格朗日方法对上述模型进行求解。最后通过仿真数据和真实数据对算法的有效性进行了验证，实验结果表明：仿真结果相对于其他方法而言，PSNR最高可以提升2.1 dB；从主观视觉而言，本文方法在噪声环境下具有较好的图像重建质量。因此，本文方法可以被广泛应用于各种低剂量条件下的CBCT图像重建中。

Abstract

Improving the quality of Cone-Beam Computed Tomography (CBCT) reconstructed images under complicated noise conditions was critical for CBCT systems. In this study

an image reconstruction method of CBCT based on a hybrid Poisson-Gaussian maximum likelihood function was proposed. First

a CBCT image reconstruction model suitable for describing a mixed Poisson-Gaussian noise environment was studied. The model contained a fidelity term based on a mixed Poisson-Gaussian maximum likelihood function and a constraint term based on a three-dimensional total variation regularization method. The fidelity term was used to constrain the reconstruction result to match the observed value as closely as possible in the mixed noise model

where as the constraint term was used for noise removal and preserving the edge and detail information as effectively as possible. The proposed model was further solved using the separable approximation and extended Lagrangian methods. Finally

the effectiveness of the algorithm was verified using both simulation and real data. The results indicate that the proposed method exhibited a maximum improvement of 2.1 dB as compared to other methods evaluated usingsimulated data. In visual comparisons

the proposed method demonstrated the best denoising performance. We can thus conclude that the proposed method is effective forCBCT reconstruction under low dose conditions.

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references

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