基于多级残差U-Net的稀疏SPECT图像重建

叶文权; 李斯; 凌捷

doi:10.12052/gdutxb.220016

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基于多级残差U-Net的稀疏SPECT图像重建

更新时间：2026-05-27

- 基于多级残差U-Net的稀疏SPECT图像重建
- Sparse-view SPECT Image Reconstruction Based on Multilevel-residual U-Net
- 广东工业大学学报 2023年40卷第1期页码：61-67
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(11771464);广东省自然科学基金资助项目(2021A1515012290);中山大学广东省计算科学重点实验室开放基金资助项目(2021007)
- DOI：10.12052/gdutxb.220016
  中图分类号： TP391
- 网络首发：2023-01-25，
  
  纸质出版：2023
- 稿件说明：
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叶文权, 李斯, 凌捷. 基于多级残差U-Net的稀疏SPECT图像重建[J]. 广东工业大学学报, 2023, 40(1): 61-67.

Ye Wen-quan, Li Si, Ling Jie. Sparse-view SPECT Image Reconstruction Based on Multilevel-residual U-Net[J]. 2023, 40(1): 61-67.
叶文权, 李斯, 凌捷. 基于多级残差U-Net的稀疏SPECT图像重建[J]. 广东工业大学学报, 2023, 40(1): 61-67. DOI： 10.12052/gdutxb.220016.

Ye Wen-quan, Li Si, Ling Jie. Sparse-view SPECT Image Reconstruction Based on Multilevel-residual U-Net[J]. 2023, 40(1): 61-67. DOI： 10.12052/gdutxb.220016.

摘要

低剂量单光子发射型断层扫描(Single-Photon Emission Computed Tomography，SPECT) 能够减少放射性示踪剂对人体的辐射损害，因此其临床应用变得愈发重要。SPECT扫描可通过投影角度稀疏采样实现低剂量成像；若直接对稀疏采样投影数据进行迭代重建，投影角度的缺失将导致重建图像中出现严重的射线伪影。现今主流的临床方法普遍在图像重建优化模型中引入特定的正则项以抑制射线伪影，然而该类方法不具有通用性，并且正则项过度依赖于经验选取。本文提出一种新颖的神经网络结构以学习稀疏采样投影数据与全角度采样投影数据之间的映射关系；通过所提网络结构合成缺失角度的投影数据，来提升重建图像的质量。数值实验表明，相较于传统迭代重建方法，论文重建方法所得图像的结构相似性(Structural Similarity

SSIM)提高了59%，标准均方误差(Normalized Mean-Square-Error

NMSE)降低了67%，峰值信噪比(Peak Signal-to-Noise Ratio

PSNR)提高了2.48 dB。因此，所提方法能较好地改善稀疏采样投影数据成像后的图像质量。

Abstract

Low-dose single-photon emission computed tomography (SPECT) imaging can reduce the radiation damage to human bodies caused by radioactive tracers

and hence it is becoming more and more important in clinical practice. In a SPECT system

low-dose imaging can be achieved by acquiring projection data of sparse-view. The sparse-view projection data

if directly reconstructed by conventional iterative reconstruction methods

will inevitably lead to severe ray artifacts in the image domain. Existing clinical reconstruction methods usually introduce specific regularization to the optimization model to suppress ray artifacts. However

this type of methods may not adapt to projection data with various dosage

and the form of regularization heavily depends on prior knowledge. A novel neural network architecture is proposed to learn the mapping from the sparse-view projection data to the full-view projection data. The projection data of missing view angle is synthesized by the proposed neural network to improve the quality of reconstructed images. Numerical experiments show that

compared with the traditional iterative reconstruction method

the SSIM of the reconstructed image is increased by 59%

the NMSE is reduced by 67%

and the PSNR is increased by 2.48 dB. Therefore

the proposed method can better improve the image quality of sparse-view projection data.

关键词

Keywords

references

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