Combining GAN Loss with Pre-trained Model for Semi-supervised SPECT Reconstruction

Xu Jin-hua; Li Si

doi:10.12052/gdutxb.230186

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Combining GAN Loss with Pre-trained Model for Semi-supervised SPECT Reconstruction

更新时间：2026-05-27

- Combining GAN Loss with Pre-trained Model for Semi-supervised SPECT Reconstruction
- Vol. 41, Issue 6, Pages: 101-107(2024)
- 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：
- DOI：10.12052/gdutxb.230186
  CLC： TP391
- Published：2024
- 稿件说明：
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徐锦华, 李斯. 结合GAN损失与预训练模型的半监督SPECT重建方法[J]. 广东工业大学学报, 2024, 41(6): 101-107.

Xu Jin-hua, Li Si. Combining GAN Loss with Pre-trained Model for Semi-supervised SPECT Reconstruction[J]. 2024, 41(6): 101-107.
徐锦华, 李斯. 结合GAN损失与预训练模型的半监督SPECT重建方法[J]. 广东工业大学学报, 2024, 41(6): 101-107. DOI： 10.12052/gdutxb.230186.

Xu Jin-hua, Li Si. Combining GAN Loss with Pre-trained Model for Semi-supervised SPECT Reconstruction[J]. 2024, 41(6): 101-107. DOI： 10.12052/gdutxb.230186.

摘要

单光子发射计算机断层扫描(Single-Photon Emission Computerized Tomography，SPECT)检查中所使用的放射性示踪剂会对人体造成辐射损害，因此，低剂量SPECT在核医学成像中受到广泛关注。在低剂量成像条件下，投影数据受到严重的噪声污染。已有大量研究工作探索基于全监督的深度学习重建方法以抑制图像噪声。全监督方法所得的图像质量取决于标签的数量与质量。然而，用作监督标签的正常剂量图像在临床上难以获取。为克服上述困难，本文提出一种带有GAN(Generative Adversarial Network)损失的预训练平均教师方法，以实现低剂量SPECT重建。所提方法为平均教师模型引入基于Swin-Conv-Unet的预训练模型，以提高未标记训练数据的可靠性。教师模型通过一致性正则化监督学生模型；预训练模型使用少量标记数据进行训练，并通过GAN损失提高监督可靠性。数值实验验证所提方法在抑制噪声和保持特征方面的性能，相较于平均教师方法，论文重建方法所得图像的结构相似性指数提高了2%，均方根误差降低了9%，峰值信噪比提高了0.77 dB。数据集由SIMIND仿真软件使用XCAT数字模体生成。

Abstract

The radioactive tracers used in Single-Photon Emission computerized Tomography (SPECT) scans can cause radiation exposure to the human body. Therefore

low-dose SPECT has attracted widespread attention in nuclear medicine imaging. Under low-dose imaging conditions

projection data is heavily contaminated by severe noise. There has been a significant amount of studies that explore fully supervised deep learning reconstruction methods to suppress image noise. The quality of images obtained by fully supervised methods depend on the quantity and quality of the labels. However

it is challenging to obtain the normal-dose images with supervised labels in clinical practice. To overcome the challenge

we propose a pre-trained mean teacher method with GAN loss to achieve low-dose SPECT reconstruction. The proposed method introduces a Swin-Conv-Unet-based pre-trained model into the mean teacher model to enhance the reliability of unlabeled training data. The teacher model supervises the student model through consistency regularization; the pre-trained model is trained with a small amount of labeled data and enhances the supervision reliability through GAN loss. Numerical experiments validate the performance of the proposed method in noise suppression and feature preservation. When compared with the mean teacher method

the SSIM of the reconstructed images is increased by 2%

the RMSE is reduced by 9%

and the PSNR is increased by 0.77 dB. The dataset is generated by the SIMIND simulation software using XCAT digital phantoms.

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references

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