Fair evaluation of different sparse-view CT reconstruction models
10.3969/j.issn.1005-202X.2025.06.013
- VernacularTitle:公平评估不同稀疏角度CT重建模型
- Author:
Ximing CAO
1
;
Menghuang WEN
1
;
Jianhua MA
1
;
Zhaoying BIAN
1
Author Information
1. 南方医科大学生物医学工程学院,广东 广州 510515
- Publication Type:Journal Article
- Keywords:
computed tomography;
sparse-view reconstruction;
deep learning;
model evaluation
- From:
Chinese Journal of Medical Physics
2025;42(6):796-800
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the performance of reconstruction networks with different sparse views under the condition of keeping the same number of model parameters.Methods The number of network channels and network layers were adjusted to make the parameter quantity of each network similar when keeping the structure of each image-domain network and dual-domain network unchanged.The reconstruction performance of each network at different sparsity levels was compared.The AAPM Low-Dose CT Grand Challenge datasets were used in the experiment,including 10 976 images for training,979 images for validation,and 4 256 images for testing.The performance of each model was evaluated visually in combination with objective metrics such as peak signal-to-noise ratio,structural similarity and root mean square error.Results Before adjusting the model parameters,the hybrid domain network Tensor-Net obtained the best visual evaluation and objective evaluation metrics.After parament adjustment,with a similar number of parameters,Tensor-Net outperformed the other models at various projection angles in image anatomical detail recovery,but its structural similarity was slightly lower than that of RED-CNN.The parameters of the hybrid domain model Dual-FBPConvNet were all worse than those of FBPConvNet.Conclusion The hybrid domain model is advantageous in sparse-view CT reconstruction,but it faces more serious overfitting problems.Using a larger image domain model can achieve results similar to those of hybrid domain model.
