Research on denoising of hemorrhagic stroke CT images based on improved Wasserstein generative adversarial networks
Objective To enhance the image quality of unpaired-reference hemorrhagic stroke CT images,a denoising algorithm for CT images based on an improved Wasserstein generative adversarial network(W-GAN)is proposed.Methods Using the W-GAN network as the framework,the visual geometry group(VGG)network is introduced in the generator part to calculate the perceptual loss module,and the discriminator part is improved by adding self-attention mechanisms and spectral normalization convolutions.The model is used to denoise low-dose CT data,obtaining images close to normal dose.Subsequently,transfer learning is performed on the unpaired-reference hemorrhagic stroke data using the trained model,and the final images obtained are evaluated using no-reference image quality assessment.The assessments are conducted using three no-reference image quality evaluation methods:total variation(TV),blind/referenceless image spatial quality evaluator(BRISQUE),and contrastive language-image pre-training image quality assessment(CLIP-IQA).Results The final results show improvements of 0.016 5,0.127 2,and 0.007 compared to the input on the three reference free image quality evaluation metrics of TV,BRISQUE,and CLIP-IQA,respectively.Conclusions The improved W-GAN network model proposed in this paper can be used for the transfer learning task of denoising low-dose CT images of hemorrhagic stroke,achieving good performance improvement,and providing a potential tool to assist physicians in diagnosing hemorrhagic stroke.
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