Compared to CT,the lesions of acute ischemic stroke are more clearly visible on MRI.Given that some patients have conditions unsuitable for MRI,such as the presence of metal in the body or claustrophobia,it can hinder the diagnosis of patients.Combined radiomics and Diffusion Generative Adversarial Network(DiffusionGAN),we propose an Edge-Aware DiffusionGAN based on radiomics,achieving the conversion of CT to MRI for patients with acute ischemic stroke.The algorithm addresses the issues of insufficient lesion information and blurry edges in existing CT-to-MRI conversion methods.Specifically,it employs radiomics to locate lesions on CT,assists in MRI generation with feature maps,and introduces the Edge-Aware DiffusionGAN to enhance edge perception in the generated MRI.Experimental results demonstrate that the generated MRI achieves a peak signal-to-noise ratio(PSNR)of 69.607,a structural similarity index(SSIM)of 0.821,and a Pearson correlation coefficient(PCC)of 0.974,significantly outperforming existing models.Medical evaluations reveal no erroneous lesions in the generated MRI,with an accuracy of 87.91%in positive/negative classifi-cation,indicating that this innovative algorithm provides a new way to solve the problem of stroke diagnosis in special cases where MRI is not applicable.
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