The lesions of acute ischemic stroke are not clearly discernible on Computed Tomography(CT), but they are distinctly visible in Magnetic Resonance Imaging(MRI). However, in cases where patients have a metal implant in their body, MRI detection becomes unfeasible, delaying the patient's treatment. MRI generated from CT plays a crucial role in the diagnosis and treatment of acute ischemic stroke. However, MRI obtained from CT using the current medical image cross-modal generation method lacks lesion information and exhibits blurred boundaries. To address these issues, this study proposes a CT-generated MRI algorithm for acute ischemic stroke based on radiomics and diffusion Generative Adversarial Network(GAN). Through imaging radiomics, lesion features are enhanced on CT to highlight lesion information generated by MRI. Additionally, gradient loss is introduced to increase the edge perception constraints between generated and real images, thereby enhancing the quality of the generated MRI. The results of experiments conducted on the ISLES2018 challenge dataset show that the overall Peak Signal-to-Noise Ratio(PSNR)of the generated MRI is 23.051 dB, the Structural Similarity(SSIM) is 0.798, the Pearson Correlation Coefficient(PCC) is 0.969, and the Mutual Information(MI)of the lesion region is 2.075. These results indicate that the proposed algorithm is optimal compared with the existing generation models. In addition, three experienced radiologists identified the lesions on the generated MRI and classified them as positive or negative. No incorrect lesions are identified in the generated MRI, and the classification accuracy reached 86.61%, demonstrating its potential as an auxiliary tool to assist doctors in diagnosis
