Cardiac MRI Image Enhancement Based on GAN Network

Magnetic Resonance Imaging (MRI) is a common medical imaging technique extensively employed for diagnosing and treating diseases. However, doctors nowadays face significant challenges and increased pressures in their diagnostic endeavors. The assurance of MRI image quality encounters impediments arising from noise, blurring, and artifacts. Consequently, the demand for high professionals and experience among physicians engaged in MRI imaging diagnosis becomes imperative. To address these challenges, this study centers on the application of deep learning techniques to enhance the quality of MRI images. The resultant improvement in image quality not only enhances the reliability of MRI images but also facilitates more facile and valuable diagnoses for medical practitioners. Our investigation primarily delves into an enhancement method for MRI images grounded in generative adversarial networks (GANs). Acknowledging the frequency domain imaging characteristics of MRI, we introduce a frequency domain enhancement network to mitigate mixed interference during conversion. Additionally, we propose a generator structure that combines frequency and spatial domains. The primary focus is on tasks encompassing Gaussian denoising, deblurring detail enhancement, and artifact removal. The efficacy of the proposed model algorithm is substantiated through experimental results, demonstrating its capacity to significantly enhance the imaging quality of MRI images and providing robust support for the automatic analysis and diagnosis of medical images.