A novel hybrid generative adversarial network for CT and MRI super-resolution reconstruction

作者：Yueyue Xiao, Chunxiao Chen, Liang Wang, Jie Yu, Xue Fu, Yuan Zou, Zhe Lin, Kunpeng Wang · 发表于：Physics in Medicine and Biology · 年份：2023 · DOI：10.1088/1361-6560/acdc7e · 被引用次数：14 · 研究领域：Advanced Image Processing Techniques、Image and Signal Denoising Methods、Photoacoustic and Ultrasonic Imaging

Abstract Objective . Computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in medical imaging modalities, and provide valuable information for clinical diagnosis and treatment. However, due to hardware limitations and radiation safety concerns, the acquired images are often limited in resolution. Super-resolution reconstruction (SR) techniques have been developed to enhance the resolution of CT and MRI slices, which can potentially improve diagnostic accuracy. To capture more useful feature information and reconstruct higher quality super-resolution images, we proposed a novel hybrid framework SR model based on generative adversarial networks. Approach . The proposed SR model combines frequency domain and perceptual loss functions, which can work in both frequency domain and image domain (spatial domain). The proposed SR model consists of 4 parts: (i) the discrete Fourier transform (DFT) operation transforms the image from the image domain to frequency domain; (ii) a complex residual U-net performs SR in the frequency domain; (iii) the inverse discrete Fourier transform (iDFT) operation based on data fusion transforms the image from the frequency domain to image domain; (iv) an enhanced residual U-net network is used for SR of image domain. Main results . Experimental results on bladder MRI slices, abdomen CT slices, and brain MRI slices show that the proposed SR model outperforms state-of-the-art SR methods in terms of visual quality and objective qua...