MRI Super-Resolution using Implicit Neural Representation with Frequency Domain Enhancement

Abstract

High resolution (HR) Magnetic Resonance Imaging (MRI) is a popular diagnostic tool, which provides detail structural information and rich textures, benefiting accurate diagnosis and disease detection. However, obtaining HR MRI remains a challenge due to longer scan time and lower peak signal-to-noise ratio (PSNR). Recently, Single Image Super-Resolution (SISR) has generated interest, which shows promising ability for recovering an HR image only relies on a Low Resolution (LR) image. MR images have some characteristics different with natural images: derived from frequency domain, simpler textures and structural information. However, Most of previous methods treat MR images as same as natural images, they only apply SR methods on natural images to MR images and fail to preserve low-frequency information and capture high-frequency details. In this paper, we mimic the process of an MRI machine produces an MRI in practice and propose an Implicit Neural Representation based module, which enable reconstruct high frequency contents effectively while preserving low frequency contents unchanged. Moreover, vanilla L1 loss cannot reflect the differences for each frequency, to address this problem, we design a frequency loss to disentangle each frequency and calculate the differences respectively. Finally, to further capture high frequency contents, we propose High-Frequency Pixel Loss, which can decouples the HF contents from pixel domain and emphasize the HF differences between SR and HR images. Extensive experiments show the effectiveness of our proposed method in terms of visual quality and PSNR score, which produces sharper edges and clearer details compared to previous works.