FIDNet: Frequency-Informed Diffusion Network for Low-Field MRI Enhancement

Low-field magnetic resonance imaging (MRI) systems are highly practical in resource-constrained scenarios due to their low cost and portability, which is regarded as an important alternative to conventional high-field MRI. However, they suffer from significant challenges caused by low signal intensity, resulting in the low signal-to-noise ratio (SNR) and bringing severe noise interference within the image. Due to the presence of complex composite noise in low-field MRI, conventional diffusion models, designed to restore image quality via generative processes based on Gaussian denoising, cannot effectively address the complex noise in this setting. To address this problem, in this article, we propose a frequency-informed diffusion network (FIDNet) for low-field MRI enhancement. The key innovation of FIDNet lies in its dual-domain design, in which frequency-domain information from the original k-space measurement signals are incorporated to guide the diffusion-based reconstruction in the image domain. To achieve this goal, we introduce a frequency-domain binary transformation (FDBT) module, which converts high-magnitude components of the K-space measurement data into a binary representation and maps them to a unified distribution that is compatible with image-domain features. By doing so, the incorporation of frequency-domain priors could facilitate the denoising process, which is particularly beneficial for addressing the complex and composite noise present in the image domain. In addition, to further remove the complex noise and preserve significant signals, we propose an adaptive Fourier filtering (AFF) block, which utilizes adaptive filters to dynamically adjust filtering parameters based on the characteristics of the MRI image. Generally speaking, the extensive experiments on various datasets demonstrate the proposed FIDNet achieves remarkable performance with great generalization capacity. Our code is available at https://github.com/zhoutao960906/FIDNet