Zero-Shot Low-Field MRI Enhancement via Denoising Diffusion Driven Neural Representation

Xiyue Lin, Chenhe Du, Qing Wu, Xuanyu Tian, Jingyi Yu, Yuyao Zhang, Hongjiang Wei

Abstract: Recently, there have been significant advancements in the development of portable low-field (LF) magnetic resonance imaging (MRI) systems. These systems aim to provide low-cost, unshielded, and bedside diagnostic solutions. MRI experiences a diminished signal-to-noise ratio (SNR) at reduced field strengths, which results in severe signal deterioration and poor reconstruction. Therefore, reconstructing a high-field-equivalent image from a low-field MRI is a complex challenge due to the ill-posed nature of the task. In this paper, we introduce diffusion model driven neural representation. We decompose the low-field MRI enhancement problem into a data consistency subproblem and a prior subproblem and solve them in an iterative framework. The diffusion model provides high-quality high-field (HF) MR images prior, while the implicit neural representation ensures data consistency. Experimental results on simulated LF data and clinical LF data indicate that our proposed method is capable of achieving zero-shot LF MRI enhancement, showing some potential for clinical applications.