bibtext: @InProceedings{ Lin_Zeroshot_MICCAI2024,
                   author = { Lin, Xiyue and Du, Chenhe and Wu, Qing and Tian, Xuanyu and Yu, Jingyi and Zhang, Yuyao and Wei, Hongjiang },
                   title = { { Zero-shot Low-field MRI Enhancement via Denoising Diffusion Driven Neural Representation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15007 },
                   month = {October},
                   pages = { pending },
                }
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 capable of achieving zero-shot LF MRI enhancement, showing some potential for clinical applications.
title: Zero-shot Low-field MRI Enhancement via Denoising Diffusion Driven Neural Representation
authors: Lin, Xiyue Du, Chenhe Wu, Qing Tian, Xuanyu Yu, Jingyi Zhang, Yuyao Wei, Hongjiang