ShanghaiTech University Knowledge Management System
| Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation | |
| 2024-09-25 | |
| 状态 | 已发表 |
| 摘要 | Motion correction (MoCo) in radial MRI is a challenging problem due to the unpredictability of subject's motion. Current state-of-the-art (SOTA) MoCo algorithms often use extensive high-quality MR images to pre-train neural networks, obtaining excellent reconstructions. However, the need for large-scale datasets significantly increases costs and limits model generalization. In this work, we propose Moner, an unsupervised MoCo method that jointly solves artifact-free MR images and accurate motion from undersampled, rigid motion-corrupted k-space data, without requiring training data. Our core idea is to leverage the continuous prior of implicit neural representation (INR) to constrain this ill-posed inverse problem, enabling ideal solutions. Specifically, we incorporate a quasi-static motion model into the INR, granting its ability to correct subject's motion. To stabilize model optimization, we reformulate radial MRI as a back-projection problem using the Fourier-slice theorem. Additionally, we propose a novel coarse-to-fine hash encoding strategy, significantly enhancing MoCo accuracy. Experiments on multiple MRI datasets show our Moner achieves performance comparable to SOTA MoCo techniques on in-domain data, while demonstrating significant improvements on out-of-domain data. |
| 语种 | 英语 |
| DOI | arXiv:2409.16921 |
| 相关网址 | 查看原文 |
| 出处 | Arxiv |
| 收录类别 | PPRN.PPRN |
| WOS记录号 | PPRN:98871711 |
| WOS类目 | Computer Science, Software Engineering ; Engineering, Electrical& Electronic |
| 资助项目 | National Key R&D Program of China[2024YFC2421100] ; National Natural Science Foundation of China["62471296","62071299"] |
| 文献类型 | 预印本 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/433537 |
| 专题 | 信息科学与技术学院_博士生 信息科学与技术学院_PI研究组_虞晶怡组 信息科学与技术学院_PI研究组_张玉瑶组 |
| 通讯作者 | Wei, Hongjiang |
| 作者单位 | 1.ShanghaiTech Univ, Shanghai, Peoples R China 2.Shanghai Jiao Tong Univ, Shanghai, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Qing,Du, Chenhe,Tian, Xuanyu,et al. Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation. 2024. |
| 条目包含的文件 | ||||||
| 条目无相关文件。 | ||||||
修改评论
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。