Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation
<|reference_start|>Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation: 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.<|reference_end|>
@article{wu2024moner:,
    title={Moner: Motion Correction in Undersampled Radial MRI with Unsupervised
  Neural Representation},
    author={Qing Wu, Chenhe Du, XuanYu Tian, Jingyi Yu, Yuyao Zhang, Hongjiang Wei},
    journal={arXiv preprint arXiv:2409.16921},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16921},
    primaryClass={eess.IV cs.CV}
}