Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation

• URL: http://arxiv.org/abs/2409.16921v3
• Date: Thu, 06 Feb 2025 05:19:51 GMT
• Title: Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation
• Authors: Qing Wu, Chenhe Du, Xuanyu Tian, Jingyi Yu, Yuyao Zhang, Hongjiang Wei,
• Abstract summary: Motion correction (MoCo) in radial MRI is a challenging problem due to the unpredictability of subject's motion. We propose Moner, an unsupervised MoCo method that jointly solves artifact-free MR images and accurate motion from rigid motion-corrupted k-space data. Experiments on multiple MRI datasets show our Moner achieves performance comparable to SOTA MoCo techniques on in-domain data.
• Score: 31.431342571006123
• License:
• Abstract: 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. The code is available at: https://github.com/iwuqing/Moner

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