用双域优化提升MRI图像清晰度，有效消除运动伪影。
DDO-IN: Dual Domains Optimization for Implicit Neural Network to Eliminate Motion Artifact in Magnetic Resonance Imaging
- 结合像素与频率域信息，通过隐式神经表示重建图像
- 在fastMRI数据集上显著优于现有方法，保留细节更清晰
- 适合医学影像修复、临床诊断辅助等场景
磁共振成像（MRI）中的运动伪影严重影响临床诊断的准确性。现有方法难以同时保持精细结构细节与图像锐利度。本文提出一种新型双域优化（DDO）方法，融合像素域与频率域信息，利用隐式神经表示（INRs）恢复无伪影的MRI图像。具体而言，利用k空间低频成分作为参考以捕捉准确组织纹理，高频与像素信息则用于细节恢复。此外，设计互补掩码与动态损失权重机制，实现从全局到局部注意力的平滑过渡，有效抑制伪影并保留有用细节。在NYU fastMRI数据集上的实验表明，该方法在多个评估指标上优于现有技术。代码已公开于https://anonymous.4open.science/r/DDO-IN-A73B。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) motion artifacts can seriously affect clinical diagnostics, making it challenging to interpret images accurately. Existing methods for eliminating motion artifacts struggle to retain fine structural details and simultaneously lack the necessary vividness and sharpness. In this study, we present a novel dual-domain optimization (DDO) approach that integrates information from the pixel and frequency domains guiding the recovery of clean magnetic resonance images through implicit neural representations(INRs). Specifically, our approach leverages the low-frequency components in the k-space as a reference to capture accurate tissue textures, while high-frequency and pixel information contribute to recover details. Furthermore, we design complementary masks and dynamic loss weighting transitioning from global to local attention that effectively suppress artifacts while retaining useful details for reconstruction. Experimental results on the NYU fastMRI dataset demonstrate that our method outperforms existing approaches in multiple evaluation metrics. Our code is available at https://anonymous.4open.science/r/DDO-IN-A73B.
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