自动检测并修正6点VIBE MRI中的水脂混淆问题。
MAGO-SP: Detection and Correction of Water-Fat Swaps in Magnitude-Only VIBE MRI
- 用合成水脂混淆数据训练分割网络，识别组织类型。
- 通过扩散模型生成信号先验，提升水信号重建精度。
- 在肥胖和低体重人群上效果显著，适合大规模临床研究。
体积插值屏气检查（VIBE）MRI可用于水与脂肪信号的分离估计。两点VIBE可生成水脂分离图像，六点VIBE还能估算有效横向弛豫率R2*和质子密度脂肪分数（PDFF），这些是健康与疾病的重要成像指标。但信号重建中的模糊性可能导致水脂混淆，阻碍了VIBE-MRI在大规模临床数据和人群研究中自动化分析的应用。本研究提出一个自动化流程，用于检测并纠正非增强型VIBE图像中的水脂混淆。该三步流程首先利用带有Perlin噪声混合的脂肪与水体积生成的合成混淆数据，训练分割网络以分类体数据为“脂肪样”或“水样”；其次，采用去噪扩散图像到图像网络预测水信号作为先验；最后，将此先验融入物理约束模型，实现准确的水脂信号恢复。本方法在六点VIBE中水脂混淆检测误差率低于1%。值得注意的是，混淆现象在低体重及Ⅲ度肥胖人群中尤为严重。所提校正算法确保化学相位MR成像中解的正确选择，保障了可靠PDFF估计，为自动化大规模人群影像分析奠定了技术基础。
原文摘要 · Abstract (English)
Volume Interpolated Breath-Hold Examination (VIBE) MRI generates images suitable for water and fat signal composition estimation. While the two-point VIBE provides water-fat-separated images, the six-point VIBE allows estimation of the effective transversal relaxation rate R2* and the proton density fat fraction (PDFF), which are imaging markers for health and disease. Ambiguity during signal reconstruction can lead to water-fat swaps. This shortcoming challenges the application of VIBE-MRI for automated PDFF analyses of large-scale clinical data and of population studies. This study develops an automated pipeline to detect and correct water-fat swaps in non-contrast-enhanced VIBE images. Our three-step pipeline begins with training a segmentation network to classify volumes as "fat-like" or "water-like," using synthetic water-fat swaps generated by merging fat and water volumes with Perlin noise. Next, a denoising diffusion image-to-image network predicts water volumes as signal priors for correction. Finally, we integrate this prior into a physics-constrained model to recover accurate water and fat signals. Our approach achieves a < 1% error rate in water-fat swap detection for a 6-point VIBE. Notably, swaps disproportionately affect individuals in the Underweight and Class 3 Obesity BMI categories. Our correction algorithm ensures accurate solution selection in chemical phase MRIs, enabling reliable PDFF estimation. This forms a solid technical foundation for automated large-scale population imaging analysis.
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