用深度学习精准去除脑部磁共振波谱的水脂干扰信号
WALINET: A water and lipid identification convolutional Neural Network for nuisance signal removal in 1H MR Spectroscopic Imaging
- 基于改进Y-NET结构的深度神经网络，自动识别并移除水脂信号
- 处理速度提升至8秒，脂质去除误差降低41%，代谢物信号保留更好
- 适合需要快速高精度代谢成像的临床研究与自动化流程
质子磁共振波谱成像（1H-MRSI）可无创实现代谢的谱-空间映射。但全脑1H-MRSI长期面临代谢物峰与头皮脂质信号重叠、水信号过强导致谱图畸变的问题。亟需高效方法在高分辨率下准确去除脂质和水信号，同时保留代谢物信号。尽管深度学习在其他MRSI处理中表现优异，其在该任务中的潜力尚未探索。本文提出WALINET（WAter and LIpid neural NETwork），一种基于改进Y-NET的深度学习方法，用于全脑1H-MRSI中的水脂抑制。在模拟数据与在体数据上，与当前最优的L2正则化脂质去除及Hankel-Lanczos奇异值分解（HLSVD）水抑制方法相比，WALINET处理时间仅需8秒，相较传统HLSVD+L2的42分钟大幅提升。定量分析显示：1）脂质去除效果更优，模拟数据中归一化均方误差（NRMSE）降低41%；2）代谢物信号保留更佳，模拟数据中NRMSE降低71%，在体数据中信噪比（SNR）提升155%，条件均方根误差（CRLB）降低50%。健康人与患者代谢图谱显示，WALINET生成图像具有更清晰的灰白质对比度和更明显的结构细节。结论表明，相较于现有先进方法，WALINET在全脑1H-MRSI的噪声信号抑制与代谢物定量方面表现更优，为深度学习在MRSI处理中的新应用，具备自动化高通量流程的潜力。
原文摘要 · Abstract (English)
Purpose. Proton Magnetic Resonance Spectroscopic Imaging (1H-MRSI) provides non-invasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain 1H-MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution 1H-MRSI to accurately remove lipid and water signals while preserving the metabolite signal. The potential of supervised neural networks for this task remains unexplored, despite their success for other MRSI processing. Methods. We introduce a deep-learning method based on a modified Y-NET network for water and lipid removal in whole-brain 1H-MRSI. The WALINET (WAter and LIpid neural NETwork) was compared to conventional methods such as the state-of-the-art lipid L2 regularization and Hankel-Lanczos singular value decomposition (HLSVD) water suppression. Methods were evaluated on simulated and in-vivo whole-brain MRSI using NMRSE, SNR, CRLB, and FWHM metrics. Results. WALINET is significantly faster and needs 8s for high-resolution whole-brain MRSI, compared to 42 minutes for conventional HLSVD+L2. Quantitative analysis shows WALINET has better performance than HLSVD+L2: 1) more lipid removal with 41% lower NRMSE, 2) better metabolite signal preservation with 71% lower NRMSE in simulated data, 155% higher SNR and 50% lower CRLB in in-vivo data. Metabolic maps obtained by WALINET in healthy subjects and patients show better gray/white-matter contrast with more visible structural details. Conclusions. WALINET has superior performance for nuisance signal removal and metabolite quantification on whole-brain 1H-MRSI compared to conventional state-of-the-art techniques. This represents a new application of deep-learning for MRSI processing, with potential for automated high-throughput workflow.
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