Zhao Ruiyang, Wang Zepeng, Anderson Aaron, Huesmann Graham, Lam Fan
IEEE Trans Med Imaging. 2025 Mar 28;PP. doi: 10.1109/TMI.2025.3555928.
We present a new method that integrates subspace modeling and a pre-learned spatiotemporal denoiser for reconstruction from highly noisy magnetic resonance spectroscopic imaging (MRSI) data. The subspace model imposes an explicit low-dimensional representation of the high-dimensional spatiospectral functions of interest for noise reduction, while the denoiser serves as a complementary spatiotemporal prior to constrain the subspace reconstruction. A self-supervised learning strategy was proposed to train a denoiser that can distinguish the spatio-temporally correlated signals from uncorrelated noise. An iterative reconstruction formalism was developed based on the Plug-and-Play (PnP)-ADMM framework to synergize the subspace constraint, plug-in denoiser and spatiospectral encoding model. We evaluated the proposed method using numerical simulations and in vivo data, demonstrating improved performance over state-of-the-art subspacebased methods. We also provided theoretical analysis on the utility of combining subspace projection and iterative denoising in terms of both algorithm convergence and performance. Our work demonstrated the potential of integrating self-supervised denoising priors and low-dimensional representations for high-dimensional imaging problems.
我们提出了一种新方法，该方法集成了子空间建模和预学习的时空去噪器，用于从高噪声磁共振波谱成像（MRSI）数据中进行重建。子空间模型对感兴趣的高维时空谱函数施加显式的低维表示以进行降噪，而去噪器作为一种互补的时空先验来约束子空间重建。我们提出了一种自监督学习策略来训练一个能够区分时空相关信号和不相关噪声的去噪器。基于即插即用（PnP）-交替方向乘子法（ADMM）框架开发了一种迭代重建形式，以协同子空间约束、插入式去噪器和时空谱编码模型。我们使用数值模拟和体内数据评估了所提出的方法，证明其性能优于基于子空间的现有方法。我们还从算法收敛性和性能方面对子空间投影和迭代去噪相结合的效用进行了理论分析。我们的工作展示了将自监督去噪先验和低维表示集成到高维成像问题中的潜力。
