Department of Biotechnology and Laboratory Science in Medicine, National Yang-Ming University, No. 155, Sec. 2, Linong Street, Taipei City 112, Taiwan.
Phys Med Biol. 2020 May 11;65(10):105003. doi: 10.1088/1361-6560/ab8105.
The clinical value of multiple b-value diffusion-weighted (DW) magnetic resonance imaging (MRI) has been shown in many studies. However, DW-MRI often suffers from low signal-to-noise ratio, especially at high b-values. To address this limitation, we present an image denoising method based on the concept of deep image prior (DIP). In this method, high-quality prior images obtained from the same patient were used as the network input, and all noisy DW images were used as the network output. Our aim is to denoise all b-value DW images simultaneously. By using early stopping, we expect the DIP-based model to learn the content of images instead of the noise. The performance of the proposed DIP method was evaluated using both simulated and real DW-MRI data. We simulated a digital phantom and generated noise-free DW-MRI data according to the intravoxel incoherent motion model. Different levels of Rician noise were then simulated. The proposed DIP method was compared with the image denoising method using local principal component analysis (LPCA). The simulation results show that the proposed DIP method outperforms the LPCA method in terms of mean-squared error and parameter estimation. The results of real DW-MRI data show that the proposed DIP method can improve the quality of IVIM parametric images. DIP is a feasible method for denoising multiple b-value DW-MRI data.
多 b 值扩散加权（DW）磁共振成像（MRI）的临床价值已在许多研究中得到证实。然而，DW-MRI 通常存在信噪比低的问题，尤其是在高 b 值时。为了解决这个局限性，我们提出了一种基于深度图像先验（DIP）概念的图像去噪方法。在这种方法中，使用来自同一患者的高质量先验图像作为网络输入，所有有噪声的 DW 图像作为网络输出。我们的目标是同时对所有 b 值 DW 图像进行去噪。通过使用提前停止，我们期望基于 DIP 的模型学习图像的内容而不是噪声。使用模拟和真实 DW-MRI 数据评估了所提出的 DIP 方法的性能。我们模拟了一个数字体模，并根据体素内不相干运动模型生成无噪声 DW-MRI 数据。然后模拟了不同水平的瑞利噪声。将所提出的 DIP 方法与使用局部主成分分析（LPCA）的图像去噪方法进行了比较。模拟结果表明，在所提出的 DIP 方法在均方误差和参数估计方面优于 LPCA 方法。真实 DW-MRI 数据的结果表明，所提出的 DIP 方法可以提高 IVIM 参数图像的质量。DIP 是一种可行的多 b 值 DW-MRI 数据去噪方法。
