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.

中文翻译：

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使用深图像先验对多b值扩散加权MR图像进行去噪。

许多研究显示了多b值扩散加权（DW）磁共振成像（MRI）的临床价值。但是，DW-MRI通常会遭受低信噪比的困扰，尤其是在高b值时。为了解决这个限制，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，将从同一患者获得的高质量先前图像用作网络输入，并将所有嘈杂的DW图像用作网络输出。我们的目标是同时对所有b值DW图像进行去噪。通过使用早期停止，我们希望基于DIP的模型能够学习图像的内容而不是噪声。使用模拟和实际DW-MRI数据评估了拟议DIP方法的性能。我们根据体素不相干运动模型模拟了数字体模并生成了无噪声的DW-MRI数据。然后，模拟了不同水平的里斯噪声。使用局部主成分分析（LPCA）将提出的DIP方法与图像去噪方法进行了比较。仿真结果表明，该方法在均方误差和参数估计方面均优于LPCA方法。实际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是对多个b值DW-MRI数据进行去噪的可行方法。仿真结果表明，该方法在均方误差和参数估计方面均优于LPCA方法。实际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是对多个b值DW-MRI数据进行去噪的可行方法。仿真结果表明，该方法在均方误差和参数估计方面均优于LPCA方法。实际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是对多个b值DW-MRI数据进行去噪的可行方法。