Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, high-quality patient-based datasets for network training. In this paper, we propose a novel deep learning-based method for undersampled MR image reconstruction that does not require pre-training procedure and pre-training datasets. The proposed reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP) is based on the DIP framework and thereby reduces the dependence on datasets. Moreover, RWS-DIP explores and introduces structure and sparsity priors into network learning to improve the efficiency of learning. By employing a high-resolution reference image as the network input, RWS-DIP incorporates structural information into network. RWS-DIP also uses the wavelet sparsity to further enrich the implicit regularization of traditional DIP by formulating the training of network parameters as a constrained optimization problem, which is solved using the alternating direction method of multipliers (ADMM) algorithm. Experiments on in vivo MR scans have demonstrated that the RWS-DIP method can reconstruct MR images more accurately and preserve features and textures from undersampled -space measurements.

中文翻译：

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使用小波稀疏约束深度图像先验的参考驱动欠采样 MR 图像重建


深度学习已显示出显着提高欠采样磁共振 (MR) 图像重建性能的潜力。然而，将深度学习应用于临床场景的一大挑战是需要大量、高质量的基于患者的数据集进行网络训练。在本文中，我们提出了一种基于深度学习的新颖的欠采样 MR 图像重建方法，该方法不需要预训练过程和预训练数据集。所提出的使用小波稀疏约束深度图像先验（RWS-DIP）的参考驱动方法基于DIP框架，从而减少了对数据集的依赖。此外，RWS-DIP探索并将结构和稀疏先验引入网络学习中，以提高学习效率。通过采用高分辨率参考图像作为网络输入，RWS-DIP 将结构信息合并到网络中。 RWS-DIP还利用小波稀疏性进一步丰富了传统DIP的隐式正则化，将网络参数的训练表示为约束优化问题，并使用乘子交替方向法（ADMM）算法来解决。体内MR 扫描实验表明，RWS-DIP 方法可以更准确地重建 MR 图像，并保留欠采样的特征和纹理-空间测量。