IEEE J Biomed Health Inform. 2021 Sep;25(9):3321-3331. doi: 10.1109/JBHI.2021.3087407. Epub 2021 Sep 3.
The visual quality of magnetic resonance images (MRIs) is crucial for clinical diagnosis and scientific research. The main source of quality degradation is the noise generated during MRI acquisition. Although denoising MRI by deep learning methods shows great superiority compared with traditional methods, the deep learning methods reported to date in the literature cannot simultaneously leverage long-range and hierarchical information, and cannot adequately utilize the similarity in 3D MRI. In this paper, we address the two issues by proposing a deep adaptive blending network (DABN) characterized by a large receptive field residual dense block and an adaptive blending method. We first propose the large receptive field residual dense block that can capture long-range information and fuse hierarchical features simultaneously. Then we propose the adaptive blending method that produces denoised pixels by adaptively filtering 3D MRI, which explicitly utilizes the similarity in 3D MRI. Residual is also considered as a compensating item after adaptive filtering. The blending adaptive filter and residual are predicted by a network consisting of several large receptive field residual dense blocks. Experimental results show that the proposed DABN outperforms state-of-the-art denoising methods in both clinical and simulated MRI data.
磁共振图像（MRI）的视觉质量对于临床诊断和科学研究至关重要。质量下降的主要原因是 MRI 采集过程中产生的噪声。尽管基于深度学习的方法在降噪方面与传统方法相比具有很大的优势，但目前文献中报道的深度学习方法不能同时利用远距离和层次信息，也不能充分利用 3D MRI 的相似性。在本文中，我们通过提出一个具有大感受野残差密集块和自适应混合方法的深度自适应混合网络（DABN）来解决这两个问题。我们首先提出了大感受野残差密集块，它可以同时捕获远距离信息和融合层次特征。然后我们提出了自适应混合方法，通过自适应滤波 3D MRI 来生成去噪像素，从而明确利用 3D MRI 的相似性。自适应滤波后的残差也被视为补偿项。自适应混合滤波器和残差由由几个大感受野残差密集块组成的网络进行预测。实验结果表明，所提出的 DABN 在临床和模拟 MRI 数据上均优于最先进的去噪方法。
