Du Congcong, Qiao Zhiwei
School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China.
School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China.
Magn Reson Imaging. 2023 Apr;97:24-30. doi: 10.1016/j.mri.2022.12.008. Epub 2022 Dec 7.
Electron paramagnetic resonance imaging (EPRI) is an advanced tumor oxygen concentration imaging method. Now, the bottleneck problem of EPRI is that the scanning time is too long. Sparse reconstruction is an effective and fast imaging method, which means reconstructing images from sparse-view projections. However, the EPRI images sparsely reconstructed by the classic filtered back projection (FBP) algorithm often contain severe streak artifacts, which affect subsequent image processing. In this work, we propose a feature pyramid attention-based, residual, dense, deep convolutional network (FRD-Net) to suppress the streak artifacts in the FBP-reconstructed images. This network combines residual connection, attention mechanism, dense connections and introduces perceptual loss. The EPRI image with streak artifacts is used as the input of the network and the output-label is the corresponding high-quality image densely reconstructed by the FBP algorithm. After training, the FRD-Net gets the capability of suppressing streak artifacts. The real data reconstruction experiments show that the FRD-Net can better improve the sparse reconstruction accuracy, compared with three existing representative deep networks.
电子顺磁共振成像（EPRI）是一种先进的肿瘤氧浓度成像方法。目前，EPRI的瓶颈问题在于扫描时间过长。稀疏重建是一种有效且快速的成像方法，即从稀疏视图投影重建图像。然而，通过经典滤波反投影（FBP）算法稀疏重建的EPRI图像通常包含严重的条纹伪影，这会影响后续的图像处理。在这项工作中，我们提出了一种基于特征金字塔注意力的残差密集深度卷积网络（FRD-Net）来抑制FBP重建图像中的条纹伪影。该网络结合了残差连接、注意力机制、密集连接并引入了感知损失。带有条纹伪影的EPRI图像用作网络的输入，输出标签是通过FBP算法密集重建的相应高质量图像。经过训练后，FRD-Net获得了抑制条纹伪影的能力。实际数据重建实验表明，与现有的三个代表性深度网络相比，FRD-Net能够更好地提高稀疏重建精度。
