Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB)
Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB)
复制标题
DOI：
10.1016/j.mri.2020.05.002
复制
发表时间：
2020-09-01
影响因子：
2.5
通讯作者：
Deng, Jie
中科院分区：
文献类型：
--
作者：
Liu, Junchi;Kocak, Mehmet;Deng, Jie
目的：磁共振成像（MRI）采集固有地对运动敏感，并且运动伪影减少对于改善MRI中的图像质量至关重要。研究方法：我们开发了一种具有密集连接多分辨率块的深度残差网络（DRN-DCMB）模型，以减少在注射造影剂前后在不同成像平面上采集的T1加权（T1 W）自旋回波图像中的运动伪影。DRN-DCMB网络由多个多分辨率块组成，这些块以前馈方式与密集连接相连。使用单个残差单元将整个网络的输入和输出与一个快捷连接连接起来，以预测残差图像（即伪影图像）。使用5个无运动T1 W图像堆栈（对比前轴向和矢状，以及对比后轴向、冠状和矢状图像）和模拟运动伪影训练模型。结果如下：在其他86个具有模拟伪影的测试图像堆栈中，我们的DRN-DCMB模型优于其他最先进的深度学习模型，具有显着更高的结构相似性指数（SSIM）和信噪比（ISNR）的改善。DRN-DCMB模型还应用于121个测试图像堆栈，这些图像堆栈具有不同程度的真实的运动伪影。随机混合通过DRN-DCMB模型采集的图像和处理的图像，并由神经放射科医生盲法评价图像质量。DRN-DCMB模型显著提高了整体图像质量，降低了运动伪影的严重程度，提高了图像清晰度，同时保持了图像对比度。结论：我们的DRN-DCMB模型提供了一种有效的方法，减少运动伪影，提高整体的临床脑MRI图像质量。
Objective: Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI. Methods: We developed a deep residual network with densely connected multi-resolution blocks (DRN-DCMB) model to reduce the motion artifacts in T1 weighted (T1W) spin echo images acquired on different imaging planes before and after contrast injection. The DRN-DCMB network consisted of multiple multi-resolution blocks connected with dense connections in a feedforward manner. A single residual unit was used to connect the input and output of the entire network with one shortcut connection to predict a residual image (i.e. artifact image). The model was trained with five motion-free T1W image stacks (pre-contrast axial and sagittal, and post-contrast axial, coronal, and sagittal images) with simulated motion artifacts. Results: In other 86 testing image stacks with simulated artifacts, our DRN-DCMB model outperformed other state-of-the-art deep learning models with significantly higher structural similarity index (SSIM) and improvement in signal-to-noise ratio (ISNR). The DRN-DCMB model was also applied to 121 testing image stacks appeared with various degrees of real motion artifacts. The acquired images and processed images by the DRN-DCMB model were randomly mixed, and image quality was blindly evaluated by a neuroradiologist. The DRN-DCMB model significantly improved the overall image quality, reduced the severity of the motion artifacts, and improved the image sharpness, while kept the image contrast. Conclusion: Our DRN-DCMB model provided an effective method for reducing motion artifacts and improving the overall clinical image quality of brain MRI.