论文标题
使用空间变化噪声相关的卷积神经网络的膝盖接头的三维快速回声磁共振图像
Denoising of Three-Dimensional Fast Spin Echo Magnetic Resonance Images of Knee Joints using Spatial-Variant Noise-Relevant Residual Learning of Convolution Neural Network
论文作者
论文摘要
二维(2D)快速自旋回波(FSE)技术在膝关节的临床磁共振成像(MRI)中起着核心作用。此外,三维(3D)FSE提供了膝关节的高分辨率磁共振(MR)图像,但与2D FSE相比,信号噪声比降低。深入学习的deoising方法是一种有前途的MR图像的方法,但是由于在获得MR图像的真实噪声分布方面的挑战,通常会使用合成噪声对其进行训练。在这项研究中,使用2-Nex获取的固有真实噪声信息用于开发基于卷积神经网络(CNN)的残留学习的深度学习模型,并且该模型用于抑制膝关节3D FSE MR图像中的噪声。拟议的CNN对平行运输和残留块使用了两步残差学习,旨在从2-Nex训练数据中全面学习真实的噪声特征。消融研究的结果验证了网络设计。与当前最新方法相比,基于峰值信噪比和结构相似性指数量度,与当前的最新方法相比,获得的新方法改善了3D FSE膝关节MR图像的脱氧性能。通过放射学评估验证了使用新方法降解后改善的图像质量。开发了使用2-Nex采集中固有的空间变化噪声信息的深CNN。该方法显示了对膝盖进行临床MRI评估的希望,并具有对其他解剖结构进行评估的潜在应用。
Two-dimensional (2D) fast spin echo (FSE) techniques play a central role in the clinical magnetic resonance imaging (MRI) of knee joints. Moreover, three-dimensional (3D) FSE provides high-isotropic-resolution magnetic resonance (MR) images of knee joints, but it has a reduced signal-to-noise ratio compared to 2D FSE. Deep-learning denoising methods are a promising approach for denoising MR images, but they are often trained using synthetic noise due to challenges in obtaining true noise distributions for MR images. In this study, inherent true noise information from 2-NEX acquisition was used to develop a deep-learning model based on residual learning of convolutional neural network (CNN), and this model was used to suppress the noise in 3D FSE MR images of knee joints. The proposed CNN used two-step residual learning over parallel transporting and residual blocks and was designed to comprehensively learn real noise features from 2-NEX training data. The results of an ablation study validated the network design. The new method achieved improved denoising performance of 3D FSE knee MR images compared with current state-of-the-art methods, based on the peak signal-to-noise ratio and structural similarity index measure. The improved image quality after denoising using the new method was verified by radiological evaluation. A deep CNN using the inherent spatial-varying noise information in 2-NEX acquisitions was developed. This method showed promise for clinical MRI assessments of the knee, and has potential applications for the assessment of other anatomical structures.