IDDNet:一个基于双域深度交互方法的多模态快速磁共振重建卷积神经网络

IDDNet:a deep interactive dual-domain convolutional neural network with auxiliary modality for fast MRI reconstruction

从欠采样k空间矩阵中精确重建完整图像是加速磁共振成像(MRI)的一种可行方法。近年来,许多基于深度学习的方法被用于加速MRI重建。在这些方法中,跨域方法已被证明是有效的。然而,现有的跨域重建算法将图像域和k空间网络顺序级联,忽略了不同域之间的相互作用,导致重建精度不足。为有效利用来自多个MR域和模态的相关信息,本文提出了一个具有辅助模态的深度交互双域网络(IDDNet)以加速MRI重建。IDDNet首先从图像域的低分辨率目标模态中提取浅层特征来获得视觉表征。在接下来的特征处理中,本文设计了一种双分支并行交互网络架构,同时从双域的相关信息中提取深度特征,从而避免了顺序连接时不同域间的冗余优先级。此外,该模型利用辅助模态的附加信息来细化结构,提高重建精度。在MICCAI BraTS 2019脑部和fastMRI膝关节数据集上,采取不同采样模板和加速速率的大量实验表明,IDDNet实现了优异的MRI重建性能。

Reconstructing a complete image accurately from an undersampled k-space matrix is a viable approach for magnetic resonance imaging(MRI)acceleration.In recent years,numerous deep learning(DL)-based methods have been employed to improve MRI reconstruction.Among these methods,the cross-domain method has been proven to be effective.However,existing cross-domain reconstruction algorithms sequentially link the image domain and k-space networks,disregarding the interplay between different domains,consequently leading to a deficiency in reconstruction accuracy.In this work,we propose a deep interactive dual-domain network(IDDNet)with an auxiliary modality for accelerating MRI reconstruction to effectively extract pertinent information from multiple MR domains and modalities.The IDDNet first extracts shallow features from low-resolution target modalities in the image domain to obtain visual representation information.In the following feature processing,a parallel interactive architecture with dual branches is designed to extract deep features from relevant information of dual domains simultaneously to avoid redundant priority priors in sequential links.Furthermore,the model uses additional information from the auxiliary modality to refine the structure and improve the reconstruction accuracy.Numerous experiments at different sampling masks and acceleration rates on the MICCAI BraTS 2019 brain and fastMRI knee datasets show that IDDNet achieves excellent accelerated MRI reconstruction performance.