Undersampled MR image reconstruction using an enhanced recursive residual network
Undersampled MR image reconstruction using an enhanced recursive residual network
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使用增强型递归残差网络进行欠采样 MR 图像重建
DOI：
10.1016/j.jmr.2019.07.020
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发表时间：
2019-08-01
影响因子：
2.2
通讯作者：
Chen, Zhong
中科院分区：
文献类型：
--
作者：
Bao, Lijun;Ye, Fuze;Chen, Zhong
当使用侵略性欠采样时，很难恢复具有可靠精细特征的高质量图像。本文提出了一种增强递归残差网络（ERRN），该网络通过高频特征引导、纠错单元和密集连接对基本递归残差网络进行改进。feature guidance的目的是基于从标签数据中先验学习到的图像来预测底层解剖结构，对残差学习起到补充作用。ERRN适用于两种重要的应用：压缩感知（CS） MRI和超分辨率（SR） MRI，同时在框架中添加了特定应用的误差校正单元，即CS-MRI的数据一致性和SR-MRI的反向投影，因为它们的采样方案不同。我们提出的网络使用实值大脑数据集、复杂值膝盖数据集、病理大脑数据和体内大鼠大脑数据进行评估，这些数据具有不同的欠采样掩码和速率。实验结果表明，与最先进的卷积神经网络和传统的基于优化的方法相比，ERRN在所有情况下都具有更好的重建效果，具有明显的恢复结构特征和最高的图像质量指标，特别是在欠采样率超过5倍的情况下。因此，良好的框架设计可以使网络具有结构灵活、参数少、对各种欠采样方案性能优异、泛化时过拟合程度低的特点，有利于在MRI扫描仪上进行实时重建。(C) 2019 Elsevier Inc.版权所有。
When using aggressive undersampling, it is difficult to recover the high quality image with reliably fine features. In this paper, we propose an enhanced recursive residual network (ERRN) that improves the basic recursive residual network with a high-frequency feature guidance, an error-correction unit and dense connections. The feature guidance is designed to predict the underlying anatomy based on image a priori learned from the label data, playing a complementary role to the residual learning. The ERRN is adapted for two important applications: compressed sensing (CS) MRI and super resolution (SR) MRI, while an application-specific error-correction unit is added into the framework, i.e. data consistency for CS-MRI and back projection for SR-MRI due to their different sampling schemes. Our proposed network was evaluated using a real-valued brain dataset, a complex-valued knee dataset, pathological brain data and in vivo rat brain data with different undersampling masks and rates. Experimental results demonstrated that ERRN presented superior reconstructions at all cases with distinctly restored structural features and highest image quality metrics compared to both the state-of-the-art convolutional neural networks and the conventional optimization-based methods, particularly for the undersampling rate over 5-fold. Thus, an excellent framework design can endow the network with a flexible architecture, fewer parameters, outstanding performances for various undersampling schemes, and reduced overfitting in generalization, which will facilitate real-time reconstruction on MRI scanners. (C) 2019 Elsevier Inc. All rights reserved.