Over-and-Under Complete Convolutional RNN for MRI Reconstruction.
Over-and-Under Complete Convolutional RNN for MRI Reconstruction.
复制标题
用于MRI重建的上下完全卷积RNN。
DOI：
10.1007/978-3-030-87231-1_2
复制
发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
Patel VM
中科院分区：
文献类型：
--
作者：
Guo P;Valanarasu JMJ;Wang P;Zhou J;Jiang S;Patel VM
由于欠采样操作会引入各种伪影，因此从欠采样数据重建磁共振 (MR) 图像是一个具有挑战性的问题。最近基于深度学习的 MR 图像重建方法通常利用通用自动编码器架构，该架构捕获初始层的低级特征和更深层的高级特征。这种网络主要关注全局特征，这对于重建完全采样的图像来说可能不是最佳的。在本文中，我们提出了一种上下完全卷积递归神经网络（OUCR），它由过完备和欠完备卷积递归神经网络（CRNN）组成。过完备分支通过限制网络的感受野来特别关注学习局部结构。将其与不完整分支相结合会产生一个更加关注低级特征而不会丢失全局结构的网络。对两个数据集的大量实验表明，所提出的方法比压缩感知和流行的基于深度学习的方法取得了显着改进，且可训练参数数量较少。
Reconstructing magnetic resonance (MR) images from under-sampled data is a challenging problem due to various artifacts introduced by the under-sampling operation. Recent deep learning-based methods for MR image reconstruction usually leverage a generic auto-encoder architecture which captures low-level features at the initial layers and high-level features at the deeper layers. Such networks focus much on global features which may not be optimal to reconstruct the fully-sampled image. In this paper, we propose an Over-and-Under Complete Convolutional Recurrent Neural Network (OUCR), which consists of an overcomplete and an undercomplete Convolutional Recurrent Neural Network (CRNN). The overcomplete branch gives special attention in learning local structures by restraining the receptive field of the network. Combining it with the undercomplete branch leads to a network which focuses more on low-level features without losing out on the global structures. Extensive experiments on two datasets demonstrate that the proposed method achieves significant improvements over the compressed sensing and popular deep learning-based methods with less number of trainable parameters.
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DOI：
10.1158/1078-0432.ccr-18-1233
发表时间：
2019-01-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Jiang S;Eberhart CG;Lim M;Heo HY;Zhang Y;Blair L;Wen Z;Holdhoff M;Lin D;Huang P;Qin H;Quinones-Hinojosa A;Weingart JD;Barker PB;Pomper MG;Laterra J;van Zijl PCM;Blakeley JO;Zhou J
通讯作者：
Zhou J
DOI：
10.1007/978-3-030-59713-9_11
发表时间：
2020-10
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
Guo P;Wang P;Zhou J;Patel VM;Jiang S
通讯作者：
Jiang S
影响因子：
10.6
作者：
Schlemper, Jo;Caballero, Jose;Rueckert, Daniel
通讯作者：
Rueckert, Daniel
影响因子：
3.3
作者：
Eo, Taejoon;Jun, Yohan;Hwang, Dosik
通讯作者：
Hwang, Dosik
影响因子：
2.5
作者：
Majumdar, Angshul
通讯作者：
Majumdar, Angshul