Adjacent slices feature transformer network for single anisotropic 3D brain MRI image super-resolution

2022 ◽  
Vol 72 ◽  
pp. 103339
Author(s):  
Lulu Wang ◽  
Huazheng Zhu ◽  
Zhongshi He ◽  
Yuanyuan Jia ◽  
Jinglong Du
IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57856-57867 ◽  
Author(s):  
Hong Zheng ◽  
Kun Zeng ◽  
Di Guo ◽  
Jiaxi Ying ◽  
Yu Yang ◽  
...  

2017 ◽  
Vol 11 (12) ◽  
pp. 1291-1301 ◽  
Author(s):  
Zifei Liang ◽  
Xiaohai He ◽  
Qizhi Teng ◽  
Dan Wu ◽  
Lingbo Qing

2017 ◽  
Vol 4 (16) ◽  
pp. 153485
Author(s):  
Dongxing Bao ◽  
Xiaoming Li ◽  
Jin Li
Keyword(s):  

2020 ◽  
Vol 32 ◽  
pp. 03044
Author(s):  
Vanita Mane ◽  
Suchit Jadhav ◽  
Praneya Lal

Single image super-resolution using deep learning techniques has shown very high reconstruction performance over the last few years. We propose a novel three-dimensional convolutional neural network called 3D FSRCNN based on FSRCNN, which reinstates the high-resolution quality of structural MRI. The 3D neural network generates output brain images of high-resolution (HR) from a low-resolution (LR) input image. A simple design ensures less time complexity and high reconstruction quality. The network is trained using T1-weighted structural MRI images from the human connectome project dataset which is a large publicly available brain MRI database.


Author(s):  
Mingfeng Jiang ◽  
Minghao Zhi ◽  
Liying Wei ◽  
Xiaocheng Yang ◽  
Jucheng Zhang ◽  
...  

Author(s):  
Hyunduk KIM ◽  
Sang-Heon LEE ◽  
Myoung-Kyu SOHN ◽  
Dong-Ju KIM ◽  
Byungmin KIM

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
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...  

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