A Hybrid Residual Attention Convolutional Neural Network for Compressed Sensing Magnetic Resonance Image Reconstruction
We propose a dual-domain deep learning technique for accelerating compressed sensing magnetic resonance image reconstruction. An advanced convolutional neural network with residual connectivity and an attention mechanism was developed for frequency and image domains. First, the sensor domain subnetw...
Main Authors: | Md. Biddut Hossain, Ki-Chul Kwon, Rupali Kiran Shinde, Shariar Md Imtiaz, Nam Kim |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-03-01
|
Series: | Diagnostics |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-4418/13/7/1306 |
Similar Items
-
De-Aliasing and Accelerated Sparse Magnetic Resonance Image Reconstruction Using Fully Dense CNN with Attention Gates
by: Md. Biddut Hossain, et al.
Published: (2022-12-01) -
Residual Augmented Attentional U-Shaped Network for Spectral Reconstruction from RGB Images
by: Jiaojiao Li, et al.
Published: (2020-12-01) -
Compressed Sensing Image Reconstruction Based on Convolutional Neural Network
by: Yuhong Liu, et al.
Published: (2019-01-01) -
A Systematic Review and Identification of the Challenges of Deep Learning Techniques for Undersampled Magnetic Resonance Image Reconstruction
by: Md. Biddut Hossain, et al.
Published: (2024-01-01) -
Underwater Acoustic Target Recognition Based on Data Augmentation and Residual CNN
by: Qihai Yao, et al.
Published: (2023-03-01)