# MRI Reconstruction via Cascaded Channel-wise Attention Network

arXiv: https://arxiv.org/abs/1810.08229 · HTML: https://mlsanity.com/paper/1810.08229/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 20 of 20)

- [UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2107.00781/) (2021)
- [Task Transformer Network for Joint MRI Reconstruction and Super-Resolution](https://mlsanity.com/paper/2106.06742/) (2021)
- [Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction](https://mlsanity.com/paper/2309.13839/) (2023)
- [High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss](https://mlsanity.com/paper/2107.09989/) (2021)
- [MAC-ReconNet: A Multiple Acquisition Context based Convolutional Neural Network for MR Image Reconstruction using Dynamic Weight Prediction](https://mlsanity.com/paper/2111.05055/) (2021)
- [CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction](https://mlsanity.com/paper/2306.00530/) (2023)
- [Generating Robust Adversarial Examples against Online Social Networks (OSNs)](https://mlsanity.com/paper/2310.12708/) (2023)
- [DP-MDM: Detail-Preserving MR Reconstruction via Multiple Diffusion Models](https://mlsanity.com/paper/2405.05763/) (2024)
- [Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives](https://mlsanity.com/paper/2105.01800/) (2021)
- [Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction](https://mlsanity.com/paper/2111.15498/) (2021)
- [GA-HQS: MRI reconstruction via a generically accelerated unfolding approach](https://mlsanity.com/paper/2304.02883/) (2023)
- [Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI](https://mlsanity.com/paper/2301.02033/) (2023)
- [SFT-KD-Recon: Learning a Student-friendly Teacher for Knowledge Distillation in Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/2304.05057/) (2023)
- [Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks](https://mlsanity.com/paper/2307.06771/) (2023)
- [HyperCoil-Recon: A Hypernetwork-based Adaptive Coil Configuration Task Switching Network for MRI Reconstruction](https://mlsanity.com/paper/2308.04821/) (2023)
- [MCU-Net: A Multi-prior Collaborative Deep Unfolding Network with Gates-controlled Spatial Attention for Accelerated MR Image Reconstruction](https://mlsanity.com/paper/2402.03383/) (2024)
- [Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks](https://mlsanity.com/paper/2103.07672/) (2021)
- [ERNAS: An Evolutionary Neural Architecture Search for Magnetic Resonance Image Reconstructions](https://mlsanity.com/paper/2206.07280/) (2022)
- [Accelerating Cardiac MRI Reconstruction with CMRatt: An Attention-Driven Approach](https://mlsanity.com/paper/2404.06941/) (2024)
- [SHFormer: Dynamic Spectral Filtering Convolutional Neural Network and High-pass Kernel Generation Transformer for Adaptive MRI Reconstruction](https://mlsanity.com/paper/2607.20159/) (2026)

## Abstract (arXiv)

We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate. This can practically benefit patient due to reduced time of MRI scan, but it is also challenging since quality of reconstruction may be compromised. Currently, deep learning based methods dominate MRI reconstruction over traditional approaches such as Compressed Sensing, but they rarely show satisfactory performance in the case of low undersampled k-space data. One explanation is that these methods treat channel-wise features equally, which results in degraded representation ability of the neural network. To solve this problem, we propose a new model called MRI Cascaded Channel-wise Attention Network (MICCAN), highlighted by three components: (i) a variant of U-net with Channel-wise Attention (UCA) module, (ii) a long skip connection and (iii) a combined loss. Our model is able to attend to salient information by filtering irrelevant features and also concentrate on high-frequency information by enforcing low-frequency information bypassed to the final output. We conduct both quantitative evaluation and qualitative analysis of our method on a cardiac dataset. The experiment shows that our method achieves very promising results in terms of three common metrics on the MRI reconstruction with low undersampled k-space data.