# Deep De-Aliasing for Fast Compressive Sensing MRI

arXiv: https://arxiv.org/abs/1705.07137 · HTML: https://mlsanity.com/paper/1705.07137/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2017.

## Built on by (most cited first, 9 of 9)

- [TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method](https://mlsanity.com/paper/2402.11274/) (2024)
- [A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects](https://mlsanity.com/paper/2206.07219/) (2022)
- [Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives](https://mlsanity.com/paper/2105.01800/) (2021)
- [K-Space Transformer for Undersampled MRI Reconstruction](https://mlsanity.com/paper/2206.06947/) (2022)
- [Deep MRI Reconstruction with Radial Subsampling](https://mlsanity.com/paper/2108.07619/) (2021)
- [Deep multi-modal aggregation network for MR image reconstruction with auxiliary modality](https://mlsanity.com/paper/2110.08080/) (2021)
- [Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation](https://mlsanity.com/paper/2104.01889/) (2021)
- [Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems Perspective](https://mlsanity.com/paper/2105.13191/) (2021)
- [Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data](https://mlsanity.com/paper/2510.06335/) (2025)

## Abstract (arXiv)

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience. This can also potentially increase the image quality by reducing the motion artefacts and contrast washout. However, once an image field of view and the desired resolution are chosen, the minimum scanning time is normally determined by the requirement of acquiring sufficient raw data to meet the Nyquist-Shannon sampling criteria. Compressive Sensing (CS) theory has been perfectly matched to the MRI scanning sequence design with much less required raw data for the image reconstruction. Inspired by recent advances in deep learning for solving various inverse problems, we propose a conditional Generative Adversarial Networks-based deep learning framework for de-aliasing and reconstructing MRI images from highly undersampled data with great promise to accelerate the data acquisition process. By coupling an innovative content loss with the adversarial loss our de-aliasing results are more realistic. Furthermore, we propose a refinement learning procedure for training the generator network, which can stabilise the training with fast convergence and less parameter tuning. We demonstrate that the proposed framework outperforms state-of-the-art CS-MRI methods, in terms of reconstruction error and perceptual image quality. In addition, our method can reconstruct each image in 0.22ms--0.37ms, which is promising for real-time applications.