# AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI

arXiv: https://arxiv.org/abs/2302.08861 · HTML: https://mlsanity.com/paper/2302.08861/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 16 of 16)

- [fastMRI: An Open Dataset and Benchmarks for Accelerated MRI](https://mlsanity.com/paper/1811.08839/) (2018)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [Learning a Variational Network for Reconstruction of Accelerated MRI Data](https://mlsanity.com/paper/1704.00447/) (2017)
- [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021)
- [Image reconstruction by domain transform manifold learning](https://mlsanity.com/paper/1704.08841/) (2017)
- [Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss](https://mlsanity.com/paper/1709.00753/) (2017)
- [A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction](https://mlsanity.com/paper/1703.00555/) (2017)
- [DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior](https://mlsanity.com/paper/2001.03799/) (2020)
- [One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI](https://mlsanity.com/paper/2112.04721/) (2021)
- [Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data](https://mlsanity.com/paper/2012.08931/) (2020)
- [Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization](https://mlsanity.com/paper/2107.11650/) (2021)
- [Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction](https://mlsanity.com/paper/1503.02945/) (2015)
- [pISTA-SENSE-ResNet for Parallel MRI Reconstruction](https://mlsanity.com/paper/1910.00650/) (2019)
- [Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks](https://mlsanity.com/paper/1903.11431/) (2019)
- [Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI](https://mlsanity.com/paper/2203.05725/) (2022)
- [Transformer Compressed Sensing via Global Image Tokens](https://mlsanity.com/paper/2203.12861/) (2022)

## Built on by (most cited first, 1 of 1)

- [Single Image Compressed Sensing MRI via a Self-Supervised Deep Denoising Approach](https://mlsanity.com/paper/2311.13144/) (2023)

## Abstract (arXiv)

Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sensing (CS), which relies on the random sampling of k-space to produce incoherent (noise-like) artefacts. Due to hardware constraints, 1D Cartesian phase-encode under-sampling schemes are popular for 2D CS-MRI. However, 1D under-sampling limits 2D incoherence between measurements, yielding structured aliasing artefacts (ghosts) that may be difficult to remove assuming a 2D sparsity model. Reconstruction algorithms typically deploy direction-insensitive 2D regularisation for these direction-associated artefacts. Recognising that phase-encode artefacts can be separated into contiguous 1D signals, we develop two decoupling techniques that enable explicit 1D regularisation and leverage the excellent 1D incoherence characteristics. We also derive a combined 1D + 2D reconstruction technique that takes advantage of spatial relationships within the image. Experiments conducted on retrospectively under-sampled brain and knee data demonstrate that combination of the proposed 1D AliasNet modules with existing 2D deep learned (DL) recovery techniques leads to an improvement in image quality. We also find AliasNet enables a superior scaling of performance compared to increasing the size of the original 2D network layers. AliasNet therefore improves the regularisation of aliasing artefacts arising from phase-encode under-sampling, by tailoring the network architecture to account for their expected appearance. The proposed 1D + 2D approach is compatible with any existing 2D DL recovery technique deployed for this application.