# Dual-Domain Multi-Contrast MRI Reconstruction with Synthesis-based Fusion Network

arXiv: https://arxiv.org/abs/2312.00661 · HTML: https://mlsanity.com/paper/2312.00661/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021)
- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [End-to-End Variational Networks for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2004.06688/) (2020)
- [DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior](https://mlsanity.com/paper/2001.03799/) (2020)
- [DeepFLASH: An Efficient Network for Learning-based Medical Image Registration](https://mlsanity.com/paper/2004.02097/) (2020)
- [DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction](https://mlsanity.com/paper/2201.10776/) (2022)
- [Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction](https://mlsanity.com/paper/1806.11216/) (2018)
- [Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction](https://mlsanity.com/paper/2302.09244/) (2023)
- [Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/1911.01458/) (2019)
- [Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction](https://mlsanity.com/paper/1503.02945/) (2015)
- [DONet: Dual-Octave Network for Fast MR Image Reconstruction](https://mlsanity.com/paper/2105.05980/) (2021)
- [Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI](https://mlsanity.com/paper/2203.05725/) (2022)
- [JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI](https://mlsanity.com/paper/2210.12548/) (2022)
- [Multi-modality imaging with structure-promoting regularisers](https://mlsanity.com/paper/2007.11689/) (2020)

## Abstract (arXiv)

Purpose: To develop an efficient dual-domain reconstruction framework for multi-contrast MRI, with the focus on minimising cross-contrast misalignment in both the image and the frequency domains to enhance optimisation. Theory and Methods: Our proposed framework, based on deep learning, facilitates the optimisation for under-sampled target contrast using fully-sampled reference contrast that is quicker to acquire. The method consists of three key steps: 1) Learning to synthesise data resembling the target contrast from the reference contrast; 2) Registering the multi-contrast data to reduce inter-scan motion; and 3) Utilising the registered data for reconstructing the target contrast. These steps involve learning in both domains with regularisation applied to ensure their consistency. We also compare the reconstruction performance with existing deep learning-based methods using a dataset of brain MRI scans. Results: Extensive experiments demonstrate the superiority of our proposed framework, for up to an 8-fold acceleration rate, compared to state-of-the-art algorithms. Comprehensive analysis and ablation studies further present the effectiveness of the proposed components. Conclusion:Our dual-domain framework offers a promising approach to multi-contrast MRI reconstruction. It can also be integrated with existing methods to further enhance the reconstruction.