# Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI

arXiv: https://arxiv.org/abs/2111.06400 · HTML: https://mlsanity.com/paper/2111.06400/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 11 Nov 2021.

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

- [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)
- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1712.01751/) (2017)
- [Deep learning for undersampled MRI reconstruction](https://mlsanity.com/paper/1709.02576/) (2017)
- [Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis](https://mlsanity.com/paper/2002.05000/) (2020)
- [DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior](https://mlsanity.com/paper/2001.03799/) (2020)
- [Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition](https://mlsanity.com/paper/1902.03051/) (2019)
- [Learning-based Optimization of the Under-sampling Pattern in MRI](https://mlsanity.com/paper/1901.01960/) (2019)
- [A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction](https://mlsanity.com/paper/1804.03596/) (2018)
- [Learning-Based Compressive MRI](https://mlsanity.com/paper/1805.01266/) (2018)
- [Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction](https://mlsanity.com/paper/1806.11216/) (2018)
- [Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation](https://mlsanity.com/paper/1511.06631/) (2015)
- [Statistically Segregated k-Space Sampling for Accelerating Multiple-Acquisition MRI](https://mlsanity.com/paper/1710.00532/) (2017)
- [Multi-modality imaging with structure-promoting regularisers](https://mlsanity.com/paper/2007.11689/) (2020)
- [Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI](https://mlsanity.com/paper/2103.16744/) (2021)

## Built on by (most cited first, 2 of 2)

- [Cross-Modality High-Frequency Transformer for MR Image Super-Resolution](https://mlsanity.com/paper/2203.15314/) (2022)
- [DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network](https://mlsanity.com/paper/2203.06920/) (2022)

## Abstract (arXiv)

Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modalities. Most of the state-of-the-art approaches have achieved improvement through the development of network architectures for fixed under-sampling patterns, without fully exploiting the complementary information between modalities. Although existing under-sampling pattern learning algorithms can be simply modified to allow the fully-sampled T1-weighted MR image to assist the pattern learning, no significant improvement on the reconstruction task can be achieved. To this end, we propose an iterative framework to optimize the under-sampling pattern for MRI acquisition of another modality that can complement the fully-sampled T1-weighted MR image at different under-sampling factors, while jointly optimizing the T1-assisted MRI reconstruction model. Specifically, our proposed method exploits the difference of latent information between the two modalities for determining the sampling patterns that can maximize the assistance power of T1-weighted MR image in improving the MRI reconstruction. We have demonstrated superior performance of our learned under-sampling patterns on a public dataset, compared to commonly used under-sampling patterns and state-of-the-art methods that can jointly optimize both the reconstruction network and the under-sampling pattern, up to 8-fold under-sampling factor.