# k-t CLAIR: Self-Consistency Guided Multi-Prior Learning for Dynamic Parallel MR Image Reconstruction

arXiv: https://arxiv.org/abs/2310.11050 · HTML: https://mlsanity.com/paper/2310.11050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 17 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [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)
- [Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1712.01751/) (2017)
- [GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction](https://mlsanity.com/paper/1910.12325/) (2019)
- [HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction](https://mlsanity.com/paper/2203.08213/) (2022)
- [DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training](https://mlsanity.com/paper/1810.00302/) (2018)
- [CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction](https://mlsanity.com/paper/2309.10836/) (2023)
- [Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction](https://mlsanity.com/paper/2012.11974/) (2020)
- [k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations](https://mlsanity.com/paper/1907.09425/) (2019)
- [CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2306.11238/) (2023)

## Abstract (arXiv)

Cardiac magnetic resonance imaging (CMR) has been widely used in clinical practice for the medical diagnosis of cardiac diseases. However, the long acquisition time hinders its development in real-time applications. Here, we propose a novel self-consistency guided multi-prior learning framework named k-t CLAIR to exploit spatiotemporal correlations from highly undersampled data for accelerated dynamic parallel MRI reconstruction. The k-t CLAIR progressively reconstructs faithful images by leveraging multiple complementary priors learned in the x-t, x-f, and k-t domains in an iterative fashion, as dynamic MRI exhibits high spatiotemporal redundancy. Additionally, k-t CLAIR incorporates calibration information for prior learning, resulting in a more consistent reconstruction. Experimental results on cardiac cine and T1W/T2W images demonstrate that k-t CLAIR achieves high-quality dynamic MR reconstruction in terms of both quantitative and qualitative performance.