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

arXiv 2310.11050 ↗First posted 17 Oct 2023eess.IV cs.CV physics.med-phcited by 0

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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.

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References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  3. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 387
  4. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
  5. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 232
  6. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 202
  7. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  8. 2019GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstructioncited by 45
  9. 2022HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstructioncited by 30
  10. 2018DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Trainingcited by 26
  11. 2023CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstructioncited by 21
  12. 2020Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstructioncited by 10
2 more
  1. 2019k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlationscited by 6
  2. 2023CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstructioncited by 3

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