369-05-003 ISMRM Abstract

Hybrid CNN-Transformer Architectures Enable Superior Acceleration in Hyperpolarized ¹²⁹Xe MRI Reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Acquisition & Reconstruction - AI methods

369-05-003 · Hyperpolarized Gases · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row J

Keywords: AI/ML Image Reconstruction Image Reconstruction Machine Learning/Artificial Intelligence Deep learning reconstruction Hyperpolarized 129Xe MRI

Accepted

Ramtin Babaeipour¹, Alexei Ouriadov^2,3,4, Matthew Fox^2,4,5

¹Western University, London, Canada

²Physics and Astronomy, Western University, London, Canada

³Biomedical Engineering, Western University, London, Canada

⁴Lawson Research Institute, London, Canada

⁵Medical Biophysics, Western University, London, Canada

Presenting Author: Samuel Perron

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References

1. 1 Perron, S. & Ouriadov, A. Hyperpolarized 129Xe MRI at low field: Current status and future directions. Journal of Magnetic Resonance 348, 107387, doi:https://doi.org/10.1016/j.jmr.2023.107387 (2023). [doi]

2. 2 Duan, C. et al. Fast and accurate reconstruction of human lung gas MRI with deep learning. Magn Reson Med 82, 2273-2285, doi:10.1002/mrm.27889 (2019). [doi]

3. 3 Stewart, N. J. et al. Improving Xenon-129 lung ventilation image SNR with deep-learning based image reconstruction. Magnetic Resonance in Medicine 92, 2546-2559, doi:https://doi.org/10.1002/mrm.30250 (2024). [doi]

4. 4 Huang, J. et al. Swin transformer for fast MRI. Neurocomputing 493, 281-304, doi:https://doi.org/10.1016/j.neucom.2022.04.051 (2022). [doi]

5. 5 Wu, Z. et al. Deep learning based MRI reconstruction with transformer. Computer Methods and Programs in Biomedicine 233, 107452, doi:https://doi.org/10.1016/j.cmpb.2023.107452 (2023). [doi]

6. 6 Guo, P., Mei, Y., Zhou, J., Jiang, S. & Patel, V. M. ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer. IEEE Trans Med Imaging 43, 582-593, doi:10.1109/tmi.2023.3314747 (2024). [doi]

7. 7 Eo, T. et al. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magnetic Resonance in Medicine 80, 2188-2201, doi:https://doi.org/10.1002/mrm.27201 (2018). [doi]

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http://echo.ismrm.org/p/ISMRM2026/369-05-003