Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models
<|reference_start|>Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models: Deep learning methods for accelerated MRI achieve state-of-the-art results but largely ignore additional speedups possible with noncartesian sampling trajectories. To address this gap, we created a generative diffusion model-based reconstruction algorithm for multi-coil highly undersampled spiral MRI. This model uses conditioning during training as well as frequency-based guidance to ensure consistency between images and measurements. Evaluated on retrospective data, we show high quality (structural similarity > 0.87) in reconstructed images with ultrafast scan times (0.02 seconds for a 2D image). We use this algorithm to identify a set of optimal variable-density spiral trajectories and show large improvements in image quality compared to conventional reconstruction using the non-uniform fast Fourier transform. By combining efficient spiral sampling trajectories, multicoil imaging, and deep learning reconstruction, these methods could enable the extremely high acceleration factors needed for real-time 3D imaging.<|reference_end|>
@article{chan2024learning,
    title={Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI
  using Diffusion Models},
    author={Trevor J. Chan, Chamith S. Rajapakse},
    journal={2024 IEEE International Symposium on Biomedical Imaging (ISBI)},
    year={2024},
    doi={10.1109/ISBI56570.2024.10635304.},
    archivePrefix={arXiv},
    eprint={2404.12361},
    primaryClass={cs.AI physics.med-ph}
}