  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.
