Accelerating MRI with Spiral Trajectory Optimization and Reconstruction using Diffusion Models

Motivation: Deep learning methods for accelerated MRI achieve state-of-the-art results but largely ignore additional speedups possible with noncartesian sampling trajectories. Goal(s): To create a generative diffusion model-based reconstruction algorithm for multi-coil undersampled spiral MRI. Approach: We train a conditional diffusion model and use frequency-based guidance to ensure consistency between images and measurements. Results: Evaluated on retrospective data, we show high quality (SSIM > 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 nufft reconstruction. Impact: We apply diffusion models to the task of non-cartesian reconstruction. Combining efficient spiral sampling trajectories, multicoil imaging, and deep learning reconstruction enables drastically accelerated imaging. Potential applications of this technology include real-time 3D imaging.