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.

利用生成扩散模型的重建算法结合条件训练和基于频率的引导实现了高质量的超快速扫描下的重建图像，进而识别出一组优化的变密度螺旋轨迹，与非均匀快速傅里叶变换相比，图像质量大幅提高，这些方法可实现实时三维成像所需的极高加速因子。