Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models

Trevor J. Chan; Chamith S. Rajapakse

doi:10.1109/isbi56570.2024.10635304.

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arXiv · 2404.12361

Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models

Trevor J. Chan·Chamith S. Rajapakse

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Abstract

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.

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Trevor J. Chan, Chamith S. Rajapakse. 2024-04-18. Learning the Domain Specific Inverse NUFFT for Accelerated Spiral MRI using Diffusion Models. https://doi.org/10.1109/isbi56570.2024.10635304.

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