| Autor: |
Chan, Trevor J., Rajapakse, Chamith S. |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
2024 IEEE International Symposium on Biomedical Imaging (ISBI) |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/ISBI56570.2024.10635304. |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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