This implementation demonstrates reduced runtime in MRI reconstruction with GRAPPA, highlighting efficiency over existing methods.
Motivation: Existing open-source GRAPPA implementations, such as pygrappa, are slow in some cases. Goal(s): To develop a high-performance, open-source GRAPPA algorithm for faster reconstruction of uniformly undersampled data. Approach: We implemented PyTorch-based GRAPPA algorithm that can use either CPU or GPU. Results: Tests with 8- and 32-channel MRI data showed that our implementation delivers similar image quality to pygrappa but with significantly reduced runtime. Impact: This approach enables faster MRI reconstructions, making it suitable for many applications.
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Bu et al. (2025) studied this question.
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