Abstract #1970

A fast Pytorch based GRAPPA implementation for uniformly undersampled k-space

Yansong Bu^1,2, Zihao Wang^1,2, Jianzhong Li³, Yilong Liu⁴, and Mengye Lyu^1,2

¹Shenzhen Technology University, Shenzhen, China, ²College of Applied Sciences, Shenzhen University, Shenzhen, China, ³Shenzhen GoldenStone Medical Technology Co. ,Ltd., Shenzhen, China, ⁴Guangdong-Hongkong-Macau Institute of CNS Regeneration, Key Laboratory of CNS Regeneration (Ministry of Education), Jinan University, Guangzhou 510632, P. R. China, Guangzhou, China

Synopsis

Keywords: Software Tools, Software Tools

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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