gGRAPPA: A Flexible, GPU-Accelerated Python Package for Fast and Efficient Generalized GRAPPA Reconstruction

Motivation: Existing open-source MR reconstruction tools often fail to efficiently utilize GPU resources and lack support for generalized GRAPPA implementations. Many tools are limited to 2D or 3D reconstruction, and few incorporate advanced techniques such as 2D-CAIPIRINHA, which enhances imaging capabilities. Goal(s): gGRAPPA aims to provide a fast, flexible, and open-source tool for generalized GRAPPA/CAIPI reconstruction. Approach: By utilizing PyTorch, gGRAPPA runs multiple convolutional windows in batch mode to optimize GPU memory usage and accelerate reconstruction times. Results: gGRAPPA achieves up to a 65x speedup over CPU implementations and a 6x speedup compared to non-batched GPU methods, enabling efficient and fast reconstruction MRI scans. Impact: gGRAPPA provides a fast, flexible, and open-source solution for GRAPPA MRI reconstruction on GPU, significantly accelerating reconstruction times and enabling ultra high-resolution imaging reconstruction, thereby supporting advanced research applications across diverse MRI protocols.