Enhancing MRI reconstruction efficiency through multi-GPU parallelization

Dynamic cardiac MRI (cMRI) is essential for diagnosing cardiovascular diseases, demanding high resolution and image quality. However, achieving superior quality increases data volume and reconstruction time. To tackle this, we propose a solution using parallel imaging and Compressed Sensing (CS) with high-capacity computing devices (e.g., GPUs) for accelerated reconstruction of undersampled data. GPU mem- ory limitations, especially in 3D cMRI, present challenges. Our scalable approach splits the reconstruction problem and employs multiple GPUs (or multiple multi-core CPUs) to per- form multiple optimizations in parallel using the well-known NESTA algorithm, while preserving smoothness between ad- jacent frames in the temporal dimension. Preliminary results on 5D cMRI reconstruction show that our parallel proposal achieves equivalent reconstruction quality in less time, en- abling larger data processing and cost reduction with smaller, more affordable GPUs, as opposed to a single, highly expensive GPU. Moreover, the adoption of the OpenCLIPER frame- work eliminates proprietary GPU technologies. Exploration of adaptability to other sampling schemes opens new possibilities in this field.