SImilarity-driven Multi-dimensional Binning Algorithm (SIMBA) is a recently proposed technique for identifying motion-consistent clusters in free-running whole-heart MRA acquisitions, where the clusters are subsequently reconstructed using conventional approaches. Physics-guided deep learning (PG-DL) reconstruction has gained popularity with its superior performance at higher acceleration rates and may further improve the image quality of free-running whole-heart MRA in conjunction with SIMBA. However, PG-DL is difficult to apply to 3D non-Cartesian acquisitions due to hardware limitations. In this work we enable distributed memory-efficient PG-DL for large-scale SIMBA datasets, showing preliminary results in which PG-DL improves image quality compared to conventional methods.
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