Unrolled neural networks have been shown to improve the reconstruction quality for accelerated MRI. While they have been widely applied in 2D settings, 3D processing may further improve reconstruction quality for volumetric imaging with its ability to capture multi-dimensional interactions. However, implementation of 3D unrolled networks is generally challenging due to GPU-memory limitations and lack of availability of large databases of 3D data. In this work, we tackle both these issues by an augmentation approach that generates smaller sub-volumes from large volumetric datasets. We then compare the 3D unrolled network to its 2D counterpart, showing the improvement from 3D processing.
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