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Abstract #3480

Memory Efficient Model Based Reconstruction for Volumetric Non-Cartesian Acquisitions using Gradient Checkpointing and Blockwise Learning

Zachary Miller1 and Kevin Johnson1
1University of Wisconsin-Madison, Madison, WI, United States


The goal of this work is to reconstruct high resolution, volumetric non-cartesian acquisitions using model based deep learning. This is a challenging problem because unlike cartesian acquisitions and hybrid trajectories like stack of spirals that can be decoupled into 2D sub problems, fully non-cartesian trajectories require the complete 3D volume for data-consistency pushing GPU memory capacity to its limits even using cluster computing. Here, we investigate blockwise learning, a method that has the memory saving benefits of patch-based methods while maintaining data-consistency in k-space. We demonstrate the feasibility of this method by reconstructing high resolution pulmonary non-contrast and MRA acquisitions.

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