Memory efficient model based deep learning reconstructions for high spatial resolution 3D non-cartesian acquisitions.

ABSTRACT: Objective. Model based deep learning (MBDL) has been challenging to apply to the reconstruction of 3D non-Cartesian MRI due to GPU memory demand because the entire volume is needed for data-consistency steps embedded in the model. This requirement makes holding even a single unroll in GPU memory difficult meaning memory efficient techniques used to increase unroll number like gradient checkpointing and deep equilibrium learning will not work well for high spatial resolution 3D non-Cartesian reconstructions without modification. Here we develop a memory efficient method called block-wise learning that combines gradient checkpointing with patch-wise training to overcome this obstacle and allow for fast and high-quality 3D non-Cartesian reconstructions using MBDL.Approach. Block

SUBMITTER: Miller Z 

PROVIDER: S-EPMC10034748 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature