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-wise learning applied to a single unroll decomposes the input volume into smaller patches, gradient checkpoints each patch, passes each patch iteratively through a neural network regularizer, and then rebuilds the full volume from these output patches for data-consistency. This method is applied across unrolls during training. Block-wise learning significantly reduces memory requirements by tying GPU memory for a single unroll to user selected patch size instead of the full volume. This algorithm was used to train a MBDL architecture to reconstruct highly undersampled, 1.25 mm isotropic, pulmonary magnetic resonance angiography volumes with matrix sizes varying from 300-450 × 200-300 × 300-450 on a single GPU. We compared block-wise learning reconstructions against L1 wavelet compressed reconstructions and proxy ground truth images.Main results. MBDL with block-wise learning significantly improved image quality relative to L1 wavelet compressed sensing while simultaneously reducing average reconstruction time 38x.Significance. Block-wise learning allows for MBDL to be applied to high spatial resolution, 3D non-Cartesian datasets with improved image quality and significant reductions in reconstruction time relative to traditional iterative methods.