Figure 3. A representative test slice from reconstructions using 2D and 3D unrolled networks. 2D processing suffers from residual artifacts (red arrows), which are suppressed with the 3D processing.

Figure 2. Schematic of the supervised training on the small slabs generated from the volumetric datasets. Unrolled neural networks comprise a series of data consistency and regularizer units, arising from conventional iterative optimization algorithms. In this work, we use a 3D unrolled network. A ResNet architecture with 5 residual blocks, consisting of 2 convolution layers with former followed by ReLU and latter followed by a scaling layer is used for implicit regularization. All layers of this ResNet use 3×3×3 kernels and 64 channels.