  Compared with 2D MRI, 3D MRI provides superior volumetric spatial resolution
and signal-to-noise ratio. However, it is more challenging to reconstruct 3D
MRI images. Current methods are mainly based on convolutional neural networks
(CNN) with small kernels, which are difficult to scale up to have sufficient
fitting power for 3D MRI reconstruction due to the large image size and GPU
memory constraint. Furthermore, MRI reconstruction is a deconvolution problem,
which demands long-distance information that is difficult to capture by CNNs
with small convolution kernels. The multi-layer perceptron (MLP) can model such
long-distance information, but it requires a fixed input size. In this paper,
we proposed Recon3DMLP, a hybrid of CNN modules with small kernels for
low-frequency reconstruction and adaptive MLP (dMLP) modules with large kernels
to boost the high-frequency reconstruction, for 3D MRI reconstruction. We
further utilized the circular shift operation based on MRI physics such that
dMLP accepts arbitrary image size and can extract global information from the
entire FOV. We also propose a GPU memory efficient data fidelity module that
can reduce $>$50$\%$ memory. We compared Recon3DMLP with other CNN-based models
on a high-resolution (HR) 3D MRI dataset. Recon3DMLP improves HR 3D
reconstruction and outperforms several existing CNN-based models under similar
GPU memory consumption, which demonstrates that Recon3DMLP is a practical
solution for HR 3D MRI reconstruction.
