  The reconstruction of 3D cine-MRI is challenged by highly undersampled
k-space data in each cine frame, due to the slow speed of MR signal
acquisition. We proposed a machine learning-based framework, spatial and
temporal implicit neural representation learning (STINR-MR), for accurate 3D
cine-MRI reconstruction from highly undersampled data. STINR-MR used a joint
reconstruction and deformable registration approach to address the ill-posed
spatiotemporal reconstruction problem, by solving a reference-frame 3D MR image
and a corresponding motion model which deforms the reference frame to each cine
frame. The reference-frame image was reconstructed as a spatial implicit neural
representation (INR) network, which learns the mapping from input 3D spatial
coordinates to corresponding MR values. The dynamic motion model was
constructed via a temporal INR, as well as basis deformation vector
fields(DVFs) extracted from prior/onboard 4D-MRIs. The learned INR encodes
input time points and outputs corresponding weighting factors to combine the
basis DVFs into time-resolved motion fields. STINR-MR was evaluated using MR
data simulated from the 4D extended cardiac-torso (XCAT) digital phantom and MR
data acquired clinically from a healthy human subject. Its reconstruction
accuracy was also compared with that of the model-based non-rigid motion
estimation method (MR-MOTUS). STINR-MR can reconstruct 3D cine-MR images with
high temporal (<100 ms) and spatial (3 mm) resolutions to accurately capture
different irregular motion patterns. Compared with MR-MOTUS, STINR-MR
consistently reconstructed images with better quality, fewer artifacts, and
achieved superior tumor localization accuracy. STINR-MR provides a lightweight
and efficient framework for accurate 3D cine-MRI reconstruction, and does not
require external data for pre-training to avoid generalizability issues
encountered in deep learning methods.
