Abstract
Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D
slices has shown promise in imaging of moving subjects, e.g., fetal MRI.
However, existing slice-to-volume reconstruction methods are
time-consuming, especially when a high-resolution volume is desired.
Moreover, they are still vulnerable to severe subject motion and when
image artifacts are present in acquired slices. In this work, we present
NeSVoR, a resolution-agnostic slice-to-volume reconstruction method,
which models the underlying volume as a continuous function of spatial
coordinates with implicit neural representation. To improve robustness
to subject motion and other image artifacts, we adopt a continuous and
comprehensive slice acquisition model that takes into account rigid
inter-slice motion, point spread function, and bias fields. NeSVoR also
estimates pixel-wise and slice-wise variances of image noise and enables
removal of outliers during reconstruction and visualization of
uncertainty. Extensive experiments are performed on both simulated and
in vivo data to evaluate the proposed method. Results show that NeSVoR
achieves state-of-the-art reconstruction quality while providing two to
ten-fold acceleration in reconstruction times over the state-of-the-art
algorithms
In submission to IEEE Transactions on Medical Imaging (TMI)