Superresolution T2-weighted fetal-brain magnetic-resonance imaging (FBMRI)
traditionally relies on the availability of several orthogonal low-resolution
series of 2-dimensional thick slices (volumes). In practice, only a few
low-resolution volumes are acquired. Thus, optimization-based
image-reconstruction methods require strong regularization using hand-crafted
regularizers (e.g., TV). Yet, due to in utero fetal motion and the rapidly
changing fetal brain anatomy, the acquisition of the high-resolution images
that are required to train supervised learning methods is difficult. In this
paper, we sidestep this difficulty by providing a proof of concept of a
self-supervised single-volume superresolution framework for T2-weighted FBMRI
(SAIR). We validate SAIR quantitatively in a motion-free simulated environment.
Our results for different noise levels and resolution ratios suggest that SAIR
is comparable to multiple-volume superresolution reconstruction methods. We
also evaluate SAIR qualitatively on clinical FBMRI data. The results suggest
SAIR could be incorporated into current reconstruction pipelines.

该文提出了一种基于自监督学习的单体积超分辨率框架（SAIR）用于胎儿脑 MRI 的图像重建，实验证明 SAIR 与多体积超分辨率重建方法相当，并且可以纳入当前的重建流程。