  Fetal motion is unpredictable and rapid on the scale of conventional MR scan
times. Therefore, dynamic fetal MRI, which aims at capturing fetal motion and
dynamics of fetal function, is limited to fast imaging techniques with
compromises in image quality and resolution. Super-resolution for dynamic fetal
MRI is still a challenge, especially when multi-oriented stacks of image slices
for oversampling are not available and high temporal resolution for recording
the dynamics of the fetus or placenta is desired. Further, fetal motion makes
it difficult to acquire high-resolution images for supervised learning methods.
To address this problem, in this work, we propose STRESS (Spatio-Temporal
Resolution Enhancement with Simulated Scans), a self-supervised
super-resolution framework for dynamic fetal MRI with interleaved slice
acquisitions. Our proposed method simulates an interleaved slice acquisition
along the high-resolution axis on the originally acquired data to generate
pairs of low- and high-resolution images. Then, it trains a super-resolution
network by exploiting both spatial and temporal correlations in the MR time
series, which is used to enhance the resolution of the original data.
Evaluations on both simulated and in utero data show that our proposed method
outperforms other self-supervised super-resolution methods and improves image
quality, which is beneficial to other downstream tasks and evaluations.
