STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning
This addresses the problem of low-resolution dynamic fetal MRI for medical imaging researchers and clinicians, offering an incremental improvement in super-resolution techniques for this specific domain.
The paper tackles the challenge of super-resolution for dynamic fetal MRI, where unpredictable fetal motion limits image quality and resolution, by proposing STRESS, a self-supervised framework that simulates interleaved slice acquisitions to train a network using spatial and temporal correlations, resulting in improved image quality over other self-supervised methods.
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.