论文标题
自我观察的各向同性超级分辨率胎儿脑MRI
Self-Supervised Isotropic Superresolution Fetal Brain MRI
论文作者
论文摘要
传统上，超分辨率T2加权胎儿 - 脑磁性成像（FBMRI）依赖于几个正交低分辨率的二维厚切片（体积）的几个正交低分辨率系列。实际上，仅获取了少量低分辨率卷。因此，基于优化的图像重建方法需要使用手工制作的正规化器（例如电视）进行强正规化。然而，由于子宫胎儿运动和快速变化的胎儿大脑解剖结构，很难获得训练监督学习方法所需的高分辨率图像。在本文中，我们通过提供了一个为T2加权FBMRI（SAIR）的自我监管的单卷超分辨率框架的概念证明来避开这一困难。我们在无运动模拟环境中定量验证SAIR。我们针对不同噪声水平和分辨率比率的结果表明，SAIR与多体积超分辨率重建方法相媲美。我们还根据临床FBMRI数据对SAIR进行定性评估。结果表明，SAIR可以纳入当前的重建管道中。
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.
