Ma Laifa, Chen Liangjun, Zhao Fenqiang, Wu Zhengwang, Wang Li, Lin Weili, Zhang He, Li Kenli, Li Gang
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Proc IEEE Int Symp Biomed Imaging. 2023 Apr;2023. doi: 10.1109/isbi53787.2023.10230423. Epub 2023 Sep 1.
Robust motion correction of fetal brain MRI slices is crucial for 3D brain volume reconstruction. However, conventional methods can only handle a limited range of motion. Hence, a deep learning model based on geometric constraints is proposed in order to predict the arbitrary motion of fetal brain MRI slices in a standard anatomical space, which consists of a global motion estimation network and a relative motion estimation network. In particular, the relative motion estimation network is used to estimate the relative motion between two adjacent slices, which is exploited as a geometric constraint. Then, sharing features between two networks make the model to learn more unique feature representations for global motion correction, and a weight-learnable strategy is employed to balance the contributions of two networks. With this design, the proposed method can estimate more complicated and large motions. Moreover, to build a large simulated fetal brain stack dataset with realistic appearance for successfully training a robust motion correction model, we introduced a control point-based method to simulate fetal motion trajectories at different gestational ages, between stacks and within 2D slices. The experimental results on a large number of fetal brain stacks demonstrate the state-of-the-art performance of our method.
胎儿脑磁共振成像（MRI）切片的稳健运动校正对于三维脑容积重建至关重要。然而，传统方法只能处理有限范围的运动。因此，提出了一种基于几何约束的深度学习模型，以预测胎儿脑MRI切片在标准解剖空间中的任意运动，该模型由全局运动估计网络和相对运动估计网络组成。具体而言，相对运动估计网络用于估计相邻两个切片之间的相对运动，并将其用作几何约束。然后，两个网络之间共享特征使模型能够学习到更独特的全局运动校正特征表示，并采用权重可学习策略来平衡两个网络的贡献。通过这种设计，所提出的方法能够估计更复杂和更大的运动。此外，为了构建一个具有逼真外观的大型模拟胎儿脑堆栈数据集，以成功训练一个稳健的运动校正模型，我们引入了一种基于控制点的方法来模拟不同孕周、堆栈之间以及二维切片内的胎儿运动轨迹。在大量胎儿脑堆栈上的实验结果证明了我们方法的领先性能。
