Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging.
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging.
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DOI：
10.1109/tmi.2020.2998600
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发表时间：
2020-11
影响因子：
10.6
通讯作者：
Gholipour A
中科院分区：
文献类型：
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作者：
Singh A;Salehi SSM;Gholipour A
胎儿磁共振成像（MRI）的挑战是无法控制的，大，不规则的胎动。因此，通过视觉监测胎儿运动和重复采集来执行，以确保采集诊断质量的图像。然而，基于所显示的切片的胎儿运动的视觉监测以及在切片堆叠水平的导航是低效的。目前的过程高度依赖于操作员，增加了扫描仪的使用和成本，并显着增加了胎儿MRI扫描的长度，这使得孕妇难以忍受。为了帮助构建自动MRI运动跟踪和导航系统，以克服当前过程的局限性并改善胎儿成像，我们开发了一种基于深度学习的新的实时图像运动跟踪方法，可以直接从获取的图像中学习预测胎儿运动。我们的方法是基于一个循环神经网络，由空间和时间的编码器-解码器，推断运动参数的解剖特征提取的序列采集切片。我们将训练好的网络与用于估计的网络以及用于预测的方法进行了比较，其中包括具有不同特征的数据，例如在不同年龄扫描的不同胎儿，以及从志愿者受试者记录的运动轨迹。结果表明，我们的方法优于其他技术，并实现了实时性能，估计和预测任务的平均误差分别为3.5度和8度。我们的实时深度预测运动跟踪技术可用于评估胎动，指导切片采集，并为胎儿MRI构建导航系统。
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help build automatic MRI motion tracking and navigation systems to overcome the limitations of the current process and improve fetal imaging, we have developed a new real-time image-based motion tracking method based on deep learning that learns to predict fetal motion directly from acquired images. Our method is based on a recurrent neural network, composed of spatial and temporal encoder-decoders, that infers motion parameters from anatomical features extracted from sequences of acquired slices. We compared our trained network on held-out test sets (including data with different characteristics, e.g. different fetuses scanned at different ages, and motion trajectories recorded from volunteer subjects) with networks designed for estimation as well as methods adopted to make predictions. The results show that our method outperformed alternative techniques, and achieved real-time performance with average errors of 3.5 and 8 degrees for the estimation and prediction tasks, respectively. Our real-time deep predictive motion tracking technique can be used to assess fetal movements, to guide slice acquisitions, and to build navigation systems for fetal MRI.