论文标题
自由呼吸的肝脏扫描中时空运动预测通过经常性的多尺度编码器解码器
Spatiotemporal motion prediction in free-breathing liver scans via a recurrent multi-scale encoder decoder
论文作者
论文摘要
在这项工作中，我们提出了一个多尺度的复发编码器架构架构，以预测未来框架中呼吸诱导的器官变形。该模型是从输入图像的端到端训练的，以预测一系列运动标签。通过量化从可变形图像注册获得的位移字段来创建目标。我们报告了来自12位志愿者的MRI自由呼吸收购的结果。实验旨在研究提出的多尺度设计以及增加预测帧数量对模型整体准确性的影响。所提出的模型能够以2.07（2.95）毫米的平均准确性（2.07（2.95）mm预测血管位置，显示出与最新方法相比的性能提高。
In this work we propose a multi-scale recurrent encoder-decoder architecture to predict the breathing induced organ deformation in future frames. The model was trained end-to-end from input images to predict a sequence of motion labels. Targets were created by quantizing the displacement fields obtained from deformable image registration. We report results using MRI free-breathing acquisitions from 12 volunteers. Experiments were aimed at investigating the proposed multi-scale design and the effect of increasing the number of predicted frames on the overall accuracy of the model. The proposed model was able to predict vessel positions in the next temporal image with a mean accuracy of 2.07 (2.95) mm showing increased performance in comparison with state-of-the-art approaches.
