Dynamic medical imaging is usually limited in application due to the large
radiation doses and longer image scanning and reconstruction times. Existing
methods attempt to reduce the dynamic sequence by interpolating the volumes
between the acquired image volumes. However, these methods are limited to
either 2D images and/or are unable to support large variations in the motion
between the image volume sequences. In this paper, we present a spatiotemporal
volumetric interpolation network (SVIN) designed for 4D dynamic medical images.
SVIN introduces dual networks: first is the spatiotemporal motion network that
leverages the 3D convolutional neural network (CNN) for unsupervised parametric
volumetric registration to derive spatiotemporal motion field from two-image
volumes; the second is the sequential volumetric interpolation network, which
uses the derived motion field to interpolate image volumes, together with a new
regression-based module to characterize the periodic motion cycles in
functional organ structures. We also introduce an adaptive multi-scale
architecture to capture the volumetric large anatomy motions. Experimental
results demonstrated that our SVIN outperformed state-of-the-art temporal
medical interpolation methods and natural video interpolation methods that have
been extended to support volumetric images. Our ablation study further
exemplified that our motion network was able to better represent the large
functional motion compared with the state-of-the-art unsupervised medical
registration methods.

介绍了用于 4D 动态医学图像的间隔时间体积插值网络 (SVIN)。SVIN 是利用三维卷积神经网络 (CNN) 进行无监督参数体积配准的空间 - 时间运动网络，并使用新的回归模块来表征功能器官结构中的周期运动循环。在实验结果中，SVIN 比最先进的医疗插值方法和自然视频插值方法表现更好，并且优于最先进的无监督医疗注册方法。