4D medical image interpolation is essential for improving temporal resolution and diagnostic precision in clinical applications. Previous works ignore the problem of distribution shifts, resulting in poor generalization under different distribution. A natural solution would be to adapt the model to a new test distribution, but this cannot be done if the test input comes without a ground truth label. In this paper, we propose a novel test time training framework which uses self-supervision to adapt the model to a new distribution without requiring any labels. Indeed, before performing frame interpolation on each test video, the model is trained on the same instance using a self-supervised task, such as rotation prediction or image reconstruction. We conduct experiments on two publicly available 4D medical image interpolation datasets, Cardiac and 4D-Lung. The experimental results show that the proposed method achieves significant performance across various evaluation metrics on both datasets. It achieves higher peak signal-to-noise ratio values, 33.73dB on Cardiac and 34.02dB on 4D-Lung. Our method not only advances 4D medical image interpolation but also provides a template for domain adaptation in other fields such as image segmentation and image registration.

本研究解决了4D医学图像插值中存在的分布变化问题，从而提高了临床应用中的时间分辨率和诊断精度。提出了一种新颖的测试时间训练框架，该框架使用自我监督方法在没有标签的情况下适应新的测试分布。实验结果表明，该方法在多个评估指标上表现显著，能够有效提升4D医学图像插值的性能，并为其他领域如图像分割和图像配准的领域适应提供了模板。

4D 医学图像插值的测试时间训练