  4D medical images, which represent 3D images with temporal information, are
crucial in clinical practice for capturing dynamic changes and monitoring
long-term disease progression. However, acquiring 4D medical images poses
challenges due to factors such as radiation exposure and imaging duration,
necessitating a balance between achieving high temporal resolution and
minimizing adverse effects. Given these circumstances, not only is data
acquisition challenging, but increasing the frame rate for each dataset also
proves difficult. To address this challenge, this paper proposes a simple yet
effective Unsupervised Volumetric Interpolation framework, UVI-Net. This
framework facilitates temporal interpolation without the need for any
intermediate frames, distinguishing it from the majority of other existing
unsupervised methods. Experiments on benchmark datasets demonstrate significant
improvements across diverse evaluation metrics compared to unsupervised and
supervised baselines. Remarkably, our approach achieves this superior
performance even when trained with a dataset as small as one, highlighting its
exceptional robustness and efficiency in scenarios with sparse supervision.
This positions UVI-Net as a compelling alternative for 4D medical imaging,
particularly in settings where data availability is limited. The source code is
available at https://github.com/jungeun122333/UVI-Net.
