Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images

JungEun Kim, Hangyul Yoon, Geondo Park, Kyungsu Kim, Eunho Yang

Abstract: 4D medical images, which represent 3D images with temporal information, are crucial in clinical practice for capturing dynamic changes and monitoring long-term dis-ease progression. However, acquiring 4D medical images poses challenges due to factors such as radiation expo-sure and imaging duration, necessitating a balance be-tween 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 frame-work facilitates temporal interpolation without the need for any intermediate frames, distinguishing it from the major-ity of other existing unsupervised methods. Experiments on benchmark datasets demonstrate significant improvements across diverse evaluation metrics compared to unsuper-vised and supervised baselines. Remarkably, our approach achieves this superior performance even when trained with a dataset as small as one, highlighting its exceptional ro-bustness 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 code is available at UVI-Net.