Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images
This work addresses data scarcity in 4D medical imaging for clinical applications, offering a robust solution in settings with limited data availability, though it appears incremental as it builds on unsupervised interpolation methods.
The paper tackles the challenge of acquiring high-temporal-resolution 4D medical images by proposing UVI-Net, an unsupervised volumetric interpolation framework that achieves significant improvements over baselines, even when trained with only one dataset.
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.