Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning
<|reference_start|>Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning: In clinical practice, 2D magnetic resonance (MR) sequences are widely adopted. While individual 2D slices can be stacked to form a 3D volume, the relatively large slice spacing can pose challenges for both image visualization and subsequent analysis tasks, which often require isotropic voxel spacing. To reduce slice spacing, deep-learning-based super-resolution techniques are widely investigated. However, most current solutions require a substantial number of paired high-resolution and low-resolution images for supervised training, which are typically unavailable in real-world scenarios. In this work, we propose a self-supervised super-resolution framework for inter-slice super-resolution of MR images. Our framework is first featured by pre-training on video dataset, as temporal correlation of videos is found beneficial for modeling the spatial relation among MR slices. Then, we use public high-quality MR dataset to fine-tune our pre-trained model, for enhancing awareness of our model to medical data. Finally, given a target dataset at hand, we utilize self-supervised fine-tuning to further ensure our model works well with user-specific super-resolution tasks. The proposed method demonstrates superior performance compared to other self-supervised methods and also holds the potential to benefit various downstream applications.<|reference_end|>
@article{wang2024inter-slice,
    title={Inter-slice Super-resolution of Magnetic Resonance Images by
  Pre-training and Self-supervised Fine-tuning},
    author={Xin Wang, Zhiyun Song, Yitao Zhu, Sheng Wang, Lichi Zhang, Dinggang
  Shen, Qian Wang},
    journal={arXiv preprint arXiv:2406.05974},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05974},
    primaryClass={eess.IV cs.CV}
}