Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness
<|reference_start|>Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level Awareness: To obtain high-quality positron emission tomography (PET) while minimizing radiation exposure, a range of methods have been designed to reconstruct standard-dose PET (SPET) from corresponding low-dose PET (LPET) images. However, most current methods merely learn the mapping between single-dose-level LPET and SPET images, but omit the dose disparity of LPET images in clinical scenarios. In this paper, to reconstruct high-quality SPET images from multi-dose-level LPET images, we design a novel two-phase multi-dose-level PET reconstruction algorithm with dose level awareness, containing a pre-training phase and a SPET prediction phase. Specifically, the pre-training phase is devised to explore both fine-grained discriminative features and effective semantic representation. The SPET prediction phase adopts a coarse prediction network utilizing pre-learned dose level prior to generate preliminary result, and a refinement network to precisely preserve the details. Experiments on MICCAI 2022 Ultra-low Dose PET Imaging Challenge Dataset have demonstrated the superiority of our method.<|reference_end|>
@article{fei2024two-phase,
    title={Two-Phase Multi-Dose-Level PET Image Reconstruction with Dose Level
  Awareness},
    author={Yuchen Fei, Yanmei Luo, Yan Wang, Jiaqi Cui, Yuanyuan Xu, Jiliu Zhou,
  Dinggang Shen},
    journal={arXiv preprint arXiv:2404.01563},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01563},
    primaryClass={eess.IV cs.CV}
}