Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction
<|reference_start|>Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction: Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure through low-dose PET scans is beneficial for patient safety, it often results in insufficient statistical data. This scarcity of data poses significant challenges for accurately reconstructing high-quality images, which are essential for reliable diagnostic outcomes. In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior (JCP) to enhance the reconstruction quality of low-dose PET imaging. In light of current research findings, we present a pioneering PET reconstruction model that integrates diffusion and transformer models for joint optimization. This model combines the powerful distribution mapping abilities of diffusion models with the capacity of transformers to capture long-range dependencies, offering significant advantages for low-dose PET reconstruction. Additionally, the incorporation of the lesion refining block and penalized weighted least squares (PWLS) enhance the recovery capability of lesion regions and preserves detail information, solving blurring problems in lesion areas and texture details of most deep learning frameworks. Experimental results demonstrate the effectiveness of DTM in enhancing image quality and preserving critical clinical information for low-dose PET scans. Our approach not only reduces radiation exposure risks but also provides a more reliable PET imaging tool for early disease detection and patient management.<|reference_end|>
@article{huang2024diffusion,
    title={Diffusion Transformer Model With Compact Prior for Low-dose PET
  Reconstruction},
    author={Bin Huang, Xubiao Liu, Lei Fang, Qiegen Liu and Bingxuan Li},
    journal={arXiv preprint arXiv:2407.00944},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00944},
    primaryClass={cs.CV}
}