  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.
