Diffusion transformer model with compact prior for low-dose PET reconstruction

Abstract Objective. 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. Approach. In this research, we propose a diffusion transformer model (DTM) guided by joint compact prior 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 model 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 alternating direction method of multipliers 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. Main results . Experimental results validate the effectiveness of DTM in reconstructing low-dose PET image quality. DTM achieves state-of-the-art performance across various metrics, including PSNR, SSIM, NRMSE, CR, and COV, demonstrating its ability to reduce noise while preserving critical clinical details such as lesion structure and texture. Compared with baseline methods, DTM delivers best results in denoising and lesion preservation across various low-dose levels, including 10%, 25%, 50%, and even ultra-low-dose level such as 1%. DTM shows robust generalization performance on phantom and patient datasets, highlighting its adaptability to varying imaging conditions. Significance . This approach reduces radiation exposure while ensuring reliable imaging for early disease detection and clinical decision-making, offering a promising tool for both clinical and research applications.