Dynamic ⁶⁸ Ga-PSMA Total-Body PET Dual-Parametric Imaging Based on W-Net and an Improved Diffusion Model

Dynamic positron emission tomography (PET) parametric imaging plays a crucial role in the diagnosis of and research on tumors and neurological disorders. However, it requires long-term continuous PET/CT scans, which significantly increase the complexity of imaging and have become one of the main limitations hindering its development and clinical application. To address this issue, we propose a novel approach, namely, a dynamic 68Ga-PSMA total-body PET dual parametric imaging model based on W-Net with an improved diffusion model. We construct W-Net as the backbone network of the model. The differences in the shared downsampling module s and middle layer networks, can be divided into three categories: W-Net 1, W-Net 2, and W-Net 3. Furthermore, we extend the cold diffusion model to generate single-class images to simultaneously produce dynamic 68Ga-PSMA total-body PET K1 and Ki parametric images. Compared to other methods, the K1 parametric images achieved PSNR values improvement of 0.247~4.335 dB and MSE error reduction of 0.00026~0.01288; and for Ki parametric images, PSNR and SSIM metrics were enhanced by 0.106~1.590 dB and 0.002~0.004 respectively, while MSE errors decreased by 0.00003~0.00078. The PCC (Pearson correlation coefficient) value between the generated and original images indicates that they have a strong positive correlation.