Partial volume effect (PVE) arises from the limited spatial resolution of positron emission tomography (PET) scanners, causing significant quantitative biases that hinder accurate metabolic activity assessment. To address these problems, we proposed an unsupervised deep residual compensation model (U-DRCM) for PET partial volume correction (PVC). U-DRCM first predicted an initial blur kernel for the PVE-affected PET image based on a conditional blind deconvolution module (CBD module). Then, a conditional residual compensation module (CRC module) was introduced to compensate for the error caused by inaccurate blur kernel prediction. The whole model is unsupervised which only needs a single patient's PET image as the training label and the corresponding MR image as the network input. The performance of U-DRCM was evaluated against several established PVC approaches, including Richardson-Lucy (RL), reblurred Van-Cittert (RVC), iterative Yang (IY), neural blind deconvolution (NBD), and deep convolutional neural network (DeepPVC) using both simulated BrainWeb phantom and real clinical datasets. In the simulation study, U-DRCM consistently outperformed competing methods across multiple quantitative metrics, achieved a higher peak signal-to-noise ratio (PSNR), an improved structural similarity index (SSIM), and a lower root mean square error (RMSE). For the real clinical study, U-DRCM delivered substantial improvements in standardized uptake value (SUV) and standardized uptake value ratio (SUVR) across various brain volumes of interest (VOIs). Experimental results show that U-DRCM effectively mitigates the impact of PVE, resulting in high-quality PVC PET images with enhanced brain visualization.

The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) improves image qualities. However, implementing the cutting-edge model-based deep learning methods for TOF-PET reconstruction is challenging due to the substantial memory requirements. In this study, we presented a novel model-based deep learning approach, LMPDNet, for TOF-PET reconstruction from list-mode data. We addressed the issue of real-time parallel computation of the projection matrix for list-mode data, and proposed an iterative model-based module that utilized a dedicated network model for list-mode data. Our experimental results indicated that the proposed LMPDNet outperformed traditional iteration-based TOF-PET list-mode reconstruction algorithms. Additionally, we compared the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.