Long-axial PET/CT systems have enabled ultrahigh sensitivity and a longer axial field of view for clinical imaging and diagnosis. However, radiation risks from radiotracers and CT scans have remained a persistent concern within total-body PET/CT systems. Conventional approaches focus mainly on PET radiotracer-based dose reduction, ignoring the substantial radiation burden inherent in CT acquisition. Therefore, we proposed a hybrid ultra-low-dose imaging framework (Mamba-SUM) for total-body PET/CT systems to restore high-quality PET images from ultra-low-dose PET (ULD PET) and ultra-low-dose CT (ULD CT) images. Our method innovatively integrates the Mamba architecture with wavelet transformation, enabling effective modeling of long-range dependencies while reducing computational overhead. Specifically, ULD PET and ULD CT images are first subjected to domain decomposition. Afterward, a custom-designed Low-Frequency Enhancement Module and a High-Frequency Denoising Module work in concert to leverage cross-domain and multimodal information, enhancing structural details and suppressing noise across different frequency subbands. Finally, a Mamba-based decoder progressively reconstructs the refined features to produce high-quality PET images with improved fidelity and diagnostic value. Experimental results have illustrated that our method achieved superior performance (PSNR: 28.88 dB $\pm ~3.26$ , SSIM: $0.92~\pm ~0.16$ , p< 0.05) compared with other models (VMamba, Mamba-Swin, SwinTransformer, CycleGAN and UNet). Moreover, the statistical analysis also revealed that the data distribution of our generated PET images was consistent with that of the ground truth (Pearson Correlation Coefficient> 0.95, p< 0.05). Our Mamba-SUM has provided a computationally effective approach for total-body ultra-low-dose PET/CT imaging. The code is available at https://github.com/LEE12365/Mamba-SUM.

In total-body positron emission tomography and computed tomography (PET/CT) imaging, reducing the radiation dose of diagnostic CT scans is essential for minimizing overall radiation exposure, particularly in pediatric patients. Although deep learning-based denoising methods have shown promise in restoring low-dose CT (LDCT) to normal-dose CT (NDCT) quality, most approaches rely on structurally aligned paired data, which are difficult to acquire in clinical practice. Models trained on synthetic pairs often exhibit limited generalizability to real LDCT data. Unconditional diffusion models demonstrate outstanding generalizability, but fail to preserve structural fidelity. To address these challenges, we propose an enhanced diffusion posterior sampling (E-DPS) framework that combines a one-step denoiser U-Net with an unconditional diffusion model. Specifically, the U-Net estimator, trained on simulated LDCT-NDCT pairs, provides preliminary denoised outputs as structural constraints, whereas the diffusion model captures the prior distribution of NDCT images to enhance realism and generalizability. During inference, the U-Net predictions are integrated as constraints with tunable weights, thereby guiding diffusion posterior sampling. In addition, an intermediate-stage initialization strategy is introduced, significantly reducing the number of required sampling steps. Extensive experiments on simulated LDCT datasets across three dose levels demonstrate the superiority of our method, yielding average PSNR gains of +5.2% and +4.3% at unseen dose levels compared with state-of-the-art approaches. Moreover, on real LDCT images, E-DPS exhibits strong zero-shot generalizability, achieving better noise suppression while preserving anatomical detail. These results highlight the robustness and clinical potential of E-DPS for LDCT denoising.

The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and complementary information for single low-count PET image denoising, especially in ultralow-count (2.5%) cases. To this end, we propose a novel two-stream dual PET/MR cross-modal interactive fusion network with an optical flow pre-alignment module, namely, OIF-Net. Specifically, the learnable optical flow registration module enables the spatial manipulation of MR imaging inputs within the network without any extra training supervision. Registered MR images fundamentally solve the problem of feature misalignment in the multimodal fusion stage, which greatly benefits the subsequent denoising process. In addition, we design a spatial-channel feature enhancement module (SC-FEM) that considers the interactive impacts of multiple modalities and provides additional information flexibility in both the spatial and channel dimensions. Furthermore, instead of simply concatenating two extracted features from these two modalities as an intermediate fusion method, the proposed cross-modal feature fusion module (CM-FFM) adopts cross-attention at multiple feature levels and greatly improves the two modalities' feature fusion procedure. Extensive experimental assessments conducted on real clinical datasets, as well as an independent clinical testing dataset, demonstrate that the proposed OIF-Net outperforms the state-of-the-art methods.

The axial field of view (FOV) is a key factor that affects the quality of PET images. Due to hardware FOV restrictions, conventional short-axis PET scanners with FOVs of 20 to 35 cm can acquire only low-quality PET (LQ-PET) images in fast scanning times (2-3 minutes). To overcome hardware restrictions and improve PET image quality for better clinical diagnoses, several deep learning-based algorithms have been proposed. However, these approaches use simple convolution layers with residual learning and local attention, which insufficiently extract and fuse long-range contextual information. To this end, we propose a novel two-branch network architecture with swin transformer units and graph convolution operation, namely SW-GCN. The proposed SW-GCN provides additional spatial- and channel-wise flexibility to handle different types of input information flow. Specifically, considering the high computational cost of calculating self-attention weights in full-size PET images, in our designed spatial adaptive branch, we take the self-attention mechanism within each local partition window and introduce global information interactions between nonoverlapping windows by shifting operations to prevent the aforementioned problem. In addition, the convolutional network structure considers the information in each channel equally during the feature extraction process. In our designed channel adaptive branch, we use a Watts Strogatz topology structure to connect each feature map to only its most relevant features in each graph convolutional layer, substantially reducing information redundancy. Moreover, ensemble learning is adopted in our SW-GCN for mapping distinct features from the two well-designed branches to the enhanced PET images. We carried out extensive experiments on three single-bed position scans for 386 patients. The test results demonstrate that our proposed SW-GCN approach outperforms state-of-the-art methods in both quantitative and qualitative evaluations.