Mamba-SUM: A Mamba-Based Framework with Wavelet Transformation for Total-Body Ultra-low-dose PET/CT Imaging.

Abstract

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 ± 3.26, SSIM: 0.92 ± 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.