An Efficient Residual-based Low-dose PET Reconstruction with Spatial-Frequency Integration

Positron emission tomography (PET) is a nuclear medical imaging technique where image quality depends on the dose of radionuclides administered to the patient. While standard-dose PET (SPET) offers high-quality imaging, it also poses radiation risks. If reconstructing low-dose PET (LPET) images can guarantee the same level of imaging quality, LPET could serve as a safer alternative by reducing patient radiation exposure. Recent approaches using convolutional neural networks (CNNs) struggle to capture complex features of LPET images due to the fixed convolutional kernels and local receptive fields. Generative adversarial network (GAN)-based methods may suffer from mode collapse and unstable training. Diffusion model-based works can ensure stability and high-quality sample generation but come with high computational costs. Furthermore, all these methods focus solely on spatial domain information, neglecting crucial frequency domain information.To address these issues, we propose an efficient residual-based LPET reconstruction framework combining CNN and diffusion model. Our framework includes a multi-scale dynamic convolution network with a wavelet reconstruction module (MN-WR) to leverage multi-scale spatial and frequency domain information. We also introduce a residual-based diffusion model (ReD) to enhance local detail reconstruction while reducing computational costs. Finally, we design a wavelet-based frequency domain fusion (WFF) module to combine low-frequency edge information from MN-WR with high-frequency details from ReD. Experimental results on public LPET datasets show that our method outperforms state-of-the-art techniques and significantly reduces computational costs. Code is available at https://github.com/TAI-Medical-Lab/LPET-Reconstruction.