GL2T-Diff: Medical image translation via spatial-frequency fusion diffusion models

Diffusion Probabilistic Models (DPMs) are effective in medical image translation (MIT), but they tend to lose high-frequency details during the noise addition process, making it challenging to recover these details during the denoising process. This hinders the model’s ability to accurately preserve anatomical details during MIT tasks, which may ultimately affect the accuracy of diagnostic outcomes. To address this issue, we propose a diffusion model (GL²T-Diff) based on convolutional channel and Laplacian frequency attention mechanisms, which is designed to enhance MIT tasks by effectively preserving critical image features. We introduce two novel modules: the Global Channel Correlation Attention Module (GC²A Module) and the Laplacian Frequency Attention Module (LFA Module). The GC²A Module enhances the model’s ability to capture global dependencies between channels, while the LFA Module effectively retains high-frequency components, which are crucial for preserving anatomical structures. To leverage the complementary strengths of both GC²A Module and LFA Module, we propose the Laplacian Convolutional Attention with Phase-Amplitude Fusion (FusLCA), which facilitates effective integration of spatial and frequency domain features. Experimental results show that GL²T-Diff outperforms state-of-the-art (SOTA) methods, including those based on Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other DPMs, across the BraTS-2021/2024, IXI, and Pelvic datasets. The code is available at https://github.com/puzzlesong8277/GL2T-Diff .