Multimodal feature‐guided diffusion model for low‐count PET image denoising

BACKGROUND: To minimize radiation exposure while obtaining high-quality Positron Emission Tomography (PET) images, various methods have been developed to derive standard-count PET (SPET) images from low-count PET (LPET) images. Although deep learning methods have enhanced LPET images, they rarely utilize the rich complementary information from MR images. Even when MR images are used, these methods typically employ early, intermediate, or late fusion strategies to merge features from different CNN streams, failing to fully exploit the complementary properties of multimodal fusion. PURPOSE: In this study, we introduce a novel multimodal feature-guided diffusion model, termed MFG-Diff, designed for the denoising of LPET images with the full utilization of MRI. METHODS: MFG-Diff replaces random Gaussian noise with LPET images and introduces a novel degradation operator to simulate the physical degradation processes of PET imaging. Besides, it uses a novel cross-modal guided restoration network to fully exploit the modality-specific features provided by the LPET and MR images and utilizes a multimodal feature fusion module employing cross-attention mechanisms and positional encoding at multiple feature levels for better feature fusion. RESULTS: Under four counts (2.5%, 5.0%, 10%, and 25%), the images generated by our proposed network showed superior performance compared to those produced by other networks in both qualitative and quantitative evaluations, as well as in statistical analysis. In particular, the peak-signal-to-noise ratio of the generated PET images improved by more than 20% under a 2.5% count, the structural similarity index improved by more than 16%, and the root mean square error reduced by nearly 50%. On the other hand, our generated PET images had significant correlation (Pearson correlation coefficient, 0.9924), consistency, and excellent quantitative evaluation results with the SPET images. CONCLUSIONS: The proposed method outperformed existing state-of-the-art LPET denoising models and can be used to generate highly correlated and consistent SPET images obtained from LPET images.