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The paper introduces a supervise-assisted multi-modality fusion diffusion model (MFdiff) to restore standard-dose PET (SPET) images from low-dose PET (LPET) and MR images. MFdiff uses a multi-modality feature fusion module to learn optimized fusion features from MR images and incorporates these features as additional conditions in a diffusion model for iterative SPET image generation. A two-stage supervise-assisted learning strategy leverages both generalized priors from simulated data and specific priors from in-vivo data to improve restoration quality, demonstrating superior performance compared to existing methods.
Achieve state-of-the-art PET image restoration by fusing multi-modal (PET & MR) data within a diffusion model framework, guided by a novel two-stage supervised learning approach that handles both in-distribution and out-of-distribution data.
Positron emission tomography (PET) offers powerful functional imaging but involves radiation exposure. Efforts to reduce this exposure by lowering the radiotracer dose or scan time can degrade image quality. While using magnetic resonance (MR) images with clearer anatomical information to restore standard-dose PET (SPET) from low-dose PET (LPET) is a promising approach, it faces challenges with the inconsistencies in the structure and texture of multi-modality fusion, as well as the mismatch in out-of-distribution (OOD) data. In this paper, we propose a supervise-assisted multi-modality fusion diffusion model (MFdiff) for addressing these challenges for high-quality PET restoration. Firstly, to fully utilize auxiliary MR images without introducing extraneous details in the restored image, a multi-modality feature fusion module is designed to learn an optimized fusion feature. Secondly, using the fusion feature as an additional condition, high-quality SPET images are iteratively generated based on the diffusion model. Furthermore, we introduce a two-stage supervise-assisted learning strategy that harnesses both generalized priors from simulated in-distribution datasets and specific priors tailored to in-vivo OOD data. Experiments demonstrate that the proposed MFdiff effectively restores high-quality SPET images from multi-modality inputs and outperforms state-of-the-art methods both qualitatively and quantitatively.