Structure-guided MR-to-CT synthesis with spatial and semantic alignments for attenuation correction of whole-body PET/MR imaging

Image synthesis from Magnetic Resonance (MR) to Computed Tomography (CT) can estimate the electron density of tissues, thereby facilitating Positron Emission Tomography (PET) attenuation correction in whole-body PET/MR imaging. Whole-body MR-to-CT synthesis faces several challenges including the spatial misalignment caused by tissue variety and respiratory movements, and the complex intensity mapping due to large intensity variations across the whole body. However, existing MR-to-CT synthesis methods mainly focus on body sub-regions, making them ineffective in addressing these challenges. Here we propose a novel whole-body MR-to-CT synthesis framework, which consists of three novel modules to tackle these challenges: (1) Structure-Guided Synthesis module leverages structure-guided attention gates to enhance synthetic image quality by diminishing unnecessary contours of soft tissues; (2) Spatial Alignment module yields precise registration between paired MR and CT images by taking into account the impacts of tissue volumes and respiratory movements, thus providing well-aligned ground-truth CT images during training; (3) Semantic Alignment module utilizes contrastive learning to constrain organ-related semantic information, thereby ensuring the semantic authenticity of synthetic CT images. Extensive experiments demonstrate that our method produces visually plausible and semantically accurate CT images, outperforming existing approaches in both synthetic image quality and PET attenuation correction accuracy.