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Deep-learning-based MR-to-CT synthesis can estimate the electron density of tissues, thereby facilitating PET attenuation correction in whole-body PET/MR imaging. However, whole-body MR-to-CT synthesis faces several challenges including the issue of spatial misalignment and the complexity of intensity mapping, primarily due to the variety of tissues and organs throughout the whole body. 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.We conduct extensive experiments to demonstrate that the proposed whole-body MR-to-CT framework can produce visually plausible and semantically realistic CT images, and validate its utility in PET attenuation correction. |
