WING generates synthetic CT from MRI/CBCT by decomposing the target into lung, soft-tissue, and bone windows, fusing them via a differentiable soft-fusion operator, and refining with a Transformer, achieving state-of-the-art results on SynthRAD2025.
Enhancing Cross-Modality Synthesis: Subvolume Merging for MRI-to-CT Conversion
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abstract
Providing more precise tissue attenuation information, synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) contributes to improved radiation therapy treatment planning. In our study, we employ the advanced SwinUNETR framework for synthesizing CT from MRI images. Additionally, we introduce a three-dimensional subvolume merging technique in the prediction process. By selecting an optimal overlap percentage for adjacent subvolumes, stitching artifacts are effectively mitigated, leading to a decrease in the mean absolute error (MAE) between sCT and the labels from 52.65 HU to 47.75 HU. Furthermore, implementing a weight function with a gamma value of 0.9 results in the lowest MAE within the same overlap area. By setting the overlap percentage between 50% and 70%, we achieve a balance between image quality and computational efficiency.
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cs.CV 1years
2026 1verdicts
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WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesis
WING generates synthetic CT from MRI/CBCT by decomposing the target into lung, soft-tissue, and bone windows, fusing them via a differentiable soft-fusion operator, and refining with a Transformer, achieving state-of-the-art results on SynthRAD2025.