# Structure-Cross Guidance Module

γ-photon flow-field images reconstructed from limited coincidence events have blurred boundaries and local structures that noise can swamp; a conventional conditional diffusion model injects the degraded image only by concatenation, so the structural prior is diluted through repeated downsampling, and its frequency processing does not change with the diffusion step. The Structure-Frequency Guided Diffusion (SFGD) model feeds edge and morphology priors into the denoiser throughout via a multi-scale structure stream and windowed cross-attention, and tunes the range and gain of frequency enhancement by diffusion timestep. On a phantom-level independent test set, the full model reaches 28.325 dB PSNR and 0.926 SSIM, 5.499 dB and 0.153 above the conditional diffusion baseline.

γ-photon flow-field images reconstructed from limited coincidence events have blurred boundaries and local structures that noise can swamp; a conventional conditional diffusion model injects the degraded image only by concatenation, so the structural prior is diluted through repeated downsampling, and its frequency processing does not change with the diffusion step. The Structure-Frequency Guided Diffusion (SFGD) model feeds edge and morphology priors into the denoiser throughout via a multi-scale structure stream and windowed cross-attention, and tunes the range and gain of frequency enhancement by diffusion timestep. On a phantom-level independent test set, the full model reaches 28.325 dB PSNR and 0.926 SSIM, 5.499 dB and 0.153 above the conditional diffusion baseline.