  Non-contrast CT (NCCT) imaging may reduce image contrast and anatomical
visibility, potentially increasing diagnostic uncertainty. In contrast,
contrast-enhanced CT (CECT) facilitates the observation of regions of interest
(ROI). Leading generative models, especially the conditional diffusion model,
demonstrate remarkable capabilities in medical image modality transformation.
Typical conditional diffusion models commonly generate images with guidance of
segmentation labels for medical modal transformation. Limited access to
authentic guidance and its low cardinality can pose challenges to the practical
clinical application of conditional diffusion models. To achieve an equilibrium
of generative quality and clinical practices, we propose a novel Syncretic
generative model based on the latent diffusion model for medical image
translation (S$^2$LDM), which can realize high-fidelity reconstruction without
demand of additional condition during inference. S$^2$LDM enhances the
similarity in distinct modal images via syncretic encoding and diffusing,
promoting amalgamated information in the latent space and generating medical
images with more details in contrast-enhanced regions. However, syncretic
latent spaces in the frequency domain tend to favor lower frequencies, commonly
locate in identical anatomic structures. Thus, S$^2$LDM applies adaptive
similarity loss and dynamic similarity to guide the generation and supplements
the shortfall in high-frequency details throughout the training process.
Quantitative experiments confirm the effectiveness of our approach in medical
image translation. Our code will release lately.
