Similarity-aware Syncretic Latent Diffusion Model for Medical Image Translation with Representation Learning

TL;DR

This paper introduces S$^2$LDM, a novel latent diffusion model that improves medical image translation by enhancing similarity and detail without requiring additional guidance, addressing limitations of existing models.

Contribution

The paper proposes a syncretic latent diffusion model with adaptive similarity loss for high-fidelity medical image translation without extra guidance during inference.

Findings

Effective in translating NCCT to CECT images with high detail fidelity.

Outperforms existing models in quantitative metrics.

Enhances high-frequency details in generated images.

Abstract

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.