Multi-Conditional Diffusion Framework with Texture Constraints for Clinically-Reliable Lesion Synthesis in Virtual Imaging Trials.

Abstract

The clinical translation of AI in medical imaging faces critical challenges including scare annotated data, long-tailed pathological distributions, and privacy constraints in virtual imaging trials. To address these limitations, we propose a Multi-conditional Diffusion framework with Texture Constraints (MDTC) for synthesizing clinically reliable lesions in CT images. The key innovation lies in the joint integration of anatomical mask guidance and a Gray-Level Co-occurence Matrix-based texture classifier into the diffusion network. This dual-constraint mechanism uniquely enforces structural fidelity and pathologically heterogeneous characteristics in synthetic lesions, effectively preserves pathological heterogeneity and simultaneously enhances the authenticity of the generated lesionst in existing methods. The experiments on hepatocellular carcinoma and pulmonary nodule datasets demonstrate that the proposed MDTC method achieves favorable performance in terms of texture fidelity, a significant improvement in PSNR, and notable optimization in FID compared to classic generation models. Furthermore, downstream classifiers trained on synthetic data remain capable of maintaining the vast majority of baseline classification performance, while data augmentation for rare pulmonary nodules significantly improved the classification efficacy. This work establishes a new paradigm for generating diagnostically meaningful synthetic data, effectively alleviating critical bottlenecks in virtual imaging trial. The experimental results demonstrate that MDTC-generated lesions preserve structural authenticity and have potential to mitigate texture homogenization, thereby enabling robust downstream diagnostic model training and offering a practical, privacy preserving solution to expand virtual imaging trials toward underrepresented disease groups and real world clinical workflows.