标题： 通过对比视觉语言预训练的3D潜在扩散模型进行文本到CT生成 显示英文标题

标题： Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining

摘要： 目标：虽然近期在文本条件生成模型方面的进展已经实现了逼真的医学图像合成，但这些进步主要局限于二维成像模态（如胸部X光片）。将文本到图像的生成扩展到体积计算断层扫描（CT）仍然是一项重大挑战，因为三维医学成像具有高维度性、解剖复杂性，并且缺乏能够对齐视觉语言数据的稳健框架。 方法：我们提出了一种用于文本到CT生成的新架构，结合了潜在扩散模型和三维对比视觉语言预训练方案。我们的方法利用双编码器CLIP风格模型（该模型基于配对的CT体积和放射学报告进行训练），以建立共享嵌入空间，作为生成的条件输入。通过预训练的体积VAE，CT体积被压缩到低维潜在空间，从而实现高效的三维去噪扩散，无需外部超分辨率阶段。 结果：我们在CT-RATE数据集上评估了我们的方法，并对图像保真度、临床相关性和语义对齐进行了全面评估。我们的模型在所有任务中都表现出竞争性能，显著优于先前的文本到CT生成基线。此外，我们证明了由我们的框架合成的CT扫描可以有效增强真实数据，提高下游诊断性能。 结论：我们的研究表明，特定模态的视觉语言对齐是高质量三维医学图像生成的关键组成部分。通过整合对比预训练和体积扩散，我们的方法提供了一种可扩展且可控的解决方案，可以从文本中合成具有临床意义的CT体积，为数据增强、医学教育和自动化临床模拟开辟了新的应用领域。 显示英文摘要

摘要： Objective: While recent advances in text-conditioned generative models have enabled the synthesis of realistic medical images, progress has been largely confined to 2D modalities such as chest X-rays. Extending text-to-image generation to volumetric Computed Tomography (CT) remains a significant challenge, due to its high dimensionality, anatomical complexity, and the absence of robust frameworks that align vision-language data in 3D medical imaging. Methods: We introduce a novel architecture for Text-to-CT generation that combines a latent diffusion model with a 3D contrastive vision-language pretraining scheme. Our approach leverages a dual-encoder CLIP-style model trained on paired CT volumes and radiology reports to establish a shared embedding space, which serves as the conditioning input for generation. CT volumes are compressed into a low-dimensional latent space via a pretrained volumetric VAE, enabling efficient 3D denoising diffusion without requiring external super-resolution stages. Results: We evaluate our method on the CT-RATE dataset and conduct a comprehensive assessment of image fidelity, clinical relevance, and semantic alignment. Our model achieves competitive performance across all tasks, significantly outperforming prior baselines for text-to-CT generation. Moreover, we demonstrate that CT scans synthesized by our framework can effectively augment real data, improving downstream diagnostic performance. Conclusion: Our results show that modality-specific vision-language alignment is a key component for high-quality 3D medical image generation. By integrating contrastive pretraining and volumetric diffusion, our method offers a scalable and controllable solution for synthesizing clinically meaningful CT volumes from text, paving the way for new applications in data augmentation, medical education, and automated clinical simulation.