This work introduces a new latent diffusion model to generate high-quality 3D chest CT scans conditioned on 3D anatomical masks. The method synthesizes volumetric images of size 256x256x256 at 1 mm isotropic resolution using a single mid-range GPU, significantly lowering the computational cost compared to existing approaches. The conditioning masks delineate lung and nodule regions, enabling precise control over the output anatomical features. Experimental results demonstrate that conditioning solely on nodule masks leads to anatomically incorrect outputs, highlighting the importance of incorporating global lung structure for accurate conditional synthesis. The proposed approach supports the generation of diverse CT volumes with and without lung nodules of varying attributes, providing a valuable tool for training AI models or healthcare professionals.

翻译：本研究提出一种新的潜在扩散模型，用于在三维解剖掩模条件下生成高质量的三维胸部CT扫描。该方法使用单台中端GPU即可合成尺寸为256×256×256、各向同性分辨率为1 mm的体数据图像，与现有方法相比显著降低了计算成本。条件掩模可精确勾勒肺部与结节区域，从而实现对输出解剖特征的精准控制。实验结果表明，仅依靠结节掩模作为条件会导致解剖结构错误的输出，这凸显了整合全局肺部结构对于实现精准条件合成的重要性。所提出的方法支持生成具有不同属性（含或不含肺结节）的多样化CT体数据，为训练人工智能模型或医疗专业人员提供了有价值的工具。