LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance

Anna Oliveras, Roger Marí, Rafael Redondo, Oriol Guardià-Olivella, Ana Tost, Bhalaji Nagarajan, Carolina Migliorelli, Vicent Ribas, Petia Radeva

Keywords: Generative models, latent diffusion, CT scans, lung cancer

TL;DR: LAND is an efficient latent diffusion model that generates high-quality synthetic 3D chest CT scans from anatomical masks, enabling precise control over lung and nodule features on a single mid-range GPU.

Abstract: 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 $256\times256\times256$ 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.

Submission Number: 5