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

TL;DR

This paper presents LAND, a diffusion-based model for generating high-quality 3D chest CT scans conditioned on anatomical masks, enabling controlled and diverse synthesis with reduced computational requirements.

Contribution

The introduction of a novel latent diffusion model that synthesizes 3D chest CTs conditioned on anatomical masks, with improved control and efficiency over prior methods.

Findings

Conditioning on lung and nodule masks improves anatomical accuracy.

The model can generate diverse CT scans with varying nodule attributes.

Using global lung structure is crucial for accurate conditional synthesis.

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 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.