# Lung-DDPM+: Efficient Thoracic CT Image Synthesis using Diffusion Probabilistic Model arXiv: https://arxiv.org/abs/2508.09327 · HTML: https://mlsanity.com/paper/2508.09327/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. - Builds on 22 of its 43 references that are in the map. - First posted on arXiv 12 Aug 2025. ## Placed in - Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/) ## Builds on (most cited first, 22 of 22) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022) - [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022) - [DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis](https://mlsanity.com/paper/2008.05865/) (2020) - [Learning to Efficiently Sample from Diffusion Probabilistic Models](https://mlsanity.com/paper/2106.03802/) (2021) - [Accelerating Diffusion Models via Early Stop of the Diffusion Process](https://mlsanity.com/paper/2205.12524/) (2022) - [Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis](https://mlsanity.com/paper/2305.18453/) (2023) - [PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior](https://mlsanity.com/paper/2106.06406/) (2021) - [Towards Generalizable Tumor Synthesis](https://mlsanity.com/paper/2402.19470/) (2024) - [Simple and Effective VAE Training with Calibrated Decoders](https://mlsanity.com/paper/2006.13202/) (2020) - [Label-Free Liver Tumor Segmentation](https://mlsanity.com/paper/2303.14869/) (2023) - [Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation](https://mlsanity.com/paper/2004.12537/) (2020) - [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022) - [Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices](https://mlsanity.com/paper/2410.11795/) (2024) - [COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network](https://mlsanity.com/paper/2007.14638/) (2020) - [Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study](https://mlsanity.com/paper/2303.17114/) (2023) - [Generative Enhancement for 3D Medical Images](https://mlsanity.com/paper/2403.12852/) (2024) - [Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis](https://mlsanity.com/paper/2502.15204/) (2025) - [Physics-Inspired Generative Models in Medical Imaging: A Review](https://mlsanity.com/paper/2407.10856/) (2024) ## Built on by (most cited first, 4 of 4) - [Auditing Sybil: Explaining Deep Lung Cancer Risk Prediction Through Generative Interventional Attributions](https://mlsanity.com/paper/2602.02560/) (2026) - [CTForensics: A Comprehensive Dataset and Method for AI-Generated CT Image Detection](https://mlsanity.com/paper/2603.01878/) (2026) - [HUydra: Full-Range Lung CT Synthesis via Multiple HU Interval Generative Modelling](https://mlsanity.com/paper/2603.23041/) (2026) - [DiffSegLung: Diffusion Radiomic Distillation for Unsupervised Lung Pathology Segmentation](https://mlsanity.com/paper/2605.11758/) (2026) ## Abstract (arXiv) Generative artificial intelligence (AI) has been playing an important role in various domains. Leveraging its high capability to generate high-fidelity and diverse synthetic data, generative AI is widely applied in diagnostic tasks, such as lung cancer diagnosis using computed tomography (CT). However, existing generative models for lung cancer diagnosis suffer from low efficiency and anatomical imprecision, which limit their clinical applicability. To address these drawbacks, we propose Lung-DDPM+, an improved version of our previous model, Lung-DDPM. This novel approach is a denoising diffusion probabilistic model (DDPM) guided by nodule semantic layouts and accelerated by a pulmonary DPM-solver, enabling the method to focus on lesion areas while achieving a better trade-off between sampling efficiency and quality. Evaluation results on the public LIDC-IDRI dataset suggest that the proposed method achieves 8× fewer FLOPs (floating point operations per second), 6.8× lower GPU memory consumption, and 14× faster sampling compared to Lung-DDPM. Moreover, it maintains comparable sample quality to both Lung-DDPM and other state-of-the-art (SOTA) generative models in two downstream segmentation tasks. We also conducted a Visual Turing Test by an experienced radiologist, showing the advanced quality and fidelity of synthetic samples generated by the proposed method. These experimental results demonstrate that Lung-DDPM+ can effectively generate high-quality thoracic CT images with lung nodules, highlighting its potential for broader applications, such as general tumor synthesis and lesion generation in medical imaging. The code and pretrained models are available at https://github.com/Manem-Lab/Lung-DDPM-PLUS.