提出高效3D扩散模型，加速医学影像生成且保持高保真度。
VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation
- 采用可变步长去噪机制，显著提升推理速度
- 在缺失MRI合成任务中达SOTA，Dice最高0.88
- 适合医疗影像生成场景，尤其受限于算力与时间的场景
扩散模型虽能生成高质量合成数据，但推理速度慢。本文提出3D可变步长去噪扩散概率模型（VS-DDPM），在保持生成质量的同时大幅加速推理。我们在BraTS2025和SynthRAD2025挑战赛的四个任务上测试：缺失MRI、肿瘤去除、MRI-to-sCT和CBCT-to-sCT。VS-DDPM在缺失MRI合成任务中表现最优，增强肿瘤、肿瘤核心和整体肿瘤的Dice分数分别为0.80、0.83、0.88，结构相似性指数（SSIM）达0.95。在肿瘤去除任务中，均方根误差（RMSE）为0.053，峰值信噪比（PSNR）为26.77，SSIM为0.918。在MRI-to-sCT和CBCT-to-sCT任务中表现良好但未达最优，可能因预处理/后处理流程敏感或损失函数配置所致。结果表明，VS-DDPM是高保真3D医学图像合成的鲁棒且可调方案。代码开源：https://github.com/andre-fs-ferreira/SynthRAD_by_Faking_it。
原文摘要 · Abstract (English)
Diffusion models produce high-quality synthetic data but suffer from slow inference. We propose 3D Variable-Step Denoising Diffusion Probabilistic Model (VS-DDPM) a framework engineered to maintain generative quality while accelerating inference by several factors. We tested our approach on four tasks (missing MRI, tumor removal, MRI-to-sCT, and CBCT-to-sCT) within the BraTS2025 and SynthRAD2025 challenges. Designed for high efficiency under hardware and time constrains imposed by both challenges. VS-DDPM achieved state-of-the-art (SOTA) performance in missing MRI synthesis, yielding Dice scores of 0.80, 0.83, and 0.88 for the enhancing tumor, tumor core, and whole tumor regions, respectively, alongside a structural similarity index (SSIM) of 0.95. For MRI tumor removal, the model attained a root mean squared error (RMSE) of 0.053, a peak signal-to-noise ratio (PSNR) of 26.77, and an SSIM of 0.918. While the framework demonstrated competitive performance in MRI-to-sCT and CBCT-to-sCT tasks, it did not reach SOTA benchmarks, potentially due to sensitivities in data pre and post-processing pipelines or specific loss function configurations. These results demonstrate that VS-DDPM provides a robust and tunable solution for high-fidelity 3D medical image synthesis. The code is available in https://github.com/andre-fs-ferreira/SynthRAD_by_Faking_it.
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