用3D扩散模型快速生成脑肿瘤MRI增强图像，省去对比剂使用。
An Efficient 3D Latent Diffusion Model for T1-contrast Enhanced MRI Generation
- 在潜空间训练3D修正流扩散模型，提升生成效率。
- 生成图像与真实增强图相似度高（SSIM>0.9），且速度比传统模型快5倍以上。
- 适合医学影像生成、无对比剂MRI研究者参考。
目的：钆基对比剂（GBCAs）常用于T1加权MRI以增强病灶可视化，但对肾功能不全患者存在风险，且对比剂注射差异会导致成像不一致。本研究提出一种高效的3D深度学习框架，从非增强多参数MRI生成T1增强图像（T1C）。方法：提出3D潜在修正流模型（T1C-RFlow），将T1w和T2-FLAIR图像输入预训练自编码器获取高效潜空间表示，随后在该空间训练修正流扩散模型。模型在整合BraTS 2024胶质瘤（GLI；1480例）、脑膜瘤（MEN；1141例）及转移瘤（MET；1475例）的数据集上训练，患者按2860（训练）、612（验证）、614（测试）划分。结果：定性与定量评估表明，T1C-RFlow优于同潜空间训练的基准3D模型（pix2pix、DDPM、DiT-3D）。性能指标为：GLI：NMSE 0.044±0.047，SSIM 0.935±0.025；MEN：NMSE 0.046±0.029，SSIM 0.937±0.021；MET：NMSE 0.098±0.088，SSIM 0.905±0.082。T1C-RFlow在肿瘤重建上表现最佳，且去噪时间仅6.9秒/体素（200步），显著快于传统DDPM（潜空间37.7秒，1000步）和基于图像块的方法（4.3小时/体素）。意义：所提方法可在更短时间内生成接近真实T1C的合成图像，未来或可实现脑肿瘤无对比剂MRI的临床应用。
原文摘要 · Abstract (English)
Objective: Gadolinium-based contrast agents (GBCAs) are commonly employed with T1w MRI to enhance lesion visualization but are restricted in patients at risk of nephrogenic systemic fibrosis and variations in GBCA administration can introduce imaging inconsistencies. This study develops an efficient 3D deep-learning framework to generate T1-contrast enhanced images (T1C) from pre-contrast multiparametric MRI. Approach: We propose the 3D latent rectified flow (T1C-RFlow) model for generating high-quality T1C images. First, T1w and T2-FLAIR images are input into a pretrained autoencoder to acquire an efficient latent space representation. A rectified flow diffusion model is then trained in this latent space representation. The T1C-RFlow model was trained on a curated dataset comprised of the BraTS 2024 glioma (GLI; 1480 patients), meningioma (MEN; 1141 patients), and metastases (MET; 1475 patients) datasets. Selected patients were split into train (N=2860), validation (N=612), and test (N=614) sets. Results: Both qualitative and quantitative results demonstrate that the T1C-RFlow model outperforms benchmark 3D models (pix2pix, DDPM, Diffusion Transformers (DiT-3D)) trained in the same latent space. T1C-RFlow achieved the following metrics - GLI: NMSE 0.044 +/- 0.047, SSIM 0.935 +/- 0.025; MEN: NMSE 0.046 +/- 0.029, SSIM 0.937 +/- 0.021; MET: NMSE 0.098 +/- 0.088, SSIM 0.905 +/- 0.082. T1C-RFlow had the best tumor reconstruction performance and significantly faster denoising times (6.9 s/volume, 200 steps) than conventional DDPM models in both latent space (37.7s, 1000 steps) and patch-based in image space (4.3 hr/volume). Significance: Our proposed method generates synthetic T1C images that closely resemble ground truth T1C in much less time than previous diffusion models. Further development may permit a practical method for contrast-agent-free MRI for brain tumors.
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