An efficient 3D latent diffusion model for T1-contrast enhanced MRI generation

Gadolinium-based contrast agents (GBCAs) are commonly employed with T1-weighted (T1w) MRI to enhance lesion visualization but are restricted in patients at risk of nephrogenic systemic fibrosis. In addition, 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. 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 Brain Tumor Segmentation (BraTS) 2024 glioma (GLI; 1480 patients), meningioma (MEN; 1141 patients), and metastases (MET; 1475 patients) datasets. Selected patients were split into training (N = 2860), test (N = 614), and validation (N = 612) sets. Model performance was evaluated with the normalized mean squared error (NMSE) and structural similarity index measure (SSIM). Both qualitative and quantitative results demonstrate that the T1C-RFlow model outperforms benchmark 3D models (pix2pix, denoising diffusion probability models (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. In a blinded reader study of 15 patients (5 GLI, 5 MEN, 5 MET), T1C-RFlow received the highest diagnostic-quality scores across all tumor types (3.80 ± 0.45, 3.20 ± 0.45, and 2.60 ± 0.89 on a 5-point Likert scale), significantly outperforming all baseline methods (p < .05). Further studies showed T1C-RFlow to have the best tumor reconstruction performance and significantly faster denoising times (6.9 s volume^-1, 200 steps) than conventional DDPM models in both latent space (37.7 s, 1000 steps) and patch-based in image space (4.3 h volume^-1). Our proposed method generates synthetic T1C images that closely resemble radiological features of 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. Code is made available at https://github.com/zacheidex/An-Efficient-3D-Latent-Diffusion-Model-for-T1-contrast-Enhanced-MRI-Generation.