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ADP-DiT, a novel diffusion transformer, was developed to synthesize subject-specific longitudinal MRI scans for Alzheimer's Disease progression by conditioning on clinically interpretable text prompts encoding follow-up time and patient metadata. The model leverages dual text encoders (OpenCLIP and T5) for vision-language alignment and clinical language understanding, respectively, fusing embeddings into the DiT architecture via cross-attention and adaptive layer normalization. Experiments on a large longitudinal MRI dataset demonstrate that ADP-DiT significantly improves image quality (SSIM and PSNR) and captures relevant AD progression-related changes compared to a DiT baseline.
Synthesizing realistic, time-specific brain MRIs that reflect individual Alzheimer's progression is now possible by conditioning a diffusion transformer on detailed clinical text prompts.
Alzheimer's disease (AD) progresses heterogeneously across individuals, motivating subject-specific synthesis of follow-up magnetic resonance imaging (MRI) to support progression assessment. While Diffusion Transformers (DiT), an emerging transformer-based diffusion model, offer a scalable backbone for image synthesis, longitudinal AD MRI generation with clinically interpretable control over follow-up time and participant metadata remains underexplored. We present ADP-DiT, an interval-aware, clinically text-conditioned diffusion transformer for longitudinal AD MRI synthesis. ADP-DiT encodes follow-up interval together with multi-domain demographic, diagnostic (CN/MCI/AD), and neuropsychological information as a natural-language prompt, enabling time-specific control beyond coarse diagnostic stages. To inject this conditioning effectively, we use dual text encoders-OpenCLIP for vision-language alignment and T5 for richer clinical-language understanding. Their embeddings are fused into DiT through cross-attention for fine-grained guidance and adaptive layer normalization for global modulation. We further enhance anatomical fidelity by applying rotary positional embeddings to image tokens and performing diffusion in a pre-trained SDXL-VAE latent space to enable efficient high-resolution reconstruction. On 3,321 longitudinal 3T T1-weighted scans from 712 participants (259,038 image slices), ADP-DiT achieves SSIM 0.8739 and PSNR 29.32 dB, improving over a DiT baseline by +0.1087 SSIM and +6.08 dB PSNR while capturing progression-related changes such as ventricular enlargement and shrinking hippocampus. These results suggest that integrating comprehensive, subject-specific clinical conditions with architectures can improve longitudinal AD MRI synthesis.
