ADP-DiT: Text-Guided Diffusion Transformer for Brain Image Generation in Alzheimer's Disease Progression

• URL: http://arxiv.org/abs/2604.13495v1
• Date: Wed, 15 Apr 2026 05:30:14 GMT
• Title: ADP-DiT: Text-Guided Diffusion Transformer for Brain Image Generation in Alzheimer's Disease Progression
• Abstract summary: We present ADP-DiT, an interval-aware, clinically text-conditioned diffusion transformer for longitudinal AD MRI synthesis.<n>DiT encodes follow-up interval together with multi-domain demographic, diagnostic (CN/MCI/AD), and neuropsychological information as a natural-language prompt.<n>On 3,321 longitudinal 3T T1-weighted scans from 712 participants, 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.
• Score: 0.7773571879033719
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Prob-BBDM: a Probabilistic Brownian Bridge Diffusion Model for MRI sequence image-to-image translation [1.8036000511825068]
  We propose a novel image-to-image translation model based on Brownian Bridge Diffusion Models (BBDM)<n>Our approach integrates a variational encoder-guided diffusion mechanism, leveraging probabilistic image distributions to enhance synthesis quality.<n>Prob-BBDM achieves superior performance across multiple translation tasks, reaching up to 88.46% SSIM and 26.09 dB PSNR.
  arXiv  Detail & Related papers  (2026-06-23T08:47:17Z)
• PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement [60.81732730684265]
  This paper introduces PHASOR, a volumetric diffusion framework for high-fidelity virtual contrast enhancement (VCE)<n>By treating CT volumes as coherent sequences, we leverage a video diffusion model to enhance structural coherence and volumetric accuracy.<n>To ensure anatomy-phase consistent synthesis, we introduce two complementary modules. First, anatomy-routed mixture-of-experts (AR-MoE) anchors distinct enhancement patterns to anatomical semantics, with organ-specific memory to capture salient details.<n>Second, intensity-phase aware representation alignment (IP-REPA) highlights intricate contrast signals while mitigating the impact of imperfect spatial alignment.
  arXiv  Detail & Related papers  (2026-04-01T15:57:18Z)
• 3D-LLDM: Label-Guided 3D Latent Diffusion Model for Improving High-Resolution Synthetic MR Imaging in Hepatic Structure Segmentation [11.912761174094795]
  3D-LLDM is a label-guided 3D latent diffusion model that generates high-quality synthetic magnetic resonance (MR) volumes with corresponding anatomical segmentation masks.<n>Our approach uses hepatobiliary phase MR images enhanced with the Gd-EOBPA contrast agent to derive structural masks for the liver, portal vein, hepatic vein, and hepatobiliary carcinoma.<n>When used for data augmentation, the synthetic volumes improve carcinoma segmentation by up to 11.153% Dice score across five CNN architectures.
  arXiv  Detail & Related papers  (2026-03-25T02:03:10Z)
• Anatomically Guided Latent Diffusion for Brain MRI Progression Modeling [10.62087466710015]
  Anatomically Guided Latent Diffusion Model (AG-LDM) is a segmentation-guided framework that enforces anatomically consistent progression.<n>A lightweight 3D tissue segmentation model (WarpSeg) provides explicit anatomical supervision during both autoencoder fine-tuning and diffusion model training.
  arXiv  Detail & Related papers  (2026-01-21T01:45:36Z)
• Multi-modal MRI-Based Alzheimer's Disease Diagnosis with Transformer-based Image Synthesis and Transfer Learning [0.0]
  Alzheimer's disease (AD) is a progressive neurodegenerative disorder in which pathological changes begin many years before the onset of clinical symptoms.<n>I propose a 3D TransUNet image synthesis framework that predicts FA and MD maps directly from T1w MRI.<n>My model generates high-fidelity maps, achieving a structural similarity index (SSIM) exceeding 0.93 and a strong Pearson correlation (>0.94) with ground-truth dMRI.
  arXiv  Detail & Related papers  (2026-01-09T22:26:38Z)
• Dual-Encoder Transformer-Based Multimodal Learning for Ischemic Stroke Lesion Segmentation Using Diffusion MRI [5.332404648315838]
  We study ischemic stroke lesion segmentation using multimodal diffusion MRI from the ISLES 2022 dataset.<n>Several state-of-the-art convolutional and transformer-based architectures, including U-Net variants, Swin-UNet, and TransUNet, are benchmarked.<n>Results show that transformer-based models outperform convolutional baselines, and the proposed dual-encoder TransUNet achieves the best performance, reaching a Dice score of 85.4% on the test set.
  arXiv  Detail & Related papers  (2025-12-23T15:24:31Z)
• FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI [44.4791295950757]
  We develop an unsupervised anomaly detection (UAD) approach for brain MRI.<n>We conduct the first systematic frequency-domain analysis of pathological signatures.<n>We show that Frequency-Decomposition Preprocessing (FDP) framework can leverage frequency-domain reconstruction for simultaneous pathology suppression and anatomical preservation.
  arXiv  Detail & Related papers  (2025-11-17T02:40:14Z)
• EqDiff-CT: Equivariant Conditional Diffusion model for CT Image Synthesis from CBCT [43.92108185590778]
  Cone-beam computed tomography (CBCT) is widely used for imageguided radiotherapy (IGRT)<n>We propose a novel diffusion-based conditional generative model, coined EqDiff-CT, to synthesize high-quality CT images from CBCT.
  arXiv  Detail & Related papers  (2025-09-26T05:51:59Z)
• Synthesizing Late-Stage Contrast Enhancement in Breast MRI: A Comprehensive Pipeline Leveraging Temporal Contrast Enhancement Dynamics [0.3499870393443268]
  This study presents a pipeline for synthesizing late-phase DCE-MRI images from early-phase data.<n>The proposed approach introduces a novel loss function, Time Intensity Loss (TI-loss), leveraging the temporal behavior of contrast agents to guide the training of a generative model.<n>Two metrics are proposed to evaluate image quality: the Contrast Agent Pattern Score ($mathcalCP_s$), which validates enhancement patterns in annotated regions, and the Average Difference in Enhancement ($mathcalED$), measuring differences between real and generated enhancements.
  arXiv  Detail & Related papers  (2024-09-03T04:31:49Z)
• SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging [59.78761085714715]
  This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification. The proposed framework has been validated through comprehensive experiments on two clinical datasets. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
  arXiv  Detail & Related papers  (2024-02-27T06:32:56Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN [59.60954255038335]
  The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators. Experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.
  arXiv  Detail & Related papers  (2020-06-26T02:50:09Z)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.