Deep generative priors for 3D brain analysis

• URL: http://arxiv.org/abs/2510.15119v1
• Date: Thu, 16 Oct 2025 20:20:50 GMT
• Title: Deep generative priors for 3D brain analysis
• Abstract summary: We present the first general-purpose application of diffusion models as priors for solving medical imaging inverse problems.<n>Our approach leverages a score-based diffusion prior trained extensively on diverse brain MRI data, paired with flexible forward models that capture common image processing tasks.
• Score: 9.407614414126142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have recently emerged as powerful generative models in medical imaging. However, it remains a major challenge to combine these data-driven models with domain knowledge to guide brain imaging problems. In neuroimaging, Bayesian inverse problems have long provided a successful framework for inference tasks, where incorporating domain knowledge of the imaging process enables robust performance without requiring extensive training data. However, the anatomical modeling component of these approaches typically relies on classical mathematical priors that often fail to capture the complex structure of brain anatomy. In this work, we present the first general-purpose application of diffusion models as priors for solving a wide range of medical imaging inverse problems. Our approach leverages a score-based diffusion prior trained extensively on diverse brain MRI data, paired with flexible forward models that capture common image processing tasks such as super-resolution, bias field correction, inpainting, and combinations thereof. We further demonstrate how our framework can refine outputs from existing deep learning methods to improve anatomical fidelity. Experiments on heterogeneous clinical and research MRI data show that our method achieves state-of-the-art performance producing consistent, high-quality solutions without requiring paired training datasets. These results highlight the potential of diffusion priors as versatile tools for brain MRI analysis.

Related papers

• Towards a general-purpose foundation model for fMRI analysis [58.06455456423138]
  We introduce NeuroSTORM, a framework that learns from 4D fMRI volumes and enables efficient knowledge transfer across diverse applications.<n>NeuroSTORM is pre-trained on 28.65 million fMRI frames (>9,000 hours) from over 50,000 subjects across multiple centers and ages 5 to 100.<n>It outperforms existing methods across five tasks: age/gender prediction, phenotype prediction, disease diagnosis, fMRI-to-image retrieval, and task-based fMRI.
  arXiv  Detail & Related papers  (2025-06-11T23:51:01Z)
• DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting [11.9146658587331]
  Brain inpainting offers a viable, although difficult, solution for tumor tissue processing.<n>This work proposes DiffKAN-Inpainting, an innovative method that blends diffusion models with the Kolmogorov-Arnold Networks architecture.<n>Both qualitative and quantitative results demonstrate that as compared to the state-of-the-art methods, our proposed DiffKAN-Inpainting inpaints more detailed and realistic reconstructions on the BraTS dataset.
  arXiv  Detail & Related papers  (2025-02-24T01:34:28Z)
• Bidirectional Brain Image Translation using Transfer Learning from Generic Pre-trained Models [0.0]
  In the medical domain, where obtaining labeled medical images is labor-intensive and expensive, addressing data scarcity is a major challenge.<n>Recent studies propose using transfer learning to overcome this issue.<n>In this work, transfer learning was applied to the task of MR-CT image translation and vice versa using 18 pre-trained non-medical models.
  arXiv  Detail & Related papers  (2025-01-21T20:30:15Z)
• Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis [55.959002385347645]
  Latent Drifting enables diffusion models to be conditioned for medical images fitted for the complex task of counterfactual image generation.<n>We evaluate our method on three public longitudinal benchmark datasets of brain MRI and chest X-rays for counterfactual image generation.
  arXiv  Detail & Related papers  (2024-12-30T01:59:34Z)
• BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation [6.5388528484686885]
  This study introduces a novel approach towards the creation of medical foundation models. Our method involves a novel two-stage pretraining approach using vision transformers. BrainFounder demonstrates a significant performance gain, surpassing the achievements of previous winning solutions.
  arXiv  Detail & Related papers  (2024-06-14T19:49:45Z)
• Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks [0.0]
  Brain disorders are a major challenge to global health, causing millions of deaths each year. Accurate diagnosis of these diseases relies heavily on advanced medical imaging techniques such as MRI and CT. The scarcity of annotated data poses a significant challenge in deploying machine learning models for medical diagnosis.
  arXiv  Detail & Related papers  (2024-05-06T07:44:46Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans [11.420077093805382]
  We propose to use graph neural networks (GNN) that are designed to deal with problems in non-Euclidean domains. In this study, we demonstrate how brain networks can be created from sMRI or PET images. We then present a multi-modal GNN framework where each modality has its own branch of GNN and a technique is proposed to combine the multi-modal data.
  arXiv  Detail & Related papers  (2023-07-31T02:04:05Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Ultrasound Signal Processing: From Models to Deep Learning [64.56774869055826]
  Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge.
  arXiv  Detail & Related papers  (2022-04-09T13:04:36Z)

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.