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.
Abstract (translated)
最近，扩散模型作为医学成像中的强大生成模型崭露头角。然而，如何将这些数据驱动的模型与领域知识相结合以指导脑部成像问题仍然是一个重大挑战。在神经影像学中，贝叶斯逆问题长期以来为推理任务提供了一个成功的框架，在此框架下，通过融入成像过程的知识能够使性能稳健且无需大量训练数据。然而，这些方法中的解剖建模部分通常依赖于经典的数学先验，这些先验往往无法捕捉到大脑结构的复杂性。 在这项工作中，我们首次将扩散模型作为先验用于解决广泛的医学成像逆问题，并进行了通用应用研究。我们的方法利用了在大量多样化的脑部MRI数据上训练出来的基于分数的扩散先验，并结合灵活的正向模型来捕捉诸如超分辨率、偏场校正、图像填充等常见图像处理任务，以及这些任务的不同组合。我们进一步展示了如何通过我们的框架改进现有深度学习方法的输出以提高解剖学准确性。 在异质性的临床和研究MRI数据上进行的实验表明，我们的方法能够实现业界领先的表现，在无需配对训练数据的情况下生成一致且高质量的结果。这些结果突显了扩散先验作为脑部MRI分析中多功能工具的巨大潜力。
URL
https://arxiv.org/abs/2510.15119
