无需高质量参考图，直接用噪声数据训练扩散模型重建MRI。
A Self-supervised Diffusion Bridge for MRI Reconstruction
- 从噪声测量中自监督学习，无需清晰图像作为参考。
- 在压缩感知MRI上优于传统去噪扩散模型，重建更精准。
- 适合无高质量对照图像的医学影像重建场景。
扩散桥（Diffusion Bridges, DBs）是一类通过插值两个配对图像分布实现快速采样的扩散模型。传统DBs训练需高质量参考图像，限制了其在缺乏此类参考的场景中的应用。本文提出SelfDB，一种直接在可用噪声测量数据上训练DB的新方法，无需任何高质量参考图像。SelfDB通过进一步对现有测量数据进行两次子采样，训练神经网络逆转对应的退化过程，并以原始测量数据为训练目标。我们在压缩感知MRI任务上验证了SelfDB，结果表明其性能显著优于去噪扩散模型。
原文摘要 · Abstract (English)
Diffusion bridges (DBs) are a class of diffusion models that enable faster sampling by interpolating between two paired image distributions. Training traditional DBs for image reconstruction requires high-quality reference images, which limits their applicability to settings where such references are unavailable. We propose SelfDB as a novel self-supervised method for training DBs directly on available noisy measurements without any high-quality reference images. SelfDB formulates the diffusion process by further sub-sampling the available measurements two additional times and training a neural network to reverse the corresponding degradation process by using the available measurements as the training targets. We validate SelfDB on compressed sensing MRI, showing its superior performance compared to the denoising diffusion models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。