用深度学习加速稀疏视角CT重建，提升精度与可解释性
Deep Guess acceleration for explainable image reconstruction in sparse-view CT
- 用训练好的神经网络生成优化算法的初始猜测，加速迭代过程
- 在极稀疏数据下重建效果优于传统方法和当前主流数据驱动模型
- 无需真实图像即可运行，对噪声鲁棒，适合临床实际场景
稀疏视角计算机断层扫描（CT）是一种旨在降低医疗成像中X射线辐射剂量的新兴技术。传统滤波反投影算法因数据稀疏导致严重伪影。尽管基于模型的迭代重建（MBIR）通过正则化能更好抑制噪声，但计算成本过高，难以用于临床。本文提出一种新型加速方法——深度猜测（Deep Guess）方案，利用训练好的神经网络同时加快正则化MBIR并提升重建精度。该方法将先进的深度学习工具用于为非凸模型的近端算法提供智能初始猜测，从而在少数迭代内得到可解释的重建图像。在真实CT图像上的实验表明，在极稀疏成像协议下，该方法显著超越仅依赖变分模型的基准方法以及多项当前最先进的数据驱动方法。此外，我们还实现了无真值的部署，并验证了所提框架对噪声的鲁棒性。
原文摘要 · Abstract (English)
Sparse-view Computed Tomography (CT) is an emerging protocol designed to reduce X-ray dose radiation in medical imaging. Traditional Filtered Back Projection algorithm reconstructions suffer from severe artifacts due to sparse data. In contrast, Model-Based Iterative Reconstruction (MBIR) algorithms, though better at mitigating noise through regularization, are too computationally costly for clinical use. This paper introduces a novel technique, denoted as the Deep Guess acceleration scheme, using a trained neural network both to quicken the regularized MBIR and to enhance the reconstruction accuracy. We integrate state-of-the-art deep learning tools to initialize a clever starting guess for a proximal algorithm solving a non-convex model and thus computing an interpretable solution image in a few iterations. Experimental results on real CT images demonstrate the Deep Guess effectiveness in (very) sparse tomographic protocols, where it overcomes its mere variational counterpart and many data-driven approaches at the state of the art. We also consider a ground truth-free implementation and test the robustness of the proposed framework to noise.
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。