标题： 稀疏视图CT中可解释图像重建的深度猜测加速方法 显示英文标题

标题： Deep Guess acceleration for explainable image reconstruction in sparse-view CT

摘要： 稀疏视图计算机断层扫描（CT）是一种新兴的协议，旨在减少医学成像中的X射线剂量辐射。传统的滤波反投影算法重建由于数据稀疏而受到严重伪影的影响。相比之下，基于模型的迭代重建（MBIR）算法虽然通过正则化更好地抑制了噪声，但计算成本太高，不适合临床使用。本文介绍了一种新技术，称为深度猜测加速方案，使用训练好的神经网络来加快正则化的MBIR并提高重建精度。我们集成了最先进的深度学习工具，为求解非凸模型的邻近算法提供一个聪明的初始猜测，从而在少数迭代中计算出可解释的解决方案图像。在真实CT图像上的实验结果表明，深度猜测在（非常）稀疏的断层扫描协议中是有效的，它克服了其单纯的变分对应方法和许多最先进的数据驱动方法。我们还考虑了一个无真实值的实现，并测试了所提出框架对噪声的鲁棒性。 显示英文摘要

摘要： 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.