This paper presents a new method for reconstructing regions of interest (ROI) from a limited number of computed tomography (CT) measurements. Classical model-based iterative reconstruction methods lead to images with predictable features. Still, they often suffer from tedious parameterization and slow convergence. On the contrary, deep learning methods are fast, and they can reach high reconstruction quality by leveraging information from large datasets, but they lack interpretability. At the crossroads of both methods, deep unfolding networks have been recently proposed. Their design includes the physics of the imaging system and the steps of an iterative optimization algorithm. Motivated by the success of these networks for various applications, we introduce an unfolding neural network called U-RDBFB designed for ROI CT reconstruction from limited data. Few-view truncated data are effectively handled thanks to a robust non-convex data fidelity term combined with a sparsity-inducing regularization function. We unfold the Dual Block coordinate Forward-Backward (DBFB) algorithm, embedded in an iterative reweighted scheme, allowing the learning of key parameters in a supervised manner. Our experiments show an improvement over several state-of-the-art methods, including a model-based iterative scheme, a multi-scale deep learning architecture, and other deep unfolding methods.

翻译：本文提出了一种新方法，用于从有限数量的计算机断层扫描(CT)测量中重建感兴趣区域(ROI)。经典的基于模型的迭代重建方法能够生成具有可预测特征的图像，但往往存在参数化繁琐和收敛速度慢的问题。相比之下，深度学习方法速度快，且能通过利用大规模数据集中的信息达到较高的重建质量，但缺乏可解释性。在两者的交汇处，近期提出了深度展开网络，其设计融合了成像系统的物理机制与迭代优化算法的步骤。受此类网络在多种应用中取得成功的启发，我们引入了一种名为U-RDBFB的展开神经网络，专用于有限数据下的ROI CT重建。通过将鲁棒非凸数据保真项与稀疏诱导正则化函数相结合，有效处理了少视角截断数据。我们展开嵌入迭代重加权框架中的对偶坐标前向后向(DBFB)算法，从而实现以监督方式学习关键参数。实验结果表明，与包括基于模型的迭代方案、多尺度深度学习架构及其他深度展开方法在内的多种最先进方法相比，本文方法具有显著优势。