标题： 用于微波断层成像的自学习正则化 显示英文标题

标题： Learned Regularization for Microwave Tomography

摘要： 微波断层成像（MWT）旨在从测量的散射电磁场中重建组织的介电特性。 这个反问题高度非线性且不适定，对传统的基于优化的方法提出了重大挑战，尽管这些方法基于物理模型，但通常无法恢复精细的结构细节。 最近的深度学习策略，包括端到端和后处理网络，已提高了重建质量，但通常需要大量配对的训练数据，并可能在泛化方面遇到困难。 为克服这些限制，我们提出了一种物理信息混合框架，该框架在数据一致性驱动的变分方案中集成了扩散模型作为学习正则化。 具体而言，我们引入了 单步扩散正则化（SSD-Reg），一种新颖的方法，将扩散先验嵌入迭代重建过程中，能够在不需要配对数据的情况下恢复复杂的解剖结构。 SSD-Reg保持了对基本物理规律和学习结构分布的保真度，提高了准确性、稳定性和鲁棒性。 大量实验表明，SSD-Reg作为即插即用（PnP）模块实现，为解决功能图像重建中的不适定性提供了灵活有效的解决方案。 显示英文摘要

摘要： Microwave Tomography (MWT) aims to reconstruct the dielectric properties of tissues from measured scattered electromagnetic fields. This inverse problem is highly nonlinear and ill-posed, posing significant challenges for conventional optimization-based methods, which, despite being grounded in physical models, often fail to recover fine structural details. Recent deep learning strategies, including end-to-end and post-processing networks, have improved reconstruction quality but typically require large paired training datasets and may struggle to generalize. To overcome these limitations, we propose a physics-informed hybrid framework that integrates diffusion models as learned regularization within a data-consistency-driven variational scheme. Specifically, we introduce Single-Step Diffusion Regularization (SSD-Reg), a novel approach that embeds diffusion priors into the iterative reconstruction process, enabling the recovery of complex anatomical structures without the need for paired data. SSD-Reg maintains fidelity to both the governing physics and learned structural distributions, improving accuracy, stability, and robustness. Extensive experiments demonstrate that SSD-Reg, implemented as a Plug-and-Play (PnP) module, provides a flexible and effective solution for tackling the ill-posedness inherent in functional image reconstruction.