QuantEIT：用于胸部电阻抗断层成像的超轻量量子辅助推断
QuantEIT: Ultra-Lightweight Quantum-Assisted Inference for Chest Electrical Impedance Tomography
AI总结：
QuantEIT首次将并行2量子比特量子电路与单线性层结合，以无监督、免训练方式实现EIT图像重建，仅用0.2%参数即超越传统方法并增强抗噪性。
摘要：
电阻抗断层成像（EIT）是一种无创、低成本、具有高时间分辨率的床旁成像模态，适用于床旁监测。然而，其固有的病态逆问题给精确图像重建带来了重大挑战。基于深度学习（DL）的方法已展现出潜力，但通常依赖具有大量参数的复杂网络架构，限制了效率和可扩展性。在此，我们提出了一种用于EIT图像重建的超轻量量子辅助推断（QuantEIT）框架。QuantEIT利用量子辅助网络（QA-Net），结合并行的2量子比特量子电路来生成具有表达力的潜在表示，这些表示充当隐式非线性先验，随后通过单个线性层进行电导率重建。该设计大幅降低了模型复杂度和参数数量。独特的是，QuantEIT以无监督、无需训练数据的方式运行，并首次将量子电路集成到EIT图像重建中。在模拟和真实的2D及3D EIT肺部成像数据上进行的大量实验表明，QuantEIT优于传统方法，仅使用0.2%的参数即可达到相当或更优的重建精度，并且对噪声具有更强的鲁棒性。
英文摘要：
Electrical Impedance Tomography (EIT) is a non-invasive, low-cost bedside imaging modality with high temporal resolution, making it suitable for bedside monitoring. However, its inherently ill-posed inverse problem poses significant challenges for accurate image reconstruction. Deep learning (DL)-based approaches have shown promise but often rely on complex network architectures with a large number of parameters, limiting efficiency and scalability. Here, we propose an Ultra-Lightweight Quantum-Assisted Inference (QuantEIT) framework for EIT image reconstruction. QuantEIT leverages a Quantum-Assisted Network (QA-Net), combining parallel 2-qubit quantum circuits to generate expressive latent representations that serve as implicit nonlinear priors, followed by a single linear layer for conductivity reconstruction. This design drastically reduces model complexity and parameter number. Uniquely, QuantEIT operates in an unsupervised, training-data-free manner and represents the first integration of quantum circuits into EIT image reconstruction. Extensive experiments on simulated and real-world 2D and 3D EIT lung imaging data demonstrate that QuantEIT outperforms conventional methods, achieving comparable or superior reconstruction accuracy using only 0.2% of the parameters, with enhanced robustness to noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
