We present a principled framework for confidence estimation in computed tomography (CT) reconstruction. Based on the sequential likelihood mixing framework (Kirschner et al., 2025), we establish confidence regions with theoretical coverage guarantees for deep-learning-based CT reconstructions. We consider a realistic forward model following the Beer-Lambert law, i.e., a log-linear forward model with Poisson noise, closely reflecting clinical and scientific imaging conditions. The framework is general and applies to both classical algorithms and deep learning reconstruction methods, including U-Nets, U-Net ensembles, and generative Diffusion models. Empirically, we demonstrate that deep reconstruction methods yield substantially tighter confidence regions than classical reconstructions, without sacrificing theoretical coverage guarantees. Our approach allows the detection of hallucinations in reconstructed images and provides interpretable visualizations of confidence regions. This establishes deep models not only as powerful estimators, but also as reliable tools for uncertainty-aware medical imaging.

翻译：我们提出了一种用于计算机断层成像（CT）重建中置信度估计的原理性框架。基于序列似然混合框架（Kirschner等人，2025），我们为基于深度学习的CT重建建立了具有理论覆盖保证的置信区域。我们采用遵循比尔-朗伯定律的真实前向模型，即具有泊松噪声的对数线性前向模型，该模型紧密反映了临床与科学成像条件。该框架具有通用性，适用于经典算法与深度学习重建方法，包括U-Net、U-Net集成以及生成式扩散模型。实证研究表明，深度重建方法能在不牺牲理论覆盖保证的前提下，产生比经典重建方法显著更紧凑的置信区域。我们的方法能够检测重建图像中的幻觉伪影，并提供可解释的置信区域可视化。这确立了深度模型不仅作为强大的估计器，同时也可作为不确定性感知医学成像的可靠工具。