为深度学习CT重建提供有理论保证的置信区间，可检测图像幻觉。
Principled Confidence Estimation for Deep Computed Tomography
- 基于泊松噪声的物理模型，构建具理论覆盖保证的置信区域。
- 深度方法生成的置信区间比传统方法窄得多，且保持严格覆盖率。
- 适用于U-Net、扩散模型等，适合医疗影像中需可信度评估的场景。
我们提出一种针对计算机断层扫描（CT）重建的系统性置信度估计框架。基于顺序似然混合框架（Kirschner et al., 2025），该框架在遵循贝-朗伯定律的真实前向模型下，为基于深度学习的CT重建建立具有理论覆盖保证的置信区域。该模型为对数线性形式并包含泊松噪声，贴近临床与科研成像实际。该方法通用性强，适用于经典算法及深度学习重建方法，包括U-Net、U-Net集成和生成式扩散模型。实验证明，深度重建方法在不牺牲理论覆盖性的前提下，可获得显著更紧的置信区域。本方法可识别重建图像中的幻觉，并提供可解释的置信区域可视化。这使深度模型不仅成为强大估计器，更成为具备不确定性感知能力的可靠医疗影像工具。
原文摘要 · Abstract (English)
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.
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