发表机构
Dundee International Institute, Central South University; Central South University（中南大学邓迪国际学院; 中南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对辐射高斯点云在稀疏视图CT重建中缺乏可信度问题，利用透射X射线成像特性赋予其变分密度后验，通过系统校准研究表明所得不确定性能与真实误差排序相符，还剖析相关现象并指出校准后验指向剂量自适应停止规则。
AI 中文摘要
辐射高斯点云使稀疏视图CT重建快速，但现有方法输出的点估计没有重建可信度的概念。我们利用透射X射线成像的特性，为辐射高斯点云配备变分密度后验，其预测方差在体积空间和投影空间中以闭式精确传播。我们对高斯点云CT进行了首次系统校准研究，结果表明所得体素不确定性在15个官方基准场景中的14个上与真实重建误差排序相符。我们剖析了未校准采集分数仍能选择可接受视图的原因，并指出校准后的后验指向剂量自适应停止规则。
英文摘要
Does an uncertainty map identify where a reconstruction is wrong? In sparse-view computed tomography (CT), we find a sharp gap between whole-volume evaluation and error localization inside the object. We derive clamp-aware analytic moments for factorized Gaussian-density distributions, with a variance pass through existing rendering interfaces that is $7.9\times$ faster than a 16-sample estimator. On a 15-scene benchmark, median variance--error Spearman correlation falls from $0.846$ over the whole volume to $0.108$ in foreground. The pattern recurs across representations and acquisition settings. Two analyses help explain the discrepancy: region contrast dominates global covariance, while $72$--$96\%$ of in-object squared error is shared across independently trained members. Spread is unchanged by a common error, although shared error alone does not determine ranking. Correcting the offset between the deployed reconstruction and predictive mean improves foreground correlation by only $0.0014$. The diagnosis separates two remedies. A log-normal control improves scale transfer without restoring localization; a supervised error predictor raises foreground correlation to $0.427$ on the benchmark and $0.566$ on eight held-out human subjects under simulated acquisition. A verified retrospective re-execution on eight additional subjects retains a median of $0.537$, with the same frozen predictors. The central lesson is to validate uncertainty in the region, and against the error target, for which it will be used.
