Reliable confidence measures of metrics derived from medical imaging reconstruction pipelines would improve the standard of decision-making in many clinical workflows. Conformal Prediction (CP) provides a robust framework for producing calibrated prediction intervals, but standard CP formulations face a critical challenge in the imaging pipeline: common mismatches between image reconstruction objectives and downstream metrics can introduce systematic prediction deviations from ground truth values, known as bias. These biases in turn compromise the efficiency of prediction intervals, which is a problem that has been unexplored in the CP literature. In this study, we formalize the behavior of symmetric (where bounds expand equally in both directions) and asymmetric (where bounds expand unequally) formulations for common non-conformity scores in CP in the presence of bias, and argue that this measurable bias must inform the choice of CP formulation. We theoretically and empirically demonstrate that symmetric intervals are inflated by a factor of two times the magnitude of bias while asymmetric intervals remain unaffected by bias, and provide conditions under which each formulation produces tighter intervals. We empirically validated our theoretical analyses on sparse-view CT reconstruction for downstream radiotherapy planning. Our work enables users of medical imaging pipelines to proactively select optimal CP formulations, thereby improving interval length efficiency for critical downstream metrics.

翻译：从医学影像重建流程中获取的度量指标若能具备可靠的置信度评估，将显著提升众多临床工作流程中的决策标准。保形预测（CP）为生成校准的预测区间提供了稳健的框架，但标准的CP公式在成像流程中面临一个关键挑战：图像重建目标与下游度量指标之间常见的失配可能引入系统性的预测偏差（即偏置），使其偏离真实值。这些偏置进而会损害预测区间的效率，而这一问题在CP文献中尚未被探讨。在本研究中，我们形式化地分析了在存在偏置的情况下，CP中常见非共形度量的对称（区间边界向两侧等量扩展）与不对称（区间边界向两侧不等量扩展）公式的行为，并论证这种可测量的偏置必须指导CP公式的选择。我们从理论和实证上证明，对称区间会因偏置幅度两倍的因子而膨胀，而不对称区间则不受偏置影响，同时给出了每种公式能产生更紧凑区间的条件。我们在用于下游放射治疗计划的稀疏视图CT重建任务上，通过实验验证了我们的理论分析。我们的工作使医学成像流程的用户能够主动选择最优的CP公式，从而为关键的下游度量指标提升区间长度的效率。