发表机构
School of Systems Science and Industrial Engineering, Binghamton University（宾夕法尼亚大学系统科学与工业工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出可靠性感知框架，通过数据学习阈值将配准质量转化为风险类别，用多种指标评估可靠性，经训练阈值用于测试患者，表明仿射配准更优，可靠性过滤配准可改善对齐，还做了多方面分析。
AI 中文摘要
多模态CT-MRI配准对图像引导放疗等至关重要，但多数流程仅报告总体质量指标。我们提出一个可靠性感知框架，利用数据学习的阈值将配准质量转换为绿色/黄色/红色风险类别。在来自脑、腹部和颈部解剖结构的18名患者的90对切片上，使用刚性和仿射变换将CT图像配准到T1加权MRI。使用Delta NMI、Delta SSIM、Dice重叠、配准稳定性和反向一致性误差评估可靠性，并组合成单个分数R。从训练患者中学习的阈值原封不动地应用于保留的测试患者。在NMI和SSIM上，仿射配准优于刚性配准，绿色分类分别为44%和33%。与未过滤的方法相比，可靠性过滤的配准改善了平均对齐轮廓。按解剖结构分析显示出很大差异，腹部配准的可靠性比脑部配准更强。权重敏感性分析确定Dice重叠是主要的可靠性组成部分。所提出的框架为多模态配准提供了一个可解释的质量控制层，而风险阈值反映的是统计而非临床验证。
英文摘要
Multimodal CT-MRI registration is central to image-guided radiotherapy, surgical navigation, and diagnostic workflows, but most pipelines report only aggregate quality metrics without per-case reliability signals. We propose a reliability-aware framework that converts registration quality into Green/Yellow/Red risk categories using data-learned thresholds. CT images were registered to T1-weighted MRI using rigid and affine transformations on 90 paired slices from 18 patients across brain, abdominal, and neck anatomies. Reliability was assessed using Delta NMI, Delta SSIM, Dice overlap, registration stability, and inverse consistency error, combined into a single score R. Thresholds learned from training patients were applied unchanged to held-out test patients. Affine registration outperformed rigid registration on NMI and SSIM, yielding 44% Green classifications versus 33% for rigid. Reliability-filtered registrations improved the average alignment profile compared with unfiltered methods. Per-anatomy analysis showed substantial variation, with stronger reliability for abdominal registrations than brain registrations. Weight sensitivity analysis identified Dice overlap as the dominant reliability component. The proposed framework provides an interpretable quality-control layer for multimodal registration, while risk thresholds reflect statistical rather than clinical validation.
