发表机构
Johns Hopkins University; Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学; 约翰斯·霍普金斯大学医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对三叉神经痛MRI-MRA配准，提出以ROI为中心的神经血管评估基准，基于149例患者数据，发现传统评估误导，强调局部、血管感知、对比度和视野敏感的评估原则。
AI 中文摘要
三叉神经痛（TN）的术前评估通常需要联合解读结构MRI（用于显示三叉神经及周围脑池解剖结构）和飞行时间MRA（用于突出血管结构）。尽管MRI-MRA融合在可视化神经血管压迫方面具有临床吸引力，但这一任务在传统的全脑配准评估中难以被充分体现，因为临床相关目标是一个小的三叉神经ROI，血管标注是部分且临床聚焦的，局部TOF-MRA对比度可变，且视野不匹配可能限制可变形配准。我们将TN MRI-MRA融合问题形式化为一个以ROI为中心的神经血管配准评估问题，并基于149名患者的临床医生标注的双侧三叉神经ROI构建了一个基准。使用局部基于图像的指标、分割派生的血管定位指标、预测体积分析以及对比度和视野分层比较，评估了六个代表性配准流程。传统的评估总结往往具有误导性：局部图像相似性、血管-背景可分离性和下游血管定位并未对方法进行一致排序；单侧血管距离在部分标注下受预测血管范围影响强烈；局部MRA对比度决定了血管可分离性指标何时具有信息量。可变形细化相对于仿射配准仅提供了小的、依赖视野的改进，而读者审查显示，局部有利的血管距离可能与全局不合理的配准共存。这些发现表明，TN MRI-MRA配准应作为一项局部的、血管感知的、对比度敏感的和视野感知的可视化任务来评估，而非作为通用的多模态脑配准。我们的代码可在以下网址公开获取：此https URL。
英文摘要
Preoperative evaluation of trigeminal neuralgia (TN) often requires joint interpretation of structural MRI, which depicts the trigeminal nerve and surrounding cisternal anatomy, and time-of-flight MRA, which highlights vascular structures. Although MRI-MRA fusion is clinically attractive for visualizing neurovascular compression, this task is poorly captured by conventional whole-brain registration evaluation because the clinically relevant target is a small trigeminal ROI, vessel annotations are partial and clinically focused, local TOF-MRA contrast is variable, and field-of-view mismatch can limit deformable alignment. We formulate TN MRI-MRA fusion as an ROI-centered neurovascular registration-evaluation problem and construct a benchmark from 149 patients with clinician-annotated bilateral trigeminal ROIs. Six representative registration pipelines were evaluated using local image-based metrics, segmentation-derived vessel-localization metrics, prediction-volume analysis, and contrast- and FOV-stratified comparisons. Conventional evaluation summaries were often misleading: local image similarity, vessel-background separability, and downstream vessel localization did not co-rank methods; one-sided vessel distances were strongly affected by predicted vessel extent under partial annotations; and local MRA contrast determined when vessel-separability metrics were informative. Deformable refinement provided only a small, FOV-dependent benefit over affine alignment, while reader review showed that locally favorable vessel distances could coexist with globally implausible registrations. These findings indicate that TN MRI-MRA registration should be evaluated as a local, vessel-aware, contrast-sensitive, and FOV-aware visualization task rather than as generic multimodal brain registration. Our code is publicly available at https://github.com/jhuldr/TN-Reg-Benchmark.
CommentsIncludes supplementary material. Code: https://github.com/jhuldr/TN-Reg-Benchmark
