MRI and MRA alignment improves nerve and vessel imaging in trigeminal neuralgia
Evaluation Principles for MRI-MRA Registration in Trigeminal Neuralgia: An ROI-Centered Neurovascular Benchmark
Computer Vision and Pattern Recognition
Summary
Trigeminal neuralgia is a condition where nerves and blood vessels near the face cause pain, and doctors use MRI and MRA scans to see these structures. Combining these scans is tricky because the important areas are small and the scans show different details. The authors studied different ways to align MRI and MRA images specifically around the nerve region and found that common methods might not work well for this task. They suggest focusing on local blood vessels and scan differences to better assess how well the scans match. Their work helps improve how doctors visually examine these images before surgery.
trigeminal neuralgiaMRIMRAimage registrationregion of interestneurovascularimage fusiondeformable alignmenttime-of-flightcontrast
Authors
Xupeng Zhang, Xihang Wang, Michael Xie, Haoyuan Liang, Hau Ern Lien, Oishika Das, James Feghali, Risheng Xu, Peirong Liu
Abstract
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.