Better brain scan alignment improves nerve and blood vessel views
Evaluation Principles for MRI-MRA Registration in Trigeminal Neuralgia: An ROI-Centered Neurovascular Benchmark
Computer Vision and Pattern Recognition
Summary
Trigeminal neuralgia is a painful nerve condition that doctors often study using two types of brain scans to see nerves and blood vessels. The researchers found that usual ways to align these scans don't work well because they focus on the whole brain instead of the tiny nerve area that matters most. They created a special test and showed that alignment methods need to focus on nerve regions, blood vessels, and scan details to be useful. Their work helps make better combined images for doctors studying this pain disorder.
What this means in practice
- •For medical imaging teams: Improve alignment of MRI and MRA scans focusing on trigeminal nerve areas for better surgical planning support in trigeminal neuralgia.
- •For radiology software developers: Create specialized tools that evaluate scan fusion quality by nerve region and vessel contrast to enhance clinical neurovascular visualization.
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.