The trigeminal root: an anatomical study using magnetic resonance imaging

The practical limits of high-quality magnetic resonance imaging for the diagnosis and classification of trigeminal neuralgia

BACKGROUND

Neurovascular compression (NVC) has been the primary hypothesis for the underlying mechanism of classical trigeminal neuralgia (TN). However, a substantial body of literature has emerged highlighting notable exceptions to this hypothesis. The purpose of this study is to assess the reliability and diagnostic accuracy of high resolution, high contrast MRI-determined neurovascular contact for TN.

METHODS

We performed a retrospective, randomized, and blinded parallel characterization of neurovascular interaction and diagnosis in a population of TN patients and controls using four expert reviewers. Performance statistics were calculated, as well as assessments for generalizability using shuffled bootstraps.

RESULTS

Fair to moderate agreement (ICC: 0.32-0.68) about diagnosis between reviewers was observed using MRIs from 47 TN patients and 47 controls. On average reviewers performed no better than chance when diagnosing participants, with an accuracy of 0.57 (95% CI 0.40, 0.59) per patient.

CONCLUSION

While MRI is useful in determining structural causes in secondary TN, expert reviewers do no better to only slightly better than chance with distinguishing TN with MRI, despite moderate agreement. Further, the causal role of NVC for TN is not clear, limiting the applicability of MRI to diagnose or prognosticate treatment of TN.

Meckel Cave: An Anatomical Study Using Magnetic Resonance Imaging

OBJECTIVE

To our knowledge, few studies have investigated anatomy of the Meckel cave with neuroimaging modalities. The present study aimed to characterize it using magnetic resonance imaging (MRI).

PATIENTS AND METHODS

Following conventional MRI examination, a total of 101 patients underwent T2-weighted imaging in thin-sliced coronal and sagittal sections, and 11 patients underwent constructive interference steady-state sequences in thin-sliced sagittal sections. Moreover, 3 injected cadaver heads were dissected.

RESULTS

In the cadaver specimens, the size and extent of the cerebrospinal fluid-filled space between the Gasserian ganglion and surrounding arachnoids were difficult to define. On the T2-weighted imaging, the Meckel cave was delineated with variable morphologies and left-right asymmetry. On the sagittal images, the shape of the Meckel cave could be classified into 3 different types, bulbous, oval, and flat, with the oval being the most frequent that comprised 60%. Furthermore, on the sagittal constructive interference steady-state images, parts of the trigeminal nerve distributed in the Meckel cave were delineated in all patients. The ophthalmic, maxillary, and mandibular divisions were clearly distinguished on both sides.

CONCLUSIONS

The Meckel cave is a structure characterized by diverse morphologies and left-right asymmetry. Thin-sliced T2-weighted imaging is useful for exploring the anatomy of the Meckel cave.

Magnetic resonance neurography in the management of trigeminal neuralgia: a cohort study of 55 patients

OBJECTIVE

To explore the usefulness of magnetic resonance neurography (MRN) in the diagnosis and management of trigeminal neuralgia (TN).

STUDY DESIGN

In total, 55 patients clinically diagnosed with TN were imaged with 3.0-T magnetic resonance imaging. Images were reconstructed to show the full course of the trigeminal nerve. Clinical findings included mean duration of symptoms (41.99 months) and mean visual analog scale pain intensity (5.98). Final diagnoses were microvascular compression (19), inflammation (21), microvascular compression with inflammation (5), normal (5), tumor (1), peripheral nerve injury (2), and multiple sclerosis (2).

RESULTS

MRN had substantial impact on diagnosis and treatment in 56.4% of cases. A total of 33 patients underwent intervention for pain. MRN had substantial impact on 54.5% of the treated patients. The correlation between MRN results and intervention response was excellent in 19 patients (57.6%) and moderate in 14 (42.4%). Pain was reduced after surgery or interventional procedure in most cases (75.8%).

CONCLUSIONS

MRN is suitable for the diagnosis of clinical TN with beneficial impact on diagnosis and clinical management and moderate-to-excellent correlation with intervention response. Diagnosis of TN should focus not only on microvascular compression but also on the conditions of the peripheral branches of the trigeminal nerve.

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

Anatomical assessment of trigeminal nerve tractography using diffusion MRI: A comparison of acquisition b-values and single- and multi-fiber tracking strategies

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Investigation of the performance of multiple dMRI acquisitions and fiber models for trigeminal nerve (TGN) identification.

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Expert rating study of over 1000 TGN visualizations using seven proposed expert rating anatomical criteria.

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The two-tensor tractography method had better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method.

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TGN tracking performance was significantly different across the three b-values for almost all structures studied.

Background

The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm² and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN.

Objective

We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN.

Methods

We proposed seven anatomical rating criteria including true and false positive structures, and we performed an expert rating study of over 1000 TGN visualizations, as follows. We tracked the TGN using high-quality dMRI data from 100 healthy adult subjects from the Human Connectome Project (HCP). TGN tracking performance was compared across dMRI acquisitions with b = 1000 s/mm², b = 2000 s/mm² and b = 3000 s/mm², using single-tensor (1T) and two-tensor (2T) unscented Kalman filter (UKF) tractography. This resulted in a total of six tracking strategies. The TGN was identified using an anatomical region-of-interest (ROI) selection approach. First, in a subset of the dataset we identified ROIs that provided good TGN tracking performance across all tracking strategies. Using these ROIs, the TGN was then tracked in all subjects using the six tracking strategies. An expert rater (GX) visually assessed and scored each TGN based on seven anatomical judgment criteria. These criteria included the presence of multiple expected anatomical segments of the TGN (true positive structures), specifically branch-like structures, cisternal portion, mesencephalic trigeminal tract, and spinal cord tract of the TGN. False positive criteria included the presence of any fibers entering the temporal lobe, the inferior cerebellar peduncle, or the middle cerebellar peduncle. Expert rating scores were analyzed to compare TGN tracking performance across the six tracking strategies. Intra- and inter-rater validation was performed to assess the reliability of the expert TGN rating result.

Results

The TGN was selected using two anatomical ROIs (Meckel's Cave and cisternal portion of the TGN). The two-tensor tractography method had significantly better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied. Tracking performance was reported in terms of the percentage of subjects achieving each anatomical rating criterion. Tracking of the cisternal portion and branching structure of the TGN was generally successful, with the highest performance of over 98% using two-tensor tractography and b = 1000 or b = 2000. However, tracking the smaller mesencephalic and spinal cord tracts of the TGN was quite challenging (highest performance of 37.5% and 57.07%, using two-tensor tractography with b = 1000 and b = 2000, respectively). False positive connections to the temporal lobe (over 38% of subjects for all strategies) and cerebellar peduncles (100% of subjects for all strategies) were prevalent. High joint probability of agreement was obtained in the inter-rater (on average 83%) and intra-rater validation (on average 90%), showing a highly reliable expert rating result.

Conclusions

Overall, the results of the study suggest that researchers and clinicians may benefit from tailoring their acquisition and tracking methodology to the specific anatomical portion of the TGN that is of the greatest interest. For example, tracking of branching structures and TGN-T2 overlap can be best achieved with a two-tensor model and an acquisition using b = 1000 or b = 2000. In general, b = 1000 and b = 2000 acquisitions provided the best-rated tracking results. Further research is needed to improve both sensitivity and specificity of the depiction of the TGN anatomy using dMRI.