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

A novel indicator to predict the outcome of percutaneous stereotactic radiofrequency rhizotomy for trigeminal neuralgia patients: diffusivity metrics of MR-DTI

Magnetic resonance-diffusion tensor imaging (MR-DTI) has been used in the microvascular decompression and gamma knife radiosurgery in trigeminal neuralgia (TN) patients; however, use of percutaneous stereotactic radiofrequency rhizotomy (PSR) to target an abnormal trigeminal ganglion (ab-TG) is unreported. Fractional anisotropy (FA), mean and radial diffusivity (MD and RD, respectively), and axial diffusivity (AD) of the trigeminal nerve (CNV) were measured in 20 TN patients and 40 healthy control participants immediately post PSR, at 6-months, and at 1 year. Longitudinal alteration of the diffusivity metrics and any correlation with treatment effects, or prognoses, were analyzed. In the TN group, either low FA (value < 0.30) or a decreased range compared to the adjacent FA (dFA) > 17% defined an ab-TG. Two-to-three days post PSR, all 15 patients reported decreased pain scores with increased FA at the ab-TG (P < 0.001), but decreased MD and RD (P < 0.01 each). Treatment remained effective in 10 of 14 patients (71.4%) and 8 of 12 patients (66.7%) at the 6-month and 1-year follow-ups, respectively. In patients with ab-TGs, there was a significant difference in treatment outcomes between patients with low FA values (9 of 10; 90%) and patients with dFA (2 of 5; 40%) (P < 0.05). MR-DTI with diffusivity metrics correlated microstructural CNV abnormalities with PSR outcomes. Of all the diffusivity metrics, FA could be considered a novel objective quantitative indicator of treatment effects and a potential indicator of PSR effectiveness in TN patients.

Intra-parotid facial nerve path by MRI tractography: radio-clinical comparison in parotid tumors

PURPOSE

The objective of our study was to evaluate the ability of preoperative MRI tractography to visualize and predict the path of the facial nerve with respect to an intra-parotid mass.

METHODS

We performed an observational bicentric study from June 2019 to August 2020. All patients older than 18 years old, treated for a parotid mass with surgical indication, without MRI contraindication and who agreed to participate in the study were enrolled prospectively. All patients underwent a cervico-facial MRI with tractographic analysis. Postprocessed tractography images of the intra-parotid facial nerve were analyzed by two expert radiologists in head and neck imaging. The intraoperative anatomical description of the facial nerve path and its relationship to the mass was performed by the surgeon during the operation, with no visibility on MRI examination results. A statistical study allowed for the description of the data collected as well as the measurement of inter-observer agreement and agreement between tractography and surgery using kappa coefficients.

RESULTS

Fifty-two patients were included. The facial nerve trunk and its first two divisional branches were visualized via tractography in 93.5% of cases (n = 43). The upper distal branches were visualized in 51.1% of cases (n = 23), and the lower branches were visualized in 73.3% of cases (n = 33). Agreement with the location described per-operatively was on average 82.9% for the trunk, 74.15% for the temporal branch, and 75.21% for the cervico-facial branch.

CONCLUSION

Fiber tractography analysis by MRI of the intra-parotid facial nerve appears to be a good test for predicting the path of the nerve over the parotid mass and could be an additional tool to guide the surgeon in the operative procedure.

Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning

The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods

The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.

A unified global tractography framework for automatic visual pathway reconstruction

The human visual pathway starts from the retina, passes through the retinogeniculate visual pathway, the optic radiation, and finally connects to the primary visual cortex. Diffusion MRI tractography is the only technology that can noninvasively reconstruct the visual pathway. However, complete and accurate visual pathway reconstruction is challenging because of the skull base environment and complex fiber geometries. Specifically, the optic nerve within the complex skull base environment can cause abnormal diffusion signals. The crossing and fanning fibers at the optic chiasm, and a sharp turn of Meyer's loop at the optic radiation, contribute to complex fiber geometries of the visual pathway. A fiber trajectory distribution (FTD) function-based tractography method of our previous work and several high sensitivity tractography methods can reveal these complex fiber geometries, but are accompanied by false-positive fibers. Thus, the related studies of the visual pathway mostly applied the expert region of interest selection strategy. However, interobserver variability is an issue in reconstructing an accurate visual pathway. In this paper, we propose a unified global tractography framework to automatically reconstruct the visual pathway. We first extend the FTD function to a high-order streamline differential equation for global trajectory estimation. At the global level, the tractography process is simplified as the estimation of global trajectory distribution coefficients by minimizing the cost between trajectory distribution and the selected directions under the prior guidance by introducing the tractography template as anatomic priors. Furthermore, we use a deep learning-based method and tractography template prior information to automatically generate the mask for tractography. The experimental results demonstrate that our proposed method can successfully reconstruct the visual pathway with high accuracy.

Probabilistic tractography of the extracranial branches of the trigeminal nerve using diffusion tensor imaging

PURPOSE

The peripheral course of the trigeminal nerves is complex and spans multiple bony foramen and tissue compartments throughout the face. Diffusion tensor imaging of these nerves is difficult due to the complex tissue interfaces and relatively low MR signal. The purpose of this work is to develop a method for reliable diffusion tensor imaging-based fiber tracking of the peripheral branches of the trigeminal nerve.

METHODS

We prospectively acquired imaging data from six healthy adult participants with a 3.0-Tesla system, including T2-weighted short tau inversion recovery with variable flip angle (T2-STIR-SPACE) and readout segmented echo planar diffusion weighted imaging sequences. Probabilistic tractography of the ophthalmic, infraorbital, lingual, and inferior alveolar nerves was performed manually and assessed by two observers who determined whether the fiber tracts reached defined anatomical landmarks using the T2-STIR-SPACE volume.

RESULTS

All nerves in all subjects were tracked beyond the trigeminal ganglion. Tracts in the inferior alveolar and ophthalmic nerve exhibited the strongest signal and most consistently reached the most distal landmark (58% and 67%, respectively). All tracts of the inferior alveolar and ophthalmic nerve extended beyond their respective third benchmarks. Tracts of the infraorbital nerve and lingual nerve were comparably lower-signal and did not consistently reach the furthest benchmarks (9% and 17%, respectively).

CONCLUSION

This work demonstrates a method for consistently identifying and tracking the major nerve branches of the trigeminal nerve with diffusion tensor imaging.

Vestibular paroxysmia entails vestibular nerve function, microstructure and endolymphatic space changes linked to root-entry zone neurovascular compression

Combining magnetic resonance imaging (MRI) sequences that permit the determination of vestibular nerve angulation (NA = change of nerve caliber or direction), structural nerve integrity via diffusion tensor imaging (DTI), and exclusion of endolymphatic hydrops (ELH) via delayed gadolinium-enhanced MRI of the inner ear (iMRI) could increase the diagnostic accuracy in patients with vestibular paroxysmia (VP). Thirty-six participants were examined, 18 with VP (52.6 ± 18.1 years) and 18 age-matched with normal vestibulocochlear testing (NP 50.3 ± 16.5 years). This study investigated whether (i) NA, (ii) DTI changes, or (iii) ELH occur in VP, and (iv) to what extent said parameters relate. Methods included vestibulocochlear testing and MRI data analyses for neurovascular compression (NVC) and NA verification, DTI and ELS quantification. As a result, (i) NA increased NVC specificity. (ii) DTI structural integrity was reduced on the side affected by VP (p < 0.05). (iii) 61.1% VP showed mild ELH and higher asymmetry indices than NP (p > 0.05). (iv) "Disease duration" and "total number of attacks" correlated with the decreased structural integrity of the affected nerve in DTI (p < 0.001). NVC distance within the nerve's root-entry zone correlated with nerve function (Roh = 0.72, p < 0.001), nerve integrity loss (Roh = - 0.638, p < 0.001), and ELS volume (Roh = - 0.604, p < 0.001) in VP. In conclusion, this study is the first to link eighth cranial nerve function, microstructure, and ELS changes in VP to clinical features and increased vulnerability of NVC in the root-entry zone. Combined MRI with NVC or NA verification, DTI and ELS quantification increased the diagnostic accuracy at group-level but did not suffice to diagnose VP on a single-subject level due to individual variability and lack of diagnostic specificity.

Visualization of human optic nerve by diffusion tensor mapping and degree of neuropathy

Diffusion-weighted magnetic resonance imaging of the human optic nerve and tract is technically difficult because of its small size, the inherent strong signal generated by the surrounding fat and the cerebrospinal fluid, and due to eddy current-induced distortions and subject movement artifacts. The effects of the bone canal through which the optic nerve passes, and the proximity of blood vessels, muscles and tendons are generally unknown. Also, the limited technical capabilities of the scanners and the minimization of acquisition times result in poor quality diffusion-weighted images. It is challenging for current tractography methods to accurately track optic pathway fibers that correspond to known anatomy. Despite these technical limitations and low image resolution, here we show how to visualize the optic nerve and tract and quantify nerve atrophy. Our visualization method based on the analysis of the diffusion tensor shows marked differences between a healthy male subject and a male subject with progressive optic nerve neuropathy. These differences coincide with diffusion scalar metrics and are not visible on standard morphological images. A quantification of the degree of optic nerve atrophy in a systematic way is provided and it is tested on 9 subjects from the Human Connectome Project.

Magnetic resonance imaging of the monkey fetal brain in utero

Non-human primates (NHPs) are the closest living relatives of the human and play a critical role in investigating the effects of maternal viral infection and consumption of medicines, drugs, and alcohol on fetal development. With the advance of contemporary fast MRI techniques with parallel imaging, fetal MRI is becoming a robust tool increasingly used in clinical practice and preclinical studies to examine congenital abnormalities including placental dysfunction, congenital heart disease (CHD), and brain abnormalities non-invasively. Because NHPs are usually scanned under anesthesia, the motion artifact is reduced substantially, allowing multi-parameter MRI techniques to be used intensively to examine the fetal development in a single scanning session or longitudinal studies. In this paper, the MRI techniques for scanning monkey fetal brains in utero in biomedical research are summarized. Also, a fast imaging protocol including T2-weighted imaging, diffusion MRI, resting-state functional MRI (rsfMRI) to examine rhesus monkey fetal brains in utero on a clinical 3T scanner is introduced.

Moyamoya Disease With Initial Ischemic or Hemorrhagic Attack Shows Different Brain Structural and Functional Features: A Pilot Study

Objective

Cerebral ischemia and intracranial hemorrhage are the two main phenotypes of moyamoya disease (MMD). However, the pathophysiological processes of these two MMD phenotypes are still largely unknown. Here, we aimed to use multimodal neuroimaging techniques to explore the brain structural and functional differences between the two MMD subtypes.

Methods

We included 12 patients with ischemic MMD, 10 patients with hemorrhagic MMD, and 10 healthy controls (HCs). Each patient underwent MRI scans and cognitive assessment. The cortical thickness of two MMD subtypes and HC group were compared. Arterial spin labeling (ASL) and diffusion tensor imaging (DTI) were used to inspect the cerebral blood flow (CBF) of cortical regions and the integrity of related white matter fibers, respectively. Correlation analyses were then performed among the MRI metrics and cognitive function scores.

Results

We found that only the cortical thickness in the right middle temporal gyrus (MTG) of hemorrhagic MMD was significantly greater than both ischemic MMD and HC (p < 0.05). In addition, the right MTG showed higher ASL-CBF, and its associated fiber tract (arcuate fasciculus, AF) exhibited higher fractional anisotropy (FA) values in hemorrhagic MMD. Furthermore, the cortical thickness of the right MTG was positively correlated with its ASL-CBF values (r = 0.37, p = 0.046) and the FA values of right AF (r = 0.67, p < 0.001). At last, the FA values of right AF were found to be significantly correlated with cognitive performances within patients with MMD.

Conclusions

Hemorrhagic MMD shows increased cortical thickness on the right MTG in comparison with ischemic MMD and HCs. The increased cortical thickness is associated with the higher CBF values and the increased integrity of the right AF. These findings are important to understand the clinical symptoms and pathophysiology of MMD and further applied to clinical practice.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Automation of Cranial Nerve Tractography by Filtering Tractograms for Skull Base Surgery

Fiber tractography enables the in vivo reconstruction of white matter fibers in 3 dimensions using data collected by diffusion tensor imaging, thereby helping to understand functional neuroanatomy. In a pre-operative context, it provides essential information on the trajectory of fiber bundles of medical interest, such as cranial nerves. However, the optimization of tractography parameters is a time-consuming process and requires expert neuroanatomical knowledge, making the use of tractography difficult in clinical routine. Tractogram filtering is a method used to isolate the most relevant fibers. In this work, we propose to use filtering as a post-processing of tractography to avoid the manual optimization of tracking parameters and therefore making a step forward automation of tractography. To question the feasibility of automated tractography of cranial nerves, we perform an analysis of main cranial nerves on a series of patients with skull base tumors. A quantitative evaluation of the filtering performance of two state-of-the-art and a new entropy-based methods is carried out on the basis of reference tractograms produced by experts. Our approach proves to be more stable in the selection of the optimal filtering threshold and turns out to be interesting in terms of computational time complexity.

Case Report: The Imperfect Association Between Craniofacial Lesion Burden and Pain in Fibrous Dysplasia

Patients with fibrous dysplasia (FD) often present with craniofacial lesions that affect the trigeminal nerve system. Debilitating pain, headache, and migraine are frequently experienced by FD patients with poor prognosis, while some individuals with similar bone lesions are asymptomatic. The clinical and biological factors that contribute to the etiopathogenesis of pain in craniofacial FD are largely unknown. We present two adult females with comparable craniofacial FD lesion size and location, as measured by ¹⁸F-sodium fluoride positron emission tomography/computed tomography (PET/CT), yet their respective pain phenotypes differed significantly. Over 4 weeks, the average pain reported by Patient A was 0.4/0–10 scale. Patient B reported average pain of 7.8/0–10 scale distributed across the entire skull and left facial region. Patient B did not experience pain relief from analgesics or more aggressive treatments (denosumab). In both patients, evaluation of trigeminal nerve divisions (V1, V2, and V3) with CT and magnetic resonance imaging (MRI) revealed nerve compression and displacement with more involvement of the left trigeminal branches relative to the right. First-time employment of diffusion MRI and tractography suggested reduced apparent fiber density within the cisternal segment of the trigeminal nerve, particularly for Patient B and in the left hemisphere. These cases highlight heterogeneous clinical presentation and neurobiological properties in craniofacial FD and also, the disconnect between peripheral pathology and pain severity. We hypothesize that a detailed phenotypic characterization of patients that incorporates an advanced imaging approach probing the trigeminal system may provide enhanced insights into the variable experiences with pain in craniofacial FD.

Automatic oculomotor nerve identification based on data‐driven fiber clustering

The oculomotor nerve (OCN) is the main motor nerve innervating eye muscles and can be involved in multiple flammatory, compressive, or pathologies. The diffusion magnetic resonance imaging (dMRI) tractography is now widely used to describe the trajectory of the OCN. However, the complex cranial structure leads to difficulties in fiber orientation distribution (FOD) modeling, fiber tracking, and region of interest (ROI) selection. Currently, the identification of OCN relies on expert manual operation, resulting in challenges, such as the carries high clinical, time‐consuming, and labor costs. Thus, we propose a method that can automatically identify OCN from dMRI tractography. First, we choose the multi‐shell multi‐tissue constraint spherical deconvolution (MSMT‐CSD) FOD estimation model and deterministic tractography to describe the 3D trajectory of the OCN. Then, we rely on the well‐established computational pipeline and anatomical expertise to create a data‐driven OCN tractography atlas from 40 HCP data. We identify six clusters belonging to the OCN from the atlas, including the structures of three kinds of positional relationships (pass between, pass through, and go around) with the red nuclei and two kinds of positional relationships with medial longitudinal fasciculus. Finally, we apply the proposed OCN atlas to identify the OCN automatically from 40 new HCP subjects and two patients with brainstem cavernous malformation. In terms of spatial overlap and visualization, experiment results show that the automatically and manually identified OCN fibers are consistent. Our proposed OCN atlas provides an effective tool for identifying OCN by avoiding the traditional selection strategy of ROIs.

Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning

Trigeminal neuralgia caused by paroxysmal and severe pain in the distribution of the trigeminal nerve is a rare chronic pain disorder. It is generally accepted that compression of the trigeminal root entry zone by vascular structures is the major cause of primary trigeminal neuralgia, and vascular decompression is the prior choice in neurosurgical treatment. Therefore, accurate preoperative modeling/segmentation/visualization of trigeminal nerve and its surrounding cerebrovascular is important to surgical planning. In this paper, we propose an automated method to segment trigeminal nerve and its surrounding cerebrovascular in the root entry zone, and to further reconstruct and visual these anatomical structures in three-dimensional (3D) Magnetic Resonance Angiography (MRA). The proposed method contains a two-stage neural network. Firstly, a preliminary confidence map of different anatomical structures is produced by a coarse segmentation stage. Secondly, a refinement segmentation stage is proposed to refine and optimize the coarse segmentation map. To model the spatial and morphological relationship between trigeminal nerve and cerebrovascular structures, the proposed network detects the trigeminal nerve, cerebrovasculature, and brainstem simultaneously. The method has been evaluated on a dataset including 50 MRA volumes, and the experimental results show the state-of-the-art performance of the proposed method with an average Dice similarity coefficient, Hausdorff distance, and average surface distance error of 0.8645, 0.2414, and 0.4296 on multi-tissue segmentation, respectively.

Predictability of Fused 3D-T2-SPACE and 3D-TOF-MRA Images in Identifying Conflict in Trigeminal Neuralgia

Purpose

To evaluate the efficacy of fused three-dimensional T2 sampling perfection with application-optimized contrasts using different flip-angle evolutions (3D-SPACE) and three-dimensional time-of-flight magnetic resonance angiography (3D-TOF-MRA) sequences for detecting neurovascular compression (NVC) in patients presenting with trigeminal neuralgia (TN).

Methods

A prospective study was carried in 33 consecutive patients (m/f: 17/16; mean age, 56.3 ± 10.4 years) with unilateral TN confirmed NVC and consensus by two experienced radiologists on fused 3D-SPACE and 3D-TOF-MRA sequences of 3-tesla (3-T) MRI. All patients underwent microvascular decompression (MVD), using photos and video in surgery as documents compared with MRI. Both the MRI and MVD were reported for three grades (contact, compression, distortion), vessel types (artery or vein), identification of offending vessel, site (juxtapontine, cisternal, and juxtapetrous), and location (cranial, caudal, medial, lateral). Agreement between preoperative MRI visualization and surgical findings was assessed using the kappa (K) statistic.

Results

The k-values for the agreement were excellent for the grade of NVC (k=0.82), vessel types (k=0.78), and location of conflict (k=0.74), and good for identification of the offending vessel (0.65) and the site-affected vessel (k=0.69).

Conclusion

The fused D3-SPACE and 3D-TOF-MRA images are highly effective tools for the evaluation and treatment planning of NVC in TN patients.

Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI

The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.

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.

Magnetic resonance neurography of the head and neck: state of the art, anatomy, pathology and future perspectives

Magnetic resonance neurography allows for the selective visualization of peripheral nerves and is increasingly being investigated. Whereas in the past, the imaging of the extracranial cranial and occipital nerve branches was inadequate, more and more techniques are now available that do allow nerve imaging. This basic review provides an overview of the literature with current state of the art, anatomical landmarks and future perspectives. Furthermore, we illustrate the possibilities of the three-dimensional CRAnial Nerve Imaging (3D CRANI) MR-sequence by means of a few case studies.

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.

Evaluation of trigeminal nerve tractography using two-fold-accelerated simultaneous multi-slice readout-segmented echo planar diffusion tensor imaging

OBJECTIVES

Simultaneous multi-slice (SMS) imaging with short repetition time (TR) accelerates diffusion tensor imaging (DTI) acquisitions. However, its impact when combined with readout-segmented echo planar imaging (RESOLVE) on the cranial nerves given the challenging skull base/posterior fossa terrain is unexplored. We evaluated the reliability of trigeminal nerve DTI metrics using SMS with RESOLVE-DTI.

METHODS

Eight healthy controls and six patients with unilateral trigeminal neuralgia (TN) underwent brain MRI scan. Three different RESOLVE-DTI protocols were performed on a 3-T MRI system: non-SMS (TR = 4330 ms), SMS with identical TR (4330 ms), and SMS with short TR (2400 ms). Pontine signal-to-noise ratio (SNR) and DTI metrics of the trigeminal nerve streamlines tracked by two independent raters using deterministic tractography and standardized tracking protocol were obtained. These were statistically analyzed and compared across the three protocols using intra-rater and inter-rater intraclass correlation coefficients (ICCs), one-way analysis of variance (ANOVA), post hoc analysis, and linear regression.

RESULTS

On visual screening, there were no artifacts across the trigeminal nerves. All data also cleared objective image quality assurance analysis. Pontine SNR was similar for the two SMS protocols and higher for the non-SMS RESOLVE-DTI (F_(2,36) = 4.40, p = 0.02). Intra-rater and inter-rater ICCs were very good (> 0.85). Trigeminal nerve DTI metrics were consistently measured by the three protocols, revealing significant linear relationships between non-SMS- and SMS-derived DTI metrics.

CONCLUSION

SMS RESOLVE-DTI enables fast and reliable evaluation of microstructural integrity of the trigeminal nerve, with potential application in the clinical management of TN.

KEY POINTS

• Readout-segmented diffusion-weighted echo planar imaging (RESOLVE-DTI) reduces image distortion artifacts in the posterior fossa but its long acquisition time limits clinical utility. • Simultaneous multi-slice (SMS) imaging combined with RESOLVE-DTI provides reliable trigeminal nerve tractography with potential applications in trigeminal neuralgia. • Two-fold-accelerated RESOLVE-DTI yields comparable trigeminal nerve streamlines and DTI metrics while near-halving acquisition time.

Three-Dimensional Identification of the Medial Longitudinal Fasciculus in the Human Brain: A Diffusion Tensor Imaging Study

Background: The medial longitudinal fasciculus (MLF) interacts with eye movement control circuits involved in the adjustment of horizontal, vertical, and torsional eye movements. In this study, we attempted to identify and investigate the anatomical characteristics of the MLF in human brain, using probabilistic diffusion tensor imaging (DTI) tractography. Methods: We recruited 31 normal healthy adults and used a 1.5-T scanner for DTI. To reconstruct MLFs, a seed region of interest (ROI) was placed on the interstitial nucleus of Cajal at the midbrain level. A target ROI was located on the MLF of the medulla in the reticular formation of the medulla. Mean values of fractional anisotropy, mean diffusivity, and tract volumes of MLFs were measured. Results: The component of the MLF originated from the midbrain MLF, descended through the posterior side of the medial lemniscus (ML) and terminated on the MLF of medulla on the posterior side of the ML in the medulla midline. DTI parameters of right and left MLFs were not significantly different. Conclusion: The tract of the MLF in healthy brain was identified by probabilistic DTI tractography. We believe this study will provide basic data and aid future comparative research on lesion or age-induced MLF changes.