Overcoming Challenges of Cranial Nerve Tractography: A Targeted Review

3D color multimodality fusion imaging as an augmented reality educational and surgical planning tool for extracerebral tumors

Extracerebral tumors often occur on the surface of the brain or at the skull base. It is important to identify the peritumoral sulci, gyri, and nerve fibers. Preoperative visualization of three-dimensional (3D) multimodal fusion imaging (MFI) is crucial for surgery. However, the traditional 3D-MFI brain models are homochromatic and do not allow easy identification of anatomical functional areas. In this study, 33 patients with extracerebral tumors without peritumoral edema were retrospectively recruited. They underwent 3D T1-weighted MRI, diffusion tensor imaging (DTI), and CT angiography (CTA) sequence scans. 3DSlicer, Freesurfer, and BrainSuite were used to explore 3D-color-MFI and preoperative planning. To determine the effectiveness of 3D-color-MFI as an augmented reality (AR) teaching tool for neurosurgeons and as a patient education and communication tool, questionnaires were administered to 15 neurosurgery residents and all patients, respectively. For neurosurgical residents, 3D-color-MFI provided a better understanding of surgical anatomy and more efficient techniques for removing extracerebral tumors than traditional 3D-MFI (P < 0.001). For patients, the use of 3D-color MFI can significantly improve their understanding of the surgical approach and risks (P < 0.005). 3D-color-MFI is a promising AR tool for extracerebral tumors and is more useful for learning surgical anatomy, developing surgical strategies, and improving communication with patients.

Methods of diffusion MRI tractography for localization of the anterior optic pathway: A systematic review of validated methods

The anterior optic pathway (AOP) is a system of three structures (optic nerves, optic chiasma, and optic tracts) that convey visual stimuli from the retina to the lateral geniculate nuclei. A successful reconstruction of the AOP using tractography could be helpful in several clinical scenarios, from presurgical planning and neuronavigation of sellar and parasellar surgery to monitoring the stage of fiber degeneration both in acute (e.g., traumatic optic neuropathy) or chronic conditions that affect AOP structures (e.g., amblyopia, glaucoma, demyelinating disorders or genetic optic nerve atrophies). However, its peculiar anatomy and course, as well as its surroundings, pose a serious challenge to obtaining successful tractographic reconstructions. Several AOP tractography strategies have been adopted but no standard procedure has been agreed upon. We performed a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines in order to find the combinations of acquisition and reconstruction parameters that have been performed previously and have provided the highest rate of successful reconstruction of the AOP, in order to promote their routine implementation in clinical practice. For this purpose, we reviewed data regarding how the process of anatomical validation of the tractographies was performed. The Cochrane Handbook for Systematic Reviews of Interventions was used to assess the risk of bias and thus the study quality We identified thirty-nine studies that met our inclusion criteria, and only five were considered at low risk of bias and achieved over 80% of successful reconstructions. We found a high degree of heterogeneity in the acquisition and analysis parameters used to perform AOP tractography and different combinations of them can achieve satisfactory levels of anterior optic tractographic reconstruction both in real-life research and clinical scenarios. One thousand s/mm2 was the most frequently used b value, while both deterministic and probabilistic tractography algorithms performed morphological reconstruction of the tract satisfactorily, although probabilistic algorithms estimated a more realistic percentage of crossing fibers (45.6%) in healthy subjects. A wide heterogeneity was also found regarding the method used to assess the anatomical fidelity of the AOP reconstructions. Three main strategies can be found: direct visual direct visual assessment of the tractography superimposed to a conventional MR image, surgical evaluation, and computational methods. Because the latter is less dependent on a priori knowledge of the anatomy by the operator, computational methods of validation of the anatomy should be considered whenever possible.

The use of diffusion magnetic resonance imaging tractography in supporting anatomical conflict between an uncal protrusion and the oculomotor nerve: A case report of isolated inferior rectus palsy

Background

Isolated inferior rectus muscle palsy is a rare entity and even more rarely induced by an anatomical conflict. We report here a clinical case of third cranial nerve (CN III) compression in its cisternal segment by an idiopathic uncal protrusion in a patient presenting an isolated inferior rectus muscle palsy.

Case Description

We report a case of an anatomical conflict between the uncus and the CN III in the form of a protrusion and highly asymmetrical proximity of the uncus and asymmetrically thinned nerve diameter deviated from its straight cisternal trajectory on the ipsilateral side were supported by an altered diffusion tractography along the concerned CN III. Clinical description, review of the literature, and image analysis were done including CN III fiber reconstruction using a fused image from diffusion tensor imaging images, constructive interference in steady state, and T2-fluid-attenuated inversion recovery images on a dedicated software (BrainLAB AG).

Conclusion

This case illustrates the importance of anatomical-clinical correlation in cases of CN deficits and supports the use of new neuroradiologically based interrogation methods such as CN diffusion tractography to support anatomical CN conflicts.

Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review

Optic never fibers in the visual pathway play significant roles in vision formation. Damages of optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and neurological diseases; also, there is a need to prevent the optic nerve fibers from getting damaged in neurosurgery and radiation therapy. Reconstruction of optic nerve fibers from medical images can facilitate all these clinical applications. Although many computational methods are developed for the reconstruction of optic nerve fibers, a comprehensive review of these methods is still lacking. This paper described both the two strategies for optic nerve fiber reconstruction applied in existing studies, i.e., image segmentation and fiber tracking. In comparison to image segmentation, fiber tracking can delineate more detailed structures of optic nerve fibers. For each strategy, both conventional and AI-based approaches were introduced, and the latter usually demonstrates better performance than the former. From the review, we concluded that AI-based methods are the trend for optic nerve fiber reconstruction and some new techniques like generative AI can help address the current challenges in optic nerve fiber reconstruction.

CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation

The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI) provides a valuable quantitative tool for the analysis of the morphology and course of individual CNs. Tractography-based approaches can describe and analyze the anatomical area of CNs by selecting the reference streamlines in combination with ROIs-based (regions-of-interests) or clustering-based. However, due to the slender structure of CNs and the complex anatomical environment, single-modality data based on dMRI cannot provide a complete and accurate description, resulting in low accuracy or even failure of current algorithms in performing individualized CNs segmentation. In this work, we propose a novel multimodal deep-learning-based multi-class network for automated cranial nerves tract segmentation without using tractography, ROI placement or clustering, called CNTSeg. Specifically, we introduced T1w images, fractional anisotropy (FA) images, and fiber orientation distribution function (fODF) peaks into the training data set, and design the back-end fusion module which uses the complementary information of the interphase feature fusion to improve the segmentation performance. CNTSeg has achieved the segmentation of 5 pairs of CNs (i.e. optic nerve CN II, oculomotor nerve CN III, trigeminal nerve CN V, and facial-vestibulocochlear nerve CN VII/VIII). Extensive comparisons and ablation experiments show promising results and are anatomically convincing even for difficult tracts. The code will be openly available at https://github.com/IPIS-XieLei/CNTSeg.

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.

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 facial-vestibulocochlear nerve complex identification based on data-driven tractography clustering

Small size and intricate anatomical environment are the main difficulties facing tractography of the facial-vestibulocochlear nerve complex (FVN), and lead to challenges in fiber orientation distribution (FOD) modeling, fiber tracking, region-of-interest selection, and fiber filtering. Experts need rich experience in anatomy and tractography, as well as substantial labor costs, to identify the FVN. Thus, we present a pipeline to identify the FVN automatically, in what we believe is the first study of the automated identification of the FVN. First, we created an FVN template. Forty high-resolution multishell data were used to perform data-driven fiber clustering based on the multishell multitissue constraint spherical deconvolution FOD model and deterministic tractography. We selected the brainstem and cerebellum (BS-CB) region as the seed region and removed the fibers that reach other brain regions. We then performed spectral fiber clustering twice. The first clustering was to create a BS-CB atlas and separate the fibers that pass through the cerebellopontine angle, and the other one was to extract the FVN. Second, we registered the subject-specific fibers in the space of the FVN template and assigned each fiber to the closest cluster to identify the FVN automatically by spectral embedding. We applied the proposed method to different acquirement sites, including two different healthy datasets and two tumor patient datasets. Experimental results showed that our automatic identification results have ideal colocalization with expert manual identification in terms of spatial overlap and visualization. Importantly, we successfully applied our method to tumor patient data. The FVNs identified by the proposed method were in agreement with intraoperative findings.

Mapping degeneration of the visual system in long-term follow-up after childhood hemispherectomy - A series of four cases

OBJECTIVE

Although hemidisconnection surgery may eliminate or reduce seizure activity in patients with epilepsy, there are visual, cognitive and motor deficits which affect patients' function post-operatively, with varying severity and according to pathology. Consequently, there is a need to map microstructural changes over long time periods and develop/apply methods that work with legacy data.

METHODS

In this study, we applied the novel single shell 3-Tissue method to data from a cohort of 4 patients who were scanned 20-years following childhood hemidisconnection surgery and presented with variable clinical outcomes. We have successfully reconstructed tractography of the whole visual pathway from single shell diffusion data with reduced number of gradient directions.

RESULTS

All patients presented with degeneration of the visual system characterised by low fractional anisotropy and high mean diffusivity. There were no apparent microstructural differences between both optic nerves that could explain the different level of visual function across patients. However, we provide evidence suggesting an association between the level of visual function and DTI metrics within the remaining components of the visual system, particularly the optic tract, of the contralateral hemisphere post-surgery.

SIGNIFICANCE

We believe this study suggests that diffusion MRI can be used to monitor the integrity of the visual system following hemispherectomy and if extended to larger cohorts and a greater number of time-points, including pre-surgically, can provide a clearer picture of the natural history of visual system degeneration. This knowledge may in turn help to identify patients at greatest risk of poor visual outcomes that might benefit from rehabilitation therapies.

New and Advanced Magnetic Resonance Imaging Diagnostic Imaging Techniques in the Evaluation of Cranial Nerves and the Skull Base

The skull base and cranial nerves are technically challenging to evaluate using magnetic resonance (MR) imaging, owing to a combination of anatomic complexity and artifacts. However, improvements in hardware, software and sequence development seek to address these challenges. This section will discuss cranial nerve imaging, with particular attention to the techniques, applications and limitations of MR neurography, diffusion tensor imaging and tractography. Advanced MR imaging techniques for skull base pathology will also be discussed, including diffusion-weighted imaging, perfusion and permeability imaging, with a particular focus on practical applications.

Correlation between Cranial Nerve Microstructural Characteristics and Vestibular Schwannoma Tumor Volume

BACKGROUND AND PURPOSE

Vestibular schwannomas are common cerebellopontine angle tumors arising from the vestibulocochlear nerve and can result in cranial nerve dysfunction. Conventional MR imaging does not provide information that could correlate with cranial nerve compression symptoms of hearing loss or imbalance. We used multitensor tractography to evaluate the relationship between the WM microstructural properties of cranial nerves and tumor volume in a cohort of patients with vestibular schwannomas.

MATERIALS AND METHODS

A retrospective study was performed in 258 patients with vestibular schwannomas treated at the Gamma Knife clinic at Toronto Western Hospital between 2014 and 2018. 3T MR images were analyzed in 160 surgically naïve patients with unilateral vestibular schwannomas. Multitensor tractography was used to extract DTI-derived metrics (fractional anisotropy and radial, axial, and mean diffusivities of the bilateral facial and vestibulocochlear nerves [cranial nerves VII/VIII]). ROIs were placed in the transition between cisternal and intracanalicular segments, and images were analyzed using the eXtended Streamline Tractography reconstruction method. Diffusion metrics were correlated with 3D tumor volume derived from the Gamma Knife clinic.

RESULTS

DTI analyses revealed significantly higher fractional anisotropy values and a reduction in axial diffusivity, radial diffusivity, and mean diffusivity (all P < .001) within the affected cranial nerves VII and VIII compared with unaffected side. All specific diffusivities (axial, radial, and mean diffusivity) demonstrated an inverse correlation with tumor volume (axial, radial, and mean diffusivity, P < .01).

CONCLUSIONS

Multitensor tractography allows the quantification of cranial nerve VII and VIII WM microstructural alterations in patients with vestibular schwannomas. Our findings support the hypothesis that tumor volume may cause microstructural alterations of the affected cranial nerves VII and VIII. This type of advanced imaging may represent a possible avenue to correlate diffusivities with cranial nerve function.

Machine learning-based prediction of outcomes of the endoscopic endonasal approach in Cushing disease: is the future coming?

OBJECTIVE

Machine learning (ML) is an innovative method to analyze large and complex data sets. The aim of this study was to evaluate the use of ML to identify predictors of early postsurgical and long-term outcomes in patients treated for Cushing disease (CD).

METHODS

All consecutive patients in our center who underwent surgery for CD through the endoscopic endonasal approach were retrospectively reviewed. Study endpoints were gross-tumor removal (GTR), postsurgical remission, and long-term control of disease. Several demographic, radiological, and histological factors were assessed as potential predictors. For ML-based modeling, data were randomly divided into 2 sets with an 80% to 20% ratio for bootstrapped training and testing, respectively. Several algorithms were tested and tuned for the area under the curve (AUC).

RESULTS

The study included 151 patients. GTR was achieved in 137 patients (91%), and postsurgical hypersecretion remission was achieved in 133 patients (88%). At last follow-up, 116 patients (77%) were still in remission after surgery and in 21 patients (14%), CD was controlled with complementary treatment (overall, of 131 cases, 87% were under control at follow-up). At internal validation, the endpoints were predicted with AUCs of 0.81-1.00, accuracy of 81%-100%, and Brier scores of 0.035-0.151. Tumor size and invasiveness and histological confirmation of adrenocorticotropic hormone (ACTH)-secreting cells were the main predictors for the 3 endpoints of interest.

CONCLUSIONS

ML algorithms were used to train and internally validate robust models for all the endpoints, giving accurate outcome predictions in CD cases. This analytical method seems promising for potentially improving future patient care and counseling; however, careful clinical interpretation of the results remains necessary before any clinical adoption of ML. Moreover, further studies and increased sample sizes are definitely required before the widespread adoption of ML to the study of CD.

Advances in Multidisciplinary Management of Skull Base Meningiomas

The surgical management of Skull Base Meningiomas (SBMs) has radically changed over the last two decades. Extensive surgery for patients with SBMs represents the mainstream treatment; however, it is often challenging due to narrow surgical corridors and proximity to critical neurovascular structures. Novel surgical technologies, including three-dimensional (3D) preoperative imaging, neuromonitoring, and surgical instruments, have gradually facilitated the surgical resectability of SBMs, reducing postoperative morbidity. Total removal is not always feasible considering a risky tumor location and invasion of surrounding structures and brain parenchyma. In recent years, the use of primary or adjuvant stereotactic radiosurgery (SRS) has progressively increased due to its safety and efficacy in the control of grade I and II meningiomas, especially for small to moderate size lesions. Patients with WHO grade SBMs receiving subtotal surgery can be monitored over time with surveillance imaging. Postoperative management remains highly controversial for grade II meningiomas, and depends on the presence of residual disease, with optional upfront adjuvant radiation therapy or close surveillance imaging in cases with total resection. Adjuvant radiation is strongly recommended in patients with grade III tumors. Although the currently available chemotherapy or targeted therapies available have a low efficacy, the molecular profiling of SBMs has shown genetic alterations that could be potentially targeted with novel tailored treatments. This multidisciplinary review provides an update on the advances in surgical technology, postoperative management and molecular profile of SBMs.

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.

High spatial resolution nerve-specific DTI protocol outperforms whole-brain DTI protocol for imaging the trigeminal nerve in healthy individuals

Diffusion tensor imaging (DTI) can provide markers of axonal micro-structure of the trigeminal nerve (cranial nerve five [CNV]), which may be affected in trigeminal neuralgia (TN) and other disorders. Previous attempts to image CNV have used low spatial resolution DTI protocols designed for whole-brain acquisition that are susceptible to errors from partial volume effects, particularly with adjacent cerebrospinal fluid (CSF). The purpose of this study was to develop a nerve-specific DTI protocol in healthy subjects that provides more accurate CNV tractography and diffusion quantification than whole-brain protocols. Four DTI protocols were compared in five healthy individuals (age 22-45 years, three males) on a 3 T Siemens Prisma MRI scanner: two newly developed nerve-specific high resolution (1.2 x 1.2 x 1.2 = 1.7 mm³ ) DTI protocols without (3.5 minutes) and with CSF suppression (fluid-attenuated inversion recovery [FLAIR]; 7.5 minutes) with limited slice-coverage, and two typical whole-brain protocols with either isotropic (2 x 2 x 2 = 8 mm³ ) or thicker slice anisotropic (1.9 x 1.9 x 3 = 10.8 mm³ ) voxels. Deterministic tractography was used to identify the CNV and quantify bilateral fractional anisotropy (FA), and mean (MD), axial (AD) and radial diffusivity (RD). CNV volume was determined by manual tracing on T1-weighted images. High spatial resolution nerve-specific protocols yielded better delineation of CNV, with less distortions and blurring, and markedly different diffusion parameters (42% higher FA, 35% lower MD, 27% lower RD and 43% lower AD) compared with the two lower resolution whole-brain protocols. The anisotropic whole-brain protocol showed a positive correlation between CNV FA and volume. The high resolution nerve-specific protocol with FLAIR yielded additional reductions in CNV AD and MD with a value of 1.0 x 10^-3 mm² /s, approaching that expected for healthy young adult white matter. In conclusion, high resolution nerve-specific DTI with FLAIR enhances the identification of CNV and provides more accurate quantification of diffusion compared with lower resolution whole-brain approaches.

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.

Parasellar Meningiomas

Parasellar spaces remain particularly singular, comprising the most important neurovascular structures such as the internal carotid artery and optic, oculomotor, and trigeminal nerves. Meningiomas are one of the most frequent tumors arising from parasellar spaces. In this location, meningiomas remain mostly benign tumors with WHO grade I and a meningothelial subtype. Progestin intake should be investigated and leads mostly to conservative strategies. In the case of benign nonsymptomatic tumors, observation should be proposed. Tumor growth will lead to the proposition of surgery or radiosurgery. In the case of an uncertain diagnosis and an aggressive pattern, a precise diagnosis is required. For cavernous sinus and Meckel's cave lesions, complete removal is rarely considered, leading to the proposition of an endoscopic endonasal or transcranial biopsy. Optic nerve decompression could also be proposed via these approaches. A case-by-case discussion about the best approach is recommended. A transcranial approach remains necessary for tumor removal in most cases. Vascular injury could lead to severe complications. Cerebrospinal fluid leakage, meningitis, venous sacrifice, visual impairment, and cranial nerve palsies are more frequent complications. Pituitary dysfunctions are rare in preoperative assessment and in postoperative follow-up but should be assessed in the case of meningiomas located close to the pituitary axis. Long-term follow-up is required given the frequent incomplete tumor removal and the risk of delayed recurrence. Radiosurgery is relevant for small and well-limited meningiomas or intra-cavernous sinus postoperative residue, whereas radiation therapy and proton beam therapy are indicated for large, extended, nonoperable meningiomas. The place of the peptide receptor radionuclide therapyneeds to be defined. Targeted therapy should be considered in rare, recurrent, and aggressive parasellar meningiomas.

Full tractography for detecting the position of cranial nerves in preoperative planning for skull base surgery: technical note

OBJECTIVE

Diffusion imaging tractography has allowed the in vivo description of brain white matter. One of its applications is preoperative planning for brain tumor resection. Due to a limited spatial and angular resolution, it is difficult for fiber tracking to delineate fiber crossing areas and small-scale structures, in particular brainstem tracts and cranial nerves. New methods are being developed but these involve extensive multistep tractography pipelines including the patient-specific design of multiple regions of interest (ROIs). The authors propose a new practical full tractography method that could be implemented in routine presurgical planning for skull base surgery.

METHODS

A Philips MRI machine provided diffusion-weighted and anatomical sequences for 2 healthy volunteers and 2 skull base tumor patients. Tractography of the full brainstem, the cerebellum, and cranial nerves was performed using the software DSI Studio, generalized-q-sampling reconstruction, orientation distribution function (ODF) of fibers, and a quantitative anisotropy-based generalized deterministic algorithm. No ROI or extensive manual filtering of spurious fibers was used. Tractography rendering was displayed in a tridimensional space with directional color code. This approach was also tested on diffusion data from the Human Connectome Project (HCP) database.

RESULTS

The brainstem, the cerebellum, and the cisternal segments of most cranial nerves were depicted in all participants. In cases of skull base tumors, the tridimensional rendering permitted the visualization of the whole anatomical environment and cranial nerve displacement, thus helping the surgical strategy.

CONCLUSIONS

As opposed to classical ROI-based methods, this novel full tractography approach could enable routine enhanced surgical planning or brain imaging for skull base tumors.

Tractography for Surgical Neuro-Oncology Planning: Towards a Gold Standard

Magnetic resonance imaging tractography permits in vivo visualization of white matter structures. Aside from its academic value, tractography has been proven particularly useful to neurosurgeons for preoperative planning. Preoperative tractography permits both qualitative and quantitative analyses of tumor effects upon surrounding white matter, allowing the surgeon to specifically tailor their operative approach. Despite its benefits, there is controversy pertaining to methodology, implementation, and interpretation of results in this context. High-definition fiber tractography (HDFT) is one of several non-tensor tractography approaches permitting visualization of crossing white matter trajectories at high resolutions, dispensing with the well-known shortcomings of diffusion tensor imaging (DTI) tractography. In this article, we provide an overview of the advantages of HDFT in a neurosurgical context, derived from our considerable experience implementing the technique for academic and clinical purposes. We highlight nuances of qualitative and quantitative approaches to using HDFT for brain tumor surgery planning, and integration of tractography with complementary operative adjuncts, and consider areas requiring further research.