Interactive diffusion tensor tractography visualization for neurosurgical planning

The Feasibility and Accuracy of Holographic Navigation with Laser Crosshair Simulator Registration on a Mixed-Reality Display

Addressing conventional neurosurgical navigation systems' high costs and complexity, this study explores the feasibility and accuracy of a simplified, cost-effective mixed reality navigation (MRN) system based on a laser crosshair simulator (LCS). A new automatic registration method was developed, featuring coplanar laser emitters and a recognizable target pattern. The workflow was integrated into Microsoft's HoloLens-2 for practical application. The study assessed the system's precision by utilizing life-sized 3D-printed head phantoms based on computed tomography (CT) or magnetic resonance imaging (MRI) data from 19 patients (female/male: 7/12, average age: 54.4 ± 18.5 years) with intracranial lesions. Six to seven CT/MRI-visible scalp markers were used as reference points per case. The LCS-MRN's accuracy was evaluated through landmark-based and lesion-based analyses, using metrics such as target registration error (TRE) and Dice similarity coefficient (DSC). The system demonstrated immersive capabilities for observing intracranial structures across all cases. Analysis of 124 landmarks showed a TRE of 3.0 ± 0.5 mm, consistent across various surgical positions. The DSC of 0.83 ± 0.12 correlated significantly with lesion volume (Spearman rho = 0.813, p < 0.001). Therefore, the LCS-MRN system is a viable tool for neurosurgical planning, highlighting its low user dependency, cost-efficiency, and accuracy, with prospects for future clinical application enhancements.

Current State of Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Traumatic Brain Injury Prognostication

Advanced imaging techniques are needed to assist in providing a prognosis for patients with traumatic brain injury (TBI), particularly mild TBI (mTBI). Diffusion tensor imaging (DTI) is one promising advanced imaging technique, but has shown variable results in patients with TBI and is not without limitations, especially when considering individual patients. Efforts to resolve these limitations are being explored and include developing advanced diffusion techniques, creating a normative database, improving study design, and testing machine learning algorithms. This article will review the fundamentals of DTI, providing an overview of the current state of its utility in evaluating and providing prognosis in patients with TBI.

[Tractography in functional neuronavigation]

The review addresses the combined use of tractography and neuronavigation. Fundamentals of diffusion tensor imaging are given, technical aspects of fiber tracking in general and in depicting separate subcortical tracts are described. Main advantages of the method and possible causes of errors are highlighted. Precision assessment of this technology is given by comparing with results of subcortical neurostimulation. Surgical tactics is described depending on distance between the tumor and subcortical pathways.

Tractography methods and findings in brain tumors and traumatic brain injury

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

Personalised, image-guided, noninvasive brain stimulation in gliomas: Rationale, challenges and opportunities

Malignant brain tumours are among the most aggressive human cancers, and despite intensive efforts made over the last decades, patients’ survival has scarcely improved. Recently, high-grade gliomas (HGG) have been found to be electrically integrated with healthy brain tissue, a communication that facilitates tumour mitosis and invasion. This link to neuronal activity has provided new insights into HGG pathophysiology and opened prospects for therapeutic interventions based on electrical modulation of neural and synaptic activity in the proximity of tumour cells, which could potentially slow tumour growth. Noninvasive brain stimulation (NiBS), a group of techniques used in research and clinical settings to safely modulate brain activity and plasticity via electromagnetic or electrical stimulation, represents an appealing class of interventions to characterise and target the electrical properties of tumour-neuron interactions. Beyond neuronal activity, NiBS may also modulate function of a range of substrates and dynamics that locally interacts with HGG (e.g., vascular architecture, perfusion and blood-brain barrier permeability). Here we discuss emerging applications of NiBS in patients with brain tumours, covering potential mechanisms of action at both cellular, regional, network and whole-brain levels, also offering a conceptual roadmap for future research to prolong survival or promote wellbeing via personalised NiBS interventions.

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

PURPOSE

Diffusion weighted MRI imaging (DWI) is often subject to low signal-to-noise ratios (SNRs) and artifacts. Recent work has produced software tools that can correct individual problems, but these tools have not been combined with each other and with quality assurance (QA). A single integrated pipeline is proposed to perform DWI preprocessing with a spectrum of tools and produce an intuitive QA document.

METHODS

The proposed pipeline, built around the FSL, MRTrix3, and ANTs software packages, performs DWI denoising; inter-scan intensity normalization; susceptibility-, eddy current-, and motion-induced artifact correction; and slice-wise signal drop-out imputation. To perform QA on the raw and preprocessed data and each preprocessing operation, the pipeline documents qualitative visualizations, quantitative plots, gradient verifications, and tensor goodness-of-fit and fractional anisotropy analyses.

RESULTS

Raw DWI data were preprocessed and quality checked with the proposed pipeline and demonstrated improved SNRs; physiologic intensity ratios; corrected susceptibility-, eddy current-, and motion-induced artifacts; imputed signal-lost slices; and improved tensor fits. The pipeline identified incorrect gradient configurations and file-type conversion errors and was shown to be effective on externally available datasets.

CONCLUSIONS

The proposed pipeline is a single integrated pipeline that combines established diffusion preprocessing tools from major MRI-focused software packages with intuitive QA.

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.

On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data

Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.

Longitudinal Disconnection Tractograms to Investigate the Functional Consequences of White Matter Damage: An Automated Pipeline

BACKGROUND AND PURPOSE

Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded.

METHODS

We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage.

RESULTS

The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration, which aligns images using an approach sensitive to white matter microstructure.

CONCLUSIONS

Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.

Tractography in Neurosurgery: A Systematic Review of Current Applications

The ability to visualize the brain's fiber connections noninvasively in vivo is relatively young compared with other possibilities of functional magnetic resonance imaging. Although many studies showed tractography to be of promising value for neurosurgical care, the implications remain inconclusive. An overview of current applications is presented in this systematic review. A search was conducted for (("tractography" or "fiber tracking" or "fibre tracking") and "neurosurgery") that produced 751 results. We identified 260 relevant articles and added 20 more from other sources. Most publications concerned surgical planning for resection of tumors (n = 193) and vascular lesions (n = 15). Preoperative use of transcranial magnetic stimulation was discussed in 22 of these articles. Tractography in skull base surgery presents a special challenge (n = 29). Fewer publications evaluated traumatic brain injury (TBI) (n = 25) and spontaneous intracranial bleeding (n = 22). Twenty-three articles focused on tractography in pediatric neurosurgery. Most authors found tractography to be a valuable addition in neurosurgical care. The accuracy of the technique has increased over time. There are articles suggesting that tractography improves patient outcome after tumor resection. However, no reliable biomarkers have yet been described. The better rehabilitation potential after TBI and spontaneous intracranial bleeding compared with brain tumors offers an insight into the process of neurorehabilitation. Tractography and diffusion measurements in some studies showed a correlation with patient outcome that might help uncover the neuroanatomical principles of rehabilitation itself. Alternative corticofugal and cortico-cortical networks have been implicated in motor recovery after ischemic stroke, suggesting more complex mechanisms in neurorehabilitation that go beyond current models. Hence tractography may potentially be able to predict clinical deficits and rehabilitation potential, as well as finding possible explanations for neurologic disorders in retrospect. However, large variations of the results indicate a lack of data to establish robust diagnostical concepts at this point. Therefore, in vivo tractography should still be interpreted with caution and by experienced surgeons.

Altered Rich-Club Organization and Regional Topology Are Associated With Cognitive Decline in Patients With Frontal and Temporal Gliomas

Objectives

Gliomas are widely considered to be related to the altered topological organization of functional networks before operations. Tumors are usually thought to cause multimodal cognitive impairments. The structure is thought to form the basics of function, and the aim of this study was to reveal the rich-club organization and topological patterns of white matter (WM) structural networks associated with cognitive impairments in patients with frontal and temporal gliomas.

Methods

Graph theory approaches were utilized to reveal the global and regional topological organization and rich-club organization of WM structural networks of 14 controls (CN), 13 frontal tumors (FTumor), and 18 temporal tumors (TTumor). Linear regression was used to assess the relationship between cognitive performances and altered topological parameters.

Results

When compared with CN, both FTumor and TTumor showed no alterations in small-world properties and global network efficiency, but instead showed altered local network efficiency. Second, FTumor and TTumor patients showed similar deficits in the nodal shortest path in the left rolandic operculum and degree centrality (DC) of the right dorsolateral and medial superior frontal gyrus (SFGmed). Third, compared to FTumor patients, TTumor patients showed a significantly higher DC in the right dorsolateral and SFGmed, a higher level of betweenness in the right SFGmed, and higher nodal efficiency in the left middle frontal gyrus and right SFGmed. Finally, rich-club organization was disrupted, with increased structural connectivity among rich-club nodes and reduced structural connectivity among peripheral nodes in FTumor and TTumor patients. Altered local efficiency in TTumor correlated with memory function, while altered local efficiency in FTumor correlated with the information processing speed.

Conclusion

Both FTumor and TTumor presented an intact global topology and altered regional topology related to cognitive impairment and may also share the convergent and divergent regional topological organization of WM structural networks. This suggested that a compensatory mechanism plays a key role in global topology formation in both FTumor and TTumor patients, and as such, development of a structural connectome for patients with brain tumors would be an invaluable medical resource and allow clinicians to make comprehensive preoperative planning.

Fiber visualization for preoperative glioma assessment: Tractography versus local connectivity mapping

In diffusion MRI, the advent of high angular resolution diffusion imaging (HARDI) and HARDI with compressed sensing (HARDI+CS) has led to clinically practical signal acquisition techniques which allow for the assessment of white matter architecture in routine patient studies. However, the reconstruction and visualization of fiber pathways by tractography has not yet been established as a standard methodology which can easily be applied. This is due to various algorithmic problems, such as a lack of robustness, error propagation and the necessity of fine-tuning parameters depending on the clinical question. In the framework of a clinical study of glioma patients, we compare two different whole-brain tracking methods to a local connectivity mapping approach which has recently shown promising results in an adaptation to diffusion MRI. The ability of the three methods to correctly depict fiber affection is analyzed by comparing visualization results to representations of local diffusion profiles provided by orientation distribution functions (ODFs). Our results suggest that methods beyond fiber tractography, which visualize local connectedness rather than global connectivity, should be evaluated further for pre-surgical assessment of fiber affection.

The Value of White Matter Tractography by Diffusion Tensor Imaging in Altering a Neurosurgeon's Operative Plan

OBJECTIVE

To investigate if the implementation of white matter (WM) fiber tractography by diffusion tensor imaging in presurgical planning for supratentorial tumors proximal to eloquent WM tracts can alter a neurosurgeon's operative strategy.

METHODS

A retrospective review was conducted of patients with supratentorial brain tumors within eloquent WM tracts who underwent diffusion tensor imaging (DTI) tractography as part of their preoperative assessment. These patients were classified into 3 different DTI groups per the radiology reports: group 1, intact WM tracts; group 2, deviated and/or displaced WM bundles; and group 3, patients with an established WM injury (interrupted and/or destroyed tracts). A blinded prospective behavioral study followed, in which 4 neurosurgeons reviewed the preoperative images at 2 different times (magnetic resonance imaging without DTI, followed by a review of the DTI). They provided estimations about the DTI group of each individual eloquent WM category in every patient, and their planned surgical approach.

RESULTS

Fifteen patients (mean age, 58.3 years) were included in the study. The neurosurgeons provided a correct DTI group estimation in 53%, 60%, and 57% of the cases that involved motor/sensory pathway tracts, optic tracts, and language tracts, respectively. The neurosurgeons underestimated DTI group 3 in the motor category and in the optic category 75% of the time. DTI did not alter the planned surgical approach.

CONCLUSIONS

DTI WM tractography helped neurosurgeons to correctly identify patients with interrupted motor and optic pathway tracts so they could be more aggressive with the extent of tumor resection, despite its inability to alter the operative approach.

Clinical Uses of Diffusion Tensor Imaging Fiber Tracking Merged Neuronavigation with Lesions Adjacent to Corticospinal Tract : A Retrospective Cohort Study

OBJECTIVE

To investigate the efficiency of diffusion tensor imaging (DTI) fiber-tracking based neuronavigation and assess its usefulness in the preoperative surgical planning, prognostic prediction, intraoperative course and outcome improvement.

METHODS

Seventeen patients with cerebral masses adjacent to corticospinal tract (CST) were given standard magnetic resonance imaging and DTI examination. By incorporation of DTI data, the relation between tumor and adjacent white matter tracts was reconstructed and assessed in the neuronavigation system. Distance from tumor border to CST was measured.

RESULTS

The sub-portion of CST in closest proximity to tumor was found displaced in all patients. The chief disruptive changes were classified as follows : complete interruption, partial interruption, or simple displacement. Partial interruption was evident in seven patients (41.2%) whose lesions were close to cortex. In the other 10 patients (58.8%), delineated CSTs were intact but distorted. No complete CST interruption was identified. Overall, the mean distance from resection border to CST was 6.12 mm (range, 0-21), as opposed to 8.18 mm (range, 2-21) with simple displacement and 2.33 mm (range, 0-5) with partial interruption. The clinical outcomes were analyzed in groups stratified by intervening distances (close, <5 mm; moderated, 5-10 mm; far, >10 mm). For the primary brain tumor patients, the proportion of completely resected tumors increased progressively from close to far grouping (42.9%, 50%, and 100%, respectively). Five patients out of seven (71.4%) experienced new neurologic deficits postoperatively in the close group. At meantime, motor deterioration was found in six cases in the close group. All patients in the far and moderate groups received excellent (modified Rankin Scale [mRS] score, 0-1) or good (mRS score, 2-3) rankings, but only 57.1% of patients in the close group earned good outcome scores.

CONCLUSION

DTI fiber tracking based neuronavigation has merit in assessing the relation between lesions and adjacent white matter tracts, allowing prediction of patient outcomes based on lesion-CST distance. It has also proven beneficial in formulating surgical strategies.

Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.

Advances in the Visualization and the Study of the Pyramidal Tract with Magnetic Resonance Tractography

Over the last several years, specific radiological techniques have been used for the analysis of the central nervous system pathways. They involve a magnetic resonance sequence called diffusion tensor imaging. In order to process the data provided by this sequence it is necessary to use software that can post-process the image and render three-dimensional images of the central nervous system pathways. Thanks to this sequence it has been possible to isolate over the years many nerve pathways that cross the brain tissue, particularly those which occupy a significant space. This sequence could have a large variety of uses, such as helping with the study of brain anatomy, assisting with surgery planning, or establishing a relationship between the nerve fibers and tumoral lesions. However, there has been an increasing number of cases that report a low reliability related to the tractographic representation of this technique. Our goal with this article is to analyse a specific nerve pathway, the piramidal tract, in order to assess the coherence between the images obtained and the anatomy that is already known from the perspective of the radiological image, and to compare this tract between different patients.

Neuroimaging of Traumatic Brain Injury

The purpose of this article is to review conventional and advanced neuroimaging techniques performed in the setting of traumatic brain injury (TBI). The primary goal for the treatment of patients with suspected TBI is to prevent secondary injury. In the setting of a moderate to severe TBI, the most appropriate initial neuroimaging examination is a noncontrast head computed tomography (CT), which can reveal life-threatening injuries and direct emergent neurosurgical intervention. We will focus much of the article on advanced neuroimaging techniques including perfusion imaging and diffusion tensor imaging and discuss their potentials and challenges. We believe that advanced neuroimaging techniques may improve the accuracy of diagnosis of TBI and improve management of TBI.

Quantitative evaluation of fiber tractography with a Delaunay triangulation-based interpolation approach

The recent challenge in high angular resolution diffusion imaging (HARDI) is to find a tractography process that provides information about the neural architecture within the white matter of the brain in a clinically feasible measurement time. The great success of the HARDI technique comes from its capability to overcome the problem of crossing fiber detection. However, it requires a large number of diffusion-weighted (DW) images which is problematic for clinical time and hardware. The main contribution of this paper is to develop a full tractography framework that gives an accurate estimate of the crossing fiber problem with the aim of reducing data acquisition time. We explore the interpolation in the gradient direction domain as a method to estimate the HARDI signal from a reduced set of DW images. The experimentation was performed in a first time on simulated data for a quantitative evaluation using the Tractometer system. We used, also, in vivo human brain data to demonstrate the potential of our pipeline. Results on both simulated and real data illustrate the effectiveness of our approach to perform the brain connectivity. Overall, we have shown that the proposed approach achieves competitive results to other tractography methods according to Tractometer connectivity metrics. Graphical Abstract.

An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan

This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

Effect of different spatial normalization approaches on tractography and structural brain networks

To facilitate the comparison of white matter morphologic connectivity across target populations, it is invaluable to map the data to a standardized neuroanatomical space. Here, we evaluated direct streamline normalization (DSN), where the warping was applied directly to the streamlines, with two publically available approaches that spatially normalize the diffusion data and then reconstruct the streamlines. Prior work has shown that streamlines generated after normalization from reoriented diffusion data do not reliably match the streamlines generated in native space. To test the impact of these different normalization methods on quantitative tractography measures, we compared the reproducibility of the resulting normalized connectivity matrices and network metrics with those originally obtained in native space. The two methods that reconstruct streamlines after normalization led to significant differences in network metrics with large to huge standardized effect sizes, reflecting a dramatic alteration of the same subject’s native connectivity. In contrast, after normalizing with DSN we found no significant difference in network metrics compared with native space with only very small-to-small standardized effect sizes. DSN readily outperformed the other methods at preserving native space connectivity and introduced novel opportunities to define connectome networks without relying on gray matter parcellations.

Direct streamline normalization (DSN) directly warps the streamlines into any template space by using the transformations output from Advanced Normalization Tools (ANTs). DSN overcomes the limitations of diffusion weighted images (DWI) spatial normalization. It allows DWIs to be acquired with any desired sampling scheme. Fiber orientation distributions (FODs) or orientation distribution functions (ODFs) can also be reconstructed using any desired method and streamlines generated using any algorithm. Most importantly, it avoids the problem of generating tracts from FODs or ODFs that have become distorted because of spatial normalization. Our results show that DSN has minimal influence on tractography measures such as tract count and structure and does not significantly alter structural networks with only very small to small effect sizes. We have developed a framework in Python that works with most diffusion software platforms. It is available at http://github.com/clintg6/DSN.

A comparison of three fiber tract delineation methods and their impact on white matter analysis

Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.

Advantages and Disadvantages in Image Processing with Free Software in Radiology

Currently, there are sophisticated applications that make it possible to visualize medical images and even to manipulate them. These software applications are of great interest, both from a teaching and a radiological perspective. In addition, some of these applications are known as Free Open Source Software because they are free and the source code is freely available, and therefore it can be easily obtained even on personal computers. Two examples of free open source software are Osirix Lite® and 3D Slicer®. However, this last group of free applications have limitations in its use. For the radiological field, manipulating and post-processing images is increasingly important. Consequently, sophisticated computing tools that combine software and hardware to process medical images are needed. In radiology, graphic workstations allow their users to process, review, analyse, communicate and exchange multidimensional digital images acquired with different image-capturing radiological devices. These radiological devices are basically CT (Computerised Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), etc. Nevertheless, the programs included in these workstations have a high cost which always depends on the software provider and is always subject to its norms and requirements. With this study, we aim to present the advantages and disadvantages of these radiological image visualization systems in the advanced management of radiological studies. We will compare the features of the VITREA2® and AW VolumeShare 5® radiology workstation with free open source software applications like OsiriX® and 3D Slicer®, with examples from specific studies.

How much is enough-Can resting state fMRI provide a demarcation for neurosurgical resection in glioma?

This study represents a systematic review of the insights provided by resting state functional MRI (rs-fMRI) use in the glioma population. Following PRISMA guidelines, 45 studies were included in the review and were classified in glioma-related neuronal changes (n=28) and eloquent area localization (n=17). Despite the heterogeneous nature of the studies, there is considerable evidence of diffuse functional reorganization occurring in the setting of gliomas with local and interhemispheric functional connectivity alterations involving different functional networks. The studies showed evidence of decreased long distance functional connectivity and increased global local efficiency occurring in the setting of gliomas. The tumour grade seems to correlate with distinct functional connectivity changes. Overall, there is a potential clinical utility of rs-fMRI for identifying the functional brain network disruptions occurring in the setting of gliomas. Further studies utilizing standardized analytical methods are required to elucidate the mechanism through which gliomas induce global changes in brain connectivity.

SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research

Diffusion MRI (dMRI) is the only noninvasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomic assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here, we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer (www.slicer.org). More information, videos, tutorials, and sample data are available at dmri.slicer.org Cancer Res; 77(21); e101-3. ©2017 AACR.

Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas

This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced. The smaller set of remaining streamlines is then segmented using the original metric, thus eliminating any false positives from the preprocessing stage. As a result, a single-thread implementation of the algorithm can segment a dataset of almost 9 million streamlines in less than 6 minutes. Moreover, parallel versions of our algorithm for multicore processors and graphics processing units further reduce the segmentation time to less than 22 seconds and to 5 seconds, respectively. This performance enables the use of the algorithm in truly interactive applications for visualization, analysis, and segmentation of large white matter tractography datasets.

Microstructure-informed slow diffusion tractography in humans enhances visualisation of fibre pathways

Conventional fibre tractography methods based on diffusion tensor imaging exploit diffusion anisotropy and directionality in the range of low diffusion weightings (b-values). High b-value Biexponential Diffusion Tensor Analysis reported previously has demonstrated that fractional anisotropy of the slow diffusion component is essentially higher than that of conventional diffusion tensor imaging whereas popular compartment models associate this slow diffusion component with axonal water fraction. One of the primary aims of this study is to elucidate the feasibility and potential benefits of "microstructure-informed" whole-brain slow-diffusion fibre tracking (SDIFT) in humans. In vivo diffusion-weighted images in humans were acquired in the extended range of diffusion weightings≤6000smm^-2 at 3T. Fast and slow diffusion tensors were reconstructed using the bi-exponential tensor decomposition, and a detailed statistical analysis of the relevant whole-brain tensor metrics was performed. We visualised three-dimensional fibre tracts in in vivo human brains using deterministic streamlining via the major eigenvector of the slow diffusion tensor. In particular, we demonstrated that slow-diffusion fibre tracking provided considerably higher fibre counts of long association fibres and allowed one to reconstruct more short association fibres than conventional diffusion tensor imaging. SDIFT is suggested to be useful as a complimentary method capable to enhance reliability and visualisation of the evaluated fibre pathways. It is especially informative in precortical areas where the uncertainty of the mono-exponential tensor evaluation becomes too high due to decreased anisotropy of low b-value diffusion in these areas. Benefits can be expected in assessment of the residual axonal integrity in tissues affected by various pathological conditions, in surgical planning, and in evaluation of cortical connectivity, in particular, between Brodmann's areas.

Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model

Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography.

We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography.

We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema.

Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors.

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Reconstruction of edematous white matter from diffusion MRI is investigated.

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The performance of two–tensor unscented Kalman filter (UKF) tractography is assessed.

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The two–tensor model in UKF is analyzed in phantom and patient data experiments.

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Practical guidance on employing the UKF method in neurosurgical patients is provided

White matter tractography for neurosurgical planning: A topography-based review of the current state of the art

We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.

A systematic evaluation of intraoperative white matter tract shift in pediatric epilepsy surgery using high-field MRI and probabilistic high angular resolution diffusion imaging tractography

OBJECTIVE Characterization of intraoperative white matter tract (WMT) shift has the potential to compensate for neuronavigation inaccuracies using preoperative brain imaging. This study aimed to quantify and characterize intraoperative WMT shift from the global hemispheric to the regional tract-based scale and to investigate the impact of intraoperative factors (IOFs). METHODS High angular resolution diffusion imaging (HARDI) diffusion-weighted data were acquired over 5 consecutive perioperative time points (MR₁ to MR₅) in 16 epilepsy patients (8 male; mean age 9.8 years, range 3.8-15.8 years) using diagnostic and intraoperative 3-T MRI scanners. MR₁ was the preoperative planning scan. MR₂ was the first intraoperative scan acquired with the patient's head fixed in the surgical position. MR₃ was the second intraoperative scan acquired following craniotomy and durotomy, prior to lesion resection. MR₄ was the last intraoperative scan acquired following lesion resection, prior to wound closure. MR₅ was a postoperative scan acquired at the 3-month follow-up visit. Ten association WMT/WMT segments and 1 projection WMT were generated via a probabilistic tractography algorithm from each MRI scan. Image registration was performed through pairwise MRI alignments using the skull segmentation. The MR₁ and MR₂ pairing represented the first surgical stage. The MR₂ and MR₃ pairing represented the second surgical stage. The MR₃ and MR₄ (or MR₅) pairing represented the third surgical stage. The WMT shift was quantified by measuring displacements between a pair of WMT centerlines. Linear mixed-effects regression analyses were carried out for 6 IOFs: head rotation, craniotomy size, durotomy size, resected lesion volume, presence of brain edema, and CSF loss via ventricular penetration. RESULTS The average WMT shift in the operative hemisphere was 2.37 mm (range 1.92-3.03 mm) during the first surgical stage, 2.19 mm (range 1.90-3.65 mm) during the second surgical stage, and 2.92 mm (range 2.19-4.32 mm) during the third surgical stage. Greater WMT shift occurred in the operative than the nonoperative hemisphere, in the WMTs adjacent to the surgical lesion rather than those remote to it, and in the superficial rather than the deep segment of the pyramidal tract. Durotomy size and resection size were significant, independent IOFs affecting WMT shift. The presence of brain edema was a marginally significant IOF. Craniotomy size, degree of head rotation, and ventricular penetration were not significant IOFs affecting WMT shift. CONCLUSIONS WMT shift occurs noticeably in tracts adjacent to the surgical lesions, and those motor tracts superficially placed in the operative hemisphere. Intraoperative probabilistic HARDI tractography following craniotomy, durotomy, and lesion resection may compensate for intraoperative WMT shift and improve neuronavigation accuracy.

Automated white matter fiber tract identification in patients with brain tumors

We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography.

Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.

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Spectral clustering machine learning approach for white matter tract identification

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Data-driven white matter parcellation learned from healthy subjects tractography

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White matter parcellation applied to 18 consecutive patients with brain tumors

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Arcuate fasciculus and corticospinal tracts identified in all patients

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All tracts within 3 mm of corresponding patient-specific functional activations

Analytical performance bounds for multi-tensor diffusion-MRI

PURPOSE

To examine the effects of MR acquisition parameters on brain white matter fiber orientation estimation and parameter of clinical interest in crossing fiber areas based on the Multi-Tensor Model (MTM).

MATERIAL AND METHODS

We compute the Cramér-Rao Bound (CRB) for the MTM and the parameter of clinical interest such as the Fractional Anisotropy (FA) and the dominant fiber orientations, assuming that the diffusion MRI data are recorded by a multi-coil, multi-shell acquisition system. Considering the sum-of-squares method for the reconstructed magnitude image, we introduce an approximate closed-form formula for Fisher Information Matrix that has the simplicity and easy interpretation advantages. In addition, we propose to generalize the FA and the mean diffusivity to the multi-tensor model.

RESULTS

We show the application of the CRB to reduce the scan time while preserving a good estimation precision. We provide results showing how the increase of the number of acquisition coils compensates the decrease of the number of diffusion gradient directions. We analyze the impact of the b-value and the Signal-to-Noise Ratio (SNR). The analysis shows that the estimation error variance decreases with a quadratic rate with the SNR, and that the optimum b-values are not unique but depend on the target parameter, the context, and eventually the target cost function.

CONCLUSION

In this study we highlight the importance of choosing the appropriate acquisition parameters especially when dealing with crossing fiber areas. We also provide a methodology for the optimal tuning of these parameters using the CRB.

Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning

BACKGROUND

Advances in white matter tractography enhance neurosurgical planning and glioma resection, but white matter tractography is limited by biological variables such as edema, mass effect, and tract infiltration or selection biases related to regions of interest or fractional anisotropy values.

OBJECTIVE

To provide an automated tract identification paradigm that corrects for artifacts created by tumor edema and infiltration and provides a consistent, accurate method of fiber bundle identification.

METHODS

An automated tract identification paradigm was developed and evaluated for glioma surgery. A fiber bundle atlas was generated from 6 healthy participants. Fibers of a test set (including 3 healthy participants and 10 patients with brain tumors) were clustered adaptively with this atlas. Reliability of the identified tracts in both groups was assessed by comparison with 2 experts with the Cohen κ used to quantify concurrence. We evaluated 6 major fiber bundles: cingulum bundle, fornix, uncinate fasciculus, arcuate fasciculus, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus, the last 3 tracts mediating language function.

RESULTS

The automated paradigm demonstrated a reliable and practical method to identify white mater tracts, despite mass effect, edema, and tract infiltration. When the tumor demonstrated significant mass effect or shift, the automated approach was useful for providing an initialization to guide the expert with identification of the specific tract of interest.

CONCLUSION

We report a reliable paradigm for the automated identification of white matter pathways in patients with gliomas. This approach should enhance the neurosurgical objective of maximal safe resections.

ABBREVIATIONS

AF, arcuate fasciculusDTI, diffusion tensor imagingIFOF, inferior fronto-occipital fasciculusILF, inferior longitudinal fasciculusROI, region of interestWM, white matter.

Diffusion Tensor Imaging of TBI: Potentials and Challenges

Neuroimaging plays a critical role in the setting in traumatic brain injury (TBI). Diffusion tensor imaging (DTI) is an advanced magnetic resonance imaging technique that is capable of providing rich information on the brain's neuroanatomic connectome. The purpose of this article is to systematically review the role of DTI and advanced diffusion techniques in the setting of TBI, including diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging, diffusion spectrum imaging, and q-ball imaging. We discuss clinical applications of DTI and review the DTI literature as it pertains to TBI. Despite the continued advancements in DTI and related diffusion techniques over the past 20 years, DTI techniques are sensitive for TBI at the group level only and there is insufficient evidence that DTI plays a role at the individual level. We conclude by discussing future directions in DTI research in TBI including the role of machine learning in the pattern classification of TBI.

An automated string-based approach to extracting and characterizing White Matter fiber-bundles

In this paper, we propose an automated approach to extracting White Matter (WM) fiber-bundles through clustering and model characterization. The key novelties of our approach are: a new string-based formalism, allowing an alternative representation of WM fibers, a new string dissimilarity metric, a WM fiber clustering technique, and a new model-based characterization algorithm. Thanks to these novelties, the complex problem of WM fiber-bundle extraction and characterization reduces to a much simpler and well-known string extraction and analysis problem. Interestingly, while several past approaches extract fiber-bundles by grouping available fibers on the basis of provided atlases (and, therefore, cannot capture possibly existing fiber-bundles nor represented in the atlases), our approach first clusters available fibers once and for all, and then tries to associate obtained clusters with models provided directly and dynamically by users. This more dynamic and interactive way of proceeding can help the detection of fiber-bundles autonomously proposed by our approach and not present in the initial models provided by experts.

Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography

PURPOSE

The aim of this study was to present a tractography algorithm using a two-tensor unscented Kalman filter (UKF) to improve the modeling of the corticospinal tract (CST) by tracking through regions of peritumoral edema and crossing fibers.

METHODS

Ten patients with brain tumors in the vicinity of motor cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-T magnetic resonance imaging (MRI) including functional MRI (fMRI) and a diffusion-weighted data set with 31 directions. Fiber tracking was performed using both single-tensor streamline and two-tensor UKF tractography methods. A two-region-of-interest approach was used to delineate the CST. Results from the two tractography methods were compared visually and quantitatively. fMRI was applied to identify the functional fiber tracts.

RESULTS

Single-tensor streamline tractography underestimated the extent of tracts running through the edematous areas and could only track the medial projections of the CST. In contrast, two-tensor UKF tractography tracked fanning projections of the CST despite peritumoral edema and crossing fibers. Based on visual inspection, the two-tensor UKF tractography delineated tracts that were closer to motor fMRI activations, and it was apparently more sensitive than single-tensor streamline tractography to define the tracts directed to the motor sites. The volume of the CST was significantly larger on two-tensor UKF than on single-tensor streamline tractography ([Formula: see text]).

CONCLUSION

Two-tensor UKF tractography tracks a larger volume CST than single-tensor streamline tractography in the setting of peritumoral edema and crossing fibers in brain tumor patients.

Merits and Limits of Tractography Techniques for the Uninitiated

The implementation of fiber tracking or tractography modules in commercial navigation systems resulted in a broad availability of visualization possibilities for major white matter tracts in the neurosurgical community. Unfortunately the implemented algorithms and tracking approaches do not represent the state of the art of tractography strategies and may lead to false tracking results. The application of advanced tractography techniques for neurosurgical procedures poses even additional challenges that relate to effects of the individual anatomy that might be altered by edema and tumor, to stereotactic inaccuracies due to image distortion, as well as to registration inaccuracies and brain shift.

Strengths and weaknesses of state of the art fiber tractography pipelines--A comprehensive in-vivo and phantom evaluation study using Tractometer

Many different tractography approaches and corresponding isolated evaluation attempts have been presented over the last years, but a comparative and quantitative evaluation of tractography algorithms still remains a challenge, particularly in-vivo. The recently presented evaluation framework Tractometer is the first attempt to approach this challenge in a quantitative, comparative, persistent and open-access way. Tractometer is currently based on the evaluation of several global connectivity and tract-overlap metrics on hardware phantom data. The work presented in this paper focuses on extending Tractometer with a metric that enables the assessment of the local consistency of tractograms with the underlying image data that is not only applicable to phantom dataset but allows the quantitative and purely data-driven evaluation of in-vivo tractography. We furthermore present an extensive reference-based evaluation study of 25,000 tractograms obtained on phantom and in-vivo datasets using the presented local metric as well as all the methods already established in Tractometer. The experiments showed that the presented local metric successfully reflects the behavior of in-vivo tractography under different conditions and that it is consistent with the results of previous studies. Additionally our experiments enabled a multitude of conclusions with implications for fiber tractography in general, including recommendations regarding optimal choice of a local modeling technique, tractography algorithm, and parameterization, confirming and complementing the results of earlier studies.

[Diffusion-weighted imaging and diffusion tensor imaging in preoperative diagnostics]

The extent of anisotropy and the direction of diffusion of H protons characterize the tissue structure and qualitative and quantitative evaluation of the appropriate parameters provide inferences for the membrane permeability, axon thickness, integrity and connectivity. The technique of fiber tracking is implemented in the presurgical diagnostics for lesions in the brain to show the relationship of the lesion to the fiber system. The benefit of this technique for preoperative and intraoperative localization of the pyramidal tracts, the primary motor areas, the optic radiation and the speech regions could be confirmed. In focal gliomas the fibers are often displaced to the edge of the tumor; however, in diffusely growing tumors the fiber system is infiltrated by tumor cells. In high-grade gliomas destruction of the fibers often occurs. Limitations are always present in brain regions where several fiber systems cross in different directions, such as the centrum semiovale. Appropriate neuroanatomical knowledge must be present in order to be able to immediately recognize such possible deviant pathways of the fiber tracking.

Magnetic Resonance Sequences and Rapid Acquisition for MR-Guided Interventions

Interventional MR uses rapid imaging to guide diagnostic and therapeutic procedures. One of the attractions of MR-guidance is the abundance of inherent contrast mechanisms available. Dynamic procedural guidance with real-time imaging has pushed the limits of MR technology, demanding rapid acquisition and reconstruction paired with interactive control and device visualization. This article reviews the technical aspects of real-time MR sequences that enable MR-guided interventions.

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery

BACKGROUND AND PURPOSE

Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop.

METHODS

Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods.

RESULTS

The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues.

CONCLUSIONS

The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.

Designing an operating theatre for awake procedures: A solution to improve multimodality information input

OBJECTIVE

Many neurosurgical procedures are now performed with the patient aware in order to allow interactions between the patient and healthcare professionals. These procedures include awake brain surgery and spinal cord stimulation (SCS), lead placement for treatment of refractory chronic back and leg pain. Neurosurgical procedures under local anaesthesia require optimal intraoperative cooperation of the patient and all personnel involved in surgery. In addition to accommodating this extra source of intraoperative information all other necessary sources of data relevant to the procedure must be presented. The concept of an operating room dedicated to neurosurgical procedures performed aware and accommodating these concepts is presented, and some evidence for improvements in outcome presented, deriving from a series of patients implanted with spinal cord stimulators before and after the operating theatre was brought into service.

RESULTS AND DISCUSSION

In addition to the description, two videos demonstrate the facility online. Beyond this qualitative evidence, quantitative improvement in patient outcome is evidenced by the series presented: 91.3% of patients operated in the awake anaesthesia-dedicated theatre obtained adequate low back pain coverage, versus 60.0% for patients operated before (p = 0.028).

CONCLUSION

The concept of such an operating room is a step in improving the outcome by improving the presentation of all types of information to the operating room staff most notably in the example of aware procedures.

Emerging Applications of Bedside 3D Printing in Plastic Surgery

Modern imaging techniques are an essential component of preoperative planning in plastic and reconstructive surgery. However, conventional modalities, including three-dimensional (3D) reconstructions, are limited by their representation on 2D workstations. 3D printing, also known as rapid prototyping or additive manufacturing, was once the province of industry to fabricate models from a computer-aided design (CAD) in a layer-by-layer manner. The early adopters in clinical practice have embraced the medical imaging-guided 3D-printed biomodels for their ability to provide tactile feedback and a superior appreciation of visuospatial relationship between anatomical structures. With increasing accessibility, investigators are able to convert standard imaging data into a CAD file using various 3D reconstruction softwares and ultimately fabricate 3D models using 3D printing techniques, such as stereolithography, multijet modeling, selective laser sintering, binder jet technique, and fused deposition modeling. However, many clinicians have questioned whether the cost-to-benefit ratio justifies its ongoing use. The cost and size of 3D printers have rapidly decreased over the past decade in parallel with the expiration of key 3D printing patents. Significant improvements in clinical imaging and user-friendly 3D software have permitted computer-aided 3D modeling of anatomical structures and implants without outsourcing in many cases. These developments offer immense potential for the application of 3D printing at the bedside for a variety of clinical applications. In this review, existing uses of 3D printing in plastic surgery practice spanning the spectrum from templates for facial transplantation surgery through to the formation of bespoke craniofacial implants to optimize post-operative esthetics are described. Furthermore, we discuss the potential of 3D printing to become an essential office-based tool in plastic surgery to assist in preoperative planning, developing intraoperative guidance tools, teaching patients and surgical trainees, and producing patient-specific prosthetics in everyday surgical practice.

Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography

Background

Diffusion imaging tractography is increasingly used to trace critical fiber tracts in brain tumor patients to reduce the risk of post-operative neurological deficit. However, the effects of peritumoral edema pose a challenge to conventional tractography using the standard diffusion tensor model. The aim of this study was to present a novel technique using a two-tensor unscented Kalman filter (UKF) algorithm to track the arcuate fasciculus (AF) in brain tumor patients with peritumoral edema.

Methods

Ten right-handed patients with left-sided brain tumors in the vicinity of language-related cortex and evidence of significant peritumoral edema were retrospectively selected for the study. All patients underwent 3-Tesla magnetic resonance imaging (MRI) including a diffusion-weighted dataset with 31 directions. Fiber tractography was performed using both single-tensor streamline and two-tensor UKF tractography. A two-regions-of-interest approach was applied to perform the delineation of the AF. Results from the two different tractography algorithms were compared visually and quantitatively.

Results

Using single-tensor streamline tractography, the AF appeared disrupted in four patients and contained few fibers in the remaining six patients. Two-tensor UKF tractography delineated an AF that traversed edematous brain areas in all patients. The volume of the AF was significantly larger on two-tensor UKF than on single-tensor streamline tractography (p < 0.01).

Conclusions

Two-tensor UKF tractography provides the ability to trace a larger volume AF than single-tensor streamline tractography in the setting of peritumoral edema in brain tumor patients.

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We compared two tractography methods in datasets from 10 brain tumor patients.

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All patients had peritumoral edema affecting the arcuate fasciculus (AF), a fiber tract related to language function.

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AF volume from unscented Kalman filter (UKF) two-tensor tractography was larger than in standard DTI tractography.

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Two-tensor UKF tractography may trace the AF more completely in patients with peritumoral edema.