Fiber tracking from DTI using linear state space models: detectability of the pyramidal tract

Preoperative tractography algorithm for safe resection of tumors located in the descending motor pathways zone

BACKGROUND

Diffusion tensor imaging (DTI) tractography facilitates maximal safe resection and optimizes planning to avoid injury during subcortical dissection along descending motor pathways (DMPs). We provide an affordable, safe, and timely algorithm for preoperative DTI motor reconstruction for gliomas adjacent to DMPs.

METHODS

Preoperative DTI reconstructions were extracted from a prospectively acquired registry of glioma resections adjacent to DMPs. The surgeries were performed over a 7-year period. Demographic, clinical, and radiographic data were extracted from patients' electronic medical records.

RESULTS

Nineteen patients (12 male) underwent preoperative tractography between January 1, 2013, and May 31, 2020. The average age was 44.5 years (range, 19-81 years). A complete radiological resection was achieved in nine patients, a subtotal resection in five, a partial resection in three, and a biopsy in two. Histopathological diagnoses included 10 patients with high-grade glioma and nine with low-grade glioma. A total of 16 perirolandic locations (10 frontal and six frontoparietal) were recorded, as well as two in the insula and one in the basal ganglia. In 9 patients (47.3%), the lesion was in the dominant hemisphere. The median preoperative and postoperative Karnofsky Performance Scores were 78 and 80, respectively. Motor function was unchanged or improved over time in 15 cases (78.9%).

CONCLUSION

This protocol of DTI reconstruction for glioma removal near the DMP shows good results in low-term neurological functional outcomes.

Subcortical Stimulation in Brain Tumor Surgery: A closer look beneath the surface

INTRODUCTION

Maximizing a patient's onco-functional balance is the central tenet of brain tumor surgery. As a result, numerous surgical adjuncts have been developed to facilitate identification of the tumor-brain interface and preservation of functional anatomy. Among these, intraoperative neurophysiologic monitoring (IONM) with direct cortical and subcortical stimulation remains the gold standard for real time, functional mapping of motor and language activity. However, stimulation techniques are not standardized and vary significantly across institutions. This is particularly true with subcortical stimulation for mapping of motor function.

METHODS

We review the state of subcortical IONM and mapping techniques. Historical and predicate literature were reviewed as well as new and emerging techniques. We discuss their evolution, clinical utility, and limitations to direct future research and application.

RESULTS

We evaluate and discuss the background and current clinical use of direct cortical and subcortical stimulation techniques and protocols and identify current trends and limitations. We focus specifically on methods of subcortical stimulation given the heterogeneity in the published literature. We also suggest directions to optimize the clinical utility of these tools.

CONCLUSION

Despite significant heterogeneity in published techniques, trends support the use of the Taniguchi method for subcortical stimulation. Novel dynamic stimulation techniques may improve accuracy. Prospective studies to define standardized guidelines are needed.

Physical and digital phantoms for validating tractography and assessing artifacts

Fiber tractography is widely used to non-invasively map white-matter bundles in vivo using diffusion-weighted magnetic resonance imaging (dMRI). As it is the case for all scientific methods, proper validation is a key prerequisite for the successful application of fiber tractography, be it in the area of basic neuroscience or in a clinical setting. It is well-known that the indirect estimation of the fiber tracts from the local diffusion signal is highly ambiguous and extremely challenging. Furthermore, the validation of fiber tractography methods is hampered by the lack of a real ground truth, which is caused by the extremely complex brain microstructure that is not directly observable non-invasively and that is the basis of the huge network of long-range fiber connections in the brain that are the actual target of fiber tractography methods. As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for?", "What would the ideal phantom for validation fiber tractography look like?" and "What phantoms, phantom datasets and tools used for their creation are available to the research community?". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.

Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery using Diffusion Tractography and Convolutional Neural Networks

Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classi?cation performance of CNN models on diffusion weighted imaging (DWI) streamlines de?ned by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbalanced dataset, we evaluated different CNN architectures with multiple loss functions (e.g., focal loss and center loss) and a soft attention mechanism, and compared our models with current state-ofthe-art methods. Through extensive experiments on streamlines collected from 70 healthy children and 70 children with focal epilepsy, we demonstrated that our deep CNN model with focal and central losses and soft attention outperforms all existing models in the literature and provides clinically acceptable accuracy (73 -100%) for the objective detection of functionally-important white matter pathways including ESM determined eloquent areas such as primary motor, aphasia, speech arrest, auditory, and visual functions. The ?ndings of this study encourage further investigations to determine if DWICNN analysis can serve as a noninvasive diagnostic tool during pediatric presurgical planning by estimating not only the location of essential cortices at the gyral level, but also the underlying ?bers connecting these cortical areas, to minimize or predict postsurgical functional de?cits. This study translates an advanced CNN model to clinical practice in the pediatric population where currently available approaches (e.g., ESM, fMRI) are suboptimal. The implementation will be released at https://github. com/HaotianMXu/Brain-?ber-classi?cation-using-CNNs.

Tracking and validation techniques for topographically organized tractography

Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.

Semiparametric Bayesian models for human brain mapping

Functional magnetic resonance imaging (fMRI) has led to enormous progress in human brain mapping. Adequate analysis of the massive spatiotemporal data sets generated by this imaging technique, combining parametric and non-parametric components, imposes challenging problems in statistical modelling. Complex hierarchical Bayesian models in combination with computer-intensive Markov chain Monte Carlo inference are promising tools.

The purpose of this paper is twofold. First, it provides a review of general semiparametric Bayesian models for the analysis of fMRI data. Most approaches focus on important but separate temporal or spatial aspects of the overall problem, or they proceed by stepwise procedures. Therefore, as a second aim, we suggest a complete spatiotemporal model for analysing fMRI data within a unified semiparametric Bayesian framework. An application to data from a visual stimulation experiment illustrates our approach and demonstrates its computational feasibility.

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.

Awake craniotomy for supratentorial gliomas: why, when and how?

Awake craniotomy has become an increasingly utilized procedure in the treatment of supratentorial intra-axial tumors. The popularity of this procedure is partially attributable to improvements in intraoperative technology and anesthetic techniques. The application of awake craniotomy to the field of neuro-oncology has decreased iatrogenic postoperative neurological deficits, allowed for safe maximal tumor resection and improved healthcare resource stewardship by permitting early patient discharge. In this article, we review recent evidence for the utility of awake craniotomy in the resection of gliomas and describe the senior author's experience in performing this procedure. Furthermore, we explore innovative applications of awake craniotomy to outpatient tumor resections and the conduct of neurosurgery in resource-poor settings. We conclude that awake craniotomy is an effective and versatile neurosurgical procedure with expanding applications in neuro-oncology.

Validation of High-Resolution Tractography Against In Vivo Tracing in the Macaque Visual Cortex

Diffusion magnetic resonance imaging (MRI) allows for the noninvasive in vivo examination of anatomical connections in the human brain, which has an important role in understanding brain function. Validation of this technique is vital, but has proved difficult due to the lack of an adequate gold standard. In this work, the macaque visual system was used as a model as an extensive body of literature of in vivo and postmortem tracer studies has established a detailed understanding of the underlying connections. We performed probabilistic tractography on high angular resolution diffusion imaging data of 2 ex vivo, in vitro macaque brains. Comparisons were made between identified connections at different thresholds of probabilistic connection “strength,” and with various tracking optimization strategies previously proposed in the literature, and known connections from the detailed visual system wiring map described by Felleman and Van Essen (1991; FVE91). On average, 74% of connections that were identified by FVE91 were reproduced by performing the most successfully optimized probabilistic diffusion MRI tractography. Further comparison with the results of a more recent tracer study ( Markov et al. 2012) suggests that the fidelity of tractography in estimating the presence or absence of interareal connections may be greater than this.

Tractography from HARDI using an intrinsic unscented Kalman filter

A novel adaptation of the unscented Kalman filter (UKF) was recently introduced in literature for simultaneous multitensor estimation and fiber tractography from diffusion MRI. This technique has the advantage over other tractography methods in terms of computational efficiency, due to the fact that the UKF simultaneously estimates the diffusion tensors and propagates the most consistent direction to track along. This UKF and its variants reported later in literature however are not intrinsic to the space of diffusion tensors. Lack of this key property can possibly lead to inaccuracies in the multitensor estimation as well as in the tractography. In this paper, we propose a novel intrinsic unscented Kalman filter (IUKF) in the space of diffusion tensors which are symmetric positive definite matrices, that can be used for simultaneous recursive estimation of multitensors and propagation of directional information for use in fiber tractography from diffusion weighted MR data. In addition to being more accurate, IUKF retains all the advantages of UKF mentioned above. We demonstrate the accuracy and effectiveness of the proposed method via experiments publicly available phantom data from the fiber cup-challenge (MICCAI 2009) and diffusion weighted MR scans acquired from human brains and rat spinal cords.

Use of diffusion tensor imaging in glioma resection

Diffusion tensor imaging (DTI) is increasingly used in the resection of both high- and low-grade gliomas. Whereas conventional MRI techniques provide only anatomical information, DTI offers data on CNS connectivity by enabling visualization of important white matter tracts in the brain. Importantly, DTI allows neurosurgeons to better guide their surgical approach and resection. Here, the authors review basic scientific principles of DTI, include a primer on the technology and image acquisition, and outline the modality's evolution as a frequently used tool for glioma resection. Current literature supporting its use is summarized, highlighting important clinical studies on the application of DTI in preoperative planning for glioma resection, preoperative diagnosis, and postoperative outcomes. The authors conclude with a review of future directions for this technology.

Intraoperative cortico–subcortical stimulations in surgery of low-grade gliomas

In order to increase the impact of surgery on the natural history of low-grade glioma, resection should be of maximum importance. Nevertheless, since low-grade gliomas are frequently located in eloquent structures, function needs to be preserved. Therefore, studying the functional organization of the brain is mandatory for each patient due to the inter-individual anatomofunctional variability, increased in tumors due to cerebral plasticity. This strategy enables performance of a resection according to functional boundaries. However, preoperative neurofunctional imaging only allows the study of the gray matter. Consequently, since low-grade glioma invades cortical and subcortical structures and shows an infiltrative progression along the fibers, the goal of this review is to focus on the techniques able to map both cortical and subcortical regions. In addition to diffusion tensor imaging, which gives only anatomical information and still needs to be validated, intraoperative direct cortico-subcortical electrostimulation is the sole current method allowing a reliable study of the individual anatomofunctional connectivity, concerning sensorimotor, language and other cognitive functions. Its actual contribution is detailed, both in clinical issues, especially the improvement of the benefit/risk ratio of low-grade glioma resection, and in fundamental applications--namely, a new door to the connectionism and cerebral plasticity.

White matter fiber tracking directed by interpolating splines and a methodological framework for evaluation

Image-based tractography of white matter (WM) fiber bundles in the brain using diffusion weighted MRI (DW-MRI) has become a useful tool in basic and clinical neuroscience. However, proper tracking is challenging due to the anatomical complexity of fiber pathways, the coarse resolution of clinically applicable whole-brain in vivo imaging techniques, and the difficulties associated with verification. In this study we introduce a new tractography algorithm using splines (denoted Spline). Spline reconstructs smooth fiber trajectories iteratively, in contrast to most other tractography algorithms that create piecewise linear fiber tract segments, followed by spline fitting. Using DW-MRI recordings from eight healthy elderly people participating in a longitudinal study of cognitive aging, we compare our Spline algorithm to two state-of-the-art tracking methods from the TrackVis software suite. The comparison is done quantitatively using diffusion metrics (fractional anisotropy, FA), with both (1) tract averaging, (2) longitudinal linear mixed-effects model fitting, and (3) detailed along-tract analysis. Further validation is done on recordings from a diffusion hardware phantom, mimicking a coronal brain slice, with a known ground truth. Results from the longitudinal aging study showed high sensitivity of Spline tracking to individual aging patterns of mean FA when combined with linear mixed-effects modeling, moderately strong differences in the along-tract analysis of specific tracts, whereas the tract-averaged comparison using simple linear OLS regression revealed less differences between Spline and the two other tractography algorithms. In the brain phantom experiments with a ground truth, we demonstrated improved tracking ability of Spline compared to the two reference tractography algorithms being tested.

Anatomical location of the medial lemniscus and spinothalamic tract at the pons in the human brain: a diffusion tensor tractography study

Using diffusion tensor tractography, we investigated the anatomical location of medial lemniscus (ML) and spinothalamic tract (STT) at pons. We recruited 47 healthy volunteers. Evaluation of the anatomical location of ML and STT was performed using the highest probabilistic location at the upper, middle, and lower pons. According to findings, MLs were located around the middle to medial one-third, between midline and lateral boundary of pons in the pontine tegmentum and STTs were located posterolaterally to ML.

Determination of three-dimensional muscle architectures: validation of the DTI-based fiber tractography method by manual digitization

In the last decade, diffusion tensor imaging (DTI) has been used increasingly to investigate three-dimensional (3D) muscle architectures. So far there is no study that has proved the validity of this method to determine fascicle lengths and pennation angles within a whole muscle. To verify the DTI method, fascicle lengths of m. soleus as well as their pennation angles have been measured using two different methods. First, the 3D muscle architecture was analyzed in vivo applying the DTI method with subsequent deterministic fiber tractography. In a second step, the muscle architecture of the same muscle was analyzed using a standard manual digitization system (MicroScribe MLX). Comparing both methods, we found differences for the median pennation angles (P < 0.001) but not for the median fascicle lengths (P = 0.216). Despite the statistical results, we conclude that the DTI method is appropriate to determine the global fiber orientation. The difference in median pennation angles determined with both methods is only about 1.2° (median pennation angle of MicroScribe: 9.7°; DTI: 8.5°) and probably has no practical relevance for muscle simulation studies. Determining fascicle lengths requires additional restriction and further development of the DTI method.

Local Tests for Identifying Anisotropic Diffusion Areas in Human Brain with DTI

Diffusion tensor imaging (DTI) plays a key role in analyzing the physical structures of biological tissues, particularly in reconstructing fiber tracts of the human brain in vivo. On the one hand, eigenvalues of diffusion tensors (DTs) estimated from diffusion weighted imaging (DWI) data usually contain systematic bias, which subsequently biases the diffusivity measurements popularly adopted in fiber tracking algorithms. On the other hand, correctly accounting for the spatial information is important in the construction of these diffusivity measurements since the fiber tracts are typically spatially structured. This paper aims to establish test-based approaches to identify anisotropic water diffusion areas in the human brain. These areas in turn indicate the areas passed by fiber tracts. Our proposed test statistic not only takes into account the bias components in eigenvalue estimates, but also incorporates the spatial information of neighboring voxels. Under mild regularity conditions, we demonstrate that the proposed test statistic asymptotically follows a χ² distribution under the null hypothesis. Simulation and real DTI data examples are provided to illustrate the efficacy of our proposed methods.

[Structural connectivity and diffusion tensor imaging in autism spectrum disorders]

Over the past years, diffusion tensor imaging (DTI) has become an important brain-imaging technique in neuropsychiatric research. DTI allows noninvasive visualization of white matter tracts. In addition, with DTI it is possible to quantify the structural integrity of the investigated fiber tracts. In child and adolescent psychiatry, DTI has become an increasingly important research tool, especially for conditions like autism spectrum disorders (ASD). Yet, correct interpretation of DTI findings can be challenging, especially for clinicians. Thus, the present review article explains the basic principles of this frequently used imaging technique as well as essential indices, like fractional anisotropy, radial, mean, and axial diffusivity and its two main applications, fibertracking and whole brain analysis. The strengths and weaknesses as well as future perspectives are discussed in light of DTI studies in children and adolescents with ASD.

Accuracy of diffusion tensor magnetic resonance imaging-based tractography for surgery of gliomas near the pyramidal tract: a significant correlation between subcortical electrical stimulation and postoperative tractography

BACKGROUND

Diffusion tensor (DT) imaging-based fiber tracking is a noninvasive magnetic resonance technique that can delineate the course of white matter fibers.

OBJECTIVE

To evaluate the accuracy and usefulness of this DT imaging-based fiber tracking for surgery in patients with gliomas near the pyramidal tract (PT).

METHODS

Subjects comprised 32 patients with gliomas near the PT. DT imaging-based fiber tracks of the PT were generated before and within 3 days after surgery in all patients. A tractography-integrated navigation system was used during the operation. Cortical and subcortical motor-evoked potentials (MEPs) were also monitored during resection to maximize the preservation of motor function. The threshold intensity for subcortical MEPs was examined by searching the stimulus points and changing the stimulus intensity. Minimum distance between the resection border and the illustrated PT was measured on postoperative tractography.

RESULTS

In all subjects, DT imaging-based tractography of the PT was successfully performed, preoperatively demonstrating the relationship between tumors and the PT. With the use of the tractography-integrated navigation system and intraoperative MEPs, motor function was preserved postoperatively in all patients. A significant correlation was seen between threshold intensity for subcortical MEPs and the distance between the resection border and PT on postoperative DT imaging.

CONCLUSION

DT imaging-based fiber tracking is a reliable and accurate method for mapping the course of subcortical PTs. Fiber tracking and intraoperative MEPs were useful for preserving motor function in patients with gliomas near the PT.

Predictive Value and Safety of Intraoperative Neurophysiological Monitoring With Motor Evoked Potentials in Glioma Surgery

BACKGROUND:

Resection of gliomas in or adjacent to the motor system is widely performed with intraoperative neuromonitoring (IOM). Despite the fact that data on the safety of IOM are available, the significance and predictive value of the procedure are still under discussion. Moreover, cases of false-negative monitoring affect the surgeon's confidence in IOM.

OBJECTIVE:

To examine cases of false-negative IOM to reveal structural explanations.

METHODS:

Between 2007 and 2010, we resected 115 consecutive supratentorial gliomas in or close to eloquent motor areas using direct cortical stimulation for monitoring of motor evoked potentials (MEPs). The monitoring data were reviewed and related to new postoperative motor deficit and postoperative imaging. Clinical outcomes were assessed during follow-up.

RESULTS:

Monitoring of MEPs was successful in 112 cases (97.4%). Postoperatively, 30.3% of patients had a new motor deficit, which remained permanent in 12.5%. Progression-free follow-up was 9.7 months (range, 2 weeks-40.6 months). In 65.2% of all cases, MEPs were stable throughout the operation, but 8.9% showed a new temporary motor deficit, whereas 4.5% (5 patients) presented with permanently deteriorated motor function representing false-negative monitoring at first glance. However, these cases were caused by secondary hemorrhage, ischemia, or resection of the supplementary motor area.

CONCLUSION:

Continuous MEP monitoring provides reliable monitoring of the motor system, influences the course of operation in some cases, and has to be regarded as the standard for IOM of the motor system. In our series, we found no false-negative MEP results.

A full bi-tensor neural tractography algorithm using the unscented Kalman filter

We describe a technique that uses tractography to visualize neural pathways in human brains by extending an existing framework that uses overlapping Gaussian tensors to model the signal. At each point on the fiber, an unscented Kalman filter is used to find the most consistent direction as a mixture of previous estimates and of the local model. In our previous framework, the diffusion ellipsoid had a cylindrical shape, i.e., the diffusion tensor's second and third eigenvalues were identical. In this paper, we extend the tensor representation so that the diffusion tensor is represented by an arbitrary ellipsoid. Experiments on synthetic data show a reduction in the angular error at fiber crossings and branchings. Tests on in vivo data demonstrate the ability to trace fibers in areas containing crossings or branchings, and the tests also confirm the superiority of using a full tensor representation over the simplified model.

Segmentation of fiber tracts based on an accuracy analysis on diffusion tensor software phantoms

Due to its unique sensitivity to tissue microstructure, one of the primary applications of diffusion-weighted magnetic resonance imaging is the reconstruction of neural fiber pathways by means of fiber-tracking algorithms. In this work, we make use of realistic diffusion-tensor software phantoms in order to carry out an analysis of the precision of streamline tractography by systematically varying certain properties of the simulated image data (noise, tensor anisotropy, and image resolution) as well as certain fiber-tracking parameters (number of seed points and step length). Building upon the gained knowledge about the precision of the analyzed fiber-tracking algorithm, we proceed by suggesting a fuzzy segmentation algorithm for diffusion tensor images which better estimates the precise spatial extent of a tracked fiber bundle. The presented segmentation algorithm utilizes information given by the estimated main diffusion direction in a voxel and the respective uncertainty, and its validity is confirmed by both qualitative and quantitative analyses.

Filtered multitensor tractography

We describe a technique that uses tractography to drive the local fiber model estimation. Existing techniques use independent estimation at each voxel so there is no running knowledge of confidence in the estimated model fit. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by those previous. To do this we perform tractography within a filter framework and use a discrete mixture of Gaussian tensors to model the signal. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model to the signal and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Using two- and three-fiber models we demonstrate in synthetic experiments that this approach significantly improves the angular resolution at crossings and branchings. In vivo experiments confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization.

Two-tensor tractography using a constrained filter

We describe a technique to simultaneously estimate a weighted, positive-definite multi-tensor fiber model and perform tractography. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by the previous. To do this we model the signal as a weighted mixture of Gaussian tensors and perform tractography within a filter framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model and propagate in the most consistent direction. Further, we modify the Kalman filter to enforce model constraints, i.e. positive eigenvalues and convex weights. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Synthetic experiments demonstrate that this approach significantly improves the angular resolution at crossings and branchings while consistently estimating the mixture weights. In vivo experiments confirm the ability to trace out fibers in areas known to contain such crossing and branching while providing inherent path regularization.

Intraoperative mapping for tumor resection

This article describes the rationale, indications, and modality for intraoperative brain mapping for safe and effective surgical removal of tumors located within functional brain areas.

A filtered approach to neural tractography using the Watson directional function

We propose a technique to simultaneously estimate the local fiber orientations and perform multi-fiber tractography. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the measured signal or estimated fiber orientation. Further, to overcome noise, many algorithms use a filter as a post-processing step to obtain a smooth trajectory. We formulate fiber tracking as causal estimation: at each step of tracing the fiber, the current estimate of the signal is guided by the previous. To do this, we model the signal as a discrete mixture of Watson directional functions and perform tractography within a filtering framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the signal and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides an accurate estimate of the local structure at each point along the fiber. We choose the Watson function since it provides a compact representation of the signal parameterized by the principal diffusion direction and a scaling parameter describing anisotropy, and also allows analytic reconstruction of the oriented diffusion function from those parameters. Using a mixture of two and three components (corresponding to two-fiber and three-fiber models) we demonstrate in synthetic experiments that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments examine the corpus callosum and internal capsule and confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization.

Present day's standards in microsurgery of low-grade gliomas

Low-grade gliomas are slow growing intrinsic lesions that induces a progressive functional reshaping of the brain. Surgical removal of these lesions requires the combined efforts of a multidiscipinary team of neurosurgeon, neuroradiologist, neuropsychologist, neurophysiologist, and neurooncologists that all together contribute in the definition of the location, extension, and extent of functional involvement that a specific lesion has induced in a particular patient. Each tumor has induced particular and specific changes of the functional network, that varies among patients. This requires that each treatment plan should be tailored to the tumor and to the patient. When this is reached, surgery should be accomplished according to functional and anatomical boundaries, and has to aim to the maximal resection with the maximal patient functional preservation. This can be reached at the time of the initial surgery, depending on the functional organization of the brain, or may require additional surgeries, eventually intermingled with adjuvant treatments. The use of so called brain mapping techniques extend surgical indications, improve extent of resection with greater oncological impact, minimization of morbidity and increase in quality of life. To achieve the goal of a satisfactory tumor resection associated with the full preservation of the patients abilities, a series of neuropsychological, neurophysiological, neuroradiological and intraoperative investigations have to be performed. In this chapter, we will describe the rationale, the indications and the modality for performing a safe and rewarding surgical removal of low-grade gliomas by using these techniques, as well as the functional and oncological results.

Intraoperative mapping and monitoring of brain functions for the resection of low-grade gliomas: technical considerations

Low-grade gliomas ([LGGs] WHO Grade II) are slow-growing intrinsic cerebral lesions that diffusely infiltrate the brain parenchyma along white matter tracts and almost invariably show a progression toward malignancy. The treatment of these tumors forces the neurosurgeon to face uncommon difficulties and is still a subject of debate. At the authors' institution, resection is the first option in the treatment of LGGs. It requires the combined efforts of a multidisciplinary team of neurosurgeons, neuroradiologists, neuropsychologists, and neurophysiologists, who together contribute to the definition of the location, extension, and extent of functional involvement that a specific lesion has caused in a particular patient. In fact, each tumor induces specific modifications of the brain functional network, with high interindividual variability. This requires that each treatment plan is tailored to the characteristics of the tumor and of the patient. Consequently, surgery is performed according to functional and anatomical boundaries to achieve the maximal resection with maximal functional preservation. The identification of eloquent cerebral areas, which are involved in motor, language, memory, and visuospatial functions and have to be preserved during surgery, is performed through the intraoperative use of brain mapping techniques. The use of these techniques extends surgical indications and improves the extent of resection, while minimizing the postoperative morbidity and safeguarding the patient's quality of life. In this paper the authors present their paradigm for the surgical treatment of LGGs, focusing on the intraoperative neurophysiological monitoring protocol as well as on the brain mapping technique. They briefly discuss the results that have been obtained at their institution since 2005 as well as the main critical points they have encountered when using this approach.

Evaluation of bayesian tensor estimation using tensor coherence

Fiber tractography, a unique and non-invasive method to estimate axonal fibers within white matter, constructs the putative streamlines from diffusion tensor MRI by interconnecting voxels according to the propagation direction defined by the diffusion tensor. This direction has uncertainties due to the properties of underlying fiber bundles, neighboring structures and image noise. Therefore, robust estimation of the diffusion direction is essential to reconstruct reliable fiber pathways. For this purpose, we propose a tensor estimation method using a Bayesian framework, which includes an a priori probability distribution based on tensor coherence indices, to utilize both the neighborhood direction information and the inertia moment as regularization terms. The reliability of the proposed tensor estimation was evaluated using Monte Carlo simulations in terms of accuracy and precision with four synthetic tensor fields at various SNRs and in vivo human data of brain and calf muscle. Proposed Bayesian estimation demonstrated the relative robustness to noise and the higher reliability compared to the simple tensor regression.

A diffusion tensor imaging tractography algorithm based on Navier-Stokes fluid mechanics

We introduce a fluid mechanics based tractography method for estimating the most likely connection paths between points in diffusion tensor imaging (DTI) volumes. We customize the Navier-Stokes equations to include information from the diffusion tensor and simulate an artificial fluid flow through the DTI image volume. We then estimate the most likely connection paths between points in the DTI volume using a metric derived from the fluid velocity vector field. We validate our algorithm using digital DTI phantoms based on a helical shape. Our method segmented the structure of the phantom with less distortion than was produced using implementations of heat-based partial differential equation (PDE) and streamline based methods. In addition, our method was able to successfully segment divergent and crossing fiber geometries, closely following the ideal path through a digital helical phantom in the presence of multiple crossing tracts. To assess the performance of our algorithm on anatomical data, we applied our method to DTI volumes from normal human subjects. Our method produced paths that were consistent with both known anatomy and directionally encoded color images of the DTI dataset.

Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling

Standard particle filtering technique have previously been applied to the problem of fiber tracking by Brun et al. [Brun, A., Bjornemo, M., Kikinis, R., Westin, C.F., 2002. White matter tractography using sequential importance sampling. In: Proceedings of the ISMRM Annual Meeting, p. 1131] and Bjornemo et al. [Bjornemo, M., Brun, A., Kikinis, R., Westin, C.F., 2002. Regularized stochastic white matter tractography using diffusion tensor MRI, In: Proc. MICCAI, pp. 435-442]. However, these previous attempts have not utilised the full power of the technique, and as a result the fiber paths were tracked in a goal directed way. In this paper, we provide an advanced technique by presenting a fast and novel probabilistic method for white matter fiber tracking in diffusion weighted MRI (DWI), which takes advantage of the weighting and resampling mechanism of particle filtering. We formulate fiber tracking using a non-linear state space model which captures both smoothness regularity of the fibers and the uncertainties in the local fiber orientations due to noise and partial volume effects. Global fiber tracking is then posed as a problem of particle filtering. To model the posterior distribution, we classify voxels of the white matter as either prolate or oblate tensors. We then construct the orientation distributions for prolate and oblate tensors separately. Finally, the importance density function for particle filtering is modeled using the von Mises-Fisher distribution on a unit sphere. Fast and efficient sampling is achieved using Ulrich-Wood's simulation algorithm. Given a seed point, the method is able to rapidly locate the globally optimal fiber and also provides a probability map for potential connections. The proposed method is validated and compared to alternative methods both on synthetic data and real-world brain MRI datasets.

Neural tractography using an unscented Kalman filter

We describe a technique to simultaneously estimate a local neural fiber model and trace out its path. Existing techniques estimate the local fiber orientation at each voxel independently so there is no running knowledge of confidence in the estimated fiber model. We formulate fiber tracking as recursive estimation: at each step of tracing the fiber, the current estimate is guided by the previous. To do this we model the signal as a mixture of Gaussian tensors and perform tractography within a filter framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the local model and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides a causal estimate of the local structure at each point along the fiber. Synthetic experiments demonstrate that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments confirm the ability to trace out fibers in areas known to contain such crossing and branching while providing inherent path regularization.

Whole brain-based computerized neuroimaging in ALS and other motor neuron disorders

Advanced neuroimaging applications to patients suffering from ALS and other motor neuron disorders (MND) have a high potential in terms of understanding the pathophysiology and visualizing the in vivo pathoanatomy of the diseases. In this context, particularly observer-independent computerized analyses of magnetic resonance imaging (MRI) data are of special interest since they overcome shortcomings of region-of-interest-based techniques. For three-dimensional structural T1-weighted MRI of the whole brain, voxel-based morphometry (VBM) has proven the most valuable approach to analyse regional volume alterations of the grey or white matter at group level. For the analysis of the white matter integrity with respect to tissue diffusivity and white matter connectivity including fibre tracking algorithms, diffusion tensor imaging (DTI) which can also be performed on a whole brain-basis is of the highest potential to date. Both VBM and DTI have been applied to various MND, in particular ALS, in multiple studies over recent years and have substantially broadened our knowledge about their in vivo pathoanatomy and mechanisms of neurodegeneration. Especially both the degree of damage to motor areas and the involvement of non-motor areas are of interest to be subjected to quantitative assessment, in order to establish quantitative surrogate markers for disease progression usable in clinical trials. Here, the technical state-of-the-art and the results of VBM and DTI studies in MND as the current state are reviewed, and future perspectives for further neuroimaging applications are highlighted.

Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings

In this article, the underlying theory of clinical diffusion MR imaging, including diffusion tensor imaging (DTI) and fiber tractography, is reviewed. First, a brief explanation of the basic physics of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping is provided. This is followed by an overview of the additional information that can be derived from the diffusion tensor, including diffusion anisotropy, color-encoded fiber orientation maps, and 3D fiber tractography. This article provides the requisite background for the second article in this 2-part review to appear next month, which covers the major technical factors that affect image quality in diffusion MR imaging, including the acquisition sequence, magnet field strength, gradient amplitude and slew rate, and multichannel radio-frequency coils and parallel imaging. The emphasis is on optimizing these factors for state-of-the-art DWI and DTI based on the best available evidence in the literature.

Motor control via spared peri-infarct corticospinal tract in patients with pontine infarct

We investigated the motor control pathway in 3 patients with pontine infarct using diffusion tensor tractography and functional magnetic resonance imaging. The motor tracts of the affected hemisphere were observed to pass along the same spared peri-infarct corticospinal tract area of the pons on both the first and second diffusion tensor tractography. It seems that the main motor function of these patients is controlled via the spared peri-infarct corticospinal tract.

Accuracy of diffusion tensor magnetic resonance imaging tractography assessed using intraoperative subcortical stimulation mapping and magnetic source imaging

OBJECT

Resecting brain tumors involves the risk of damaging the descending motor pathway. Diffusion tensor (DT)-imaged fiber tracking is a noninvasive magnetic resonance (MR) technique that can delineate the subcortical course of the motor pathway. The goal of this study was to use intraoperative subcortical stimulation mapping of the motor tract and magnetic source imaging to validate the utility of DT-imaged fiber tracking as a tool for presurgical planning.

METHODS

Diffusion tensor-imaged fiber tracks of the motor tract were generated preoperatively in nine patients with gliomas. A mask of the resultant fiber tracks was overlaid on high-resolution T1- and T2-weighted anatomical MR images and used for stereotactic surgical navigation. Magnetic source imaging was performed in seven of the patients to identify functional somatosensory cortices. During resection, subcortical stimulation mapping of the motor pathway was performed within the white matter using a bipolar electrode.

RESULTS

A total of 16 subcortical motor stimulations were stereotactically identified in nine patients. The mean distance between the stimulation sites and the DT-imaged fiber tracks was 8.7 +/- 3.1 mm (+/- standard deviation). The measured distance between subcortical stimulation sites and DT-imaged fiber tracks combines tracking technique errors and all errors encountered with stereotactic navigation.

CONCLUSIONS

Fiber tracks delineated using DT imaging can be used to identify the motor tract in deep white matter and define a safety margin around the tract.

Motor outcome according to the integrity of the corticospinal tract determined by diffusion tensor tractography in the early stage of corona radiata infarct

Diffusion tensor tractography (DTT) is useful for exploring the state of the corticospinal tract (CST). An accurate estimation of the integrity of the CST in the early stage of a cerebral infarct would enable a determination of motor recovery. DTT was performed to classify CST integrity following a corona radiata infarct to evaluate if the procedure could characterize the motor outcome of the affected hand. Fifty-five patients with completely paralyzed hands due to a corona radiata infarct were recruited for the study, and DTT images were obtained within 7-30 days after a stroke. The DTI findings for the patients were classified into four groups. In type A, the CST was preserved around the infarct; in type B, the CST originated from a cortex other than the primary motor cortex; in type C, the CST was interrupted at the infarct; in type D, the CST failed to reach the infarct due to degeneration. Six months after a stroke, the motor function of the affected hand was evaluated with the motricity index (MI) for the hand, the Medical Research Council score (MRC) for finger extensors and the modified Brunnstrom classification (MBC). These indices were significantly influenced by the DTT type (p<0.05). The highest MI, MRC and MBC were seen in the DTT type A patients; the lowest MI, MRC and MBC were seen in the DTT type D patients (p<0.05). The integrity of the corticospinal tract determined by DTT obtained during the early stage of a corona radiata infarct seems to be helpful in predicting the motor outcome of the affected hand.