White matter fiber tractography via anisotropic diffusion simulation in the human brain

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.

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.

The evolving utility of diffusion tensor tractography in the surgical management of temporal lobe epilepsy: a review

BACKGROUND

Diffusion tensor imaging (DTI) is a relatively new imaging modality that has found many peri-operative applications in neurosurgery.

METHODS

A comprehensive survey of the applications of diffusion tensor imaging (DTI) in planning for temporal lobe epilepsy surgery was conducted. The presentation of this literature is supplemented by a case illustration.

RESULTS

The authors have found that DTI is well utilized in epilepsy surgery, primarily in the tractography of Meyer's loop. DTI has also been used to demonstrate extratemporal connections that may be responsible for surgical failure as well as perioperative planning. The tractographic anatomy of the temporal lobe is discussed and presented with original DTI pictures.

CONCLUSIONS

The uses of DTI in epilepsy surgery are varied and rapidly evolving. A discussion of the technology, its limitations, and its applications is well warranted and presented in this article.

Fiberfox: facilitating the creation of realistic white matter software phantoms

PURPOSE

Phantom-based validation of diffusion-weighted image processing techniques is an important key to innovation in the field and is widely used. Openly available and user friendly tools for the flexible generation of tailor-made datasets for the specific tasks at hand can greatly facilitate the work of researchers around the world.

METHODS

We present an open-source framework, Fiberfox, that enables (1) the intuitive definition of arbitrary artificial white matter fiber tracts, (2) signal generation from those fibers by means of the most recent multi-compartment modeling techniques, and (3) simulation of the actual MR acquisition that allows for the introduction of realistic MRI-related effects into the final image.

RESULTS

We show that real acquisitions can be closely approximated by simulating the acquisition of the well-known FiberCup phantom. We further demonstrate the advantages of our framework by evaluating the effects of imaging artifacts and acquisition settings on the outcome of 12 tractography algorithms.

CONCLUSION

Our findings suggest that experiments on a realistic software phantom might change the conclusions drawn from earlier hardware phantom experiments. Fiberfox may find application in validating and further developing methods such as tractography, super-resolution, diffusion modeling or artifact correction.

Fasciculography: robust prior-free real-time normalized volumetric neural tract parcellation

Fiber tracking in diffusion tensor magnetic resonance images (DTIs) reveals 3-D structural connectivity of the brain conveniently and thus is a viable tool for investigating neural differences. Unfortunately, local noise, image artifacts and numerical tracking errors during integration-based techniques are cumulative. Prematurely terminated fibers and under-sampled fiber bundles result in incomplete reconstruction of white matter fiber tracts and hence incorrect anatomical measurements. Quantitative cross-subject tract analysis, which is critical for abnormality detection, is complicated by inefficient and inaccurate tract reconstruction and normalization from fiber bundles. Because of the above problems, we propose a parcellation method that aims for lower sensitivity to initialization and local orientation error by directly segmenting full white matter tracts (Fasciculography), rather than reconstructing individual curves, from diffusion tensor fields. A fast, robust volumetric, and intrinsically normalized solution is achieved by noise-filtering using a generic parametrized tract model to prevent premature tract termination. At the same time, orientation information reduces the search space, significantly speeding up the tract parcellation process with less human intervention. Detailed comparisons against streamline tracking, shortest-path tracking, and nonrigid registration using synthetic and real DTIs confirmed the superior properties of Fasciculography. Since a normalized tract can be delineated interactively in a just few seconds using the proposed method, accurate high volume tract comparisons become feasible.

A Hough transform global probabilistic approach to multiple-subject diffusion MRI tractography

A global probabilistic fiber tracking approach based on the voting process provided by the Hough transform is introduced in this work. The proposed framework tests candidate 3D curves in the volume, assigning to each one a score computed from the diffusion images, and then selects the curves with the highest scores as the potential anatomical connections. The algorithm avoids local minima by performing an exhaustive search at the desired resolution. The technique is easily extended to multiple subjects, considering a single representative volume where the registered high-angular resolution diffusion images (HARDI) from all the subjects are non-linearly combined, thereby obtaining population-representative tracts. The tractography algorithm is run only once for the multiple subjects, and no tract alignment is necessary. We present experimental results on HARDI volumes, ranging from simulated and 1.5T physical phantoms to 7T and 4T human brain and 7T monkey brain datasets.

The role of magnetic resonance imaging and spectroscopy in hepatic encephalopathy

Hepatic encephalopathy (HE) is a diverse manifestation of acute and chronic liver failure, ranging from cognitive impairment, only detectable on psychometric evaluation through to confusion, coma and death from cerebral oedema. While there is widespread acceptance of its importance, there is little consensus on how best to diagnose and monitor HE. Clinical descriptions, psychometric testing, electroencephalography and magnetic resonance (MR) imaging (and lately, MR spectroscopy) have all been proposed. MR techniques, in contrast to other modalities, have the benefit of objectivity and of being able to interrogate the brain directly with respect to changes in brain size, function and the metabolic disturbances thought to underlie HE, particularly in the context of astrocyte swelling. Modern clinical MRI scanners with multinuclear MR spectroscopy capabilities and brain mapping software can demonstrate structural and functional cellular changes using volumetric MRI, magnetization transfer MRI, diffusion-weighting MRI, functional MRI with oxygenation measurements and in vivo and in vitro (1)H and (31)P MR spectroscopy. This review describes the relative merits of these techniques and provides guidance on the directions for future research and translation into clinical practice.

Diffusion simulation-based fiber tracking using time-of-arrival maps: a comparison with standard methods

OBJECT

We propose a new tracking method based on time-of-arrival (TOA) maps derived from simulated diffusion processes.

MATERIALS AND METHODS

The proposed diffusion simulation-based tracking consists of three steps that are successively evaluated on small overlapping sub-regions in a diffusion tensor field. First, the diffusion process is simulated for several time steps. Second, a TOA map is created to store simulation results for the individual time steps that are required for the tract reconstruction. Third, the fiber pathway is reconstructed on the TOA map and concatenated between neighboring sub-regions. This new approach is compared with probabilistic and streamline tracking. All methods are applied to synthetic phantom data for an easier evaluation of their fiber reconstruction quality.

RESULTS

The comparison of the tracking results did show severe problems for the streamline approach in the reconstruction of crossing fibers, for example. The probabilistic method was able to resolve the crossing, but could not handle strong curvature. The new diffusion simulation-based tracking could reconstruct all problematic fiber constellations.

CONCLUSION

The proposed diffusion simulation-based tracking method used the whole tensor information of a neighborhood of voxels and is, therefore, able to handle problematic tracking situations better than established methods.

Brain tractography using Q-ball imaging and graph theory: Improved connectivities through fibre crossings via a model-based approach

Brain tractography techniques utilize a set of diffusion-weighted magnetic resonance images to reconstruct white matter tracts, non-invasively and in-vivo. Streamline tracking techniques propagate curves from a seed to imply connectivity to distal voxels. Alternative approaches have been developed that attempt to quantify connection strength between all voxels and the seed. Tractography based on graph theory is amongst them. Despite its potential, graph-based tracking through complex fibre configurations has not been extensively studied and existing methods have inherent limitations. Anatomically unlikely connections may be identified in fibre crossing regions, by assigning relatively high connection strengths to all crossing populations. In this study, we discuss these limitations and present a new approach for robustly propagating through fibre crossings, as described by the orientation distribution functions (ODFs) derived from Q-ball imaging. Each image voxel is treated as a graph node and graph arcs connect neighbouring voxels. Weights representative of both structural and diffusivity features are assigned to each arc. To account for the existence of crossing fibre populations within a voxel, complex ODFs are decomposed into components representative of single-fibre populations and an image multigraph is created. The multigraph is searched exhaustively, but efficiently, to find the strongest paths and assign connectivity strengths between a seed and all the other image voxels. A comparison with the existing graph-based tractography as well as Q-ball driven front evolution tractography is performed on simulated data, and on human Q-ball imaging data acquired from five healthy volunteers. The new approach improves the connection strengths through fibre crossing regions, reducing the strengths of paths that are less anatomically plausible.

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.

Reconstruction of the human visual system based on DTI fiber tracking

PURPOSE

To apply and to evaluate the newly developed advanced fast marching algorithm (aFM) in vivo by reconstructing the human visual pathway, which is characterized by areas of extensive fiber crossing and branching, i.e., the optic chiasm and the lateral geniculate nucleus (LGN).

MATERIALS AND METHODS

Diffusion tensor images were acquired in 10 healthy volunteers. Due to the proximity to bony structures and air-filled spaces of the optic chiasm, a high sensitivity encoding (SENSE) reduction factor was applied to reduce image distortions in this area. To reconstruct the visual system, three different seed areas were chosen separately. The results obtained by the aFM tracking algorithm were compared and validated with known anatomy.

RESULTS

The visual system could be reconstructed reproducibly in all subjects and the reconstructed fiber pathways are in good agreement with known anatomy.

CONCLUSION

The present work shows that the advanced aFM, which is especially designed for overcoming tracking limitations within areas of extensive fiber crossing, handles the fiber crossing and branching within the optic chiasm and the LGN correctly, thus allowing the reconstruction of the entire human visual fiber pathway, from the intraorbital segment of the optic nerves to the visual cortex.

Model-based variational smoothing and segmentation for diffusion tensor imaging in the brain

This article applies a unified approach to variational smoothing and segmentation to brain diffusion tensor image data along user-selected attributes derived from the tensor, with the aim of extracting detailed brain structure information. The application of this framework simultaneously segments and denoises to produce edges and smoothed regions within the white matter of the brain that are relatively homogeneous with respect to the diffusion tensor attributes of choice. This approach enables the visualization of a smoothed, scale invariant representation of the tensor data field in a variety of diverse forms. In addition to known attributes such as fractional anisotropy, these representations include selected directional tensor components and additionally associated continuous valued edge fields that might be used for further segmentation. A comparison is presented of the results of three different data model selections with respect to their ability to resolve white matter structure. The resulting images are integrated to provide better perspective of the model properties (edges, smoothed image, and so forth) and their relationship to the underlying brain anatomy. The improvement in brain image quality is illustrated both qualitatively and quantitatively, and the robust performance of the algorithm in the presence of added noise is shown. Smoothing occurs without loss of edge features because of the simultaneous segmentation aspect of the variational approach, and the output enables better delineation of tensors representative of local and long-range association, projection, and commissural fiber systems.