Visualization Strategies for Major White Matter Tracts for Intraoperative Use

TLDR: Streamline representation of major fiber tract systems along with high-resolution anatomical data provides a reliable orientation for the neurosurgeon and integration of fiber tract data into a neuronavigation setup allows removal of tumors adjacent to eloquent brain areas with low morbidity.

Abstract: Streamline representation of major fiber tract systems along with high-resolution anatomical data provides a reliable orientation for the neurosurgeon. For intraoperative visualization of these data either on navigation screens near the surgical field or directly in the surgical field applying heads-up displays of operating microscopes, wrapping of all streamlines of interest to render an individual object representing the whole fiber bundle is the most suitable representation. Integration of fiber tract data into a neuronavigation setup allows removal of tumors adjacent to eloquent brain areas with low morbidity.

Figures

Fig. 5 Implementation of fiber tracking in the navigation software; A: initial tracking starting

Fig. 6 Generation of a 3-D object representing the left pyramidal tract; A: final tracking result

Table 1 different visualization techniques of DTI data

Full text

Visualization strategies for major white matter
tracts for intraoperative use
Christopher Nimsky
1,2
, Oliver Ganslandt
1,2
, Frank Enders
2
, Dorit Merhof
2
, Thilo
Hammen
2
, Michael Buchfelder
1,2
1
Department of Neurosurgery, University Erlangen-Nuremberg
2
Neurocenter, University Erlangen-Nuremberg
3
Department of Neurology, University Erlang
First publ. in: International Journal of Computer Assisted Radiology and Surgery 1 (2006), 1, pp. 13-22
Konstanzer Online-Publikations-System (KOPS)
URN: http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-91936
URL: http://kops.ub.uni-konstanz.de/volltexte/2009/9193/

2
Streamline representation of major fiber tract systems along with high-resolution anatomical data
provides a reliable orientation for the neurosurgeon. For intraoperative visualization of these data
either on navigation screens near the surgical field or directly in the surgical field applying heads-
up displays of operating microscopes, wrapping of all streamlines of interest to render an
individual object representing the whole fiber bundle is the most suitable representation.
Integration of fiber tract data into a neuronavigation setup allows removal of tumors adjacent to
eloquent brain areas with low morbidity.
Keywords: diffusion tensor imaging, glyph representation, fiber tracking,
functional neuronavigation, visualization strategies, major white matter tracts

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Introduction
In white matter of the brain, diffusion is anisotropic, i.e. the water diffusion is not
equal in all three orthogonal directions. This is due to the strong aligned
microstructure, including cell membranes and the myelin sheath surrounding
myelineated white matter, causing impediment of the water motion. Isotropic
diffusion can be graphically represented as a sphere, whereas anisotropic diffusion
can be graphically expressed as an ellipsoid, with water molecules moving farther
along the long axis of a fiber bundle and less movement perpendicularly. To
estimate the nine tensor matrix elements required for a Gaussian description of
water mobility, the diffusion gradient must be applied to at least six noncollinear
directions. The eigenvalues represent the three principal diffusion coefficients
measured along the three coordinate directions of the ellipsoid. The eigenvectors
represent the directions of the tensor. Diffusion tensor imaging (DTI) can resolve
the dominant fiber orientation in each voxel element. The direction of greatest
diffusion measured by DTI parallels the dominant orientation of the tissue
structure in each voxel, representing the mean longitudinal direction of axons in
white matter tracts. DTI can be applied to identify major white matter tracts, such
as the pyramidal tract or the visual pathway [1, 6, 17].
Avoiding postoperative neurological deficits by preserving eloquent brain areas is
a major principle in neurosurgery. Cortical eloquent brain areas can be preserved
successfully by identification of these areas by methods such as
magnetoencephalography (MEG) and functional magnetic resonance imaging
(fMRI). Registration of MEG and fMRI data with 3-D anatomical MR datasets
allows representation of the spatial information of the identified cortical eloquent
brain areas in the surgical field by using neuronavigation systems [9, 15, 22].
Heads-up displays visualizing segmented data in the surgical field are routinely
used in so-called microscope-based neuronavigation. While MEG and fMRI only
identify cortical eloquent brain areas, DTI can be used to assess the major white
matter tracts connecting to these cortical brain areas [4, 10, 11]. These major
white matter tracts have also to be preserved during surgery to avoid postoperative
deficits. Different visualization techniques of DTI data, such as glyph
representation and fiber tracking have evolved in the recent years. The aim of this

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study is to find a suitable method to visualize the course of major white matter
tracts during neurosurgical procedures.
Methods
Single-shot spin-echo diffusion weighted echo planar imaging on a 1.5 T MR
scanner (Magnetom Sonata, Siemens Medical Solutions, Erlangen, Germany) was
used for DTI. The sequence parameters were: TE 86 ms, TR 9200 ms, matrix size
128 x 128, FOV 240 mm, slice thickness 1.9 mm, bandwidth 1502 Hz/Px. A
diffusion weighting of 1000 s/mm2 (high b value) was used. One null image (low
b value: 0 s/mm2) and six diffusion weighted images were obtained with the
diffusion-encoding gradients directed along the following axes (±1,1,0), (±1,0,1),
and (1,±1,0). The voxel size was 1.9 x 1.9 x 1.9 mm; 60 slices with no intersection
gap were measured. Applying 5 averages the total DTI measurement required 5
minutes and 31 seconds.
Directly after image acquisition, the DTI data could be evaluated with the DTI
task card version 1.7 (Magnetic Resonance Center, Massachusetts General
Hospital, Boston) on a Siemens scanner using MR software MRease N4_VA21B
under syngo VB10I. The diffusion tensor information could then be represented as
color-encoded fractional anisotropy (FA) maps, which were generated by
mapping the principal eigenvector components into red, green, and blue color
channels, weighted by fractional anisotropy. Assuming the patient is lying in
supine position and the head is not tilted, then the color mapping defines white
matter tracts oriented in an anterior/posterior direction in green, a left/right
direction in red, and a superior/inferior direction in blue (Fig. 1).
Furthermore, 3-D fibertracking was generated applying a knowledge-based
multiple-ROI (region of interest) approach with user-defined seed regions.
Tracking was initiated in both retro- and orthograde directions according to the
direction of the principal eigenvector in each voxel of the ROI. These data could
be displayed on a screen in the operating theatre. Fiber tracts were visualized as
streamtubes along the B0 images in coronal, sagittal or axial orientation (Fig. 2).
In parallel DTI data were displayed using an in-house visualizing platform,
allowing generating different glyph and fibertract representations, such as evenly
spaced streamlines (Fig. 3). Additionally standard anatomical image data could be

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registered, so that glyph or streamline fiber tract visualization could be displayed
along standard anatomical image data (Fig. 4).
To integrate fibertracking in a navigation system (iPlan 2.5, BrainLab,
Heimstetten, Germany) the b=0 images were rigidly registered with the T1-
weighted MPRAGE data (magnetization prepared rapid acquisition gradient echo
sequence; TE 4.38 ms, TR 2020 ms, matrix size 256 x 256, FOV 250 mm, slice
thickness 1mm, slab 16 cm, measurement time 8 min 39 s). For fiber tracking we
implemented a tracking algorithm based on a local diffusion approach. The
algorithm is based on a tensor deflection algorithm where the trend of the current
generated fibers is also considered, which was first described by Lazar, Weinstein,
and Westin [16, 28, 29]. In contrast to the syngo approach (see above) tract
seeding is performed by defining a rectangular volume of interest (VOI) either in
the FA maps or in the co-registered standard anatomical datasets. The definition
of the VOI depends on the fiber structures to be displayed. A raster of a third of
the voxel size is applied in the VOI to define the starting points. The tract is
propagated both in the forward and reverse directions of the major eigenvector, so
that each fiber consists of two iterative calculated segments beginning at the
starting point. After termination of the iteration process the calculated fibers must
meet all general conditions regarding threshold, local angulation, and total fiber
length. Before tracking is initiated the user can adjust the FA threshold and the
minimum fiber length. The final result of the tracking calculations is a parametric
display of fibers, which are represented as streamlines, using the standard
direction color encoding: left-right oriented fibers are displayed in red, anterior-
posterior in green, and cranio-caudal in blue (Fig. 5 A/B). For further selecting the
fiber bundle of interest, it is possible to define a volume of interest in the initially
tracked fibers, so that among this VOI fibers can be retained, excluded, and
deleted. This selection of fibers of interest can be repeated as often as necessary,
allowing an interactive selection of complex volumes of interest to distinguish
single fiber bundles, disconnect branching fibers, or to remove entire areas (Fig. 5
C/D).
For representation in the navigation software the maximum outline of the fiber
tracts was segmented automatically (Fig. 6), so that this object wrapping all fibers
is displayed as a single object and could be displayed on the navigation screen
(Fig. 6 D), as well as in the surgical field.

Frequently asked questions

Q1. What are the contributions in "Visualization strategies for major white matter tracts for intraoperative use" ?

In this paper, diffusion tensor imaging ( DTI ) is used to estimate the nine tensor matrix elements required for a Gaussian description of water mobility, which must be applied to at least six noncollinear directions. 

Q2. What are the main techniques used to improve tracking?

Fiber tracking algorithms often utilize thresholds, angle criterions, regularization techniques and local filters to improve tracking results. 

Q3. Why do the streamlines vary over the domain without control?

Due to the diverging nature of tract systems, the density of streamlines varies over the domain without control resulting in sparse areas as well as cramped regions. 

Q4. What is the tensor at the current end point of the fiber?

The tensor at the current end point of the fiber is computed using trilinear interpolation which is separately performed for each tensor entry. 

Q5. What is the main reason for the low morbidity of resections?

Integration of DTI data into navigation systems allowing an immediate visualization in the surgical field has resulted in resections with low morbidity [5, 13, 23]. 

Q6. What is the basis of the wrapping?

The wrapping itself is based on the determination of a centerline of the bundle and the subsequent construction of bounding curves around the set of corresponding lines. 

Q7. How many noncollinear directions are required for a Gaussian description of water mobility?

To estimate the nine tensor matrix elements required for a Gaussian description of water mobility, the diffusion gradient must be applied to at least six noncollinear directions. 

Q8. What was the important technique used for the visualization of the fiber tract?

Integration of DTI information into navigational systems, either by registration of the color-encoded FA maps and manual segmentation of the respective fiber tracts by an expert (applied in 16 glioma patients) or by integrating the reconstructed streamlines in the navigational setup (n=20) allowed a direct visualization of the pyramidal tract or the optic radiation in the surgical field. 

Q9. What is the use of head up displays in neurosurgery?

Heads-up displays visualizing segmented data in the surgical field are routinely used in so-called microscope-based neuronavigation. 

Q10. What is the meaning of the term 'fibers'?

Note that the term 'fibers' is used for streamlines which do not represent real anatomical fibers but provide an abstract model of neural structures. 

Q11. What is the advantage of glyphs for tensors?

An even more satisfying shape for tensors are superquadric tensor glyphs which provide a better and less ambiguous spatial impression [14]. 

Q12. What is the main advantage of volume growing algorithms?

In general, volume growing algorithms start from a predefined seed region and spread out within the volume until some terminating criterion is reached. 

Q13. What is the appealing technique for representing white matter?

Fiber tracking which is maybe the most appealing and understandable technique for representing white matter has been investigated by several groups [2, 8, 20, 26, 27, 31]. 

Q14. What is the final result of the tracking calculations?

The final result of the tracking calculations is a parametric display of fibers, which are represented as streamlines, using the standard direction color encoding: left-right oriented fibers are displayed in red, anteriorposterior in green, and cranio-caudal in blue (Fig. 5 A/B). 

Q15. How can the authors preserve eloquent brain areas?

Cortical eloquent brain areas can be preserved successfully by identification of these areas by methods such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). 

Q16. What was the important visualization technique used in the MR scanner?

The DTI task card integrated in the MR scanner syngo interface allowed besides standard display of FA maps (Fig. 1) an immediate visualization of the major fiber tracts (Fig. 2), so that this information could be displayed during neurosurgical procedures, even if these data were acquired intraoperatively. 

Q17. What is the area where a tumor is reaching the nearest point of a major white matter?

This is the area where a tumor is reaching the nearest point of a major white matter tract system, because entering major white matter tracts has to be avoided during surgery to prevent postoperative new neurological deficits. 

Q18. What is the way to visualize fiber tracts?

For direct visualization of these data in the surgical field applying heads-up displays of operating microscopes, wrapping of all streamlines of interest to render an individual object representing the whole fiber bundle is mandatory.