is currently a third-year Resident in Diagnostic Radiology at the
University of California, San Francisco. He earned his SB, SM,
and PhD degrees from the Massachusetts Institute of Technology
and his MD degree from Harvard Medical School, Cambridge,
Diffusion tensor imaging (DTI) is a rapidly growing area
of interest in radiology research. By querying tissue at the
microscopic scale, DTI provides researchers and clinicians with
new biological information not previously available with
conventional magnetic resonance imaging. Basic concepts of water
diffusion and DTI are reviewed in this manuscript, followed by a
brief review of topic areas of ongoing clinical research and
developing clinical applications, including brain development,
brain injury, white matter diseases, epilepsy, neuropsychiatric
diseases, and brain tumors.
Diffusion tensor imaging (DTI) is an area of rapidly growing
research in both technical refinements and clinical applications
(Figure 1). Diffusion data reflect information on a microscopic
scale, allowing researchers an unprecedented tool to noninvasively
probe tissue microarchitecture in vivo. This manuscript aims to
provide readers with an introduction to the basic concepts of water
diffusion and diffusion tensor imaging, followed by a brief review
of areas of ongoing clinical research and developing clinical
applications, with emphasis on areas of research presented at the
88th Scientific Assembly and Annual Meeting of the Radiological
Society of North America (RNSA 2002).
Physical basis of diffusion
Molecules are constantly undergoing random motion as a result of
their thermal energy. The Einstein equation states that the mean
square molecular displacement along a given axis is equal to twice
the product of the observation time and a constant, D, known as the
diffusion coefficient, measured in mm
/sec. For the typical echo times of 50 to 100 msec and diffusion
coefficients encountered in DTI, root mean square diffusion of
water is on the scale of 10 to 15 µm. Thus, DTI can provide
information related to tissue microarchitecture at this scale, as
diffusing water molecules probe cell size, shape, and organization
during each acquisition.
If molecules are free to diffuse in all directions with equal
probability, as in pure water, diffusion is described as isotropic
and a single diffusion coefficient, D, is sufficient to describe
the expected diffusion behavior. However, in biological tissues,
the mobility of molecules is affected by the local cellular
microarchitecture. When the obstacles to mobility are different
depending on the direction a molecule travels, diffusion is
described as anisotropic, and a different diffusion coefficient may
be measured along each direction.
An example of a setting with anisotropic water diffusion is a
white matter tract,
where the tight packing and parallel orientation of axon membranes
and the presence of myelin sheaths hinder diffusion in directions
perpendicular to the axons, such that diffusion parallel to fiber
tracts occurs "faster" than diffusion perpendicular to the tracts.
Anisotropy has been observed in many other tissues, including
as well as cortical gray matter of premature infants.
The diffusion tensor
The diffusion tensor formalism
was first applied in multidimensional magnetic resonance imaging
(MRI) by Basser et al
in order to simplify analysis of anisotropic diffusion data. In
this model, diffusion at each voxel is described by a symmetric 3 *
3 matrix of diffusivities,
, known as the diffusion tensor, where the bold face is used to
is a matrix. The diagonal elements of this matrix, Dxx, Dyy, and
Dzz, correspond to the diffusion coefficients estimated with
gradients applied along the principal axes of the reference frame,
ie, the scanner frame. The off-diagonal elements, Dxy, Dyx, Dxz,
Dzx, Dyz, and Dzy, represent correlations between random motions
along the respective paired axes of the reference frame. The matrix
is symmetric, ie, Dxy = Dyx, Dxz = Dzx, and Dyz = Dzy.
In anisotropic diffusion, diffusion is directionally dependent
and correlations in diffusion between the axes of the reference
frame are possible, such that the tensor may contain all nonzero
values. In diffusion tensor analysis, the tensor is diagonalized,
ie, rotated mathematically onto another set of axes that eliminate
the off-diagonal correlations, known as the principal axes of
diffusion. Once this has been accomplished, the diagonal elements
of the rotated matrix, known as the eigenvalues, represent the
diffusion coefficients along the principal axes formed by the
corresponding eigenvectors. The largest eigenvalue is known as the
major eigenvalue, and the corresponding major eigenvector
represents the "preferred" direction of diffusion at each
The eigenvalues and eigenvectors can be graphically represented
by an ellipsoid (Figure 2). The major axis of the ellipsoid is
parallel to the major eigenvector and the minor axes parallel to
the minor eigenvectors, with the size of the ellipsoid along each
axis related to the corresponding eigenvalue. The eccentricity of
the ellipsoid thus graphically reflects the anisotropy of
diffusion. In the case of isotropic diffusion, the ellipsoid
reduces to a sphere.
Measuring diffusion with MRI
As a proton diffuses across a magnetic field gradient,
irreversible spin dephasing occurs, reducing the measured MR
By analyzing the degree of signal loss observed after application
of diffusion-sensitizing gradients, also known as motion-probing
gradients, the underlying diffusion coefficient can be estimated.
To simplify analysis, the shape, strength, and duration of
diffusion-sensitizing gradients are summarized by the b factor,
measured in seconds per millimeter squared. For typically
encountered b values of <1500 sec/mm
, attenuation is well approximated by a monoexponential model.
Thus, the diffusion coefficient D
along a given direction i is then estimated by solving the equation
I = I(0)exp(-b
), where I is the observed signal after application of the
diffusion-sensitizing gradient characterized by b
and I(0) is the signal without application of the gradient, ie, b =
In isotropic diffusion, D is independent of direction and thus
the choice of direction of the diffusion-sensitizing gradient is
arbitrary. With anisotropic diffusion, however, the observed signal
varies with different gradient directions, and a maximal signal
loss is observed when the gradient is aligned with the major axis
of diffusion. In practice, however, the principal directions of
diffusion are unknown or spatially varying, and full
characterization of diffusion characteristics requires estimation
of the complete diffusion tensor. In addition to the unweighted
image (b = 0), measurements with at least six noncollinear
gradients are required to estimate the six independent components
of the matrix, for a total of at least seven acquisitions
comprising a typical complete DTI dataset. From the imaging data,
the diffusion tensor is then filled by least-squares approximation
of its independent components and then its eigenvalues and
eigenvectors solved. Additional measurements decrease
susceptibility to noise, with additional gradient directions at a
single b value providing better signal-to-noise ratio than an
increased number of averages with fewer directions or an increased
number of b values.
Measurements derived from the diffusion tensor
Once the tensor has been estimated, many measures characterizing
different properties of diffusion can be calculated for each voxel.
Various scalar measures relating to average diffusivity and the
degree of diffusion anisotropy are described below, as well as
methods to express the three-dimensional information included in
the diffusion tensor related to the direction of maximal
The mean diffusivity is equal to the trace of the diffusion
tensor, ie, the sum of the diagonal elements, divided by 3. The
trace is a rotationally invariant measure, ie, it is independent of
the reference frame used, and is thus always equal to the average
of the three eigenvalues of the diffusion tensor, ie, the mean of
the diffusivities along the three principal axes of diffusion. The
mean diffusivity is often referred to as the apparent diffusion
coefficient (ADC), where the use of the word apparent acknowledges
the effects of tissue interactions as opposed to a true free
Examples of normal measured ADC values (*10
/sec) are 3.2 in cerebrospinal fluid (CSF), 0.83 in adult cortical
gray matter, 0.65 in adult white matter, and 1.1 to 1.6 in neonatal
Measures to quantify the degree of anisotropy are also
desirable. Commonly used measures of anisotropy derived from the
eigenvalues of the diffusion tensor include fractional anisotropy
relative anisotropy (RA),
normalized relative anisotropy (A*),
and the volume ratio (VR).
Values of FA vary from 0 (isotropic) to 1 (infinite anisotropy).
Similarly, RA varies from 0 to the square root of 2, (A*) from 0 to
1, and VR from 1 to 0. Volume ratio is often expressed as 1-VR, so
that isotropy is represented by a value of 0. Measures to evaluate
the coherence of anisotropic diffusion between a voxel and its
neighbors, such as the lattice index (LI),
have also been developed.
Of these measures, FA is most commonly used, as it has the most
favorable signal-to-noise characteristics.
A map of fractional anisotropy in the brain of an adult volunteer
is presented in Figure 3.
Directionally encoded color maps
The eigenvector of the diffusion tensor corresponding to the
largest eigenvalue is known as the major eigenvector and represents
the preferred direction of diffusion. In a white matter tract, for
example, this direction corresponds to the direction of the fiber
tract. Development of display strategies to express this
three-dimensional information on two-dimensional monitors is an
area of ongoing research. A number of methods have been described
to use color to encode the direction of the major eigenvector.
A frequently used method is to set the red, green, and blue values
at a pixel according to the right-to-left, anterior-to-posterior,
and superior-to-inferior components of the major eigenvector, with
the overall intensity modulated by the fractional anisotropy. In
this way, it is possible to separate adjacent white matter tracts
in a manner not possible with conventional MRI, as seen in the
color-encoded directional map of the brain of an adult volunteer
presented in Figure 3C.
Another technique of displaying directional information is known
as whisker plotting. At each pixel where anisotropy is present, a
short linear "whisker" is superimposed upon the image and aligned
parallel to the in-plane projection of the major eigenvector, thus
providing the observer with a detailed view of pixel-by-pixel
directional information in the imaging plane. These plots are also
known as fiber orientation plots, as each whisker indicates the
dominant orientation of fibers at the corresponding position. A
whisker plot of the brain of an adult volunteer is presented in
Figure 4. While major tracts, such as the corpus callosum, that
curve within the imaging plane are more easily visualized with
whisker plots than color-encoded images, a limitation of the
whisker plot method is the difficulty in displaying eigenvector
information perpendicular to the plane of the image, such as
corticospinal tracts in axial images.
Since the major eigenvector of the diffusion tensor in white
matter voxels is assumed to be collinear with the direction of a
white matter fiber tract, the directional information can be used
to map tracts by connecting neighboring pixels that lie along the
direction of this eigenvector.
This process, known as fiber tractography, is also an area of much
active research. All fiber tracts passing through a single voxel
can be examined, or, alternatively, all tracts connecting specified
"start" and "end" positions can be explored. If prior knowledge of
anatomic connections is incorporated, major known fiber bundles can
be mapped, such as the corticospinal tracts. An example of
tractography in the setting of a brain tumor is shown in Figure
Brief review of applications
Normal development and perinatal brain injury
Several groups have explored the normal changes in diffusion
properties of the human brain during development, as well as
alterations to these patterns following neonatal brain injury.
Simple models to describe white matter and deep gray matter
diffusion magnitude and anisotropy from infancy to adolescence have
been developed, suggesting that certain DTI milestones for brain
maturity can be established.
Rapidly decreasing brain ADC has been observed during the first
2 years of life,
with a more gradual decrease during the next few years as brain ADC
approaches adult values.
The basis of this decrease is incompletely understood but is
generally attributed to increasing myelination and the formation of
new barriers to diffusion with brain maturation, as well as
decreasing extracellular free water.
Similarly, in studies of white matter, increasing anisotropy
with age has been observed in preterm newborns,
Like ADC, the change in anisotropy is most rapid during the first 2
years of life, and more gradual thereafter.
Thus, diffusion anisotropy may provide a marker of normal brain
white matter maturation and myelination.
For example, in a study of normal children aged 4 days to 71
months, higher anisotropy was seen in compact than in noncompact
white matter at all ages, but greater increases in anisotropy were
observed with age in noncompact white matter, suggesting that
myelination is more advanced in compact white matter at birth, with
greater changes in myelination occurring in noncompact white matter
Anisotropy, however, is not only a marker for myelination, as
anisotropic diffusion is also seen in nonmyelinated nerve bundles
and white matter prior to myelination,
suggesting that in addition to myelin, the dense packing and
parallel alignment of axonal membranes contribute to anisotropy.
Increasing fiber bundle organization may also play a role.
Diffusion tensor imaging may also have a role in the evaluation
of maturation of cortical gray matter. In a study of neonates of 26
to 41 weeks gestational age,
significant anisotropy of cortical gray matter was seen in the most
premature infants, with a radially aligned major diffusion tensor
eigenvector, consistent with the relatively simple radial neuronal
architecture at that stage of development. Anisotropy essentially
disappeared by 36 weeks gestational age, correlating with the
normal increase in the complexity of cortical architecture as
neuronal connections are formed as a fetus approaches term.
Many studies of perinatal brain injury have also been performed.
Apparent diffusion coefficient imaging may be superior to
conventional imaging in evaluation of perinatal brain injury, as
one study has found that abnormal decreases in ADC seen on
diffusion imaging may better demonstrate and define the extent of
perinatal brain injury than conventional MRI, especially when
obtained between the second and fourth days of life.
In a study of premature infants imaged near birth and again near
infants with moderate white matter injury did not demonstrate the
typical decrease in brain ADC and increase in white matter
anisotropy observed in controls who were born at term. Those
neonates with minimal white matter injury showed similar decreases
in ADC to the control infants, but did not show the normal increase
in frontal white matter anisotropy, suggesting a more mild
impairment of white matter development.
In another study of perinatal brain injury, abnormally decreased
anisotropy was observed at the site of the original central white
matter injury as well as in the ipsilateral internal capsule when
compared with controls, suggesting impaired development of these
In a complementary study of two 6-year-old boys with spastic
quadriplegia secondary to known periventricular leukomalacia (PVL)
in the neonatal period, DTI demonstrated attenuation of white
matter fiber tracts projecting to and from occipital and parietal
lobes, with preservation of the corticospinal tracts, suggesting
that pathophysiology of motor disability in PVL may be related to
abnormal sensory cortex projections, rather than pyramidal motor
tracts, as generally postulated.
Ischemic and traumatic brain injury
The most well-established clinical application of diffusion
imaging is in the evaluation of ischemic brain injury. Although the
exact mechanism is yet unknown, water diffusivity may decrease by
as much as 50% in acute brain infarction. In a study of patients
with acute to early subacute middle cerebral artery (MCA) territory
more marked decreases in diffusivity were observed in white matter
than in gray matter, with conventional diffusion-weighted imaging
(DWI) underestimating the magnitude and in some cases the spatial
extent of the white matter abnormalities seen by DTI.
In addition to the well-known decrease in ADC that identifies
acutely infarcted gray and white matter, the relationship of new
stroke lesions to major white matter tracts can also be determined
by DTI, which may allow more accurate prognosis of long-term
recovery or disability.
Furthermore, decreases in anisotropy can be observed in white
matter ischemic injury,
and disruption or distortion of white matter tracts can also be
inferred in subacute stroke patients.
In longer-term follow-up, DTI may also allow direct monitoring of
Wallerian degeneration following ischemia, which is not possible
with conventional imaging. For example, in a study of 5 patients
who underwent DTI at 2 to 6 months following MCA territory
infarction, evaluation of the corticospinal tract away from the
area of infarction demonstrated decreased FA in all patients,
despite the normal appearance of this region on conventional
T2-weighted imaging in all but 1 patient.
Diffusion tensor imaging may also complement conventional
imaging in the evaluation of traumatic head injury. In 5 patients
with mild traumatic brain injuries examined within 24 hours of
injury, significant reductions in anisotropy in several brain
regions ipsilateral to the injury were observed when compared with
the contralateral side, as well as when compared with normal
Repeat imaging was performed in 2 of these patients at 1 month,
revealing a decrease in the observed anisotropy differences at
In a study of 21 patients with cortical contusion, significantly
lower FA was observed in the white matter underlying the contusion
when compared with other areas of white matter, while only
nonsignificant increases in T2-weighted signal were noted in the
In a study of 2 patients with a history of blunt head trauma
occurring 11 or 18 months before evaluation, both patients
demonstrated regions of increased white matter ADC and 1 patient
demonstrated ipsilateral decreased internal capsule FA, correlating
with their clinical motor and neuropsychiatric deficits.
Notably, both of these patients had normal concurrent conventional
MRI studies at the time of diffusion tensor imaging. Similarly, in
a case report of a patient evaluated 18 months after a traumatic
brain injury, decreased areas of anisotropy in the anterior limb of
the internal capsule correlated with the patient's persistent
sensory deficits, while preservation of FA in the posterior limb
corresponding to the pyramidal tracts correlated with the patient's
excellent recovery of motor function.
White matter diseases
Diffusion tensor imaging has been studied extensively in white
matter disease, where the possibility of identifying abnormalities
of diffusivity or anisotropy in patients with normal conventional
MRI studies may lead to future clinical applications in diagnosis
and disease monitoring, including response to therapy.
Additionally, it has been suggested that differential changes in
the major and minor eigenvalues of the diffusion tensor may be
helpful in differentiating myelin loss from axonal loss in white
matter disease states.
In DTI evaluation of patients with multiple sclerosis (MS),
abnormalities of diffusion can be seen in normal-appearing white
In a study of subjects with relapsing-remitting MS (RRMS),
decreased FA and increased ADC was observed in NAWM when compared
with controls, most significantly in the corpus callosum,
suggesting that DTI can identify brain injury in regions that
appear normal on conventional MRI.
Higher ADC and lower FA within NAWM were also observed in
a larger study including patients with RRMS, secondary progressive
MS (SPMS), and primary progressive MS (PPMS), where a correlation
of these diffusion abnormalities with white matter lesion volume
was also demonstrated.
While no differences in ADC or FA of NAWM were found between
patients with RRMS, SPMS, and PPMS, a measure of disability did
correlate positively with lesion ADC and negatively with lesion FA
in patients with SPMS, suggesting that DTI may have a role in
monitoring disease in advanced stages. Diffusion differences
between MS phenotypes have also been observed. In a DTI study of
patients with SPMS and PPMS, ADC measures in NAWM, normal appearing
gray matter (NAGM), and hyperintense lesions on T2-weighted
conventional MRI were significantly higher in patients with SPMS
than in patients with PPMS.
Changes in mean diffusivity of white matter over time may also
provide a marker for disease progression in cerebral autosomal
dominant arteriopathy with subcortical infarcts and
Additionally, increased diffusivity and decreased anisotropy have
been observed in the putamen and thalamus of CADASIL patients,
where thalamic diffusivity correlated with white matter diffusivity
and infarct load, and inversely correlated with Mini Mental Status
Examination (MMSE) score, suggesting secondary degeneration of
thalamocortical pathways resulting from ischemic white matter
In patients with amyotrophic lateral sclerosis and essentially
normal conventional MRI studies, bilaterally decreased anisotropy
in the anterior aspect of the posterior limb of the internal
capsule has been seen when compared with controls.
Decreased anisotropy has also been identified within foci of
periventricular T2 prolongation in B12 leukoencephalopathy, as well
as in the adjacent normal-appearing white matter.
Finally, in a DTI study of 12 patients with conventional MRI
findings consistent with reversible posterior leukoencephalopathy
syndrome, increased mean diffusivity and reduced anisotropy were
seen in the posterior white matter.
In the 1 patient re-examined after symptoms resolved, diffusivity
had corrected and anisotropy had largely corrected.
As many as 20% of patients with refractory epilepsy may have
normal conventional MR imaging studies. In a study of 30 refractory
partial seizure patients with normal conventional MRI evaluation,
foci of abnormally increased diffusivity were found in 8 of 30
patients, and abnormal FA in 2 of 30 patients.
In 10 refractory partial seizures patients with known acquired
brain lesions on conventional MRI also included in this study, foci
of abnormal diffusivity corresponding to the brain lesions were
noted in all 10, with 9 of 10 also demonstrating corresponding
abnormalities of anisotropy.
In the subgroup of patients with normal MRI and abnormal DTI, 6 of
the 8 diffusivity foci and 1 of the 2 anisotropy foci concurred
with electroclinical localization of seizure focus in the
corresponding patient, suggesting that DTI may help identify
targets for surgical intervention in many patients with normal MRI
This is supported by a case report of a patient with refractory
complex partial seizures and a normal conventional MRI evaluation,
in whom DTI demonstrated an area of abnormally increased
diffusivity in the right orbitofrontal region that correlated with
A good clinical outcome was observed in this patient following
surgical resection of this area, with pathology demonstrating
Diffusion tensor imaging offers the opportunity to explore
subtle connectivity abnormalities that may underlie many
neuropsychiatric diseases and which are not evident on conventional
imaging. As with white matter diseases, this may lead to future
clinical applications in diagnosis and disease monitoring. Examples
of white matter abnormalities identified with DTI include
abnormally decreased FA in the rolandic operculum of patients with
persistent developmental stuttering
and in the left temporoparietal region of adults with developmental
dyslexia and normal conventional MRI studies.
In the latter study, anisotropy also correlated with a measure of
reading skill. Decreased anisotropy in frontal and left
temporoparietal regions has also been observed in adolescents with
disruptive behavior disorder, correlating with performance on
cognitive tests, suggesting abnormalities in these white matter
pathways may underlie this disorder.
In substance abuse, reduced anisotropy within frontal white matter
has been seen in cocaine-dependent patients as compared with
controls, consistent with the hypothesis that decision-making
deficits seen in cocaine dependence may result from impaired
Diffusion tensor imaging data also support abnormal cortical
connectivity in schizophrenia, especially involving the frontal
lobes. In a study of 14 men with schizophrenia, decreased FA in the
inferior frontal white matter was associated with impulsivity and
increased ADC was associated with aggression in men with
Another DTI study demonstrated disruption in the normal pattern of
connectivity between temporal and frontal brain regions in
schizophrenia, as evidenced by loss of the left-greater-than-right
asymmetry of FA seen in normal subjects within the uncinate
fasciculus, the major white matter tract connecting these regions.
Diffusion tensor imaging of the corpus callosum demonstrated
increased mean diffusivity and decreased FA in the splenium but not
the genu of the corpus callosum in 20 schizophrenic patients
compared with 25 controls, suggesting that focal disruption of
commisural connectivity may also be present in schizophrenia.
Decreased white matter anisotropy has also been seen in prefrontal
as well as more diffusely,
in patients with schizophrenia.
Diffusion imaging may have a role in predicting response to
treatment in geriatric depression. In a study of elderly patients
with major depression, lower FA in the region of the frontostriatal
white matter tracts was associated with lower age-adjusted
remission rate following treatment with citalopram.
Diffusion tensor imaging has also helped advance understanding
of Alzheimer disease (AD). Reduced integrity of association white
matter tracts (splenium of corpus callosum, superior longitudinal
fasciculus, and left cingulum) as measured by decreased lattice
index, has also been found in patients with probable AD,
with anisotropy of the splenium correlating with MMSE score. Others
have observed similar results in the posterior corpus callosum,
and significantly lower FA has also been observed in the temporal
subcortical white matter and anterior and posterior cingulate
bundles of AD patients.
More widespread increases in white matter ADC have also been
reported in AD patients, as well as abnormally elevated hippocampal
ADC in patients with mild cognitive impairment, many of whom may
have preclinical AD.
Finally, diffusion imaging may come to play a role in the
diagnosis and monitoring of Creutzfeldt-Jakob disease (CJD). In a
study of 4 patients with CJD, half of whom had normal conventional
MRI studies, reduced ADC was seen in the basal ganglia bilaterally
in all patients, with additional abnormalities seen in thalamus and
cerebral cortex, and with the most extensive abnormalities seen in
the patient with the longest duration of symptoms.
A typical ADC map from a patient with CJD is shown in Figure 6.
Diffusion tensor imaging and tractography offer a unique
opportunity in preoperative planning for brain tumor resection, as
major white matter tracts can be identified in relation to tumors,
and evidence of white matter tract edema, infiltration,
displacement, and disruption may be observed.
An example of fiber tractography of the corticospinal tracts in a
patient with a glioma is shown in Figure 5. In addition, DTI
identification of white matter tracts can be incorporated into
intraoperative neuronavigation systems used during tumor resection.
Other research suggests that measurements of ADC may help
differentiate between necrotic and viable portions of tumors, with
higher ADC seen in necrotic portions of head and neck tumors,
as well as brain tumors,
opening the door for serial ADC measurements as a possible way to
follow response to therapy. Apparent diffusion coefficient
measurements may also be used to help differentiate tumor
pathology, where the ADC of lymphoma was found to be less than that
of carcinomas, carcinoma ADC less than benign solid tumor ADC, and
benign solid tumor less than benign cystic lesions.
The same authors achieved 86% accuracy, 84% sensitivity, and 91%
sensitivity in predicting malignancy by applying an ADC threshold
of 1.22 * 10
Much work remains in understanding the underlying mechanisms of
diffusion anisotropy observed in biological tissues. The relative
contributions of intracellular and extracellular compartments
remain to be elucidated. Additionally, the monoexponential model
used at low b values begins to fail for b values greatly exceeding
, where data seem to support a biexponential model. Unfortunately,
multicompartment models of "fast" and "slow" diffusion do not seem
to correlate with known biologic compartments and the biexponential
model also fails for even larger b values, suggesting that more
complex models of restricted diffusion may be required to
understand diffusion at ultra-high b values.
Improved methods of fiber tracking also need to be developed,
especially techniques to deal with the crossing of dense white
matter tracts, where the tensor formalism no longer holds, and
partial volume effects, resulting from the relatively low
resolution obtained today with DTI. Additionally, a better
understanding of noise and motion contributions to DTI is also
issues which may become clearer as faster sequences and higher
field imaging are developed.
Diffusion tensor imaging is a rich new technique for the
exploration of tissue properties at the microarchitectural level,
providing a new window through which to observe development,
pathophysiology, and brain connectivity. Exciting opportunities in
diagnosis and disease monitoring not possible with conventional MR
imaging are emerging, and as DTI becomes more widely available,
many more applications will undoubtedly develop.
The author would like to thank Pratik Mukherjee for his
assistance in the preparation of this manuscript, and Dan Vigneron,
Duan Xu, Roland Henry, Jeffrey Berman, Mitchel Berger, Michael
Geschwind, and Julie Maas for providing figures.