Rheumatology 2009;48:378–382
Advance Access publication 27 January 2009
doi:10.1093/rheumatology/ken499
Diffusion tensor anisotropy magnetic resonance imaging: a new tool to
assess synovial inflammation
Vikas Agarwal1, Manoj Kumar2, Jitesh K. Singh3, Ram K. S. Rathore3, Ramnath Misra1 and
Rakesh K. Gupta2
Objective. Diffusion tensor imaging (DTI) has been used to study the structure of ordered biological tissue. DTI-derived metrics correlate
with inflammatory cytokines and adhesion molecules, expressed in the brain abscess. We aimed to study the role of DTI-derived
metrics in delineating the synovitis and their correlation with inflammatory proteins expressed in the SF of chronic inflammatory arthritis
patients.
Methods. DTI was performed on 18 patients and 6 healthy controls. A follow-up DTI at 6 months was performed in 10 patients. Quantification
of inflammatory cytokines (TNF-, IL-1) and soluble intercellular adhesion molecule-1 (sICAM-1) in SF and their correlation with DTI-derived
metrics was performed.
Results. DTI-derived metrics, fractional anisotropy (FA), cylindrical isotropy (CL), planar anisotropy (CP) and spherical isotropy (CS), were
significantly altered in the inflamed synovium of the patients as compared to the healthy controls. Significant correlation between FA and TNF (r ¼ 0.68, P ¼ 0.002) and IL-1 (r ¼ 0.48, P < 0.05) and inverse correlations between mean diffusivity (MD) and TNF- (r ¼ 0.54, P < 0.05)
and CS and TNF- (r ¼ 0.53, P < 0.05) and CP and IL-1 and sICAM (r ¼ 0.48, P < 0.05 and r ¼ 0.49, P < 0.05, respectively) were observed.
A significant correlation between post-contrast signal intensity (PCI) and IL-1 and sICAM-1 (r ¼ 0.61, P ¼ 0.01 and r ¼ 0.46, P ¼ 0.05) and
volume and sICAM-1 (r ¼ 0.45, P ¼ 0.05) was observed, respectively.
Conclusion. Results of this pilot study suggest that the DTI-derived metrics have the potential to delineate synovial inflammation; however, it
is not superior to conventional MRI for its detection and assessment of therapeutic response.
KEY
WORDS:
Diffusion tensor imaging, Fractional anisotropy, Inflammatory cytokines, Synovial fluid, Synovial inflammation, Inflammatory arthritis.
correlate with disease activity [8–10]. Few studies have reported a
relationship between synovial volume measurement and synovial
inflammation histologically and bone erosion score after 1 yr
[6, 11]. Synovial quality, assessed by dynamic contrast-enhanced
MRI, provides information regarding the severity of inflammation
in the synovium [12, 13]. However, the consensus regarding
dynamic contrast-enhanced MRI sequences, maximum slice thickness, timing of contrast enhancement and plane of imaging are
still evolving [6]. Moreover, the enhancement rate is dependent
upon other factors like synovial volume occupied by the plasma,
pre-contrast T1 relaxation time of the synovium and the fraction
of the synovial volume occupied by extravascular, extracellular
fluid, thereby making it difficult to evaluate the severity of the
inflammation [14].
Fat-suppressed T2-weighted MRI may delineate synovial
thickening; however, at times it may be difficult to differentiate
it from joint effusion as both demonstrate increased signal intensity. Gadolinium-enhanced imaging is highly accurate in differentiating proliferative synovium from the joint effusion. However,
one drawback regarding the use of gadolinium contrast materials
is its potential to cause severe adverse effects, including nephrogenic systemic fibrosis [15]. Moreover, patients with renal dysfunction or prior history of hypersensitivity to contrast material may
not be suitable for the contrast study.
Diffusion tensor imaging (DTI) is a non-invasive imaging
technique that does not require administration of the contrast
material and measures diffusion of water molecules in vivo and
provides microstructural information of the tissue [16, 17]. It has
been used to study the structure of ordered biological tissue, such
as brain [17] myocardium [18] and intervertebral disc [19]. Water
molecules exhibit preferential diffusion along certain directions in
tissues because of the presence of membranes and other structures
that restrict the molecular diffusion. For example, water molecules
diffuse more rapidly along the length of fibres compared with the
perpendicular directions. This directional dependence is referred
to as anisotropic diffusion. Diffusion anisotropy can be exploited
to gain information about the tissue organization at a microscopic
level [20]. Unlike in pure liquids where diffusion is isotropic and
Introduction
Synovium is the primary site of inflammation in diverse chronic
inflammatory arthritis. Inflammation is mediated by complex
interactions of inflammatory cytokines and chemokines secreted
from the lymphocytes, macrophages and neutrophils [1]. As a result
of this, leucocytes accumulate in the synovium and the synovial
lining cells become activated and proliferate in numbers. These
activated cells and leucocytes secrete many pro-inflammatory
cytokines, such as IL-1, TNF-, soluble intercellular adhesion
molecule (sICAM), IL-12, IFN-, and various enzymes, such
as Matrix metalloproteinases [2, 3], leading to joint effusion,
cartilage damage and bone erosions [4].
A complete understanding of the inflamed synovium can be
achieved by synovial histology only. However, it is an invasive
procedure and obtaining serial specimen is not possible and not
every joint is accessible for synovial biopsy. Therefore, noninvasive techniques like MRI, ultrasonography, PET and bone
scintigraphy have been used to assess the inflammation in the
joints over a period of time [5]. Ultrasonography, though rapid
and easy to perform, is constrained by the fact that it is less sensitive and operator dependent. Similarly, bone scintigraphy is less
specific whereas PET, though highly sensitive, is still experimental
[6]. Presently, MRI is the most sensitive technique available to
assess joint inflammation. T1-weighted spin echo sequence early
after intravenous contrast material administration helps in differentiation of synovial inflammation from the joint effusion [7].
MRI-based measurements of bone marrow oedema, synovial
membrane volume and synovial quality have been suggested to
1
Department of Clinical Immunology, 2Department of Radiodiagnosis, Sanjay
Gandhi Postgraduate Institute of Medical Sciences, Lucknow and 3Department of
Mathematics and Statistics, Indian Institute of Technology, Kanpur, UP, India.
Submitted 24 July 2008; revised version accepted 10 December 2008.
Correspondence to: Rakesh K. Gupta, Department of Radiodiagnosis, Sanjay
Gandhi Post Graduate Institute of Medical Sciences, Raebareily Road, Lucknow,
UP, India, 226014. E-mail: [email protected], [email protected]
378
ß The Author 2009. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: [email protected]
DTI to assess synovial inflammation
can be characterized by a single diffusion parameter, anisotropic
diffusion, such as that observed in tissues, is described by a 3 3
symmetric matrix. Determination of the complete diffusion matrix
requires the calculation of six independent matrix elements. This is
realized by acquiring MRI data with diffusion gradients applied
along in at least six non-collinear directions and one data set
acquired in the absence of diffusion gradients (or weak gradients)
[21, 22]. The two commonly used rotationally invariant scalar
parameters that are derived from diffusion tensor are the mean
diffusivity (MD) and fractional anisotropy (FA). MD is the trace
of the diffusion matrix and is the average measure of the molecular motion independent of tissue directionality and is affected
by the cellular size and integrity [21–23]. FA is a measure of the
diffusion anisotropy and depends upon the vectrality of the
tissue structure. The minimum value of FA is 0 when diffusion is
equally probable in all directions (isotropic diffusion) and has a
maximum value of 1 for highly anisotropic structures such as
thin fibres. Anisotropic structures such as white matter tracks in
brain and spinal cord are characterized by large FA values (but
less than 1).
In avascular tissues that exhibit a high degree of structural
anisotropy, such as the fibre cells of eye lens or collagen fibre
architecture in cartilage, DTI can also be used to probe underlying
microstructure [24]. DTI reveals the tissue structure under the
assumption that the direction of the primary eigenvector of the
diffusion tensor is parallel to the local fibre orientation. DTI helps
in detecting abnormality at a much earlier stage when it is occult
on conventional MRI [25, 26]. We have earlier reported increased
FA values in the brain abscess cavity and suggested the presence
of oriented structures [27]. These oriented structures may be due
to the presence of various neuroinflammatory molecules that are
responsible for the adhesivity and the orientations of the viable
inflammatory cells present within the cavity [27, 28]. Recently, we
have proven by both in vivo and ex vivo experiments that increased
FA in brain abscess cavity was indeed due to the structured
orientation of neuroinflammatory cells in the abscess cavity, an
environment induced by the up-regulation of various adhesion
molecules: TNF, IL-1, LFA-1 and sICAMs on the inflammatory cells [29].
Taking a lead from these experiments, we hypothesize that
inflamed synovium, which expresses increased levels of inflammatory cytokines, chemokines and adhesion molecules, should
generate FA and MD values, which reflect inflammatory process
at the level of synovium. The aim of the present study was to
look for the changes in the DTI-derived metrics [FAMD, linear
anisotropy (CL), planar anisotropy (CP) and spherical anisotropy
(CS)] in the inflamed synovium of the knee joint in patients with
inflammatory arthritis and to correlate these changes with the
levels of inflammatory cytokines quantified from the SF of the
imaged knee joints.
379
MRI
Conventional MRI including DTI was acquired on a 1.5 Tesla
MRI scanner (General Electric Medical System, Milwaukee, WI,
USA) using a standard quadrature birdcage receive and transmit
radio frequency quadrature knee coil. The conventional MRI
protocol included T2-weighted fast spin echo (FSE) images
with repetition time (TR) (ms)/echo time (TE) (ms)/echo train
length (ETL)/no. of excitations (NEX) ¼ 6000/85/16/4 and spin
echo (SE) T1-weighted images with TR/TE/NEX ¼ 1000/14/2.
Both T1- and T2-weighted images (fat saturation) were acquired
from contiguous (interleaved), 3-mm-thick axial sections with
240 240 mm field of view (FOV) and image matrix of 256 256.
Post-contrast T1-weighted images (fat saturation) were acquired
after intravenous injection of gadolinium diethylenetriaminepenta
acetic acid-bismethylamide (Gd-DTPA-BMA; Omniscan,
Amersham Health, Oslo, Norway) at a dose of 0.1 mmol/kg
body weight.
DTI protocol
DTI data were acquired using a single-shot echo-planar dual SE
sequence with ramp sampling [30]. A balanced [31] rotationally
invariant [32] icosahedral diffusion gradient encoding scheme with
21 uniformly distributed directions over the unit hemisphere was
used. The b-factor was 1000 s/mm2. The acquisition parameters
were: slice thickness of 3 mm with no gap, number of slices ¼ 21,
FOV ¼ 240 240 mm, TR ¼ 8 s, TE ¼ 100 ms and NEX ¼ 8. The
acquisition matrix was 128 80 and the homodyne algorithm
was used to construct the k-space data to 128 128 and zero-filled
to generate an image matrix of 256 256. The distortioncorrected data were then interpolated to attain isotropic voxels
and decoded to obtain the tensor field for each voxel. The tensor
field data were then diagonalized using the analytical diagonalization method [33] to obtain the eigenvalues (1, 2 and 3)
the three orthonormal eigenvectors (e1, e2 and e3). The orthogonality of the computed eigenvectors and the correctness of the
eigenvalues were checked using random sampling at a number of
voxels. The correctness observed was up to an order of 1017,
indicating that no iterative refinement of the computed eigenvalues/eigenvectors was needed. The tensor field data were then
used to compute the DTI metrics, such as MD; (Equation 1), FA;
(Equation 2), CL; (Equation 3), CP; (Equation 4) and CS;
(Equation 5) for each voxel.
MD ¼
1 þ 2 þ 3
3
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1 ð1 2 Þ2 þð2 3 Þ2 þð1 3 Þ2
FAð1 ,2 ,3 Þ ¼ pffiffiffi
21 þ 22 þ 23
2
Materials and methods
Patients
Eighteen patients, 10 with RA (seven females, mean age 26 yrs)
and eight with inflammatory OA (four males, mean age 49 yrs)
were recruited for the study. A follow-up DTI was carried out
after 6 months in 10 patients (five RA and five OA). The study
was approved by the institutional review board and informed
consents were obtained from all study subjects. One of the
involved knee joints was subjected to MRI and DTI imaging
followed by aspiration of the SF from the same joint and intraarticular administration of 80 mg of methyl prednisolone acetate.
At follow-up, the same knee joint was imaged again. We imaged
six age- and sex-matched knee joints of the normal healthy
volunteers and compared their DTI-derived metrics values with
the patients.
ð1Þ
ð2Þ
CLð1 ,2 ,3 Þ ¼
1 2
1 þ 2 þ 3
ð3Þ
CPð1 ,2 ,3 Þ ¼
2 ð2 3 Þ
1 þ 2 þ 3
ð4Þ
CSð1 ,2 ,3 Þ ¼
3 3
1 þ 2 þ 3
ð5Þ
Segmentation
The segmentation method was implemented using JAVA programming language that was based on Fuzzy C-means (FCM)
380
Vikas Agarwal et al.
clustering. FCM is a soft tissue segmentation method that has
been used extensively for segmentation of MR images [34]. The
FCM approach is able to make unsupervised classification of
data in a number of clusters, by identifying different tissues in an
image without the use of an explicit threshold. In this study, postcontrast T1-weighted image was classified in four clusters: background/air signal, signal related to non-cartilage tissues, signal
related to non-enhanced cartilage and signal related to enhanced
synovium. Four masks were extracted from every image, each
related to tissue distribution. Then masks related to enhanced
synovium were taken and also those had some enhanced cartilage regions in few patients. The enhanced synovial region was
obtained by selecting the largest connected component. The
implemented software automatically quantifies the DTI measures
(FA, MD, CL, CP and CS) related to mask of enhanced synovium
[enhanced synovium mask was taken as region of interest (ROI)].
In addition, signal intensity and volume of the enhanced synovium
were measured in the same sitting (Fig. 1). Imaging was carried
out at baseline and after 6 months of therapy. The SF from the
inflamed joints was aspirated under aseptic condition from the
imaged knee joints. The aspirated SF was centrifuged at 210 g for
10 min, supernatant collected and stored at 708C till further
analysis.
Quantitation of inflammatory cytokines from the SF
The Human soluble ICAM-1, IL-1 and TNF- in the SF were
quantitatively measured by ELISA (R&D systems, Minneapolis,
MN, USA). ELISA was performed as per manufacturer’s guideline. Amount of inflammatory cytokines in samples were determined by standard plot.
Results
Synovial membrane enhancement was observed on post-contrast
T1-weighted images in all the patients compared with controls
who had minimal and small areas of synovial enhancement.
Amongst the patients, significantly high signal intensity on postcontrast T1 image with increased contrast-enhanced synovial
membrane volume was observed at baseline as compared with
during the follow-up (Figs 1 and 2, Table 1). For DTI parameters,
FA, CP, CL and CS were significantly altered in patients as
compared with the healthy subjects (Table 1). FA values in the
inflamed knee joints were higher at baseline as compared with
during the follow-up. Significant positive correlations between FA
and TNF- (r ¼ 0. 0.68, P ¼ 0.002) and IL-1 (r ¼ 0.48, P < 0.05),
CP and IL-1 and sICAM (r ¼ 0.48, P < 0.05 and r ¼ 0.49,
P < 0.05, respectively) and inverse correlations between MD and
TNF- (r ¼ 0.53, P < 0.03) were observed (Table 2). For
conventional MRI parameters a significant positive correlation
between PCI and IL-1 (r ¼ 0.61, P < 0.01) was observed.
There were 10 patients with RA with a mean 28-joint disease
activity score (DAS28) of 6.84 1.6 at baseline and mean ESR of
78 22 mm. During follow-up the DAS28 score reduced moderately to 4.2 1.54 and ESR to 46 19 mm. However, three
patients had marginal reduction (<1.2) in their baseline DAS
scores and showed persistent warm knee joint with effusion. In the
OA cohort, almost all the patients had warm and tender knee joint
with effusion at baseline with mean ESR of 73 21 mm; however,
during follow-up only two patients had cold knee joint with some
residual effusion and the mean ESR was 38 12 mm. During
follow-up a significant decrease in the FA values (P < 0.05) was
observed (Fig. 2, Table 1).
Statistical analysis
Multiple comparisons using the bonferroni post hoc test were
performed for determining the changes in DTI indices among the
study groups (control and base line and follow-up in patient).
Pearson’s correlation was performed to study the relationship
between different DTI-derived metrics and the inflammatory
cytokines measured from SF from the knee joints of inflammatory
arthritis patients. All the statistical computations were performed
using the SPSS (Statistical Package for Social Sciences; version
12.0, SPSS Inc, Chicago, IL, USA) statistical software.
Discussion
To the best of our knowledge this is the first study that demonstrates the sensitivity of the DTI-derived metrics to differentiate
between the normal and the inflamed synovium and to correlate
with inflammatory cytokines in the SF of patients with inflammatory arthritis. Compared with conventional contrast MRI, DTI
parameters were equally sensitive in delineating the inflamed
synovium and correlation with markers of inflammation in
the SF.
FIG. 1. Baseline imaging of the right knee joint of a 42-yr-old female presenting with RA. Fat-suppressed axial T2-weighted (A), pre-contrast T1-weighted (B) and postcontrast T1-weighted (C) images show joint effusion with synovial thickening. Segmented region of enhanced synovial membrane (D) as seen on post-contrast fatsuppressed T1-weighted image shows 1852.89 signal intensity and synovial volume of 1.70 CC. FA (E) and MD (F) maps form the contrast-enhanced segmented volume
showing FA value of 0.20 and MD value (103) of 1.36 mm2/s, respectively.
DTI to assess synovial inflammation
381
FIG. 2. Follow-up imaging after 6 months of the same knee joint with RA. Fat-suppressed axial T2-weighted (A), pre-contrast T1-weighted (B) and post-contrast T1weighted (C) images show joint effusion with synovial thickening. Segmented region of enhanced synovial membrane (D) as seen on post-contrast fat-suppressed T1weighted image shows 1469.45 signal intensity and synovial volume of 1.25 CC. FA (E) and MD (F) maps form the contrast enhanced segmented volume showing FA value
of 0.17 and MD value (10–3) of 1.21 mm2/s, respectively.
TABLE 1. Comparison of DTI-derived indices of control subjects and patients with inflammatory arthritis at baseline and follow-up
DTI indices
FA, mean S.D.
MD, mean S.D.
CL, mean S.D.
CP, mean S.D.
CS, mean S.D.
PCI, mean S.D.
Volume, mean S.D.
Control
Base line patient
Follow-up patient
(a)
(b)
(c)
ab
P-value
bc
0.16 0.01
0.02 0.00
0.05 .00
0.10 0.01
0.85 0.01
892.83 73.57
0.05 0.03
0.20 0.04
0.02 0.00
0.07 0.02
0.12 0.02
0.79 0.05
1521.01 312.45
1.40 0.52
0.17 0.02
0.02 0.00
0.07 0.02
0.12 0.03
0.70 0.14
1342.31 251.46
0.98 0.41
<0.001
0.47
0.01
0.03
0.01
<0.001
<0.001
0.02
0.09
0.85
0.91
0.08
0.01
<0.001
PCI: post-contrast signal intensity.
TABLE 2. Correlation between DTI-derived indices and inflammatory cytokines in
the SF (n ¼ 18)
DTI indices
FA
R-values
P-values
MD
R-values
P-values
CL
R-values
P-values
CP
R-values
P-values
CS
R-values
P-values
PCI
R-values
P-values
Volume
R-values
P-values
TNF-
IL-1
s-ICAM
0.681
0.002
0.477
0.045
0.452
0.059
0.544
0.020
0.455
0.058
0.358
0.145
0.427
0.077
0.457
0.057
0.334
0.175
0.289
0.244
0.478
0.045
0.491
0.039
0.528
0.024
0.444
0.065
0.352
0.152
0.270
0.280
0.607
0.008
0.464
0.053
0.182
0.470
0.115
0.648
0.453
0.059
PCI: post-contrast signal intensity.
Synovial inflammation in OA and RA shares many pathogenetic features including aggregation of inflammatory cells in the
synovium, synovial proliferation, activation and expression of
various cell adhesion molecules and release of pro-inflammatory
cytokines into the SF [35]. We choose TNF-, IL1- and sICAM-1
for our study as these are the key inflammatory markers in an
inflamed synovium and are expressed in abundance, though there
are many more. High FA in brain abscess has been reported to
positively correlate with pro-inflammatory cytokines, TNF-,
IL1- and sICAM-1, inside the abscess cavity and is reported to
be due to the aggregation of inflammatory cells and expression of
the neuroinflammatory and adhesion molecules [27, 29]. Similarly,
we have observed high FA, CP, CL and CS values and low
MD values at baseline in the enhanced synovium of the patients.
Fractional anisotropy represents vectrality of the tissue; however,
in the inflamed joint there is no such strong vector-like structure as
compared with brain white matter. Therefore, FA signals were
very weak in the healthy knee joints as compared with the significantly higher FA values in the inflamed knee joints of the
patients. The basic mechanism behind the increased FA in the
abscess cavity in brain abscess have been reported to be due to
aggregation of the inflammatory cells; a similar process may be
operative in the inflamed synovium as well. However, histological
evidence is needed for confirmation in case of the synovitis. The
correlation between FA and TNF- is suggestive of aggregation
of activated inflammatory cells within the inflamed joint. We
have not found a significant correlation between FA and sICAM;
this discrepancy may be due to the fact that the FA values were
measured in the synovial membrane, whereas sICAM were quantitated in the SF.
The correlation between CL values and IL-1 is indicative of
linear orientation of the inflammatory cells within the synovial
membrane. Negative correlation between MD and CS with proinflammatory cytokines in our study may be explained by the
Vikas Agarwal et al.
382
inability of the water molecules to diffuse freely due to organized
inflammatory exudates on the synovial membrane.
Recently, high CP with low CL values inside the brain abscess
cavity has been reported and it was suggested that the shape of the
diffusion tensor is predominantly planar in the abscess cavity
while it is linear in the white matter tracts [29]. We observed a
significant positive correlation between CP and IL-1 and sICAM
as compared with CL and FA values, which again suggests that
the adhered inflammatory cells on the synovial membrane
simulate the more planar model of diffusion tensor.
The MD and CS values appear more sensitive to water content
and FA values are more sensitive to tissue microstructural
orientation. We observed a significant inverse correlation between
MD values with TNF- indicative of the adhered oriented inflammatory cells on the synovial membrane. As none of our patients
of OA was likely to achieve remission and the patients with RA
showed only moderate response (as per DAS28 joint count score)
with their current DMARDs, the data appear to be consistent.
Results of the present study suggest that DTI parameters are
sensitive in detecting oriented microstructural alterations at any
tissue site and that it may be used as non-contrast technique to
delineate synovial inflammation. The limitation of the present
study is the lack of the gold standard of synovial inflammation,
i.e. histological evidence and sensitivity in detecting changes in the
severity of inflammation during the follow-up. However, results of
this pilot study convincingly suggest that the DTI-derived metrics
parameters may be used to delineate synovial inflammation.
Rheumatology key messages
DTI derived metrics have the potential to delineate synovial
inflammation.
DTI derived metrics correlate with inflammation cytokines in
inflamed joints.
Acknowledgment
M.K. acknowledges the financial assistance from the Indian
Council of Medical Research, New Delhi, India.
Disclosure statement: The authors have declared no conflicts of
interest.
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